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a/usecases/ai/digital-avatar/.gitignore b/usecases/ai/digital-avatar/.gitignore new file mode 100644 index 0000000..2cf6285 --- /dev/null +++ b/usecases/ai/digital-avatar/.gitignore @@ -0,0 +1,12 @@ +__pycache__ +.env + +ffmpeg*/ +checkpoints +cache/ +backend/musetalk/models +backend/musetalk/data/avatars +backend/wav2lip/wav2lip/results +backend/wav2lip/wav2lip/temp +weights/* +backend/liveportrait/templates \ No newline at end of file diff --git a/usecases/ai/digital-avatar/README.md b/usecases/ai/digital-avatar/README.md new file mode 100644 index 0000000..4964c7f --- /dev/null +++ b/usecases/ai/digital-avatar/README.md @@ -0,0 +1,118 @@ +# Digital Avatar + +A digital avatar that utilizes Image to Video, Text To Speech, Speech To Text, and LLM to create an interactive avatar. + +![Demo](./docs/demo.gif) + + +## Table of Contents +- [Architecture Diagram](#requirements) +- [Requirements](#requirements) + - [Minimum](#minimum) + - [Recommended](#recommended) +- [Application Ports](#application-ports) +- [Setup](#setup) + - [Prerequisite](#prerequisite) + - [Setup ENV](#setup-env) + - [Build Docker Container](#build-docker-container) + - [Start Docker Container](#start-docker-container) + - [Access the App](#access-the-app) +- [FAQ](#faq) + +## Architecture DIagram +![Archictecture Diagram](./docs/architecture.png) + +## Requirements + +### Minimum +- CPU: 13th generations of Intel Core i5 and above +- GPU: Intelยฎ Arcโ„ข A770 graphics (16GB) +- RAM: 32GB +- DISK: 128GB + +## Application Ports +Please ensure that you have these ports available before running the applications. + +| Apps | Port | +|--------------|------| +| Lipsync | 8011 | +| LivePortrait | 8012 | +| TTS | 8013 | +| STT | 8014 | +| OLLAMA | 8015 | +| Frontend | 80 | + +## Setup + +### Prerequisite +1. **OS**: Ubuntu (Validated on 22.04) + + ***Note***: If you are using different Ubuntu version, please [update the RENDER_GROUP_ID](#1-how-to-check-render-group-id) + +1. **Docker and Docker Compose**: Ensure Docker and Docker Compose are installed. Refer to [Docker installation guide](https://docs.docker.com/engine/install/). +1. **Intel GPU Drivers**: + 1. Refer to [here](../../../README.md#gpu) to install Intel GPU Drivers +1. **Download Wav2Lip Model**: Download the [Wav2Lip model](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA?e=n9ljGW) and place the file in the `weights` folder. +1. **Create Avatar**: + 1. Place an `image.png` file containing an image of a person (preferably showing at least the upper half of the body) in the assets folder. + 2. Place an `idle.mp4` file of a person with some movement such as eye blinking (to be used as a reference) in the assets folder. + +### Setup ENV +1. Create a `.env` file and copy the contents from `.env.template`: + ```bash + cp .env.template .env + ``` +2. Modify the `LLM_MODEL` in the `.env` file. Refer to [Ollama library](https://ollama.com/library) for available models. (Default is `QWEN2.5`). + +### Build Docker Container +```bash +docker compose build +``` + +### Start Docker container +```bash +docker compose up -d +``` + +### Access the App +- Navigate to http://localhost + +## Notes +### Device Workload Configurations +You can offload model inference to specific device by modifying the environment variable setting in the docker-compose.yml file. + +| Workload | Environment Variable |Supported Device | +|----------------------|----------------------|-------------------------| +| LLM | - | GPU | +| STT - Encoded Device | STT_ENCODED_DEVICE | CPU,GPU,NPU | +| STT - Decided Device | STT_DECODED_DEVICE | CPU,GPU | +| TTS | TTS_DEVICE | CPU | +| Lipsync (Wav2lip) | DEVICE | CPU, GPU | + +Example Configuration: + +* To offload the STT encoded workload to `NPU`, you can use the following configuration. + +``` +wav2lip: + ... + environment: + ... + DEVICE=CPU + ... +``` + +## FAQ +### 1. Update Render Group ID +1. Ensure the [Intel GPU driver](#prerequisite) is installed. +2. Check the group ID from `/etc/group`: + ```bash + grep render /etc/group + ``` +3. The output will be something like: + ``` + render:x:110:user + ``` +4. The group ID is the number in the third field (e.g., `110` in the example above). +5. Ensure the `RENDER_GROUP_ID` in the [docker-compose.yml](./docker-compose.yml) file matches the render group ID. + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/Dockerfile b/usecases/ai/digital-avatar/backend/liveportrait/Dockerfile new file mode 100644 index 0000000..3397feb --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/Dockerfile @@ -0,0 +1,77 @@ +FROM debian:12-slim + +ARG DEBIAN_FRONTEND=noninteractive +ARG RENDER_GROUP_ID +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install --no-install-recommends -y \ + sudo \ + wget \ + ca-certificates \ + ffmpeg \ + libsm6 \ + libxext6 \ + curl \ + git \ + build-essential \ + libssl-dev \ + zlib1g-dev \ + libbz2-dev \ + libreadline-dev \ + libsqlite3-dev \ + llvm \ + libncursesw5-dev \ + xz-utils \ + tk-dev \ + libxml2-dev \ + libxmlsec1-dev \ + libffi-dev \ + liblzma-dev \ + && addgroup --system intel --gid 1000 \ + && adduser --system --ingroup intel --uid 1000 --home /home/intel intel \ + && echo "intel ALL=(ALL:ALL) NOPASSWD:ALL" > /etc/sudoers.d/intel \ + && groupadd -g ${RENDER_GROUP_ID} render \ + && usermod -aG render intel \ + && rm -rf /var/lib/apt/lists/* \ + && mkdir -p /usr/src \ + && chown -R intel:intel /usr/src + +# Intel GPU Driver +RUN apt-get update && apt-get install -y gnupg + +RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \ + gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \ + tee /etc/apt/sources.list.d/intel-gpu-jammy.list && \ + apt update && \ + apt-get install -y --no-install-recommends libze1 intel-level-zero-gpu intel-opencl-icd clinfo libze-dev intel-ocloc + +USER intel +WORKDIR /usr/src/app + +# Set environment variables for pyenv +ENV PYENV_ROOT="/usr/src/app/.pyenv" +ENV PATH="$PYENV_ROOT/bin:$PYENV_ROOT/shims:$PATH" + +# Install pyenv +RUN curl https://pyenv.run | bash \ + && echo 'export PYENV_ROOT="$PYENV_ROOT"' >> ~/.bashrc \ + && echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc \ + && echo 'eval "$(pyenv init --path)"' >> ~/.bashrc \ + && echo 'eval "$(pyenv init -)"' >> ~/.bashrc \ + && . ~/.bashrc \ + && pyenv install 3.10.15 \ + && pyenv global 3.10.15 + +RUN python3 -m pip install --upgrade pip \ + && python3 -m pip install virtualenv + +RUN python3 -m venv /usr/src/.venv +ENV PATH="/usr/src/.venv/bin:$PATH" + +COPY --chown=intel ./backend/liveportrait . +RUN python3 -m pip install -r requirements.txt \ + && huggingface-cli download KwaiVGI/LivePortrait --local-dir liveportrait/pretrained_weights --exclude "*.git*" "README.md" "docs" + +HEALTHCHECK --interval=30s --timeout=180s --start-period=60s --retries=3 \ + CMD sh -c 'PORT=${SERVER_PORT:-8012} && wget --no-verbose -O /dev/null --tries=1 http://localhost:$PORT/healthcheck || exit 1' \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/.gitignore b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/.gitignore new file mode 100644 index 0000000..7e9e638 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/.gitignore @@ -0,0 +1,34 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +**/__pycache__/ +*.py[cod] +**/*.py[cod] +*$py.class + +# Model weights +**/*.pth +**/*.onnx + +pretrained_weights/*.md +pretrained_weights/docs +pretrained_weights/liveportrait +pretrained_weights/liveportrait_animals + +# Ipython notebook +*.ipynb + +# Temporary files or benchmark resources +animations/* +tmp/* +.vscode/launch.json +**/*.DS_Store +gradio_temp/** + +# Windows dependencies +ffmpeg/ +LivePortrait_env/ + +# XPose build files +src/utils/dependencies/XPose/models/UniPose/ops/build +src/utils/dependencies/XPose/models/UniPose/ops/dist +src/utils/dependencies/XPose/models/UniPose/ops/MultiScaleDeformableAttention.egg-info diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/LICENSE b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/LICENSE new file mode 100644 index 0000000..8450f81 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/LICENSE @@ -0,0 +1,30 @@ +MIT License + +Copyright (c) 2024 Kuaishou Visual Generation and Interaction Center + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +--- + +The code of InsightFace is released under the MIT License. +The models of InsightFace are for non-commercial research purposes only. + +If you want to use the LivePortrait project for commercial purposes, you +should remove and replace InsightFaceโ€™s detection models to fully comply with +the MIT license. diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app.py new file mode 100644 index 0000000..37c40e4 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app.py @@ -0,0 +1,493 @@ +# coding: utf-8 + +""" +The entrance of the gradio for human +""" + +import os +import tyro +import subprocess +import gradio as gr +import os.path as osp +from src.utils.helper import load_description +from src.gradio_pipeline import GradioPipeline +from src.config.crop_config import CropConfig +from src.config.argument_config import ArgumentConfig +from src.config.inference_config import InferenceConfig + + +def partial_fields(target_class, kwargs): + return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)}) + + +def fast_check_ffmpeg(): + try: + subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True) + return True + except: + return False + + +# set tyro theme +tyro.extras.set_accent_color("bright_cyan") +args = tyro.cli(ArgumentConfig) + +ffmpeg_dir = os.path.join(os.getcwd(), "ffmpeg") +if osp.exists(ffmpeg_dir): + os.environ["PATH"] += (os.pathsep + ffmpeg_dir) + +if not fast_check_ffmpeg(): + raise ImportError( + "FFmpeg is not installed. Please install FFmpeg (including ffmpeg and ffprobe) before running this script. https://ffmpeg.org/download.html" + ) +# specify configs for inference +inference_cfg = partial_fields(InferenceConfig, args.__dict__) # use attribute of args to initial InferenceConfig +crop_cfg = partial_fields(CropConfig, args.__dict__) # use attribute of args to initial CropConfig +# global_tab_selection = None + +gradio_pipeline = GradioPipeline( + inference_cfg=inference_cfg, + crop_cfg=crop_cfg, + args=args +) + +if args.gradio_temp_dir not in (None, ''): + os.environ["GRADIO_TEMP_DIR"] = args.gradio_temp_dir + os.makedirs(args.gradio_temp_dir, exist_ok=True) + + +def gpu_wrapped_execute_video(*args, **kwargs): + return gradio_pipeline.execute_video(*args, **kwargs) + + +def gpu_wrapped_execute_image_retargeting(*args, **kwargs): + return gradio_pipeline.execute_image_retargeting(*args, **kwargs) + + +def gpu_wrapped_execute_video_retargeting(*args, **kwargs): + return gradio_pipeline.execute_video_retargeting(*args, **kwargs) + + +def reset_sliders(*args, **kwargs): + return 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.5, True, True + + +# assets +title_md = "assets/gradio/gradio_title.md" +example_portrait_dir = "assets/examples/source" +example_video_dir = "assets/examples/driving" +data_examples_i2v = [ + [osp.join(example_portrait_dir, "s9.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, False], + [osp.join(example_portrait_dir, "s6.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, False], + [osp.join(example_portrait_dir, "s10.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, False], + [osp.join(example_portrait_dir, "s5.jpg"), osp.join(example_video_dir, "d18.mp4"), True, True, True, False], + [osp.join(example_portrait_dir, "s7.jpg"), osp.join(example_video_dir, "d19.mp4"), True, True, True, False], + [osp.join(example_portrait_dir, "s2.jpg"), osp.join(example_video_dir, "d13.mp4"), True, True, True, True], +] +data_examples_v2v = [ + [osp.join(example_portrait_dir, "s13.mp4"), osp.join(example_video_dir, "d0.mp4"), True, True, True, False, 3e-7], + # [osp.join(example_portrait_dir, "s14.mp4"), osp.join(example_video_dir, "d18.mp4"), True, True, True, False, False, 3e-7], + # [osp.join(example_portrait_dir, "s15.mp4"), osp.join(example_video_dir, "d19.mp4"), True, True, True, False, False, 3e-7], + [osp.join(example_portrait_dir, "s18.mp4"), osp.join(example_video_dir, "d6.mp4"), True, True, True, False, 3e-7], + # [osp.join(example_portrait_dir, "s19.mp4"), osp.join(example_video_dir, "d6.mp4"), True, True, True, False, False, 3e-7], + [osp.join(example_portrait_dir, "s20.mp4"), osp.join(example_video_dir, "d0.mp4"), True, True, True, False, 3e-7], +] +#################### interface logic #################### + +# Define components first +retargeting_source_scale = gr.Number(minimum=1.8, maximum=3.2, value=2.5, step=0.05, label="crop scale") +video_retargeting_source_scale = gr.Number(minimum=1.8, maximum=3.2, value=2.3, step=0.05, label="crop scale") +driving_smooth_observation_variance_retargeting = gr.Number(value=3e-6, label="motion smooth strength", minimum=1e-11, maximum=1e-2, step=1e-8) +video_retargeting_silence = gr.Checkbox(value=False, label="keeping the lip silent") +eye_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target eyes-open ratio") +lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-open ratio") +video_lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-open ratio") +head_pitch_slider = gr.Slider(minimum=-15.0, maximum=15.0, value=0, step=1, label="relative pitch") +head_yaw_slider = gr.Slider(minimum=-25, maximum=25, value=0, step=1, label="relative yaw") +head_roll_slider = gr.Slider(minimum=-15.0, maximum=15.0, value=0, step=1, label="relative roll") +mov_x = gr.Slider(minimum=-0.19, maximum=0.19, value=0.0, step=0.01, label="x-axis movement") +mov_y = gr.Slider(minimum=-0.19, maximum=0.19, value=0.0, step=0.01, label="y-axis movement") +mov_z = gr.Slider(minimum=0.9, maximum=1.2, value=1.0, step=0.01, label="z-axis movement") +lip_variation_zero = gr.Slider(minimum=-0.09, maximum=0.09, value=0, step=0.01, label="pouting") +lip_variation_one = gr.Slider(minimum=-20.0, maximum=15.0, value=0, step=0.01, label="pursing ๐Ÿ˜") +lip_variation_two = gr.Slider(minimum=0.0, maximum=15.0, value=0, step=0.01, label="grin ๐Ÿ˜") +lip_variation_three = gr.Slider(minimum=-90.0, maximum=120.0, value=0, step=1.0, label="lip close <-> open") +smile = gr.Slider(minimum=-0.3, maximum=1.3, value=0, step=0.01, label="smile ๐Ÿ˜„") +wink = gr.Slider(minimum=0, maximum=39, value=0, step=0.01, label="wink ๐Ÿ˜‰") +eyebrow = gr.Slider(minimum=-30, maximum=30, value=0, step=0.01, label="eyebrow ๐Ÿคจ") +eyeball_direction_x = gr.Slider(minimum=-30.0, maximum=30.0, value=0, step=0.01, label="eye gaze (horizontal) ๐Ÿ‘€") +eyeball_direction_y = gr.Slider(minimum=-63.0, maximum=63.0, value=0, step=0.01, label="eye gaze (vertical) ๐Ÿ™„") +retargeting_input_image = gr.Image(type="filepath") +retargeting_input_video = gr.Video() +output_image = gr.Image(type="numpy") +output_image_paste_back = gr.Image(type="numpy") +retargeting_output_image = gr.Image(type="numpy") +retargeting_output_image_paste_back = gr.Image(type="numpy") +output_video = gr.Video(autoplay=False) +output_video_paste_back = gr.Video(autoplay=False) + +with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Plus Jakarta Sans")])) as demo: + gr.HTML(load_description(title_md)) + + gr.Markdown(load_description("assets/gradio/gradio_description_upload.md")) + with gr.Row(): + with gr.Column(): + with gr.Tabs(): + with gr.TabItem("๐Ÿ–ผ๏ธ Source Image") as tab_image: + with gr.Accordion(open=True, label="Source Image"): + source_image_input = gr.Image(type="filepath") + gr.Examples( + examples=[ + [osp.join(example_portrait_dir, "s9.jpg")], + [osp.join(example_portrait_dir, "s6.jpg")], + [osp.join(example_portrait_dir, "s10.jpg")], + [osp.join(example_portrait_dir, "s5.jpg")], + [osp.join(example_portrait_dir, "s7.jpg")], + [osp.join(example_portrait_dir, "s12.jpg")], + [osp.join(example_portrait_dir, "s22.jpg")], + [osp.join(example_portrait_dir, "s23.jpg")], + ], + inputs=[source_image_input], + cache_examples=False, + ) + + with gr.TabItem("๐ŸŽž๏ธ Source Video") as tab_video: + with gr.Accordion(open=True, label="Source Video"): + source_video_input = gr.Video() + gr.Examples( + examples=[ + [osp.join(example_portrait_dir, "s13.mp4")], + # [osp.join(example_portrait_dir, "s14.mp4")], + # [osp.join(example_portrait_dir, "s15.mp4")], + [osp.join(example_portrait_dir, "s18.mp4")], + # [osp.join(example_portrait_dir, "s19.mp4")], + [osp.join(example_portrait_dir, "s20.mp4")], + ], + inputs=[source_video_input], + cache_examples=False, + ) + + tab_selection = gr.Textbox(visible=False) + tab_image.select(lambda: "Image", None, tab_selection) + tab_video.select(lambda: "Video", None, tab_selection) + with gr.Accordion(open=True, label="Cropping Options for Source Image or Video"): + with gr.Row(): + flag_do_crop_input = gr.Checkbox(value=True, label="do crop (source)") + scale = gr.Number(value=2.3, label="source crop scale", minimum=1.8, maximum=3.2, step=0.05) + vx_ratio = gr.Number(value=0.0, label="source crop x", minimum=-0.5, maximum=0.5, step=0.01) + vy_ratio = gr.Number(value=-0.125, label="source crop y", minimum=-0.5, maximum=0.5, step=0.01) + + with gr.Column(): + with gr.Tabs(): + with gr.TabItem("๐ŸŽž๏ธ Driving Video") as v_tab_video: + with gr.Accordion(open=True, label="Driving Video"): + driving_video_input = gr.Video() + gr.Examples( + examples=[ + [osp.join(example_video_dir, "d0.mp4")], + [osp.join(example_video_dir, "d18.mp4")], + [osp.join(example_video_dir, "d19.mp4")], + [osp.join(example_video_dir, "d14.mp4")], + [osp.join(example_video_dir, "d6.mp4")], + [osp.join(example_video_dir, "d20.mp4")], + ], + inputs=[driving_video_input], + cache_examples=False, + ) + with gr.TabItem("๐Ÿ–ผ๏ธ Driving Image") as v_tab_image: + with gr.Accordion(open=True, label="Driving Image"): + driving_image_input = gr.Image(type="filepath") + gr.Examples( + examples=[ + [osp.join(example_video_dir, "d30.jpg")], + [osp.join(example_video_dir, "d9.jpg")], + [osp.join(example_video_dir, "d19.jpg")], + [osp.join(example_video_dir, "d8.jpg")], + [osp.join(example_video_dir, "d12.jpg")], + [osp.join(example_video_dir, "d38.jpg")], + ], + inputs=[driving_image_input], + cache_examples=False, + ) + + with gr.TabItem("๐Ÿ“ Driving Pickle") as v_tab_pickle: + with gr.Accordion(open=True, label="Driving Pickle"): + driving_video_pickle_input = gr.File(type="filepath", file_types=[".pkl"]) + gr.Examples( + examples=[ + [osp.join(example_video_dir, "d1.pkl")], + [osp.join(example_video_dir, "d2.pkl")], + [osp.join(example_video_dir, "d5.pkl")], + [osp.join(example_video_dir, "d7.pkl")], + [osp.join(example_video_dir, "d8.pkl")], + ], + inputs=[driving_video_pickle_input], + cache_examples=False, + ) + + v_tab_selection = gr.Textbox(visible=False) + v_tab_video.select(lambda: "Video", None, v_tab_selection) + v_tab_image.select(lambda: "Image", None, v_tab_selection) + v_tab_pickle.select(lambda: "Pickle", None, v_tab_selection) + # with gr.Accordion(open=False, label="Animation Instructions"): + # gr.Markdown(load_description("assets/gradio/gradio_description_animation.md")) + with gr.Accordion(open=True, label="Cropping Options for Driving Video"): + with gr.Row(): + flag_crop_driving_video_input = gr.Checkbox(value=False, label="do crop (driving)") + scale_crop_driving_video = gr.Number(value=2.2, label="driving crop scale", minimum=1.8, maximum=3.2, step=0.05) + vx_ratio_crop_driving_video = gr.Number(value=0.0, label="driving crop x", minimum=-0.5, maximum=0.5, step=0.01) + vy_ratio_crop_driving_video = gr.Number(value=-0.1, label="driving crop y", minimum=-0.5, maximum=0.5, step=0.01) + + with gr.Row(): + with gr.Accordion(open=True, label="Animation Options"): + with gr.Row(): + flag_normalize_lip = gr.Checkbox(value=False, label="normalize lip") + flag_relative_input = gr.Checkbox(value=True, label="relative motion") + flag_remap_input = gr.Checkbox(value=True, label="paste-back") + flag_stitching_input = gr.Checkbox(value=True, label="stitching") + animation_region = gr.Radio(["exp", "pose", "lip", "eyes", "all"], value="all", label="animation region") + driving_option_input = gr.Radio(['expression-friendly', 'pose-friendly'], value="expression-friendly", label="driving option (i2v)") + driving_multiplier = gr.Number(value=1.0, label="driving multiplier (i2v)", minimum=0.0, maximum=2.0, step=0.02) + driving_smooth_observation_variance = gr.Number(value=3e-7, label="motion smooth strength (v2v)", minimum=1e-11, maximum=1e-2, step=1e-8) + + gr.Markdown(load_description("assets/gradio/gradio_description_animate_clear.md")) + with gr.Row(): + process_button_animation = gr.Button("๐Ÿš€ Animate", variant="primary") + with gr.Row(): + with gr.Column(): + output_video_i2v = gr.Video(autoplay=False, label="The animated video in the original image space") + with gr.Column(): + output_video_concat_i2v = gr.Video(autoplay=False, label="The animated video") + with gr.Row(): + with gr.Column(): + output_image_i2i = gr.Image(type="numpy", label="The animated image in the original image space", visible=False) + with gr.Column(): + output_image_concat_i2i = gr.Image(type="numpy", label="The animated image", visible=False) + with gr.Row(): + process_button_reset = gr.ClearButton([source_image_input, source_video_input, driving_video_pickle_input, driving_video_input, driving_image_input, output_video_i2v, output_video_concat_i2v, output_image_i2i, output_image_concat_i2i], value="๐Ÿงน Clear") + + with gr.Row(): + # Examples + gr.Markdown("## You could also choose the examples below by one click โฌ‡๏ธ") + with gr.Row(): + with gr.Tabs(): + with gr.TabItem("๐Ÿ–ผ๏ธ Portrait Animation"): + gr.Examples( + examples=data_examples_i2v, + fn=gpu_wrapped_execute_video, + inputs=[ + source_image_input, + driving_video_input, + flag_relative_input, + flag_do_crop_input, + flag_remap_input, + flag_crop_driving_video_input, + ], + outputs=[output_image, output_image_paste_back], + examples_per_page=len(data_examples_i2v), + cache_examples=False, + ) + with gr.TabItem("๐ŸŽž๏ธ Portrait Video Editing"): + gr.Examples( + examples=data_examples_v2v, + fn=gpu_wrapped_execute_video, + inputs=[ + source_video_input, + driving_video_input, + flag_relative_input, + flag_do_crop_input, + flag_remap_input, + flag_crop_driving_video_input, + driving_smooth_observation_variance, + ], + outputs=[output_image, output_image_paste_back], + examples_per_page=len(data_examples_v2v), + cache_examples=False, + ) + + # Retargeting Image + gr.Markdown(load_description("assets/gradio/gradio_description_retargeting.md"), visible=True) + with gr.Row(visible=True): + flag_do_crop_input_retargeting_image = gr.Checkbox(value=True, label="do crop (source)") + flag_stitching_retargeting_input = gr.Checkbox(value=True, label="stitching") + retargeting_source_scale.render() + eye_retargeting_slider.render() + lip_retargeting_slider.render() + with gr.Row(visible=True): + with gr.Column(): + with gr.Accordion(open=True, label="Facial movement sliders"): + with gr.Row(visible=True): + head_pitch_slider.render() + head_yaw_slider.render() + head_roll_slider.render() + with gr.Row(visible=True): + mov_x.render() + mov_y.render() + mov_z.render() + with gr.Column(): + with gr.Accordion(open=True, label="Facial expression sliders"): + with gr.Row(visible=True): + lip_variation_zero.render() + lip_variation_one.render() + lip_variation_two.render() + with gr.Row(visible=True): + lip_variation_three.render() + smile.render() + wink.render() + with gr.Row(visible=True): + eyebrow.render() + eyeball_direction_x.render() + eyeball_direction_y.render() + with gr.Row(visible=True): + reset_button = gr.Button("๐Ÿ”„ Reset") + reset_button.click( + fn=reset_sliders, + inputs=None, + outputs=[ + head_pitch_slider, head_yaw_slider, head_roll_slider, mov_x, mov_y, mov_z, + lip_variation_zero, lip_variation_one, lip_variation_two, lip_variation_three, smile, wink, eyebrow, eyeball_direction_x, eyeball_direction_y, + retargeting_source_scale, flag_stitching_retargeting_input, flag_do_crop_input_retargeting_image + ] + ) + with gr.Row(visible=True): + with gr.Column(): + with gr.Accordion(open=True, label="Retargeting Image Input"): + retargeting_input_image.render() + gr.Examples( + examples=[ + [osp.join(example_portrait_dir, "s9.jpg")], + [osp.join(example_portrait_dir, "s6.jpg")], + [osp.join(example_portrait_dir, "s10.jpg")], + [osp.join(example_portrait_dir, "s5.jpg")], + [osp.join(example_portrait_dir, "s7.jpg")], + [osp.join(example_portrait_dir, "s12.jpg")], + [osp.join(example_portrait_dir, "s22.jpg")], + # [osp.join(example_portrait_dir, "s23.jpg")], + [osp.join(example_portrait_dir, "s42.jpg")], + ], + inputs=[retargeting_input_image], + cache_examples=False, + ) + with gr.Column(): + with gr.Accordion(open=True, label="Retargeting Result"): + retargeting_output_image.render() + with gr.Column(): + with gr.Accordion(open=True, label="Paste-back Result"): + retargeting_output_image_paste_back.render() + with gr.Row(visible=True): + process_button_reset_retargeting = gr.ClearButton( + [ + retargeting_input_image, + retargeting_output_image, + retargeting_output_image_paste_back, + ], + value="๐Ÿงน Clear" + ) + + # Retargeting Video + gr.Markdown(load_description("assets/gradio/gradio_description_retargeting_video.md"), visible=True) + with gr.Row(visible=True): + flag_do_crop_input_retargeting_video = gr.Checkbox(value=True, label="do crop (source)") + video_retargeting_source_scale.render() + video_lip_retargeting_slider.render() + driving_smooth_observation_variance_retargeting.render() + video_retargeting_silence.render() + with gr.Row(visible=True): + process_button_retargeting_video = gr.Button("๐Ÿš— Retargeting Video", variant="primary") + with gr.Row(visible=True): + with gr.Column(): + with gr.Accordion(open=True, label="Retargeting Video Input"): + retargeting_input_video.render() + gr.Examples( + examples=[ + [osp.join(example_portrait_dir, "s13.mp4")], + # [osp.join(example_portrait_dir, "s18.mp4")], + # [osp.join(example_portrait_dir, "s20.mp4")], + [osp.join(example_portrait_dir, "s29.mp4")], + [osp.join(example_portrait_dir, "s32.mp4")], + [osp.join(example_video_dir, "d3.mp4")], + ], + inputs=[retargeting_input_video], + cache_examples=False, + ) + with gr.Column(): + with gr.Accordion(open=True, label="Retargeting Result"): + output_video.render() + with gr.Column(): + with gr.Accordion(open=True, label="Paste-back Result"): + output_video_paste_back.render() + with gr.Row(visible=True): + process_button_reset_retargeting = gr.ClearButton( + [ + video_lip_retargeting_slider, + retargeting_input_video, + output_video, + output_video_paste_back + ], + value="๐Ÿงน Clear" + ) + + # binding functions for buttons + process_button_animation.click( + fn=gpu_wrapped_execute_video, + inputs=[ + source_image_input, + source_video_input, + driving_video_input, + driving_image_input, + driving_video_pickle_input, + flag_normalize_lip, + flag_relative_input, + flag_do_crop_input, + flag_remap_input, + flag_stitching_input, + animation_region, + driving_option_input, + driving_multiplier, + flag_crop_driving_video_input, + scale, + vx_ratio, + vy_ratio, + scale_crop_driving_video, + vx_ratio_crop_driving_video, + vy_ratio_crop_driving_video, + driving_smooth_observation_variance, + tab_selection, + v_tab_selection, + ], + outputs=[output_video_i2v, output_video_i2v, output_video_concat_i2v, output_video_concat_i2v, output_image_i2i, output_image_i2i, output_image_concat_i2i, output_image_concat_i2i], + show_progress=True + ) + + + retargeting_input_image.change( + fn=gradio_pipeline.init_retargeting_image, + inputs=[retargeting_source_scale, eye_retargeting_slider, lip_retargeting_slider, retargeting_input_image], + outputs=[eye_retargeting_slider, lip_retargeting_slider] + ) + + sliders = [eye_retargeting_slider, lip_retargeting_slider, head_pitch_slider, head_yaw_slider, head_roll_slider, mov_x, mov_y, mov_z, lip_variation_zero, lip_variation_one, lip_variation_two, lip_variation_three, smile, wink, eyebrow, eyeball_direction_x, eyeball_direction_y] + for slider in sliders: + # NOTE: gradio >= 4.0.0 may cause slow response + slider.change( + fn=gpu_wrapped_execute_image_retargeting, + inputs=[ + eye_retargeting_slider, lip_retargeting_slider, head_pitch_slider, head_yaw_slider, head_roll_slider, mov_x, mov_y, mov_z, + lip_variation_zero, lip_variation_one, lip_variation_two, lip_variation_three, smile, wink, eyebrow, eyeball_direction_x, eyeball_direction_y, + retargeting_input_image, retargeting_source_scale, flag_stitching_retargeting_input, flag_do_crop_input_retargeting_image + ], + outputs=[retargeting_output_image, retargeting_output_image_paste_back], + ) + + process_button_retargeting_video.click( + fn=gpu_wrapped_execute_video_retargeting, + inputs=[video_lip_retargeting_slider, retargeting_input_video, video_retargeting_source_scale, driving_smooth_observation_variance_retargeting, video_retargeting_silence, flag_do_crop_input_retargeting_video], + outputs=[output_video, output_video_paste_back], + show_progress=True + ) + +demo.launch( + server_port=args.server_port, + share=args.share, + server_name=args.server_name +) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app_animals.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app_animals.py new file mode 100644 index 0000000..361044a --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/app_animals.py @@ -0,0 +1,248 @@ +# coding: utf-8 + +""" +The entrance of the gradio for animal +""" + +import os +import tyro +import subprocess +import gradio as gr +import os.path as osp +from src.utils.helper import load_description +from src.gradio_pipeline import GradioPipelineAnimal +from src.config.crop_config import CropConfig +from src.config.argument_config import ArgumentConfig +from src.config.inference_config import InferenceConfig + + +def partial_fields(target_class, kwargs): + return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)}) + + +def fast_check_ffmpeg(): + try: + subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True) + return True + except: + return False + + +# set tyro theme +tyro.extras.set_accent_color("bright_cyan") +args = tyro.cli(ArgumentConfig) + +ffmpeg_dir = os.path.join(os.getcwd(), "ffmpeg") +if osp.exists(ffmpeg_dir): + os.environ["PATH"] += (os.pathsep + ffmpeg_dir) + +if not fast_check_ffmpeg(): + raise ImportError( + "FFmpeg is not installed. Please install FFmpeg (including ffmpeg and ffprobe) before running this script. https://ffmpeg.org/download.html" + ) +# specify configs for inference +inference_cfg = partial_fields(InferenceConfig, args.__dict__) # use attribute of args to initial InferenceConfig +crop_cfg = partial_fields(CropConfig, args.__dict__) # use attribute of args to initial CropConfig + +gradio_pipeline_animal: GradioPipelineAnimal = GradioPipelineAnimal( + inference_cfg=inference_cfg, + crop_cfg=crop_cfg, + args=args +) + +if args.gradio_temp_dir not in (None, ''): + os.environ["GRADIO_TEMP_DIR"] = args.gradio_temp_dir + os.makedirs(args.gradio_temp_dir, exist_ok=True) + +def gpu_wrapped_execute_video(*args, **kwargs): + return gradio_pipeline_animal.execute_video(*args, **kwargs) + + +# assets +title_md = "assets/gradio/gradio_title.md" +example_portrait_dir = "assets/examples/source" +example_video_dir = "assets/examples/driving" +data_examples_i2v = [ + [osp.join(example_portrait_dir, "s41.jpg"), osp.join(example_video_dir, "d3.mp4"), True, False, False, False], + [osp.join(example_portrait_dir, "s40.jpg"), osp.join(example_video_dir, "d6.mp4"), True, False, False, False], + [osp.join(example_portrait_dir, "s25.jpg"), osp.join(example_video_dir, "d19.mp4"), True, False, False, False], +] +data_examples_i2v_pickle = [ + [osp.join(example_portrait_dir, "s25.jpg"), osp.join(example_video_dir, "wink.pkl"), True, False, False, False], + [osp.join(example_portrait_dir, "s40.jpg"), osp.join(example_video_dir, "talking.pkl"), True, False, False, False], + [osp.join(example_portrait_dir, "s41.jpg"), osp.join(example_video_dir, "aggrieved.pkl"), True, False, False, False], +] +#################### interface logic #################### + +# Define components first +output_image = gr.Image(type="numpy") +output_image_paste_back = gr.Image(type="numpy") +output_video_i2v = gr.Video(autoplay=False) +output_video_concat_i2v = gr.Video(autoplay=False) +output_video_i2v_gif = gr.Image(type="numpy") + + +with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Plus Jakarta Sans")])) as demo: + gr.HTML(load_description(title_md)) + + gr.Markdown(load_description("assets/gradio/gradio_description_upload_animal.md")) + with gr.Row(): + with gr.Column(): + with gr.Accordion(open=True, label="๐Ÿฑ Source Animal Image"): + source_image_input = gr.Image(type="filepath") + gr.Examples( + examples=[ + [osp.join(example_portrait_dir, "s25.jpg")], + [osp.join(example_portrait_dir, "s30.jpg")], + [osp.join(example_portrait_dir, "s31.jpg")], + [osp.join(example_portrait_dir, "s32.jpg")], + [osp.join(example_portrait_dir, "s39.jpg")], + [osp.join(example_portrait_dir, "s40.jpg")], + [osp.join(example_portrait_dir, "s41.jpg")], + [osp.join(example_portrait_dir, "s38.jpg")], + [osp.join(example_portrait_dir, "s36.jpg")], + ], + inputs=[source_image_input], + cache_examples=False, + ) + + with gr.Accordion(open=True, label="Cropping Options for Source Image"): + with gr.Row(): + flag_do_crop_input = gr.Checkbox(value=True, label="do crop (source)") + scale = gr.Number(value=2.3, label="source crop scale", minimum=1.8, maximum=3.2, step=0.05) + vx_ratio = gr.Number(value=0.0, label="source crop x", minimum=-0.5, maximum=0.5, step=0.01) + vy_ratio = gr.Number(value=-0.125, label="source crop y", minimum=-0.5, maximum=0.5, step=0.01) + + with gr.Column(): + with gr.Tabs(): + with gr.TabItem("๐Ÿ“ Driving Pickle") as tab_pickle: + with gr.Accordion(open=True, label="Driving Pickle"): + driving_video_pickle_input = gr.File() + gr.Examples( + examples=[ + [osp.join(example_video_dir, "wink.pkl")], + [osp.join(example_video_dir, "shy.pkl")], + [osp.join(example_video_dir, "aggrieved.pkl")], + [osp.join(example_video_dir, "open_lip.pkl")], + [osp.join(example_video_dir, "laugh.pkl")], + [osp.join(example_video_dir, "talking.pkl")], + [osp.join(example_video_dir, "shake_face.pkl")], + ], + inputs=[driving_video_pickle_input], + cache_examples=False, + ) + with gr.TabItem("๐ŸŽž๏ธ Driving Video") as tab_video: + with gr.Accordion(open=True, label="Driving Video"): + driving_video_input = gr.Video() + gr.Examples( + examples=[ + # [osp.join(example_video_dir, "d0.mp4")], + # [osp.join(example_video_dir, "d18.mp4")], + [osp.join(example_video_dir, "d19.mp4")], + [osp.join(example_video_dir, "d14.mp4")], + [osp.join(example_video_dir, "d6.mp4")], + [osp.join(example_video_dir, "d3.mp4")], + ], + inputs=[driving_video_input], + cache_examples=False, + ) + + tab_selection = gr.Textbox(visible=False) + tab_pickle.select(lambda: "Pickle", None, tab_selection) + tab_video.select(lambda: "Video", None, tab_selection) + with gr.Accordion(open=True, label="Cropping Options for Driving Video"): + with gr.Row(): + flag_crop_driving_video_input = gr.Checkbox(value=False, label="do crop (driving)") + scale_crop_driving_video = gr.Number(value=2.2, label="driving crop scale", minimum=1.8, maximum=3.2, step=0.05) + vx_ratio_crop_driving_video = gr.Number(value=0.0, label="driving crop x", minimum=-0.5, maximum=0.5, step=0.01) + vy_ratio_crop_driving_video = gr.Number(value=-0.1, label="driving crop y", minimum=-0.5, maximum=0.5, step=0.01) + + with gr.Row(): + with gr.Accordion(open=False, label="Animation Options"): + with gr.Row(): + flag_stitching = gr.Checkbox(value=False, label="stitching (not recommended)") + flag_remap_input = gr.Checkbox(value=False, label="paste-back (not recommended)") + driving_multiplier = gr.Number(value=1.0, label="driving multiplier", minimum=0.0, maximum=2.0, step=0.02) + + gr.Markdown(load_description("assets/gradio/gradio_description_animate_clear.md")) + with gr.Row(): + process_button_animation = gr.Button("๐Ÿš€ Animate", variant="primary") + with gr.Row(): + with gr.Column(): + with gr.Accordion(open=True, label="The animated video in the cropped image space"): + output_video_i2v.render() + with gr.Column(): + with gr.Accordion(open=True, label="The animated gif in the cropped image space"): + output_video_i2v_gif.render() + with gr.Column(): + with gr.Accordion(open=True, label="The animated video"): + output_video_concat_i2v.render() + with gr.Row(): + process_button_reset = gr.ClearButton([source_image_input, driving_video_input, output_video_i2v, output_video_concat_i2v, output_video_i2v_gif], value="๐Ÿงน Clear") + + with gr.Row(): + # Examples + gr.Markdown("## You could also choose the examples below by one click โฌ‡๏ธ") + with gr.Row(): + with gr.Tabs(): + with gr.TabItem("๐Ÿ“ Driving Pickle") as tab_video: + gr.Examples( + examples=data_examples_i2v_pickle, + fn=gpu_wrapped_execute_video, + inputs=[ + source_image_input, + driving_video_pickle_input, + flag_do_crop_input, + flag_stitching, + flag_remap_input, + flag_crop_driving_video_input, + ], + outputs=[output_image, output_image_paste_back, output_video_i2v_gif], + examples_per_page=len(data_examples_i2v_pickle), + cache_examples=False, + ) + with gr.TabItem("๐ŸŽž๏ธ Driving Video") as tab_video: + gr.Examples( + examples=data_examples_i2v, + fn=gpu_wrapped_execute_video, + inputs=[ + source_image_input, + driving_video_input, + flag_do_crop_input, + flag_stitching, + flag_remap_input, + flag_crop_driving_video_input, + ], + outputs=[output_image, output_image_paste_back, output_video_i2v_gif], + examples_per_page=len(data_examples_i2v), + cache_examples=False, + ) + + process_button_animation.click( + fn=gpu_wrapped_execute_video, + inputs=[ + source_image_input, + driving_video_input, + driving_video_pickle_input, + flag_do_crop_input, + flag_remap_input, + driving_multiplier, + flag_stitching, + flag_crop_driving_video_input, + scale, + vx_ratio, + vy_ratio, + scale_crop_driving_video, + vx_ratio_crop_driving_video, + vy_ratio_crop_driving_video, + tab_selection, + ], + outputs=[output_video_i2v, output_video_concat_i2v, output_video_i2v_gif], + show_progress=True + ) + +demo.launch( + server_port=args.server_port, + share=args.share, + server_name=args.server_name +) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/attention.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/attention.py new file mode 100644 index 0000000..dca9804 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/attention.py @@ -0,0 +1,175 @@ +import os +import torch +from functools import cache + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +# ARC GPUs can't allocate more than 4GB to a single block so we slice the attetion layers + +sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 6)) +attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4)) + +# Find something divisible with the input_tokens +@cache +def find_slice_size(slice_size, slice_block_size): + while (slice_size * slice_block_size) > attention_slice_rate: + slice_size = slice_size // 2 + if slice_size <= 1: + slice_size = 1 + break + return slice_size + +# Find slice sizes for SDPA +@cache +def find_sdpa_slice_sizes(query_shape, query_element_size): + if len(query_shape) == 3: + batch_size_attention, query_tokens, shape_three = query_shape + shape_four = 1 + else: + batch_size_attention, query_tokens, shape_three, shape_four = query_shape + + slice_block_size = query_tokens * shape_three * shape_four / 1024 / 1024 * query_element_size + block_size = batch_size_attention * slice_block_size + + split_slice_size = batch_size_attention + split_2_slice_size = query_tokens + split_3_slice_size = shape_three + + do_split = False + do_split_2 = False + do_split_3 = False + + if block_size > sdpa_slice_trigger_rate: + do_split = True + split_slice_size = find_slice_size(split_slice_size, slice_block_size) + if split_slice_size * slice_block_size > attention_slice_rate: + slice_2_block_size = split_slice_size * shape_three * shape_four / 1024 / 1024 * query_element_size + do_split_2 = True + split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size) + if split_2_slice_size * slice_2_block_size > attention_slice_rate: + slice_3_block_size = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * query_element_size + do_split_3 = True + split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size) + + return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size + +# Find slice sizes for BMM +@cache +def find_bmm_slice_sizes(input_shape, input_element_size, mat2_shape): + batch_size_attention, input_tokens, mat2_atten_shape = input_shape[0], input_shape[1], mat2_shape[2] + slice_block_size = input_tokens * mat2_atten_shape / 1024 / 1024 * input_element_size + block_size = batch_size_attention * slice_block_size + + split_slice_size = batch_size_attention + split_2_slice_size = input_tokens + split_3_slice_size = mat2_atten_shape + + do_split = False + do_split_2 = False + do_split_3 = False + + if block_size > attention_slice_rate: + do_split = True + split_slice_size = find_slice_size(split_slice_size, slice_block_size) + if split_slice_size * slice_block_size > attention_slice_rate: + slice_2_block_size = split_slice_size * mat2_atten_shape / 1024 / 1024 * input_element_size + do_split_2 = True + split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size) + if split_2_slice_size * slice_2_block_size > attention_slice_rate: + slice_3_block_size = split_slice_size * split_2_slice_size / 1024 / 1024 * input_element_size + do_split_3 = True + split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size) + + return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size + +original_torch_bmm = torch.bmm +def torch_bmm_32_bit(input, mat2, *, out=None): + if input.device.type != "xpu": + return original_torch_bmm(input, mat2, out=out) + do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_bmm_slice_sizes(input.shape, input.element_size(), mat2.shape) + + # Slice BMM + if do_split: + batch_size_attention, input_tokens, mat2_atten_shape = input.shape[0], input.shape[1], mat2.shape[2] + hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype) + for i in range(batch_size_attention // split_slice_size): + start_idx = i * split_slice_size + end_idx = (i + 1) * split_slice_size + if do_split_2: + for i2 in range(input_tokens // split_2_slice_size): # pylint: disable=invalid-name + start_idx_2 = i2 * split_2_slice_size + end_idx_2 = (i2 + 1) * split_2_slice_size + if do_split_3: + for i3 in range(mat2_atten_shape // split_3_slice_size): # pylint: disable=invalid-name + start_idx_3 = i3 * split_3_slice_size + end_idx_3 = (i3 + 1) * split_3_slice_size + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_torch_bmm( + input[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3], + mat2[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3], + out=out + ) + else: + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm( + input[start_idx:end_idx, start_idx_2:end_idx_2], + mat2[start_idx:end_idx, start_idx_2:end_idx_2], + out=out + ) + else: + hidden_states[start_idx:end_idx] = original_torch_bmm( + input[start_idx:end_idx], + mat2[start_idx:end_idx], + out=out + ) + torch.xpu.synchronize(input.device) + else: + return original_torch_bmm(input, mat2, out=out) + return hidden_states + +original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention +def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs): + if query.device.type != "xpu": + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_sdpa_slice_sizes(query.shape, query.element_size()) + + # Slice SDPA + if do_split: + batch_size_attention, query_tokens, shape_three = query.shape[0], query.shape[1], query.shape[2] + hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype) + for i in range(batch_size_attention // split_slice_size): + start_idx = i * split_slice_size + end_idx = (i + 1) * split_slice_size + if do_split_2: + for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name + start_idx_2 = i2 * split_2_slice_size + end_idx_2 = (i2 + 1) * split_2_slice_size + if do_split_3: + for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name + start_idx_3 = i3 * split_3_slice_size + end_idx_3 = (i3 + 1) * split_3_slice_size + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_scaled_dot_product_attention( + query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3], + key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3], + value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3], + attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + else: + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention( + query[start_idx:end_idx, start_idx_2:end_idx_2], + key[start_idx:end_idx, start_idx_2:end_idx_2], + value[start_idx:end_idx, start_idx_2:end_idx_2], + attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + else: + hidden_states[start_idx:end_idx] = original_scaled_dot_product_attention( + query[start_idx:end_idx], + key[start_idx:end_idx], + value[start_idx:end_idx], + attn_mask=attn_mask[start_idx:end_idx] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal, **kwargs + ) + torch.xpu.synchronize(query.device) + else: + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + return hidden_states diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/xpu_override.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/xpu_override.py new file mode 100644 index 0000000..be1efea --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/intel_xpu/xpu_override.py @@ -0,0 +1,319 @@ +import sys +import os +# os.add_dll_directory(os.path.join(sys.base_exec_prefix)) + +from functools import wraps +from contextlib import nullcontext +import torch +import numpy as np +from .attention import scaled_dot_product_attention_32_bit + +devices = { "device": "xpu" } + +device_supports_fp64 = torch.xpu.has_fp64_dtype() if hasattr(torch.xpu, "has_fp64_dtype") else torch.xpu.get_device_properties("xpu").has_fp64 + +class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods + def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument + if isinstance(device_ids, list) and len(device_ids) > 1: + # errors.log.error("IPEX backend doesn't support DataParallel on multiple XPU devices") + print("IPEX error") + return module.to(devices.device) + +def return_null_context(*args, **kwargs): # pylint: disable=unused-argument + return nullcontext() + +@property +def is_cuda(self): + return self.device.type == 'xpu' or self.device.type == 'cuda' + +def check_device(device): + return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int)) + +def return_xpu(device): + return "xpu" + # return f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device(devices.device) if isinstance(device, torch.device) else devices.device + +# Autocast +original_autocast_init = torch.amp.autocast_mode.autocast.__init__ +@wraps(torch.amp.autocast_mode.autocast.__init__) +def autocast_init(self, device_type, dtype=None, enabled=True, cache_enabled=None): + if device_type == "cuda" or device_type == "xpu": + if dtype is None: + dtype = devices.dtype + return original_autocast_init(self, device_type="xpu", dtype=dtype, enabled=enabled, cache_enabled=cache_enabled) + else: + return original_autocast_init(self, device_type=device_type, dtype=dtype, enabled=enabled, cache_enabled=cache_enabled) + +# Latent Antialias CPU Offload: +original_interpolate = torch.nn.functional.interpolate +@wraps(torch.nn.functional.interpolate) +def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments + if mode in {'bicubic', 'bilinear'}: + return_device = tensor.device + return_dtype = tensor.dtype + return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype) + else: + return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias) + + +# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit): +original_from_numpy = torch.from_numpy + +@wraps(torch.from_numpy) +def from_numpy(ndarray): + if ndarray.dtype == float: + return original_from_numpy(ndarray.astype('float32')) + else: + return original_from_numpy(ndarray) + +original_as_tensor = torch.as_tensor +@wraps(torch.as_tensor) +def as_tensor(data, dtype=None, device=None): + if check_device(device): + device = return_xpu(device) + if isinstance(data, np.ndarray) and data.dtype == float and not ( + (isinstance(device, torch.device) and device.type == "cpu") or (isinstance(device, str) and "cpu" in device)): + return original_as_tensor(data, dtype=torch.float32, device=device) + else: + return original_as_tensor(data, dtype=dtype, device=device) + +if device_supports_fp64 and os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None) is None: + print("Device Supports FP64, will use default SDPA from Torch") + original_torch_bmm = torch.bmm + original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention +else: + # 32 bit attention workarounds for Alchemist: + try: + from attention import torch_bmm_32_bit as original_torch_bmm + from attention import scaled_dot_product_attention_32_bit as original_scaled_dot_product_attention + except Exception: # pylint: disable=broad-exception-caught + print("Error when import sdpa") + original_torch_bmm = torch.bmm + original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention + +# Data Type Errors: +@wraps(torch.bmm) +def torch_bmm(input, mat2, *, out=None): + if input.dtype != mat2.dtype: + mat2 = mat2.to(input.dtype) + return original_torch_bmm(input, mat2, out=out) + +@wraps(torch.nn.functional.scaled_dot_product_attention) +def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs): + if query.dtype != key.dtype: + key = key.to(dtype=query.dtype) + if query.dtype != value.dtype: + value = value.to(dtype=query.dtype) + if attn_mask is not None and query.dtype != attn_mask.dtype: + attn_mask = attn_mask.to(dtype=query.dtype) + return scaled_dot_product_attention_32_bit(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) + +# A1111 FP16 +original_functional_group_norm = torch.nn.functional.group_norm +@wraps(torch.nn.functional.group_norm) +def functional_group_norm(input, num_groups, weight=None, bias=None, eps=1e-05): + if weight is not None and input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_group_norm(input, num_groups, weight=weight, bias=bias, eps=eps) + +# A1111 BF16 +original_functional_layer_norm = torch.nn.functional.layer_norm +@wraps(torch.nn.functional.layer_norm) +def functional_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05): + if weight is not None and input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_layer_norm(input, normalized_shape, weight=weight, bias=bias, eps=eps) + +# Training +original_functional_linear = torch.nn.functional.linear +@wraps(torch.nn.functional.linear) +def functional_linear(input, weight, bias=None): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_linear(input, weight, bias=bias) + +original_functional_conv2d = torch.nn.functional.conv2d +@wraps(torch.nn.functional.conv2d) +def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if input.dtype != weight.data.dtype: + input = input.to(dtype=weight.data.dtype) + if bias is not None and bias.data.dtype != weight.data.dtype: + bias.data = bias.data.to(dtype=weight.data.dtype) + return original_functional_conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +# SwinIR BF16: +original_functional_pad = torch.nn.functional.pad +@wraps(torch.nn.functional.pad) +def functional_pad(input, pad, mode='constant', value=None): + if mode == 'reflect' and input.dtype == torch.bfloat16: + return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16) + else: + return original_functional_pad(input, pad, mode=mode, value=value) + + +original_torch_tensor = torch.tensor +@wraps(torch.tensor) +def torch_tensor(data, *args, dtype=None, device=None, **kwargs): + if check_device(device): + device = return_xpu(device) + if not device_supports_fp64: + if (isinstance(device, torch.device) and device.type == "xpu") or (isinstance(device, str) and "xpu" in device): + if dtype == torch.float64: + dtype = torch.float32 + elif dtype is None and (hasattr(data, "dtype") and (data.dtype == torch.float64 or data.dtype == float)): + dtype = torch.float32 + return original_torch_tensor(data, *args, dtype=dtype, device=device, **kwargs) + +original_Tensor_to = torch.Tensor.to +@wraps(torch.Tensor.to) +def Tensor_to(self, device=None, *args, **kwargs): + if check_device(device): + return original_Tensor_to(self, return_xpu(device), *args, **kwargs) + else: + return original_Tensor_to(self, device, *args, **kwargs) + +original_Tensor_cuda = torch.Tensor.cuda +@wraps(torch.Tensor.cuda) +def Tensor_cuda(self, device=None, *args, **kwargs): + if check_device(device): + return original_Tensor_cuda(self, return_xpu(device), *args, **kwargs) + else: + return original_Tensor_cuda(self, device, *args, **kwargs) + +original_Tensor_pin_memory = torch.Tensor.pin_memory +@wraps(torch.Tensor.pin_memory) +def Tensor_pin_memory(self, device=None, *args, **kwargs): + if device is None: + device = "xpu" + if check_device(device): + return original_Tensor_pin_memory(self, return_xpu(device), *args, **kwargs) + else: + return original_Tensor_pin_memory(self, device, *args, **kwargs) + +original_UntypedStorage_init = torch.UntypedStorage.__init__ +@wraps(torch.UntypedStorage.__init__) +def UntypedStorage_init(*args, device=None, **kwargs): + if check_device(device): + return original_UntypedStorage_init(*args, device=return_xpu(device), **kwargs) + else: + return original_UntypedStorage_init(*args, device=device, **kwargs) + +original_UntypedStorage_cuda = torch.UntypedStorage.cuda +@wraps(torch.UntypedStorage.cuda) +def UntypedStorage_cuda(self, device=None, *args, **kwargs): + if check_device(device): + return original_UntypedStorage_cuda(self, return_xpu(device), *args, **kwargs) + else: + return original_UntypedStorage_cuda(self, device, *args, **kwargs) + +original_torch_empty = torch.empty +@wraps(torch.empty) +def torch_empty(*args, device=None, **kwargs): + if check_device(device): + return original_torch_empty(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_empty(*args, device=device, **kwargs) + +original_torch_randn = torch.randn +@wraps(torch.randn) +def torch_randn(*args, device=None, dtype=None, **kwargs): + if dtype == bytes: + dtype = None + if check_device(device): + return original_torch_randn(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_randn(*args, device=device, **kwargs) + +original_torch_ones = torch.ones +@wraps(torch.ones) +def torch_ones(*args, device=None, **kwargs): + if check_device(device): + return original_torch_ones(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_ones(*args, device=device, **kwargs) + +original_torch_zeros = torch.zeros +@wraps(torch.zeros) +def torch_zeros(*args, device=None, **kwargs): + if check_device(device): + return original_torch_zeros(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_zeros(*args, device=device, **kwargs) + +original_torch_full = torch.full +@wraps(torch.full) +def torch_full(*args, device=None, **kwargs): + if check_device(device): + return original_torch_full(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_full(*args, device=device, **kwargs) + +original_torch_linspace = torch.linspace +@wraps(torch.linspace) +def torch_linspace(*args, device=None, **kwargs): + if check_device(device): + return original_torch_linspace(*args, device=return_xpu(device), **kwargs) + else: + return original_torch_linspace(*args, device=device, **kwargs) + +original_torch_Generator = torch.Generator +@wraps(torch.Generator) +def torch_Generator(device=None): + if check_device(device): + return original_torch_Generator(return_xpu(device)) + else: + return original_torch_Generator(device) + +original_torch_load = torch.load +@wraps(torch.load) +def torch_load(f, map_location=None, *args, **kwargs): + if map_location is None: + map_location = "xpu" + if check_device(map_location): + return original_torch_load(f, *args, map_location=return_xpu(map_location), **kwargs) + else: + return original_torch_load(f, *args, map_location=map_location, **kwargs) + + +# Hijack Functions: +def xpu_override(): + torch.tensor = torch_tensor + torch.Tensor.to = Tensor_to + torch.Tensor.cuda = Tensor_cuda + torch.Tensor.pin_memory = Tensor_pin_memory + torch.UntypedStorage.__init__ = UntypedStorage_init + torch.UntypedStorage.cuda = UntypedStorage_cuda + torch.empty = torch_empty + torch.randn = torch_randn + torch.ones = torch_ones + torch.zeros = torch_zeros + torch.full = torch_full + torch.linspace = torch_linspace + torch.Generator = torch_Generator + torch.load = torch_load + + torch.backends.cuda.sdp_kernel = return_null_context + torch.nn.DataParallel = DummyDataParallel + torch.UntypedStorage.is_cuda = is_cuda + torch.amp.autocast_mode.autocast.__init__ = autocast_init + + torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention + torch.nn.functional.group_norm = functional_group_norm + torch.nn.functional.layer_norm = functional_layer_norm + torch.nn.functional.linear = functional_linear + torch.nn.functional.conv2d = functional_conv2d + torch.nn.functional.interpolate = interpolate + torch.nn.functional.pad = functional_pad + + torch.bmm = torch_bmm + if not device_supports_fp64: + torch.from_numpy = from_numpy + torch.as_tensor = as_tensor diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/pretrained_weights/.gitkeep b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/pretrained_weights/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme.md b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme.md new file mode 100644 index 0000000..b7775db --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme.md @@ -0,0 +1,296 @@ +

LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

+ +
+ Jianzhu Guo 1โ€   + Dingyun Zhang 1,2  + Xiaoqiang Liu 1  + Zhizhou Zhong 1,3  + Yuan Zhang 1  +
+ +
+ Pengfei Wan 1  + Di Zhang 1  +
+ +
+ 1 Kuaishou Technology  2 University of Science and Technology of China  3 Fudan University  +
+
+ โ€  Corresponding author +
+
+ +
+ + + + + +
+ English | ็ฎ€ไฝ“ไธญๆ–‡ +
+
+ + +

+ showcase +
+ ๐Ÿ”ฅ For more results, visit our homepage ๐Ÿ”ฅ +

+ + +## ๐Ÿ”ฅ Updates +- **`2024/10/18`**: โ— We have updated the versions of the `transformers` and `gradio` libraries to avoid security vulnerabilities. Details [here](https://github.com/KwaiVGI/LivePortrait/pull/421/files). +- **`2024/08/29`**: ๐Ÿ“ฆ We update the Windows [one-click installer](https://huggingface.co/cleardusk/LivePortrait-Windows/blob/main/LivePortrait-Windows-v20240829.zip) and support auto-updates, see [changelog](https://huggingface.co/cleardusk/LivePortrait-Windows#20240829). +- **`2024/08/19`**: ๐Ÿ–ผ๏ธ We support **image driven mode** and **regional control**. For details, see [**here**](./assets/docs/changelog/2024-08-19.md). +- **`2024/08/06`**: ๐ŸŽจ We support **precise portrait editing** in the Gradio interface, inspired by [ComfyUI-AdvancedLivePortrait](https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait). See [**here**](./assets/docs/changelog/2024-08-06.md). +- **`2024/08/05`**: ๐Ÿ“ฆ Windows users can now download the [one-click installer](https://huggingface.co/cleardusk/LivePortrait-Windows/blob/main/LivePortrait-Windows-v20240806.zip) for Humans mode and **Animals mode** now! For details, see [**here**](./assets/docs/changelog/2024-08-05.md). +- **`2024/08/02`**: ๐Ÿ˜ธ We released a version of the **Animals model**, along with several other updates and improvements. Check out the details [**here**](./assets/docs/changelog/2024-08-02.md)! +- **`2024/07/25`**: ๐Ÿ“ฆ Windows users can now download the package from [HuggingFace](https://huggingface.co/cleardusk/LivePortrait-Windows/tree/main). Simply unzip and double-click `run_windows.bat` to enjoy! +- **`2024/07/24`**: ๐ŸŽจ We support pose editing for source portraits in the Gradio interface. Weโ€™ve also lowered the default detection threshold to increase recall. [Have fun](assets/docs/changelog/2024-07-24.md)! +- **`2024/07/19`**: โœจ We support ๐ŸŽž๏ธ **portrait video editing (aka v2v)**! More to see [here](assets/docs/changelog/2024-07-19.md). +- **`2024/07/17`**: ๐ŸŽ We support macOS with Apple Silicon, modified from [jeethu](https://github.com/jeethu)'s PR [#143](https://github.com/KwaiVGI/LivePortrait/pull/143). +- **`2024/07/10`**: ๐Ÿ’ช We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here](assets/docs/changelog/2024-07-10.md). +- **`2024/07/09`**: ๐Ÿค— We released the [HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait), thanks to the HF team and [Gradio](https://github.com/gradio-app/gradio)! +- **`2024/07/04`**: ๐Ÿ˜Š We released the initial version of the inference code and models. Continuous updates, stay tuned! +- **`2024/07/04`**: ๐Ÿ”ฅ We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168). + + + +## Introduction ๐Ÿ“– +This repo, named **LivePortrait**, contains the official PyTorch implementation of our paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168). +We are actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) ๐Ÿ’–. + +## Getting Started ๐Ÿ +### 1. Clone the code and prepare the environment ๐Ÿ› ๏ธ + +> [!Note] +> Make sure your system has [`git`](https://git-scm.com/), [`conda`](https://anaconda.org/anaconda/conda), and [`FFmpeg`](https://ffmpeg.org/download.html) installed. For details on FFmpeg installation, see [**how to install FFmpeg**](assets/docs/how-to-install-ffmpeg.md). + +```bash +git clone https://github.com/KwaiVGI/LivePortrait +cd LivePortrait + +# create env using conda +conda create -n LivePortrait python=3.10 +conda activate LivePortrait +``` + +#### For Linux or Windows Users +[X-Pose](https://github.com/IDEA-Research/X-Pose) requires your `torch` version to be compatible with the CUDA version. + +Firstly, check your current CUDA version by: +```bash +nvcc -V # example versions: 11.1, 11.8, 12.1, etc. +``` + +Then, install the corresponding torch version. Here are examples for different CUDA versions. Visit the [PyTorch Official Website](https://pytorch.org/get-started/previous-versions) for installation commands if your CUDA version is not listed: +```bash +# for CUDA 11.1 +pip install torch==1.10.1+cu111 torchvision==0.11.2 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html +# for CUDA 11.8 +pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu118 +# for CUDA 12.1 +pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121 +# ... +``` + +**Note**: On Windows systems, some higher versions of CUDA (such as 12.4, 12.6, etc.) may lead to unknown issues. You may consider downgrading CUDA to version 11.8 for stability. See the [downgrade guide](https://github.com/dimitribarbot/sd-webui-live-portrait/blob/main/assets/docs/how-to-install-xpose.md#cuda-toolkit-118) by [@dimitribarbot](https://github.com/dimitribarbot). + +Finally, install the remaining dependencies: +```bash +pip install -r requirements.txt +``` + +#### For macOS with Apple Silicon Users +The [X-Pose](https://github.com/IDEA-Research/X-Pose) dependency does not support macOS, so you can skip its installation. While Humans mode works as usual, Animals mode is not supported. Use the provided requirements file for macOS with Apple Silicon: +```bash +# for macOS with Apple Silicon users +pip install -r requirements_macOS.txt +``` + +### 2. Download pretrained weights ๐Ÿ“ฅ + +The easiest way to download the pretrained weights is from HuggingFace: +```bash +# !pip install -U "huggingface_hub[cli]" +huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs" +``` + +If you cannot access to Huggingface, you can use [hf-mirror](https://hf-mirror.com/) to download: +```bash +# !pip install -U "huggingface_hub[cli]" +export HF_ENDPOINT=https://hf-mirror.com +huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs" +``` + +Alternatively, you can download all pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). Unzip and place them in `./pretrained_weights`. + +Ensuring the directory structure is as or contains [**this**](assets/docs/directory-structure.md). + +### 3. Inference ๐Ÿš€ + +#### Fast hands-on (humans) ๐Ÿ‘ค +```bash +# For Linux and Windows users +python inference.py + +# For macOS users with Apple Silicon (Intel is not tested). NOTE: this maybe 20x slower than RTX 4090 +PYTORCH_ENABLE_MPS_FALLBACK=1 python inference.py +``` + +If the script runs successfully, you will get an output mp4 file named `animations/s6--d0_concat.mp4`. This file includes the following results: driving video, input image or video, and generated result. + +

+ image +

+ +Or, you can change the input by specifying the `-s` and `-d` arguments: + +```bash +# source input is an image +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 + +# source input is a video โœจ +python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d0.mp4 + +# more options to see +python inference.py -h +``` + +#### Fast hands-on (animals) ๐Ÿฑ๐Ÿถ +Animals mode is ONLY tested on Linux and Windows with NVIDIA GPU. + +You need to build an OP named `MultiScaleDeformableAttention` first, which is used by [X-Pose](https://github.com/IDEA-Research/X-Pose), a general keypoint detection framework. +```bash +cd src/utils/dependencies/XPose/models/UniPose/ops +python setup.py build install +cd - # equal to cd ../../../../../../../ +``` + +Then +```bash +python inference_animals.py -s assets/examples/source/s39.jpg -d assets/examples/driving/wink.pkl --driving_multiplier 1.75 --no_flag_stitching +``` +If the script runs successfully, you will get an output mp4 file named `animations/s39--wink_concat.mp4`. +

+ image +

+ +#### Driving video auto-cropping ๐Ÿ“ข๐Ÿ“ข๐Ÿ“ข +> [!IMPORTANT] +> To use your own driving video, we **recommend**: โฌ‡๏ธ +> - Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`. +> - Focus on the head area, similar to the example videos. +> - Minimize shoulder movement. +> - Make sure the first frame of driving video is a frontal face with **neutral expression**. + +Below is an auto-cropping case by `--flag_crop_driving_video`: +```bash +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video +``` + +If you find the results of auto-cropping is not well, you can modify the `--scale_crop_driving_video`, `--vy_ratio_crop_driving_video` options to adjust the scale and offset, or do it manually. + +#### Motion template making +You can also use the auto-generated motion template files ending with `.pkl` to speed up inference, and **protect privacy**, such as: +```bash +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl # portrait animation +python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d5.pkl # portrait video editing +``` + +### 4. Gradio interface ๐Ÿค— + +We also provide a Gradio interface for a better experience, just run by: + +```bash +# For Linux and Windows users (and macOS with Intel??) +python app.py # humans mode + +# For macOS with Apple Silicon users, Intel not supported, this maybe 20x slower than RTX 4090 +PYTORCH_ENABLE_MPS_FALLBACK=1 python app.py # humans mode +``` + +We also provide a Gradio interface of animals mode, which is only tested on Linux with NVIDIA GPU: +```bash +python app_animals.py # animals mode ๐Ÿฑ๐Ÿถ +``` + +You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs! + +๐Ÿš€ We also provide an acceleration option `--flag_do_torch_compile`. The first-time inference triggers an optimization process (about one minute), making subsequent inferences 20-30% faster. Performance gains may vary with different CUDA versions. +```bash +# enable torch.compile for faster inference +python app.py --flag_do_torch_compile +``` +**Note**: This method is not supported on Windows and macOS. + +**Or, try it out effortlessly on [HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait) ๐Ÿค—** + +### 5. Inference speed evaluation ๐Ÿš€๐Ÿš€๐Ÿš€ +We have also provided a script to evaluate the inference speed of each module: + +```bash +# For NVIDIA GPU +python speed.py +``` + +The results are [**here**](./assets/docs/speed.md). + +## Community Resources ๐Ÿค— + +Discover the invaluable resources contributed by our community to enhance your LivePortrait experience. + + +### Community-developed Projects + +| Repo (sorted by created timestamp) | Description | Author | +|------|------|--------| +| [**FacePoke**](https://github.com/jbilcke-hf/FacePoke) | A real-time head transformation app, controlled by your mouse! | [@jbilcke-hf](https://github.com/jbilcke-hf) | +| [**FaceFusion**](https://github.com/facefusion/facefusion) | FaceFusion 3.0 integregates LivePortrait as `expression_restorer` and `face_editor` processors. | [@henryruhs](https://github.com/henryruhs) | +| [**sd-webui-live-portrait**](https://github.com/dimitribarbot/sd-webui-live-portrait) | WebUI extension of LivePortrait, adding atab to the original Stable Diffusion WebUI to benefit from LivePortrait features. | [@dimitribarbot](https://github.com/dimitribarbot) | +| [**ComfyUI-LivePortraitKJ**](https://github.com/kijai/ComfyUI-LivePortraitKJ) | A ComfyUI node to use LivePortrait, with MediaPipe as as an alternative to Insightface. | [@kijai](https://github.com/kijai) | +| [**ComfyUI-AdvancedLivePortrait**](https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait) | A faster ComfyUI node with real-time preview that has inspired many other community-developed tools and projects. | [@PowerHouseMan](https://github.com/PowerHouseMan) | +| [**comfyui-liveportrait**](https://github.com/shadowcz007/comfyui-liveportrait) | A ComfyUI node to use LivePortrait, supporting multi-faces, expression interpolation etc, with a [tutorial](https://www.bilibili.com/video/BV1JW421R7sP). | [@shadowcz007](https://github.com/shadowcz007) | + +### Playgrounds, ๐Ÿค— HuggingFace Spaces and Others +- [FacePoke Space](https://huggingface.co/spaces/jbilcke-hf/FacePoke) +- [Expression Editor Space](https://huggingface.co/spaces/fffiloni/expression-editor) +- [Expression Editor Replicate](https://replicate.com/fofr/expression-editor) +- [Face Control Realtime Demo](https://fal.ai/demos/face-control) on FAL +- [Replicate Playground](https://replicate.com/fofr/live-portrait) +- Nuke can use LivePortrait through CompyUI node, details [here](https://x.com/bilawalsidhu/status/1837349806475276338) +- LivePortrait lives on [Poe](https://poe.com/LivePortrait) + +### Video Tutorials +- [Workflow of LivePortrait Video to Video](https://youtu.be/xfzK_6cTs58?si=aYjgypeJBkhc46VL) by [@curiousrefuge](https://www.youtube.com/@curiousrefuge) +- [Google Colab tutorial](https://youtu.be/59Y9ePAXTp0?si=KzEWhklBlporW7D8) by [@Planet Ai](https://www.youtube.com/@planetai217) +- [Paper reading](https://youtu.be/fD0P6UWSu8I?si=Vn5wxUa8qSu1jv4l) by [@TwoMinutePapers](https://www.youtube.com/@TwoMinutePapers) +- [ComfyUI Advanced LivePortrait](https://youtu.be/q0Vf-ZZsbzI?si=nbs3npleH-dVCt28) by [TutoView](https://www.youtube.com/@TutoView) +- [LivePortarit exploration](https://www.youtube.com/watch?v=vsvlbTEqgXQ) and [A deep dive into LivePortrait](https://youtu.be/cucaEEDYmsw?si=AtPaDWc5G-a4E8dD) by [TheoreticallyMedia](https://www.youtube.com/@TheoreticallyMedia) +- [LivePortrait hands-on tutorial](https://www.youtube.com/watch?v=uyjSTAOY7yI) by [@AI Search](https://www.youtube.com/@theAIsearch) +- [ComfyUI tutorial](https://www.youtube.com/watch?v=8-IcDDmiUMM) by [@Sebastian Kamph](https://www.youtube.com/@sebastiankamph) +- A [tutorial](https://www.bilibili.com/video/BV1cf421i7Ly) on BiliBili + +And so MANY amazing contributions from our community, too many to list them all ๐Ÿ’– + +## Acknowledgements ๐Ÿ’ +We would like to thank the contributors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model), [Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis), [SPADE](https://github.com/NVlabs/SPADE), [InsightFace](https://github.com/deepinsight/insightface) and [X-Pose](https://github.com/IDEA-Research/X-Pose) repositories, for their open research and contributions. + +## Ethics Considerations ๐Ÿ›ก๏ธ +Portrait animation technologies come with social risks, particularly the potential for misuse in creating deepfakes. To mitigate these risks, itโ€™s crucial to follow ethical guidelines and adopt responsible usage practices. At present, the synthesized results contain visual artifacts that may help in detecting deepfakes. Please note that we do not assume any legal responsibility for the use of the results generated by this project. + +## Citation ๐Ÿ’– +If you find LivePortrait useful for your research, welcome to ๐ŸŒŸ this repo and cite our work using the following BibTeX: +```bibtex +@article{guo2024liveportrait, + title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control}, + author = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di}, + journal = {arXiv preprint arXiv:2407.03168}, + year = {2024} +} +``` + +## Contact ๐Ÿ“ง +[**Jianzhu Guo (้ƒญๅปบ็ )**](https://guojianzhu.com); **guojianzhu1994@gmail.com** diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme_zh_cn.md b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme_zh_cn.md new file mode 100644 index 0000000..d5f2501 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/readme_zh_cn.md @@ -0,0 +1,309 @@ +

LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

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+ ้ƒญๅปบ็  1โ€   + ๅผ ไธ่Šธ 1,2  + ๅˆ˜ๆ™“ๅผบ 1  + ้’Ÿๆ™บ่ˆŸ 1,3  + ๅผ ๆธŠ 1  + ไธ‡้น้ฃž 1  + ๅผ ่ฟช 1  +
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+ 1 ๅฟซๆ‰‹็ง‘ๆŠ€  2 ไธญๅ›ฝ็ง‘ๅญฆๆŠ€ๆœฏๅคงๅญฆ  3 ๅคๆ—ฆๅคงๅญฆ  +
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+ โ€  ้€š่ฎฏไฝœ่€…๏ผˆProject Lead๏ผ‰ +
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+ English | ็ฎ€ไฝ“ไธญๆ–‡ +
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+ showcase +
+ ๐Ÿ”ฅ ๆ›ดๅคšๆ•ˆๆžœ๏ผŒ่ฏทๆŸฅ็œ‹ๆˆ‘ไปฌ็š„ ไธป้กต ๐Ÿ”ฅ +

+ + + +## ๐Ÿ”ฅ ๆ›ดๆ–ฐๆ—ฅๅฟ— +- **`2024/10/18`**๏ผšโ— ๆˆ‘ไปฌๆ›ดๆ–ฐไบ†`transformers`๏ผŒ`gradio`ๅบ“็š„็‰ˆๆœฌ้ฟๅ…ๅฎ‰ๅ…จๆผๆดž๏ผŒๅ…ทไฝ“ๆŸฅ็œ‹[่ฟ™้‡Œ](https://github.com/KwaiVGI/LivePortrait/pull/421/files). +- **`2024/08/29`**๏ผš๐Ÿ“ฆ ๆˆ‘ไปฌๆ›ดๆ–ฐไบ†Windows[ไธ€้”ฎๅฎ‰่ฃ…็จ‹ๅบ](https://huggingface.co/cleardusk/LivePortrait-Windows/blob/main/LivePortrait-Windows-v20240829.zip)ๅนถๆ”ฏๆŒ่‡ชๅŠจๆ›ดๆ–ฐ, ่ฏฆๆƒ…ๅปบ[่ฟ™้‡Œ](https://huggingface.co/cleardusk/LivePortrait-Windows#20240829)ใ€‚ +- **`2024/08/19`**๏ผš๐Ÿ–ผ๏ธ ๆˆ‘ไปฌๆ”ฏๆŒไบ†**ๅ›พๅƒ้ฉฑๅŠจๆจกๅผ**ๅ’Œ**ๅŒบๅŸŸๆŽงๅˆถ**ใ€‚่ฏฆๆƒ…่ฏท่ง[**่ฟ™้‡Œ**](./assets/docs/changelog/2024-08-19.md)ใ€‚ +- **`2024/08/06`**๏ผš๐ŸŽจ ๆˆ‘ไปฌๅœจGradio็•Œ้ขๆ”ฏๆŒ**็ฒพ็กฎ็š„ไบบๅƒ็ผ–่พ‘**, ๅ—ๅˆฐ[ComfyUI-AdvancedLivePortrait](https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait)ๅฏๅ‘ใ€‚่ฏฆ่ง[**่ฟ™้‡Œ**](./assets/docs/changelog/2024-08-06.md)ใ€‚ +- **`2024/08/05`**๏ผš๐Ÿ“ฆ Windows็”จๆˆท็Žฐๅœจๅฏไปฅไธ‹่ฝฝ[ไธ€้”ฎๅฎ‰่ฃ…็จ‹ๅบ](https://huggingface.co/cleardusk/LivePortrait-Windows/blob/main/LivePortrait-Windows-v20240806.zip)๏ผŒๆ”ฏๆŒ**ไบบ็ฑปๆจกๅผ**ๅ’Œ**ๅŠจ็‰ฉๆจกๅผ**๏ผ่ฏฆๆƒ…่ง[**่ฟ™้‡Œ**](./assets/docs/changelog/2024-08-05.md)ใ€‚ +- **`2024/08/02`**๏ผš๐Ÿ˜ธ ๆˆ‘ไปฌๅ‘ๅธƒไบ†**ๅŠจ็‰ฉๆจกๅž‹**็‰ˆๆœฌ๏ผŒไปฅๅŠๅ…ถไป–ไธ€ไบ›ๆ›ดๆ–ฐๅ’Œๆ”น่ฟ›ใ€‚ๆŸฅ็œ‹่ฏฆๆƒ…[**่ฟ™้‡Œ**](./assets/docs/changelog/2024-08-02.md)๏ผ +- **`2024/07/25`**๏ผš๐Ÿ“ฆ Windows็”จๆˆท็ŽฐๅœจๅฏไปฅไปŽ [HuggingFace](https://huggingface.co/cleardusk/LivePortrait-Windows/tree/main) ๆˆ– [็™พๅบฆไบ‘](https://pan.baidu.com/s/1FWsWqKe0eNfXrwjEhhCqlw?pwd=86q2) ไธ‹่ฝฝ่ฝฏไปถๅŒ…ใ€‚่งฃๅŽ‹ๅนถๅŒๅ‡ป`run_windows.bat`ๅณๅฏไบซๅ—๏ผ +- **`2024/07/24`**๏ผš๐ŸŽจ ๆˆ‘ไปฌๅœจGradio็•Œ้ขๆ”ฏๆŒๆบไบบๅƒ็š„ๅงฟๅŠฟ็ผ–่พ‘ใ€‚ๆˆ‘ไปฌ่ฟ˜้™ไฝŽไบ†้ป˜่ฎคๆฃ€ๆต‹้˜ˆๅ€ผไปฅๅขžๅŠ ๅฌๅ›ž็Ž‡ใ€‚[็Žฉๅพ—ๅผ€ๅฟƒ](assets/docs/changelog/2024-07-24.md)๏ผ +- **`2024/07/19`**๏ผšโœจ ๆˆ‘ไปฌๆ”ฏๆŒ๐ŸŽž๏ธ **ไบบๅƒ่ง†้ข‘็ผ–่พ‘๏ผˆaka v2v๏ผ‰**๏ผๆ›ดๅคšไฟกๆฏ่ง[**่ฟ™้‡Œ**](assets/docs/changelog/2024-07-19.md)ใ€‚ +- **`2024/07/17`**๏ผš๐ŸŽ ๆˆ‘ไปฌๆ”ฏๆŒmacOSๆญ่ฝฝApple Silicon๏ผŒไฟฎๆ”นๆฅ่‡ช [jeethu](https://github.com/jeethu) ็š„PR [#143](https://github.com/KwaiVGI/LivePortrait/pull/143) ใ€‚ +- **`2024/07/10`**๏ผš๐Ÿ’ชๆˆ‘ไปฌๆ”ฏๆŒ้Ÿณ้ข‘ๅ’Œ่ง†้ข‘ๆ‹ผๆŽฅใ€้ฉฑๅŠจ่ง†้ข‘่‡ชๅŠจ่ฃๅ‰ชไปฅๅŠๅˆถไฝœๆจกๆฟไปฅไฟๆŠค้š็งใ€‚ๆ›ดๅคšไฟกๆฏ่ง[่ฟ™้‡Œ](assets/docs/changelog/2024-07-10.md)ใ€‚ +- **`2024/07/09`**๏ผš๐Ÿค— ๆˆ‘ไปฌๅ‘ๅธƒไบ†[HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait)๏ผŒๆ„Ÿ่ฐขHFๅ›ข้˜Ÿๅ’Œ[Gradio](https://github.com/gradio-app/gradio)๏ผ +- **`2024/07/04`**๏ผš๐Ÿ˜Š ๆˆ‘ไปฌๅ‘ๅธƒไบ†ๅˆๅง‹็‰ˆๆœฌ็š„ๆŽจ็†ไปฃ็ ๅ’Œๆจกๅž‹ใ€‚ๆŒ็ปญๆ›ดๆ–ฐ๏ผŒๆ•ฌ่ฏทๅ…ณๆณจ๏ผ +- **`2024/07/04`**๏ผš๐Ÿ”ฅ ๆˆ‘ไปฌๅ‘ๅธƒไบ†[ไธป้กต](https://liveportrait.github.io)ๅ’Œๅœจ[arXiv](https://arxiv.org/pdf/2407.03168)ไธŠ็š„ๆŠ€ๆœฏๆŠฅๅ‘Šใ€‚ + + + +## ไป‹็ป ๐Ÿ“– +ๆญคไป“ๅบ“ๅไธบ**LivePortrait**๏ผŒๅŒ…ๅซๆˆ‘ไปฌ่ฎบๆ–‡๏ผˆ[LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168)๏ผ‰็š„ๅฎ˜ๆ–นPyTorchๅฎž็Žฐใ€‚ ๆˆ‘ไปฌๆญฃๅœจ็งฏๆžๆ›ดๆ–ฐๅ’Œๆ”น่ฟ›ๆญคไป“ๅบ“ใ€‚ๅฆ‚ๆžœๆ‚จๅ‘็Žฐไปปไฝ•้”™่ฏฏๆˆ–ๆœ‰ๅปบ่ฎฎ๏ผŒๆฌข่ฟŽๆๅ‡บ้—ฎ้ข˜ๆˆ–ๆไบคๅˆๅนถ่ฏทๆฑ‚๐Ÿ’–ใ€‚ + +## ไธŠๆ‰‹ๆŒ‡ๅ— ๐Ÿ +### 1. ๅ…‹้š†ไปฃ็ ๅ’Œๅฎ‰่ฃ…่ฟ่กŒ็Žฏๅขƒ ๐Ÿ› ๏ธ + +> [!Note] +> ็กฎไฟๆ‚จ็š„็ณป็ปŸๅทฒๅฎ‰่ฃ…[`git`](https://git-scm.com/)ใ€[`conda`](https://anaconda.org/anaconda/conda)ๅ’Œ[`FFmpeg`](https://ffmpeg.org/download.html)ใ€‚ๆœ‰ๅ…ณFFmpegๅฎ‰่ฃ…็š„่ฏฆ็ป†ไฟกๆฏ๏ผŒ่ง[**ๅฆ‚ไฝ•ๅฎ‰่ฃ…FFmpeg**](assets/docs/how-to-install-ffmpeg.md)ใ€‚ + +```bash +git clone https://github.com/KwaiVGI/LivePortrait +cd LivePortrait + +# ไฝฟ็”จcondaๅˆ›ๅปบ็Žฏๅขƒ +conda create -n LivePortrait python=3.10 +conda activate LivePortrait +``` + +#### ๅฏนไบŽLinuxๆˆ–Windows็”จๆˆท + +[X-Pose](https://github.com/IDEA-Research/X-Pose)้œ€่ฆๆ‚จ็š„`torch`็‰ˆๆœฌไธŽCUDA็‰ˆๆœฌๅ…ผๅฎนใ€‚ + +้ฆ–ๅ…ˆ๏ผŒ้€š่ฟ‡ไปฅไธ‹ๅ‘ฝไปคๆฃ€ๆŸฅๆ‚จๅฝ“ๅ‰็š„CUDA็‰ˆๆœฌ๏ผš + +```bash +nvcc -V # example versions: 11.1, 11.8, 12.1, etc. +``` + +็„ถๅŽ๏ผŒๅฎ‰่ฃ…็›ธๅบ”็‰ˆๆœฌ็š„torchใ€‚ไปฅไธ‹ๆ˜ฏไธๅŒCUDA็‰ˆๆœฌ็š„็คบไพ‹ใ€‚ๅฆ‚ๆžœๆ‚จ็š„CUDA็‰ˆๆœฌๆœชๅˆ—ๅ‡บ๏ผŒ่ฏท่ฎฟ้—ฎ[PyTorchๅฎ˜ๆ–น็ฝ‘็ซ™](https://pytorch.org/get-started/previous-versions)่Žทๅ–ๅฎ‰่ฃ…ๅ‘ฝไปค๏ผš +```bash +# for CUDA 11.1 +pip install torch==1.10.1+cu111 torchvision==0.11.2 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html +# for CUDA 11.8 +pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu118 +# for CUDA 12.1 +pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121 +# ... +``` + +**ๆณจๆ„**๏ผšๅœจWindows็ณป็ปŸไธŠ๏ผŒไธ€ไบ›่ฟ‡้ซ˜็‰ˆๆœฌ็š„CUDA(12.4ใ€12.6็ญ‰)ๅฏ่ƒฝไผšๅฏผ่‡ดๆœช็Ÿฅ็š„้—ฎ้ข˜๏ผŒๆ‚จๅฏไปฅ่€ƒ่™‘้™ไฝŽๆ‚จ็š„CUDA็‰ˆๆœฌๅˆฐ11.8๏ผŒ่ฟ™ๆ˜ฏๆˆ‘ไปฌๆต‹่ฏ•็š„ไธ€ไธช่พƒไธบ็จณๅฎš็š„็‰ˆๆœฌใ€‚้™็บงๆ–นๆณ•ๅฏไปฅๅ‚่€ƒ [@dimitribarbot](https://github.com/dimitribarbot) +ๆไพ›็š„[ๆ–‡ๆกฃ](https://github.com/dimitribarbot/sd-webui-live-portrait/blob/main/assets/docs/how-to-install-xpose.md#cuda-toolkit-118). + +ๆœ€ๅŽ๏ผŒๅฎ‰่ฃ…ๅ…ถไฝ™ไพ่ต–้กน๏ผš + +```bash +pip install -r requirements.txt +``` + +#### ๅฏนไบŽๆญ่ฝฝApple Silicon็š„macOS็”จๆˆท + +[X-Pose](https://github.com/IDEA-Research/X-Pose)ไพ่ต–้กนไธๆ”ฏๆŒmacOS๏ผŒๅ› ๆญคๆ‚จๅฏไปฅ่ทณ่ฟ‡ๅ…ถๅฎ‰่ฃ…ใ€‚ไบบ็ฑปๆจกๅผ็…งๅธธๅทฅไฝœ๏ผŒไฝ†ไธๆ”ฏๆŒๅŠจ็‰ฉๆจกๅผใ€‚ไฝฟ็”จไธบๆญ่ฝฝApple Silicon็š„macOSๆไพ›็š„requirementsๆ–‡ไปถ๏ผš + +```bash +# ๅฏนไบŽๆญ่ฝฝApple Silicon็š„macOS็”จๆˆท +pip install -r requirements_macOS.txt +``` + +### 2. ไธ‹่ฝฝ้ข„่ฎญ็ปƒๆƒ้‡(Pretrained weights) ๐Ÿ“ฅ + +ไปŽHuggingFaceไธ‹่ฝฝ้ข„่ฎญ็ปƒๆƒ้‡็š„ๆœ€็ฎ€ๅ•ๆ–นๆณ•ๆ˜ฏ๏ผš +```bash +# !pip install -U "huggingface_hub[cli]" +huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs" +``` + +่‹ฅๆ‚จไธ่ƒฝ่ฎฟ้—ฎHuggingFaceๅนณๅฐ๏ผŒไฝ ๅฏไปฅ่ฎฟ้—ฎๅ…ถ้•œๅƒ็ฝ‘็ซ™[hf-mirror](https://hf-mirror.com/)่ฟ›่กŒไธ‹่ฝฝๆ“ไฝœ๏ผš + +```bash +# !pip install -U "huggingface_hub[cli]" +export HF_ENDPOINT=https://hf-mirror.com +huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs" +``` + +ๆˆ–่€…๏ผŒๆ‚จๅฏไปฅไปŽ[Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib)ๆˆ–[็™พๅบฆไบ‘](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn)๏ผˆ่ฟ›่กŒไธญ๏ผ‰ไธ‹่ฝฝๆ‰€ๆœ‰้ข„่ฎญ็ปƒๆƒ้‡ใ€‚่งฃๅŽ‹ๅนถๅฐ†ๅฎƒไปฌๆ”พ็ฝฎๅœจ`./pretrained_weights`็›ฎๅฝ•ไธ‹ใ€‚ + +็กฎไฟ็›ฎๅฝ•็ป“ๆž„ๅฆ‚ๆ‰€็คบๅŒ…ๅซ[**ๆœฌไป“ๅบ“่ฏฅ่ทฏๅพ„**](assets/docs/directory-structure.md)ๅ…ถไธญๅฑ•็คบ็š„ๅ†…ๅฎนใ€‚ + +### 3. ๆŽจ็† ๐Ÿš€ + +#### ๅฟซ้€ŸไธŠๆ‰‹๏ผˆไบบ็ฑปๆจกๅž‹๏ผ‰๐Ÿ‘ค + +```bash +# ๅฏนไบŽLinuxๅ’ŒWindows็”จๆˆท +python inference.py + +# ๅฏนไบŽๆญ่ฝฝApple Silicon็š„macOS็”จๆˆท๏ผˆIntelๆœชๆต‹่ฏ•๏ผ‰ใ€‚ๆณจๆ„๏ผš่ฟ™ๅฏ่ƒฝๆฏ”RTX 4090ๆ…ข20ๅ€ +PYTORCH_ENABLE_MPS_FALLBACK=1 python inference.py +``` + +ๅฆ‚ๆžœ่„šๆœฌๆˆๅŠŸ่ฟ่กŒ๏ผŒๆ‚จๅฐ†ๅพ—ๅˆฐไธ€ไธชๅไธบ`animations/s6--d0_concat.mp4`็š„่พ“ๅ‡บmp4ๆ–‡ไปถใ€‚ๆญคๆ–‡ไปถๅŒ…ๅซไปฅไธ‹็ป“ๆžœ๏ผš้ฉฑๅŠจ่ง†้ข‘ใ€่พ“ๅ…ฅๅ›พๅƒๆˆ–่ง†้ข‘ไปฅๅŠ็”Ÿๆˆ็ป“ๆžœใ€‚ + +

+ image +

+ๆˆ–่€…๏ผŒๆ‚จๅฏไปฅ้€š่ฟ‡ๆŒ‡ๅฎš`-s`ๅ’Œ`-d`ๅ‚ๆ•ฐๆฅๆ›ดๆ”น่พ“ๅ…ฅ๏ผš + +```bash +# ๆบ่พ“ๅ…ฅๆ˜ฏๅ›พๅƒ +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 + +# ๆบ่พ“ๅ…ฅๆ˜ฏ่ง†้ข‘ โœจ +python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d0.mp4 + +# ๆ›ดๅคš้€‰้กน่ฏท่ง +python inference.py -h +``` + +#### ๅฟซ้€ŸไธŠๆ‰‹๏ผˆๅŠจ็‰ฉๆจกๅž‹๏ผ‰ ๐Ÿฑ๐Ÿถ + +ๅŠจ็‰ฉๆจกๅผไป…ๅœจLinuxๅ’ŒWindowsไธŠ็ป่ฟ‡ๆต‹่ฏ•๏ผŒๅนถไธ”้œ€่ฆNVIDIA GPUใ€‚ + +ๆ‚จ้œ€่ฆ้ฆ–ๅ…ˆๆž„ๅปบไธ€ไธชๅไธบ`MultiScaleDeformableAttention`็š„OP๏ผŒ่ฏฅOP็”ฑ[X-Pose](https://github.com/IDEA-Research/X-Pose)ไฝฟ็”จ๏ผŒ่ฟ™ๆ˜ฏไธ€ไธช้€š็”จ็š„ๅ…ณ้”ฎ็‚นๆฃ€ๆต‹ๆก†ๆžถใ€‚ + +```bash +cd src/utils/dependencies/XPose/models/UniPose/ops +python setup.py build install +cd - # ็ญ‰ๅŒไบŽ cd ../../../../../../../ +``` + +็„ถๅŽๆ‰ง่กŒ +```bash +python inference_animals.py -s assets/examples/source/s39.jpg -d assets/examples/driving/wink.pkl --driving_multiplier 1.75 --no_flag_stitching +``` +ๅฆ‚ๆžœ่„šๆœฌๆˆๅŠŸ่ฟ่กŒ๏ผŒๆ‚จๅฐ†ๅพ—ๅˆฐไธ€ไธชๅไธบ`animations/s39--wink_concat.mp4`็š„่พ“ๅ‡บmp4ๆ–‡ไปถใ€‚ +

+ image +

+ +#### ้ฉฑๅŠจ่ง†้ข‘่‡ชๅŠจ่ฃๅ‰ช ๐Ÿ“ข๐Ÿ“ข๐Ÿ“ข + +> [!IMPORTANT] +> ไฝฟ็”จๆ‚จ่‡ชๅทฑ็š„้ฉฑๅŠจ่ง†้ข‘ๆ—ถ๏ผŒๆˆ‘ไปฌ**ๆŽจ่**๏ผš โฌ‡๏ธ +> +> - ๅฐ†ๅ…ถ่ฃๅ‰ชไธบ**1:1**็š„ๅฎฝ้ซ˜ๆฏ”๏ผˆไพ‹ๅฆ‚๏ผŒ512x512ๆˆ–256x256ๅƒ็ด ๏ผ‰๏ผŒๆˆ–้€š่ฟ‡`--flag_crop_driving_video`ๅฏ็”จ่‡ชๅŠจ่ฃๅ‰ชใ€‚ +> - ไธ“ๆณจไบŽๅคด้ƒจๅŒบๅŸŸ๏ผŒ็ฑปไผผไบŽ็คบไพ‹่ง†้ข‘ใ€‚ +> - ๆœ€ๅฐๅŒ–่‚ฉ้ƒจ่ฟๅŠจใ€‚ +> - ็กฎไฟ้ฉฑๅŠจ่ง†้ข‘็š„็ฌฌไธ€ๅธงๆ˜ฏๅ…ทๆœ‰**ไธญๆ€ง่กจๆƒ…**็š„ๆญฃ้ข้ข้ƒจใ€‚ + +ไปฅไธ‹ๆ˜ฏ้€š่ฟ‡`--flag_crop_driving_video`่‡ชๅŠจ่ฃๅ‰ช็š„็คบไพ‹๏ผš + +```bash +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video +``` + +ๅฆ‚ๆžœ่‡ชๅŠจ่ฃๅ‰ช็š„็ป“ๆžœไธ็†ๆƒณ๏ผŒๆ‚จๅฏไปฅไฟฎๆ”น`--scale_crop_driving_video`ใ€`--vy_ratio_crop_driving_video`้€‰้กนๆฅ่ฐƒๆ•ดๆฏ”ไพ‹ๅ’Œๅ็งป๏ผŒๆˆ–่€…ๆ‰‹ๅŠจ่ฟ›่กŒ่ฐƒๆ•ดใ€‚ + +#### ๅŠจไฝœๆจกๆฟๅˆถไฝœ + +ๆ‚จไนŸๅฏไปฅไฝฟ็”จไปฅ`.pkl`็ป“ๅฐพ็š„่‡ชๅŠจ็”Ÿๆˆ็š„ๅŠจไฝœๆจกๆฟๆ–‡ไปถๆฅๅŠ ๅฟซๆŽจ็†้€Ÿๅบฆ๏ผŒๅนถ**ไฟๆŠค้š็ง**๏ผŒไพ‹ๅฆ‚๏ผš +```bash +python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl # ไบบๅƒๅŠจ็”ป +python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d5.pkl # ไบบๅƒ่ง†้ข‘็ผ–่พ‘ +``` + +### 4. Gradio ็•Œ้ข ๐Ÿค— + +ๆˆ‘ไปฌ่ฟ˜ๆไพ›ไบ†Gradio็•Œ้ข ๏ผŒไปฅ่Žทๅพ—ๆ›ดๅฅฝ็š„ไฝ“้ชŒ๏ผŒๅช้œ€่ฟ่กŒ๏ผš + +```bash +# ๅฏนไบŽLinuxๅ’ŒWindows็”จๆˆท๏ผˆไปฅๅŠๆญ่ฝฝIntel็š„macOS๏ผŸ๏ผŸ๏ผ‰ +python app.py # ไบบ็ฑปๆจกๅž‹ๆจกๅผ + +# ๅฏนไบŽๆญ่ฝฝApple Silicon็š„macOS็”จๆˆท๏ผŒไธๆ”ฏๆŒIntel๏ผŒ่ฟ™ๅฏ่ƒฝๆฏ”RTX 4090ๆ…ข20ๅ€ +PYTORCH_ENABLE_MPS_FALLBACK=1 python app.py # ไบบ็ฑปๆจกๅž‹ๆจกๅผ +``` + +ๆˆ‘ไปฌ่ฟ˜ไธบๅŠจ็‰ฉๆจกๅผๆไพ›ไบ†Gradio็•Œ้ข๏ผŒ่ฟ™ไป…ๅœจLinuxไธŠ็ป่ฟ‡NVIDIA GPUๆต‹่ฏ•๏ผš +```bash +python app_animals.py # animals mode ๐Ÿฑ๐Ÿถ +``` + +ๆ‚จๅฏไปฅๆŒ‡ๅฎš`--server_port`ใ€`--share`ใ€`--server_name`ๅ‚ๆ•ฐไปฅๆปก่ถณๆ‚จ็š„้œ€ๆฑ‚๏ผ + +๐Ÿš€ๆˆ‘ไปฌ่ฟ˜ๆไพ›ไบ†ไธ€ไธชๅŠ ้€Ÿ้€‰้กน`--flag_do_torch_compile`ใ€‚็ฌฌไธ€ๆฌกๆŽจ็†่งฆๅ‘ไผ˜ๅŒ–่ฟ‡็จ‹๏ผˆ็บฆไธ€ๅˆ†้’Ÿ๏ผ‰๏ผŒไฝฟๅŽ็ปญๆŽจ็†้€Ÿๅบฆๆ้ซ˜20-30%ใ€‚ไธๅŒCUDA็‰ˆๆœฌ็š„ๆ€ง่ƒฝๆๅ‡ๅฏ่ƒฝๆœ‰ๆ‰€ไธๅŒใ€‚ + +```bash +# ๅฏ็”จtorch.compileไปฅ่ฟ›่กŒๆ›ดๅฟซ็š„ๆŽจ็† +python app.py --flag_do_torch_compile +``` +**ๆณจๆ„**๏ผšๆญคๆ–นๆณ•ๅœจWindowsๅ’ŒmacOSไธŠไธๅ—ๆ”ฏๆŒใ€‚ + +**ๆˆ–่€…๏ผŒๅœจ[HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait)ไธŠ่ฝปๆพๅฐ่ฏ•**๐Ÿค—ใ€‚ + +### 5. ๆŽจ็†้€Ÿๅบฆ้ข„ไผฐ ๐Ÿš€๐Ÿš€๐Ÿš€ +ๆˆ‘ไปฌ่ฟ˜ๆไพ›ไบ†ไธ€ไธช่„šๆœฌๆฅ่ฏ„ไผฐๆฏไธชๆจกๅ—็š„ๆŽจ็†้€Ÿๅบฆ๏ผš + +```bash +# ๅฏนไบŽNVIDIA GPU +python speed.py +``` + +็ป“ๆžœๅœจ[**ๆœฌไป“ๅบ“่ฏฅๆ–‡ไปถๅฑ•็คบ**](./assets/docs/speed.md). + +## ็คพๅŒบ่ต„ๆบ ๐Ÿค— + +### ็คพๅŒบ้กน็›ฎ + +| ไป“ๅบ“ (ๆŒ‰ๆ—ถ้—ดๆŽ’ๅบ) | ๆ่ฟฐ | ไฝœ่€… | +|------|------|--------| +| [**FacePoke**](https://github.com/jbilcke-hf/FacePoke) | ไธ€ไธชๅฎžๆ—ถ็š„ๅคด้ƒจๅงฟๆ€่กจๆƒ…ๆŽงๅˆถๅบ”็”จ๏ผŒ้€š่ฟ‡้ผ ๆ ‡ๆŽงๅˆถ๏ผ | [@jbilcke-hf](https://github.com/jbilcke-hf) | +| [**FaceFusion**](https://github.com/facefusion/facefusion) | FaceFusion 3.0 ้›†ๆˆไบ† LivePortrait ไฝœไธบ `expression_restorer` ๅ’Œ `face_editor` ๅค„็†ๅ™จใ€‚ | [@henryruhs](https://github.com/henryruhs) | +| [**sd-webui-live-portrait**](https://github.com/dimitribarbot/sd-webui-live-portrait) | LivePortrait ็š„ WebUI ๆ‰ฉๅฑ•๏ผŒๅœจๅŽŸ็‰ˆ Stable Diffusion WebUI ไธญๆทปๅŠ ไบ†ไธ€ไธชๆ ‡็ญพไปฅไฝฟ็”จ LivePortrait ็š„ๅŠŸ่ƒฝใ€‚ | [@dimitribarbot](https://github.com/dimitribarbot) | +| [**ComfyUI-LivePortraitKJ**](https://github.com/kijai/ComfyUI-LivePortraitKJ) | ไธ€ไธช็”จไบŽ LivePortrait ็š„ ComfyUI ่Š‚็‚น๏ผŒไฝฟ็”จ MediaPipe ไฝœไธบ Insightface ็š„ๆ›ฟไปฃๆ–นๆกˆใ€‚ | [@kijai](https://github.com/kijai) | +| [**ComfyUI-AdvancedLivePortrait**](https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait) | ไธ€ไธชๆ›ดๅฟซ็š„ ComfyUI ่Š‚็‚น๏ผŒๅ…ทๆœ‰ๅฎžๆ—ถ้ข„่งˆๅŠŸ่ƒฝ๏ผŒๅฏๅ‘ไบ†่ฎธๅคš็คพๅŒบๅผ€ๅ‘็š„ๅทฅๅ…ทๅ’Œ้กน็›ฎใ€‚ | [@PowerHouseMan](https://github.com/PowerHouseMan) | +| [**comfyui-liveportrait**](https://github.com/shadowcz007/comfyui-liveportrait) | ไธ€ไธช็”จไบŽ LivePortrait ็š„ ComfyUI ่Š‚็‚น๏ผŒๆ”ฏๆŒๅคš้ข้ƒจใ€่กจๆƒ…ๆ’ๅ€ผ็ญ‰ๅŠŸ่ƒฝ๏ผŒๅนถๆœ‰[ๆ•™็จ‹](https://www.bilibili.com/video/BV1JW421R7sP)ใ€‚ | [@shadowcz007](https://github.com/shadowcz007) | + +### Playgrounds, ๐Ÿค— HuggingFace Spaces ไปฅๅŠๅ…ถๅฎƒ +- [FacePoke Space](https://huggingface.co/spaces/jbilcke-hf/FacePoke) +- [Expression Editor Space](https://huggingface.co/spaces/fffiloni/expression-editor) +- [Expression Editor Replicate](https://replicate.com/fofr/expression-editor) +- [Face Control Realtime Demo](https://fal.ai/demos/face-control) on FAL +- [Replicate Playground](https://replicate.com/fofr/live-portrait) +- Nuke ๅฏไปฅ้€š่ฟ‡ CompyUI ่Š‚็‚นไฝฟ็”จ LivePortrait๏ผŒ่ฏฆๆƒ…่ง[่ฟ™้‡Œ](https://x.com/bilawalsidhu/status/1837349806475276338) +- LivePortrait ๅœจ [Poe](https://poe.com/LivePortrait) ไธŠ่ฟ่กŒ + +### ่ง†้ข‘ๆ•™็จ‹ +- [LivePortrait ่ง†้ข‘่ฝฌ่ง†้ข‘็š„ๅทฅไฝœๆต็จ‹](https://youtu.be/xfzK_6cTs58?si=aYjgypeJBkhc46VL) ็”ฑ [@curiousrefuge](https://www.youtube.com/@curiousrefuge) ๅˆถไฝœ +- [Google Colab ๆ•™็จ‹](https://youtu.be/59Y9ePAXTp0?si=KzEWhklBlporW7D8) ็”ฑ [@Planet Ai](https://www.youtube.com/@planetai217) ๅˆถไฝœ +- [่ฎบๆ–‡่งฃ่ฏป](https://youtu.be/fD0P6UWSu8I?si=Vn5wxUa8qSu1jv4l) ็”ฑ [@TwoMinutePapers](https://www.youtube.com/@TwoMinutePapers) ๅˆถไฝœ +- [ComfyUI ้ซ˜็บง LivePortrait ๆ•™็จ‹](https://youtu.be/q0Vf-ZZsbzI?si=nbs3npleH-dVCt28) ็”ฑ [TutoView](https://www.youtube.com/@TutoView) ๅˆถไฝœ +- [LivePortrait ๆŽข็ดข](https://www.youtube.com/watch?v=vsvlbTEqgXQ) ๅ’Œ [LivePortrait ๆทฑๅ…ฅๆŽข่ฎจ](https://youtu.be/cucaEEDYmsw?si=AtPaDWc5G-a4E8dD) ็”ฑ [TheoreticallyMedia](https://www.youtube.com/@TheoreticallyMedia) ๅˆถไฝœ +- [LivePortrait ๅฎžๆˆ˜ๆ•™็จ‹](https://www.youtube.com/watch?v=uyjSTAOY7yI) ็”ฑ [@AI Search](https://www.youtube.com/@theAIsearch) ๅˆถไฝœ +- [ComfyUI ๆ•™็จ‹](https://www.youtube.com/watch?v=8-IcDDmiUMM) ็”ฑ [@Sebastian Kamph](https://www.youtube.com/@sebastiankamph) ๅˆถไฝœ +- B ็ซ™ไธŠ็š„[ๆ•™็จ‹](https://www.bilibili.com/video/BV1cf421i7Ly) + +่ฟ˜ๆœ‰ๆฅ่‡ช็คพๅŒบ็š„ๆ— ๆ•ฐไปคไบบๆƒŠๅน็š„่ดก็Œฎ๏ผŒๆœช่ƒฝไธ€ไธ€ๅˆ—ไธพ ๐Ÿ’– + +## ่‡ด่ฐข ๐Ÿ’ + +ๆˆ‘ไปฌ่ฆๆ„Ÿ่ฐข[FOMM](https://github.com/AliaksandrSiarohin/first-order-model)ใ€[Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis)ใ€[SPADE](https://github.com/NVlabs/SPADE)ใ€[InsightFace](https://github.com/deepinsight/insightface)ๅ’Œ[X-Pose](https://github.com/IDEA-Research/X-Pose)ไป“ๅบ“็š„็š„่ดก็Œฎ่€…๏ผŒๆ„Ÿ่ฐขไป–ไปฌ็š„ๅผ€ๆ”พ็ ”็ฉถๅ’Œ่ดก็Œฎใ€‚ + +## ้“ๅพท่€ƒ้‡ ๐Ÿ›ก๏ธ +่‚–ๅƒๅŠจ็”ปๆŠ€ๆœฏไผด้š็€็คพไผš้ฃŽ้™ฉ๏ผŒ็‰นๅˆซๆ˜ฏๅœจๅˆ›ๅปบๆทฑๅบฆไผช้€ ๏ผˆdeepfakes๏ผ‰ๆ—ถๅฏ่ƒฝ่ขซๆปฅ็”จใ€‚ไธบไบ†ๅ‡่ฝป่ฟ™ไบ›้ฃŽ้™ฉ๏ผŒ้ตๅพช้“ๅพทๆŒ‡ๅ—ๅนถ้‡‡ๅ–่ดŸ่ดฃไปป็š„ไฝฟ็”จๅฎž่ทต่‡ณๅ…ณ้‡่ฆใ€‚็›ฎๅ‰๏ผŒ็”Ÿๆˆ็š„็ป“ๆžœๅŒ…ๅซไธ€ไบ›่ง†่ง‰ไผชๅฝฑ๏ผŒ่ฟ™ไบ›ไผชๅฝฑๅฏ่ƒฝๆœ‰ๅŠฉไบŽๆฃ€ๆต‹ๆทฑๅบฆไผช้€ ใ€‚่ฏทๆณจๆ„๏ผŒๆˆ‘ไปฌไธๅฏนๆœฌ้กน็›ฎ็”Ÿๆˆ็š„็ป“ๆžœ็š„ไฝฟ็”จๆ‰ฟๆ‹…ไปปไฝ•ๆณ•ๅพ‹่ดฃไปปใ€‚ + +## ๅผ•็”จ ๐Ÿ’– + +ๅฆ‚ๆžœๆ‚จๅ‘็ŽฐLivePortraitๅฏนๆ‚จ็š„็ ”็ฉถๆœ‰็”จ๏ผŒๆฌข่ฟŽๅผ•็”จๆˆ‘ไปฌ็š„ๅทฅไฝœ๏ผŒไฝฟ็”จไปฅไธ‹BibTeX๏ผš + +```bibtex +@article{guo2024liveportrait, + title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control}, + author = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di}, + journal = {arXiv preprint arXiv:2407.03168}, + year = {2024} +} +``` + +## ่”็ณปๆ–นๅผ ๐Ÿ“ง + +[**Jianzhu Guo (้ƒญๅปบ็ )**](https://guojianzhu.com); **guojianzhu1994@gmail.com**๏ผ› diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/speed.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/speed.py new file mode 100644 index 0000000..20a3482 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/speed.py @@ -0,0 +1,195 @@ +# coding: utf-8 + +""" +Benchmark the inference speed of each module in LivePortrait. + +TODO: heavy GPT style, need to refactor +""" + +import torch +torch._dynamo.config.suppress_errors = True # Suppress errors and fall back to eager execution + +import yaml +import time +import numpy as np + +from src.utils.helper import load_model, concat_feat +from src.config.inference_config import InferenceConfig + + +def initialize_inputs(batch_size=1, device_id=0): + """ + Generate random input tensors and move them to GPU + """ + feature_3d = torch.randn(batch_size, 32, 16, 64, 64).to(device_id).half() + kp_source = torch.randn(batch_size, 21, 3).to(device_id).half() + kp_driving = torch.randn(batch_size, 21, 3).to(device_id).half() + source_image = torch.randn(batch_size, 3, 256, 256).to(device_id).half() + generator_input = torch.randn(batch_size, 256, 64, 64).to(device_id).half() + eye_close_ratio = torch.randn(batch_size, 3).to(device_id).half() + lip_close_ratio = torch.randn(batch_size, 2).to(device_id).half() + feat_stitching = concat_feat(kp_source, kp_driving).half() + feat_eye = concat_feat(kp_source, eye_close_ratio).half() + feat_lip = concat_feat(kp_source, lip_close_ratio).half() + + inputs = { + 'feature_3d': feature_3d, + 'kp_source': kp_source, + 'kp_driving': kp_driving, + 'source_image': source_image, + 'generator_input': generator_input, + 'feat_stitching': feat_stitching, + 'feat_eye': feat_eye, + 'feat_lip': feat_lip + } + + return inputs + + +def load_and_compile_models(cfg, model_config): + """ + Load and compile models for inference + """ + appearance_feature_extractor = load_model(cfg.checkpoint_F, model_config, cfg.device_id, 'appearance_feature_extractor') + motion_extractor = load_model(cfg.checkpoint_M, model_config, cfg.device_id, 'motion_extractor') + warping_module = load_model(cfg.checkpoint_W, model_config, cfg.device_id, 'warping_module') + spade_generator = load_model(cfg.checkpoint_G, model_config, cfg.device_id, 'spade_generator') + stitching_retargeting_module = load_model(cfg.checkpoint_S, model_config, cfg.device_id, 'stitching_retargeting_module') + + models_with_params = [ + ('Appearance Feature Extractor', appearance_feature_extractor), + ('Motion Extractor', motion_extractor), + ('Warping Network', warping_module), + ('SPADE Decoder', spade_generator) + ] + + compiled_models = {} + for name, model in models_with_params: + model = model.half() + model = torch.compile(model, mode='max-autotune') # Optimize for inference + model.eval() # Switch to evaluation mode + compiled_models[name] = model + + retargeting_models = ['stitching', 'eye', 'lip'] + for retarget in retargeting_models: + module = stitching_retargeting_module[retarget].half() + module = torch.compile(module, mode='max-autotune') # Optimize for inference + module.eval() # Switch to evaluation mode + stitching_retargeting_module[retarget] = module + + return compiled_models, stitching_retargeting_module + + +def warm_up_models(compiled_models, stitching_retargeting_module, inputs): + """ + Warm up models to prepare them for benchmarking + """ + print("Warm up start!") + with torch.no_grad(): + for _ in range(10): + compiled_models['Appearance Feature Extractor'](inputs['source_image']) + compiled_models['Motion Extractor'](inputs['source_image']) + compiled_models['Warping Network'](inputs['feature_3d'], inputs['kp_driving'], inputs['kp_source']) + compiled_models['SPADE Decoder'](inputs['generator_input']) # Adjust input as required + stitching_retargeting_module['stitching'](inputs['feat_stitching']) + stitching_retargeting_module['eye'](inputs['feat_eye']) + stitching_retargeting_module['lip'](inputs['feat_lip']) + print("Warm up end!") + + +def measure_inference_times(compiled_models, stitching_retargeting_module, inputs): + """ + Measure inference times for each model + """ + times = {name: [] for name in compiled_models.keys()} + times['Stitching and Retargeting Modules'] = [] + + overall_times = [] + + with torch.no_grad(): + for _ in range(100): + torch.cuda.synchronize() + overall_start = time.time() + + start = time.time() + compiled_models['Appearance Feature Extractor'](inputs['source_image']) + torch.cuda.synchronize() + times['Appearance Feature Extractor'].append(time.time() - start) + + start = time.time() + compiled_models['Motion Extractor'](inputs['source_image']) + torch.cuda.synchronize() + times['Motion Extractor'].append(time.time() - start) + + start = time.time() + compiled_models['Warping Network'](inputs['feature_3d'], inputs['kp_driving'], inputs['kp_source']) + torch.cuda.synchronize() + times['Warping Network'].append(time.time() - start) + + start = time.time() + compiled_models['SPADE Decoder'](inputs['generator_input']) # Adjust input as required + torch.cuda.synchronize() + times['SPADE Decoder'].append(time.time() - start) + + start = time.time() + stitching_retargeting_module['stitching'](inputs['feat_stitching']) + stitching_retargeting_module['eye'](inputs['feat_eye']) + stitching_retargeting_module['lip'](inputs['feat_lip']) + torch.cuda.synchronize() + times['Stitching and Retargeting Modules'].append(time.time() - start) + + overall_times.append(time.time() - overall_start) + + return times, overall_times + + +def print_benchmark_results(compiled_models, stitching_retargeting_module, retargeting_models, times, overall_times): + """ + Print benchmark results with average and standard deviation of inference times + """ + average_times = {name: np.mean(times[name]) * 1000 for name in times.keys()} + std_times = {name: np.std(times[name]) * 1000 for name in times.keys()} + + for name, model in compiled_models.items(): + num_params = sum(p.numel() for p in model.parameters()) + num_params_in_millions = num_params / 1e6 + print(f"Number of parameters for {name}: {num_params_in_millions:.2f} M") + + for index, retarget in enumerate(retargeting_models): + num_params = sum(p.numel() for p in stitching_retargeting_module[retarget].parameters()) + num_params_in_millions = num_params / 1e6 + print(f"Number of parameters for part_{index} in Stitching and Retargeting Modules: {num_params_in_millions:.2f} M") + + for name, avg_time in average_times.items(): + std_time = std_times[name] + print(f"Average inference time for {name} over 100 runs: {avg_time:.2f} ms (std: {std_time:.2f} ms)") + + +def main(): + """ + Main function to benchmark speed and model parameters + """ + # Load configuration + cfg = InferenceConfig() + model_config_path = cfg.models_config + with open(model_config_path, 'r') as file: + model_config = yaml.safe_load(file) + + # Sample input tensors + inputs = initialize_inputs(device_id = cfg.device_id) + + # Load and compile models + compiled_models, stitching_retargeting_module = load_and_compile_models(cfg, model_config) + + # Warm up models + warm_up_models(compiled_models, stitching_retargeting_module, inputs) + + # Measure inference times + times, overall_times = measure_inference_times(compiled_models, stitching_retargeting_module, inputs) + + # Print benchmark results + print_benchmark_results(compiled_models, stitching_retargeting_module, ['stitching', 'eye', 'lip'], times, overall_times) + + +if __name__ == "__main__": + main() diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/argument_config.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/argument_config.py new file mode 100644 index 0000000..33b6aca --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/argument_config.py @@ -0,0 +1,59 @@ +# coding: utf-8 + +""" +All configs for user +""" +from dataclasses import dataclass +import tyro +from typing_extensions import Annotated +from typing import Optional, Literal +from .base_config import PrintableConfig, make_abs_path + + +@dataclass(repr=False) # use repr from PrintableConfig +class ArgumentConfig(PrintableConfig): + ########## input arguments ########## + source: Annotated[str, tyro.conf.arg(aliases=["-s"])] = make_abs_path('../../assets/examples/source/s0.jpg') # path to the source portrait (human/animal) or video (human) + driving: Annotated[str, tyro.conf.arg(aliases=["-d"])] = make_abs_path('../../assets/examples/driving/d0.mp4') # path to driving video or template (.pkl format) + output_dir: Annotated[str, tyro.conf.arg(aliases=["-o"])] = 'animations/' # directory to save output video + output_name: Annotated[str, tyro.conf.arg(aliases=["-n"])] = 'video' # name of the output video + template_dir: Annotated[str, tyro.conf.arg(aliases=["-t"])] = 'templates' # name of the output video + + ########## inference arguments ########## + flag_use_half_precision: bool = True # whether to use half precision (FP16). If black boxes appear, it might be due to GPU incompatibility; set to False. + flag_crop_driving_video: bool = False # whether to crop the driving video, if the given driving info is a video + device_id: int = 0 # gpu device id + flag_force_cpu: bool = False # force cpu inference, WIP! + flag_normalize_lip: bool = False # whether to let the lip to close state before animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False + flag_source_video_eye_retargeting: bool = False # when the input is a source video, whether to let the eye-open scalar of each frame to be the same as the first source frame before the animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False, may cause the inter-frame jittering + flag_eye_retargeting: bool = False # not recommend to be True, WIP; whether to transfer the eyes-open ratio of each driving frame to the source image or the corresponding source frame + flag_lip_retargeting: bool = False # not recommend to be True, WIP; whether to transfer the lip-open ratio of each driving frame to the source image or the corresponding source frame + flag_stitching: bool = True # recommend to True if head movement is small, False if head movement is large or the source image is an animal + flag_relative_motion: bool = True # whether to use relative motion + flag_pasteback: bool = True # whether to paste-back/stitch the animated face cropping from the face-cropping space to the original image space + flag_do_crop: bool = True # whether to crop the source portrait or video to the face-cropping space + driving_option: Literal["expression-friendly", "pose-friendly"] = "expression-friendly" # "expression-friendly" or "pose-friendly"; "expression-friendly" would adapt the driving motion with the global multiplier, and could be used when the source is a human image + driving_multiplier: float = 1.0 # be used only when driving_option is "expression-friendly" + driving_smooth_observation_variance: float = 3e-7 # smooth strength scalar for the animated video when the input is a source video, the larger the number, the smoother the animated video; too much smoothness would result in loss of motion accuracy + audio_priority: Literal['source', 'driving'] = 'driving' # whether to use the audio from source or driving video + animation_region: Literal["exp", "pose", "lip", "eyes", "all"] = "all" # the region where the animation was performed, "exp" means the expression, "pose" means the head pose, "all" means all regions + ########## source crop arguments ########## + det_thresh: float = 0.15 # detection threshold + scale: float = 2.3 # the ratio of face area is smaller if scale is larger + vx_ratio: float = 0 # the ratio to move the face to left or right in cropping space + vy_ratio: float = -0.125 # the ratio to move the face to up or down in cropping space + flag_do_rot: bool = True # whether to conduct the rotation when flag_do_crop is True + source_max_dim: int = 1280 # the max dim of height and width of source image or video, you can change it to a larger number, e.g., 1920 + source_division: int = 2 # make sure the height and width of source image or video can be divided by this number + + ########## driving crop arguments ########## + scale_crop_driving_video: float = 2.2 # scale factor for cropping driving video + vx_ratio_crop_driving_video: float = 0. # adjust y offset + vy_ratio_crop_driving_video: float = -0.1 # adjust x offset + + ########## gradio arguments ########## + server_port: Annotated[int, tyro.conf.arg(aliases=["-p"])] = 8890 # port for gradio server + share: bool = False # whether to share the server to public + server_name: Optional[str] = "127.0.0.1" # set the local server name, "0.0.0.0" to broadcast all + flag_do_torch_compile: bool = False # whether to use torch.compile to accelerate generation + gradio_temp_dir: Optional[str] = None # directory to save gradio temp files diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/base_config.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/base_config.py new file mode 100644 index 0000000..216b8be --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/base_config.py @@ -0,0 +1,29 @@ +# coding: utf-8 + +""" +pretty printing class +""" + +from __future__ import annotations +import os.path as osp +from typing import Tuple + + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + + +class PrintableConfig: # pylint: disable=too-few-public-methods + """Printable Config defining str function""" + + def __repr__(self): + lines = [self.__class__.__name__ + ":"] + for key, val in vars(self).items(): + if isinstance(val, Tuple): + flattened_val = "[" + for item in val: + flattened_val += str(item) + "\n" + flattened_val = flattened_val.rstrip("\n") + val = flattened_val + "]" + lines += f"{key}: {str(val)}".split("\n") + return "\n ".join(lines) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/crop_config.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/crop_config.py new file mode 100644 index 0000000..eaf6cc2 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/crop_config.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +""" +parameters used for crop faces +""" + +from dataclasses import dataclass + +from .base_config import PrintableConfig, make_abs_path + + +@dataclass(repr=False) # use repr from PrintableConfig +class CropConfig(PrintableConfig): + insightface_root: str = make_abs_path("../../pretrained_weights/insightface") + landmark_ckpt_path: str = make_abs_path("../../pretrained_weights/liveportrait/landmark.onnx") + xpose_config_file_path: str = make_abs_path("../utils/dependencies/XPose/config_model/UniPose_SwinT.py") + xpose_embedding_cache_path: str = make_abs_path('../utils/resources/clip_embedding') + + xpose_ckpt_path: str = make_abs_path("../../pretrained_weights/liveportrait_animals/xpose.pth") + device_id: int = 0 # gpu device id + flag_force_cpu: bool = False # force cpu inference, WIP + det_thresh: float = 0.1 # detection threshold + ########## source image or video cropping option ########## + dsize: int = 512 # crop size + scale: float = 2.3 # scale factor + vx_ratio: float = 0 # vx ratio + vy_ratio: float = -0.125 # vy ratio +up, -down + max_face_num: int = 0 # max face number, 0 mean no limit + flag_do_rot: bool = True # whether to conduct the rotation when flag_do_crop is True + animal_face_type: str = "animal_face_9" # animal_face_68 -> 68 landmark points, animal_face_9 -> 9 landmarks + ########## driving video auto cropping option ########## + scale_crop_driving_video: float = 2.2 # 2.0 # scale factor for cropping driving video + vx_ratio_crop_driving_video: float = 0.0 # adjust y offset + vy_ratio_crop_driving_video: float = -0.1 # adjust x offset + direction: str = "large-small" # direction of cropping diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/inference_config.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/inference_config.py new file mode 100644 index 0000000..c4471b5 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/inference_config.py @@ -0,0 +1,69 @@ +# coding: utf-8 + +""" +config dataclass used for inference +""" + +import cv2 +from numpy import ndarray +import pickle as pkl +from dataclasses import dataclass, field +from typing import Literal, Tuple +from .base_config import PrintableConfig, make_abs_path + +def load_lip_array(): + with open(make_abs_path('../utils/resources/lip_array.pkl'), 'rb') as f: # nosec B301 -- trusted file + return pkl.load(f) # nosec B301 --trusted file + +@dataclass(repr=False) # use repr from PrintableConfig +class InferenceConfig(PrintableConfig): + # HUMAN MODEL CONFIG, NOT EXPORTED PARAMS + models_config: str = make_abs_path('./models.yaml') # portrait animation config + checkpoint_F: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/appearance_feature_extractor.pth') # path to checkpoint of F + checkpoint_M: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/motion_extractor.pth') # path to checkpoint pf M + checkpoint_G: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/spade_generator.pth') # path to checkpoint of G + checkpoint_W: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/warping_module.pth') # path to checkpoint of W + checkpoint_S: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip + + # ANIMAL MODEL CONFIG, NOT EXPORTED PARAMS + checkpoint_F_animal: str = make_abs_path('../../pretrained_weights/liveportrait_animals/base_models/appearance_feature_extractor.pth') # path to checkpoint of F + checkpoint_M_animal: str = make_abs_path('../../pretrained_weights/liveportrait_animals/base_models/motion_extractor.pth') # path to checkpoint pf M + checkpoint_G_animal: str = make_abs_path('../../pretrained_weights/liveportrait_animals/base_models/spade_generator.pth') # path to checkpoint of G + checkpoint_W_animal: str = make_abs_path('../../pretrained_weights/liveportrait_animals/base_models/warping_module.pth') # path to checkpoint of W + checkpoint_S_animal: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip, NOTE: use human temporarily! + + # EXPORTED PARAMS + flag_use_half_precision: bool = True + flag_crop_driving_video: bool = False + device_id: int = 0 + flag_normalize_lip: bool = True + flag_source_video_eye_retargeting: bool = False + flag_eye_retargeting: bool = False + flag_lip_retargeting: bool = False + flag_stitching: bool = True + flag_relative_motion: bool = True + flag_pasteback: bool = True + flag_do_crop: bool = True + flag_do_rot: bool = True + flag_force_cpu: bool = False + flag_do_torch_compile: bool = False + driving_option: str = "pose-friendly" # "expression-friendly" or "pose-friendly" + driving_multiplier: float = 1.0 + driving_smooth_observation_variance: float = 3e-7 # smooth strength scalar for the animated video when the input is a source video, the larger the number, the smoother the animated video; too much smoothness would result in loss of motion accuracy + source_max_dim: int = 1280 # the max dim of height and width of source image or video + source_division: int = 2 # make sure the height and width of source image or video can be divided by this number + animation_region: Literal["exp", "pose", "lip", "eyes", "all"] = "all" # the region where the animation was performed, "exp" means the expression, "pose" means the head pose + + # NOT EXPORTED PARAMS + lip_normalize_threshold: float = 0.03 # threshold for flag_normalize_lip + source_video_eye_retargeting_threshold: float = 0.18 # threshold for eyes retargeting if the input is a source video + anchor_frame: int = 0 # TO IMPLEMENT + + input_shape: Tuple[int, int] = (256, 256) # input shape + output_format: Literal['mp4', 'gif'] = 'mp4' # output video format + crf: int = 15 # crf for output video + output_fps: int = 25 # default output fps + + mask_crop: ndarray = field(default_factory=lambda: cv2.imread(make_abs_path('../utils/resources/mask_template.png'), cv2.IMREAD_COLOR)) + lip_array: ndarray = field(default_factory=load_lip_array) + size_gif: int = 256 # default gif size, TO IMPLEMENT diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/models.yaml b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/models.yaml new file mode 100644 index 0000000..131d1c6 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/config/models.yaml @@ -0,0 +1,43 @@ +model_params: + appearance_feature_extractor_params: # the F in the paper + image_channel: 3 + block_expansion: 64 + num_down_blocks: 2 + max_features: 512 + reshape_channel: 32 + reshape_depth: 16 + num_resblocks: 6 + motion_extractor_params: # the M in the paper + num_kp: 21 + backbone: convnextv2_tiny + warping_module_params: # the W in the paper + num_kp: 21 + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + reshape_channel: 32 + estimate_occlusion_map: True + dense_motion_params: + block_expansion: 32 + max_features: 1024 + num_blocks: 5 + reshape_depth: 16 + compress: 4 + spade_generator_params: # the G in the paper + upscale: 2 # represents upsample factor 256x256 -> 512x512 + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + stitching_retargeting_module_params: # the S in the paper + stitching: + input_size: 126 # (21*3)*2 + hidden_sizes: [128, 128, 64] + output_size: 65 # (21*3)+2(tx,ty) + lip: + input_size: 65 # (21*3)+2 + hidden_sizes: [128, 128, 64] + output_size: 63 # (21*3) + eye: + input_size: 66 # (21*3)+3 + hidden_sizes: [256, 256, 128, 128, 64] + output_size: 63 # (21*3) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/gradio_pipeline.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/gradio_pipeline.py new file mode 100644 index 0000000..4ccd951 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/gradio_pipeline.py @@ -0,0 +1,665 @@ +# coding: utf-8 + +""" +Pipeline for gradio +""" + +import os.path as osp +import os +import cv2 +from rich.progress import track +import gradio as gr +import numpy as np +import torch + +from .config.argument_config import ArgumentConfig +from .live_portrait_pipeline import LivePortraitPipeline +from .live_portrait_pipeline_animal import LivePortraitPipelineAnimal +from .utils.io import load_img_online, load_video, resize_to_limit +from .utils.filter import smooth +from .utils.rprint import rlog as log +from .utils.crop import prepare_paste_back, paste_back +from .utils.camera import get_rotation_matrix +from .utils.video import get_fps, has_audio_stream, concat_frames, images2video, add_audio_to_video +from .utils.helper import is_square_video, mkdir, dct2device, basename +from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio + + +def update_args(args, user_args): + """update the args according to user inputs + """ + for k, v in user_args.items(): + if hasattr(args, k): + setattr(args, k, v) + return args + + +class GradioPipeline(LivePortraitPipeline): + """gradio for human + """ + + def __init__(self, inference_cfg, crop_cfg, args: ArgumentConfig): + super().__init__(inference_cfg, crop_cfg) + # self.live_portrait_wrapper = self.live_portrait_wrapper + self.args = args + + @torch.no_grad() + def update_delta_new_eyeball_direction(self, eyeball_direction_x, eyeball_direction_y, delta_new, **kwargs): + if eyeball_direction_x > 0: + delta_new[0, 11, 0] += eyeball_direction_x * 0.0007 + delta_new[0, 15, 0] += eyeball_direction_x * 0.001 + else: + delta_new[0, 11, 0] += eyeball_direction_x * 0.001 + delta_new[0, 15, 0] += eyeball_direction_x * 0.0007 + + delta_new[0, 11, 1] += eyeball_direction_y * -0.001 + delta_new[0, 15, 1] += eyeball_direction_y * -0.001 + blink = -eyeball_direction_y / 2. + + delta_new[0, 11, 1] += blink * -0.001 + delta_new[0, 13, 1] += blink * 0.0003 + delta_new[0, 15, 1] += blink * -0.001 + delta_new[0, 16, 1] += blink * 0.0003 + + return delta_new + + @torch.no_grad() + def update_delta_new_smile(self, smile, delta_new, **kwargs): + delta_new[0, 20, 1] += smile * -0.01 + delta_new[0, 14, 1] += smile * -0.02 + delta_new[0, 17, 1] += smile * 0.0065 + delta_new[0, 17, 2] += smile * 0.003 + delta_new[0, 13, 1] += smile * -0.00275 + delta_new[0, 16, 1] += smile * -0.00275 + delta_new[0, 3, 1] += smile * -0.0035 + delta_new[0, 7, 1] += smile * -0.0035 + + return delta_new + + @torch.no_grad() + def update_delta_new_wink(self, wink, delta_new, **kwargs): + delta_new[0, 11, 1] += wink * 0.001 + delta_new[0, 13, 1] += wink * -0.0003 + delta_new[0, 17, 0] += wink * 0.0003 + delta_new[0, 17, 1] += wink * 0.0003 + delta_new[0, 3, 1] += wink * -0.0003 + + return delta_new + + @torch.no_grad() + def update_delta_new_eyebrow(self, eyebrow, delta_new, **kwargs): + if eyebrow > 0: + delta_new[0, 1, 1] += eyebrow * 0.001 + delta_new[0, 2, 1] += eyebrow * -0.001 + else: + delta_new[0, 1, 0] += eyebrow * -0.001 + delta_new[0, 2, 0] += eyebrow * 0.001 + delta_new[0, 1, 1] += eyebrow * 0.0003 + delta_new[0, 2, 1] += eyebrow * -0.0003 + return delta_new + + @torch.no_grad() + def update_delta_new_lip_variation_zero(self, lip_variation_zero, delta_new, **kwargs): + delta_new[0, 19, 0] += lip_variation_zero + + return delta_new + + @torch.no_grad() + def update_delta_new_lip_variation_one(self, lip_variation_one, delta_new, **kwargs): + delta_new[0, 14, 1] += lip_variation_one * 0.001 + delta_new[0, 3, 1] += lip_variation_one * -0.0005 + delta_new[0, 7, 1] += lip_variation_one * -0.0005 + delta_new[0, 17, 2] += lip_variation_one * -0.0005 + + return delta_new + + @torch.no_grad() + def update_delta_new_lip_variation_two(self, lip_variation_two, delta_new, **kwargs): + delta_new[0, 20, 2] += lip_variation_two * -0.001 + delta_new[0, 20, 1] += lip_variation_two * -0.001 + delta_new[0, 14, 1] += lip_variation_two * -0.001 + + return delta_new + + @torch.no_grad() + def update_delta_new_lip_variation_three(self, lip_variation_three, delta_new, **kwargs): + delta_new[0, 19, 1] += lip_variation_three * 0.001 + delta_new[0, 19, 2] += lip_variation_three * 0.0001 + delta_new[0, 17, 1] += lip_variation_three * -0.0001 + + return delta_new + + @torch.no_grad() + def update_delta_new_mov_x(self, mov_x, delta_new, **kwargs): + delta_new[0, 5, 0] += mov_x + + return delta_new + + @torch.no_grad() + def update_delta_new_mov_y(self, mov_y, delta_new, **kwargs): + delta_new[0, 5, 1] += mov_y + + return delta_new + + @torch.no_grad() + def execute_video( + self, + input_source_image_path=None, + input_source_video_path=None, + input_driving_video_path=None, + input_driving_image_path=None, + input_driving_video_pickle_path=None, + flag_normalize_lip=False, + flag_relative_input=True, + flag_do_crop_input=True, + flag_remap_input=True, + flag_stitching_input=True, + animation_region="all", + driving_option_input="pose-friendly", + driving_multiplier=1.0, + flag_crop_driving_video_input=True, + # flag_video_editing_head_rotation=False, + scale=2.3, + vx_ratio=0.0, + vy_ratio=-0.125, + scale_crop_driving_video=2.2, + vx_ratio_crop_driving_video=0.0, + vy_ratio_crop_driving_video=-0.1, + driving_smooth_observation_variance=3e-7, + tab_selection=None, + v_tab_selection=None + ): + """ for video-driven portrait animation or video editing + """ + if tab_selection == 'Image': + input_source_path = input_source_image_path + elif tab_selection == 'Video': + input_source_path = input_source_video_path + else: + input_source_path = input_source_image_path + + if v_tab_selection == 'Video': + input_driving_path = input_driving_video_path + elif v_tab_selection == 'Image': + input_driving_path = input_driving_image_path + elif v_tab_selection == 'Pickle': + input_driving_path = input_driving_video_pickle_path + else: + input_driving_path = input_driving_video_path + + if input_source_path is not None and input_driving_path is not None: + if osp.exists(input_driving_path) and v_tab_selection == 'Video' and not flag_crop_driving_video_input and is_square_video(input_driving_path) is False: + flag_crop_driving_video_input = True + log("The driving video is not square, it will be cropped to square automatically.") + gr.Info("The driving video is not square, it will be cropped to square automatically.", duration=2) + + args_user = { + 'source': input_source_path, + 'driving': input_driving_path, + 'flag_normalize_lip' : flag_normalize_lip, + 'flag_relative_motion': flag_relative_input, + 'flag_do_crop': flag_do_crop_input, + 'flag_pasteback': flag_remap_input, + 'flag_stitching': flag_stitching_input, + 'animation_region': animation_region, + 'driving_option': driving_option_input, + 'driving_multiplier': driving_multiplier, + 'flag_crop_driving_video': flag_crop_driving_video_input, + 'scale': scale, + 'vx_ratio': vx_ratio, + 'vy_ratio': vy_ratio, + 'scale_crop_driving_video': scale_crop_driving_video, + 'vx_ratio_crop_driving_video': vx_ratio_crop_driving_video, + 'vy_ratio_crop_driving_video': vy_ratio_crop_driving_video, + 'driving_smooth_observation_variance': driving_smooth_observation_variance, + } + # update config from user input + self.args = update_args(self.args, args_user) + self.live_portrait_wrapper.update_config(self.args.__dict__) + self.cropper.update_config(self.args.__dict__) + + output_path, output_path_concat = self.execute(self.args) + gr.Info("Run successfully!", duration=2) + if output_path.endswith(".jpg"): + return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), output_path, gr.update(visible=True), output_path_concat, gr.update(visible=True) + else: + return output_path, gr.update(visible=True), output_path_concat, gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) + else: + raise gr.Error("Please upload the source portrait or source video, and driving video ๐Ÿค—๐Ÿค—๐Ÿค—", duration=5) + + @torch.no_grad() + def execute_image_retargeting( + self, + input_eye_ratio: float, + input_lip_ratio: float, + input_head_pitch_variation: float, + input_head_yaw_variation: float, + input_head_roll_variation: float, + mov_x: float, + mov_y: float, + mov_z: float, + lip_variation_zero: float, + lip_variation_one: float, + lip_variation_two: float, + lip_variation_three: float, + smile: float, + wink: float, + eyebrow: float, + eyeball_direction_x: float, + eyeball_direction_y: float, + input_image, + retargeting_source_scale: float, + flag_stitching_retargeting_input=True, + flag_do_crop_input_retargeting_image=True): + """ for single image retargeting + """ + if input_head_pitch_variation is None or input_head_yaw_variation is None or input_head_roll_variation is None: + raise gr.Error("Invalid relative pose input ๐Ÿ’ฅ!", duration=5) + # disposable feature + f_s_user, x_s_user, R_s_user, R_d_user, x_s_info, source_lmk_user, crop_M_c2o, mask_ori, img_rgb = \ + self.prepare_retargeting_image( + input_image, input_head_pitch_variation, input_head_yaw_variation, input_head_roll_variation, retargeting_source_scale, flag_do_crop=flag_do_crop_input_retargeting_image) + + if input_eye_ratio is None or input_lip_ratio is None: + raise gr.Error("Invalid ratio input ๐Ÿ’ฅ!", duration=5) + else: + device = self.live_portrait_wrapper.device + # inference_cfg = self.live_portrait_wrapper.inference_cfg + x_s_user = x_s_user.to(device) + f_s_user = f_s_user.to(device) + R_s_user = R_s_user.to(device) + R_d_user = R_d_user.to(device) + mov_x = torch.tensor(mov_x).to(device) + mov_y = torch.tensor(mov_y).to(device) + mov_z = torch.tensor(mov_z).to(device) + eyeball_direction_x = torch.tensor(eyeball_direction_x).to(device) + eyeball_direction_y = torch.tensor(eyeball_direction_y).to(device) + smile = torch.tensor(smile).to(device) + wink = torch.tensor(wink).to(device) + eyebrow = torch.tensor(eyebrow).to(device) + lip_variation_zero = torch.tensor(lip_variation_zero).to(device) + lip_variation_one = torch.tensor(lip_variation_one).to(device) + lip_variation_two = torch.tensor(lip_variation_two).to(device) + lip_variation_three = torch.tensor(lip_variation_three).to(device) + + x_c_s = x_s_info['kp'].to(device) + delta_new = x_s_info['exp'].to(device) + scale_new = x_s_info['scale'].to(device) + t_new = x_s_info['t'].to(device) + R_d_new = (R_d_user @ R_s_user.permute(0, 2, 1)) @ R_s_user + + if eyeball_direction_x != 0 or eyeball_direction_y != 0: + delta_new = self.update_delta_new_eyeball_direction(eyeball_direction_x, eyeball_direction_y, delta_new) + if smile != 0: + delta_new = self.update_delta_new_smile(smile, delta_new) + if wink != 0: + delta_new = self.update_delta_new_wink(wink, delta_new) + if eyebrow != 0: + delta_new = self.update_delta_new_eyebrow(eyebrow, delta_new) + if lip_variation_zero != 0: + delta_new = self.update_delta_new_lip_variation_zero(lip_variation_zero, delta_new) + if lip_variation_one != 0: + delta_new = self.update_delta_new_lip_variation_one(lip_variation_one, delta_new) + if lip_variation_two != 0: + delta_new = self.update_delta_new_lip_variation_two(lip_variation_two, delta_new) + if lip_variation_three != 0: + delta_new = self.update_delta_new_lip_variation_three(lip_variation_three, delta_new) + if mov_x != 0: + delta_new = self.update_delta_new_mov_x(-mov_x, delta_new) + if mov_y !=0 : + delta_new = self.update_delta_new_mov_y(mov_y, delta_new) + + x_d_new = mov_z * scale_new * (x_c_s @ R_d_new + delta_new) + t_new + eyes_delta, lip_delta = None, None + if input_eye_ratio != self.source_eye_ratio: + combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio([[float(input_eye_ratio)]], source_lmk_user) + eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s_user, combined_eye_ratio_tensor) + if input_lip_ratio != self.source_lip_ratio: + combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio([[float(input_lip_ratio)]], source_lmk_user) + lip_delta = self.live_portrait_wrapper.retarget_lip(x_s_user, combined_lip_ratio_tensor) + print(lip_delta) + x_d_new = x_d_new + \ + (eyes_delta if eyes_delta is not None else 0) + \ + (lip_delta if lip_delta is not None else 0) + + if flag_stitching_retargeting_input: + x_d_new = self.live_portrait_wrapper.stitching(x_s_user, x_d_new) + out = self.live_portrait_wrapper.warp_decode(f_s_user, x_s_user, x_d_new) + out = self.live_portrait_wrapper.parse_output(out['out'])[0] + if flag_do_crop_input_retargeting_image: + out_to_ori_blend = paste_back(out, crop_M_c2o, img_rgb, mask_ori) + else: + out_to_ori_blend = out + return out, out_to_ori_blend + + @torch.no_grad() + def prepare_retargeting_image( + self, + input_image, + input_head_pitch_variation, input_head_yaw_variation, input_head_roll_variation, + retargeting_source_scale, + flag_do_crop=True): + """ for single image retargeting + """ + if input_image is not None: + # gr.Info("Upload successfully!", duration=2) + args_user = {'scale': retargeting_source_scale} + self.args = update_args(self.args, args_user) + self.cropper.update_config(self.args.__dict__) + inference_cfg = self.live_portrait_wrapper.inference_cfg + ######## process source portrait ######## + img_rgb = load_img_online(input_image, mode='rgb', max_dim=1280, n=2) + if flag_do_crop: + crop_info = self.cropper.crop_source_image(img_rgb, self.cropper.crop_cfg) + I_s = self.live_portrait_wrapper.prepare_source(crop_info['img_crop_256x256']) + source_lmk_user = crop_info['lmk_crop'] + crop_M_c2o = crop_info['M_c2o'] + mask_ori = prepare_paste_back(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0])) + else: + I_s = self.live_portrait_wrapper.prepare_source(img_rgb) + source_lmk_user = self.cropper.calc_lmk_from_cropped_image(img_rgb) + crop_M_c2o = None + mask_ori = None + x_s_info = self.live_portrait_wrapper.get_kp_info(I_s) + x_d_info_user_pitch = x_s_info['pitch'] + input_head_pitch_variation + x_d_info_user_yaw = x_s_info['yaw'] + input_head_yaw_variation + x_d_info_user_roll = x_s_info['roll'] + input_head_roll_variation + R_s_user = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll']) + R_d_user = get_rotation_matrix(x_d_info_user_pitch, x_d_info_user_yaw, x_d_info_user_roll) + ############################################ + f_s_user = self.live_portrait_wrapper.extract_feature_3d(I_s) + x_s_user = self.live_portrait_wrapper.transform_keypoint(x_s_info) + return f_s_user, x_s_user, R_s_user, R_d_user, x_s_info, source_lmk_user, crop_M_c2o, mask_ori, img_rgb + else: + raise gr.Error("Please upload a source portrait as the retargeting input ๐Ÿค—๐Ÿค—๐Ÿค—", duration=5) + + @torch.no_grad() + def init_retargeting_image(self, retargeting_source_scale: float, source_eye_ratio: float, source_lip_ratio:float, input_image = None): + """ initialize the retargeting slider + """ + if input_image != None: + args_user = {'scale': retargeting_source_scale} + self.args = update_args(self.args, args_user) + self.cropper.update_config(self.args.__dict__) + # inference_cfg = self.live_portrait_wrapper.inference_cfg + ######## process source portrait ######## + img_rgb = load_img_online(input_image, mode='rgb', max_dim=1280, n=16) + log(f"Load source image from {input_image}.") + crop_info = self.cropper.crop_source_image(img_rgb, self.cropper.crop_cfg) + if crop_info is None: + raise gr.Error("Source portrait NO face detected", duration=2) + source_eye_ratio = calc_eye_close_ratio(crop_info['lmk_crop'][None]) + source_lip_ratio = calc_lip_close_ratio(crop_info['lmk_crop'][None]) + self.source_eye_ratio = round(float(source_eye_ratio.mean()), 2) + self.source_lip_ratio = round(float(source_lip_ratio[0][0]), 2) + log("Calculating eyes-open and lip-open ratios successfully!") + return self.source_eye_ratio, self.source_lip_ratio + else: + return source_eye_ratio, source_lip_ratio + + @torch.no_grad() + def execute_video_retargeting(self, input_lip_ratio: float, input_video, retargeting_source_scale: float, driving_smooth_observation_variance_retargeting: float, video_retargeting_silence=False, flag_do_crop_input_retargeting_video=True): + """ retargeting the lip-open ratio of each source frame + """ + # disposable feature + device = self.live_portrait_wrapper.device + + if not video_retargeting_silence: + f_s_user_lst, x_s_user_lst, source_lmk_crop_lst, source_M_c2o_lst, mask_ori_lst, source_rgb_lst, img_crop_256x256_lst, lip_delta_retargeting_lst_smooth, source_fps, n_frames = \ + self.prepare_retargeting_video(input_video, retargeting_source_scale, device, input_lip_ratio, driving_smooth_observation_variance_retargeting, flag_do_crop=flag_do_crop_input_retargeting_video) + if input_lip_ratio is None: + raise gr.Error("Invalid ratio input ๐Ÿ’ฅ!", duration=5) + else: + inference_cfg = self.live_portrait_wrapper.inference_cfg + + I_p_pstbk_lst = None + if flag_do_crop_input_retargeting_video: + I_p_pstbk_lst = [] + I_p_lst = [] + for i in track(range(n_frames), description='Retargeting video...', total=n_frames): + x_s_user_i = x_s_user_lst[i].to(device) + f_s_user_i = f_s_user_lst[i].to(device) + + lip_delta_retargeting = lip_delta_retargeting_lst_smooth[i] + x_d_i_new = x_s_user_i + lip_delta_retargeting + x_d_i_new = self.live_portrait_wrapper.stitching(x_s_user_i, x_d_i_new) + out = self.live_portrait_wrapper.warp_decode(f_s_user_i, x_s_user_i, x_d_i_new) + I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0] + I_p_lst.append(I_p_i) + + if flag_do_crop_input_retargeting_video: + I_p_pstbk = paste_back(I_p_i, source_M_c2o_lst[i], source_rgb_lst[i], mask_ori_lst[i]) + I_p_pstbk_lst.append(I_p_pstbk) + else: + inference_cfg = self.live_portrait_wrapper.inference_cfg + f_s_user_lst, x_s_user_lst, x_d_i_new_lst, source_M_c2o_lst, mask_ori_lst, source_rgb_lst, img_crop_256x256_lst, source_fps, n_frames = \ + self.prepare_video_lip_silence(input_video, device, flag_do_crop=flag_do_crop_input_retargeting_video) + + I_p_pstbk_lst = None + if flag_do_crop_input_retargeting_video: + I_p_pstbk_lst = [] + I_p_lst = [] + for i in track(range(n_frames), description='Silencing lip...', total=n_frames): + x_s_user_i = x_s_user_lst[i].to(device) + f_s_user_i = f_s_user_lst[i].to(device) + x_d_i_new = x_d_i_new_lst[i] + x_d_i_new = self.live_portrait_wrapper.stitching(x_s_user_i, x_d_i_new) + out = self.live_portrait_wrapper.warp_decode(f_s_user_i, x_s_user_i, x_d_i_new) + I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0] + I_p_lst.append(I_p_i) + + if flag_do_crop_input_retargeting_video: + I_p_pstbk = paste_back(I_p_i, source_M_c2o_lst[i], source_rgb_lst[i], mask_ori_lst[i]) + I_p_pstbk_lst.append(I_p_pstbk) + + mkdir(self.args.output_dir) + flag_source_has_audio = has_audio_stream(input_video) + + ######### build the final concatenation result ######### + # source frame | generation + frames_concatenated = concat_frames(driving_image_lst=None, source_image_lst=img_crop_256x256_lst, I_p_lst=I_p_lst) + wfp_concat = osp.join(self.args.output_dir, f'{basename(input_video)}_retargeting_concat.mp4') + images2video(frames_concatenated, wfp=wfp_concat, fps=source_fps) + + if flag_source_has_audio: + # final result with concatenation + wfp_concat_with_audio = osp.join(self.args.output_dir, f'{basename(input_video)}_retargeting_concat_with_audio.mp4') + add_audio_to_video(wfp_concat, input_video, wfp_concat_with_audio) + os.replace(wfp_concat_with_audio, wfp_concat) + log(f"Replace {wfp_concat_with_audio} with {wfp_concat}") + + # save the animated result + wfp = osp.join(self.args.output_dir, f'{basename(input_video)}_retargeting.mp4') + if I_p_pstbk_lst is not None and len(I_p_pstbk_lst) > 0: + images2video(I_p_pstbk_lst, wfp=wfp, fps=source_fps) + else: + images2video(I_p_lst, wfp=wfp, fps=source_fps) + + ######### build the final result ######### + if flag_source_has_audio: + wfp_with_audio = osp.join(self.args.output_dir, f'{basename(input_video)}_retargeting_with_audio.mp4') + add_audio_to_video(wfp, input_video, wfp_with_audio) + os.replace(wfp_with_audio, wfp) + log(f"Replace {wfp_with_audio} with {wfp}") + gr.Info("Run successfully!", duration=2) + return wfp_concat, wfp + + @torch.no_grad() + def prepare_retargeting_video(self, input_video, retargeting_source_scale, device, input_lip_ratio, driving_smooth_observation_variance_retargeting, flag_do_crop=True): + """ for video retargeting + """ + if input_video is not None: + # gr.Info("Upload successfully!", duration=2) + args_user = {'scale': retargeting_source_scale} + self.args = update_args(self.args, args_user) + self.cropper.update_config(self.args.__dict__) + inference_cfg = self.live_portrait_wrapper.inference_cfg + ######## process source video ######## + source_rgb_lst = load_video(input_video) + source_rgb_lst = [resize_to_limit(img, inference_cfg.source_max_dim, inference_cfg.source_division) for img in source_rgb_lst] + source_fps = int(get_fps(input_video)) + n_frames = len(source_rgb_lst) + log(f"Load source video from {input_video}. FPS is {source_fps}") + + if flag_do_crop: + ret_s = self.cropper.crop_source_video(source_rgb_lst, self.cropper.crop_cfg) + log(f'Source video is cropped, {len(ret_s["frame_crop_lst"])} frames are processed.') + if len(ret_s["frame_crop_lst"]) != n_frames: + n_frames = min(len(source_rgb_lst), len(ret_s["frame_crop_lst"])) + img_crop_256x256_lst, source_lmk_crop_lst, source_M_c2o_lst = ret_s['frame_crop_lst'], ret_s['lmk_crop_lst'], ret_s['M_c2o_lst'] + mask_ori_lst = [prepare_paste_back(inference_cfg.mask_crop, source_M_c2o, dsize=(source_rgb_lst[0].shape[1], source_rgb_lst[0].shape[0])) for source_M_c2o in source_M_c2o_lst] + else: + source_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(source_rgb_lst) + img_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in source_rgb_lst] # force to resize to 256x256 + source_M_c2o_lst, mask_ori_lst = None, None + + c_s_eyes_lst, c_s_lip_lst = self.live_portrait_wrapper.calc_ratio(source_lmk_crop_lst) + # save the motion template + I_s_lst = self.live_portrait_wrapper.prepare_videos(img_crop_256x256_lst) + source_template_dct = self.make_motion_template(I_s_lst, c_s_eyes_lst, c_s_lip_lst, output_fps=source_fps) + + c_d_lip_retargeting = [input_lip_ratio] + f_s_user_lst, x_s_user_lst, lip_delta_retargeting_lst = [], [], [] + for i in track(range(n_frames), description='Preparing retargeting video...', total=n_frames): + x_s_info = source_template_dct['motion'][i] + x_s_info = dct2device(x_s_info, device) + x_s_user = x_s_info['x_s'] + + source_lmk = source_lmk_crop_lst[i] + img_crop_256x256 = img_crop_256x256_lst[i] + I_s = I_s_lst[i] + f_s_user = self.live_portrait_wrapper.extract_feature_3d(I_s) + + combined_lip_ratio_tensor_retargeting = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_retargeting, source_lmk) + lip_delta_retargeting = self.live_portrait_wrapper.retarget_lip(x_s_user, combined_lip_ratio_tensor_retargeting) + f_s_user_lst.append(f_s_user); x_s_user_lst.append(x_s_user); lip_delta_retargeting_lst.append(lip_delta_retargeting.cpu().numpy().astype(np.float32)) + lip_delta_retargeting_lst_smooth = smooth(lip_delta_retargeting_lst, lip_delta_retargeting_lst[0].shape, device, driving_smooth_observation_variance_retargeting) + + return f_s_user_lst, x_s_user_lst, source_lmk_crop_lst, source_M_c2o_lst, mask_ori_lst, source_rgb_lst, img_crop_256x256_lst, lip_delta_retargeting_lst_smooth, source_fps, n_frames + else: + # when press the clear button, go here + raise gr.Error("Please upload a source video as the retargeting input ๐Ÿค—๐Ÿค—๐Ÿค—", duration=5) + + @torch.no_grad() + def prepare_video_lip_silence(self, input_video, device, flag_do_crop=True): + """ for keeping lips in the source video silent + """ + if input_video is not None: + inference_cfg = self.live_portrait_wrapper.inference_cfg + ######## process source video ######## + source_rgb_lst = load_video(input_video) + source_rgb_lst = [resize_to_limit(img, inference_cfg.source_max_dim, inference_cfg.source_division) for img in source_rgb_lst] + source_fps = int(get_fps(input_video)) + n_frames = len(source_rgb_lst) + log(f"Load source video from {input_video}. FPS is {source_fps}") + + if flag_do_crop: + ret_s = self.cropper.crop_source_video(source_rgb_lst, self.cropper.crop_cfg) + log(f'Source video is cropped, {len(ret_s["frame_crop_lst"])} frames are processed.') + if len(ret_s["frame_crop_lst"]) != n_frames: + n_frames = min(len(source_rgb_lst), len(ret_s["frame_crop_lst"])) + img_crop_256x256_lst, source_lmk_crop_lst, source_M_c2o_lst = ret_s['frame_crop_lst'], ret_s['lmk_crop_lst'], ret_s['M_c2o_lst'] + mask_ori_lst = [prepare_paste_back(inference_cfg.mask_crop, source_M_c2o, dsize=(source_rgb_lst[0].shape[1], source_rgb_lst[0].shape[0])) for source_M_c2o in source_M_c2o_lst] + else: + source_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(source_rgb_lst) + img_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in source_rgb_lst] # force to resize to 256x256 + source_M_c2o_lst, mask_ori_lst = None, None + + c_s_eyes_lst, c_s_lip_lst = self.live_portrait_wrapper.calc_ratio(source_lmk_crop_lst) + # save the motion template + I_s_lst = self.live_portrait_wrapper.prepare_videos(img_crop_256x256_lst) + source_template_dct = self.make_motion_template(I_s_lst, c_s_eyes_lst, c_s_lip_lst, output_fps=source_fps) + + f_s_user_lst, x_s_user_lst, x_d_i_new_lst = [], [], [] + for i in track(range(n_frames), description='Preparing silencing lip...', total=n_frames): + x_s_info = source_template_dct['motion'][i] + x_s_info = dct2device(x_s_info, device) + scale_s = x_s_info['scale'] + x_s_user = x_s_info['x_s'] + x_c_s = x_s_info['kp'] + R_s = x_s_info['R'] + t_s = x_s_info['t'] + delta_new = torch.zeros_like(x_s_info['exp']) + torch.from_numpy(inference_cfg.lip_array).to(dtype=torch.float32, device=device) + for eyes_idx in [11, 13, 15, 16, 18]: + delta_new[:, eyes_idx, :] = x_s_info['exp'][:, eyes_idx, :] + source_lmk = source_lmk_crop_lst[i] + img_crop_256x256 = img_crop_256x256_lst[i] + I_s = I_s_lst[i] + f_s_user = self.live_portrait_wrapper.extract_feature_3d(I_s) + x_d_i_new = scale_s * (x_c_s @ R_s + delta_new) + t_s + f_s_user_lst.append(f_s_user); x_s_user_lst.append(x_s_user); x_d_i_new_lst.append(x_d_i_new) + return f_s_user_lst, x_s_user_lst, x_d_i_new_lst, source_M_c2o_lst, mask_ori_lst, source_rgb_lst, img_crop_256x256_lst, source_fps, n_frames + else: + # when press the clear button, go here + raise gr.Error("Please upload a source video as the input ๐Ÿค—๐Ÿค—๐Ÿค—", duration=5) + +class GradioPipelineAnimal(LivePortraitPipelineAnimal): + """gradio for animal + """ + def __init__(self, inference_cfg, crop_cfg, args: ArgumentConfig): + inference_cfg.flag_crop_driving_video = True # ensure the face_analysis_wrapper is enabled + super().__init__(inference_cfg, crop_cfg) + # self.live_portrait_wrapper_animal = self.live_portrait_wrapper_animal + self.args = args + + @torch.no_grad() + def execute_video( + self, + input_source_image_path=None, + input_driving_video_path=None, + input_driving_video_pickle_path=None, + flag_do_crop_input=False, + flag_remap_input=False, + driving_multiplier=1.0, + flag_stitching=False, + flag_crop_driving_video_input=False, + scale=2.3, + vx_ratio=0.0, + vy_ratio=-0.125, + scale_crop_driving_video=2.2, + vx_ratio_crop_driving_video=0.0, + vy_ratio_crop_driving_video=-0.1, + tab_selection=None, + ): + """ for video-driven potrait animation + """ + input_source_path = input_source_image_path + + if tab_selection == 'Video': + input_driving_path = input_driving_video_path + elif tab_selection == 'Pickle': + input_driving_path = input_driving_video_pickle_path + else: + input_driving_path = input_driving_video_pickle_path + + if input_source_path is not None and input_driving_path is not None: + if osp.exists(input_driving_path) and tab_selection == 'Video' and is_square_video(input_driving_path) is False: + flag_crop_driving_video_input = True + log("The driving video is not square, it will be cropped to square automatically.") + gr.Info("The driving video is not square, it will be cropped to square automatically.", duration=2) + + args_user = { + 'source': input_source_path, + 'driving': input_driving_path, + 'flag_do_crop': flag_do_crop_input, + 'flag_pasteback': flag_remap_input, + 'driving_multiplier': driving_multiplier, + 'flag_stitching': flag_stitching, + 'flag_crop_driving_video': flag_crop_driving_video_input, + 'scale': scale, + 'vx_ratio': vx_ratio, + 'vy_ratio': vy_ratio, + 'scale_crop_driving_video': scale_crop_driving_video, + 'vx_ratio_crop_driving_video': vx_ratio_crop_driving_video, + 'vy_ratio_crop_driving_video': vy_ratio_crop_driving_video, + } + # update config from user input + self.args = update_args(self.args, args_user) + self.live_portrait_wrapper_animal.update_config(self.args.__dict__) + self.cropper.update_config(self.args.__dict__) + # video driven animation + video_path, video_path_concat, video_gif_path = self.execute(self.args) + gr.Info("Run successfully!", duration=2) + return video_path, video_path_concat, video_gif_path + else: + raise gr.Error("Please upload the source animal image, and driving video ๐Ÿค—๐Ÿค—๐Ÿค—", duration=5) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline.py new file mode 100644 index 0000000..cefe396 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline.py @@ -0,0 +1,471 @@ +# coding: utf-8 + +""" +Pipeline of LivePortrait (Human) +""" + +import torch +torch.backends.cudnn.benchmark = True # disable CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR warning + +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) +import numpy as np +import os +import os.path as osp +from rich.progress import track + +from .config.argument_config import ArgumentConfig +from .config.inference_config import InferenceConfig +from .config.crop_config import CropConfig +from .utils.cropper import Cropper +from .utils.camera import get_rotation_matrix +from .utils.video import images2video, get_fps, add_audio_to_video, has_audio_stream +from .utils.crop import prepare_paste_back, paste_back +from .utils.io import load_image_rgb, load_video, resize_to_limit, dump, load +from .utils.helper import mkdir, basename, dct2device, is_video, is_template, remove_suffix, is_image, is_square_video, calc_motion_multiplier +from .utils.filter import smooth +from .utils.rprint import rlog as log +# from .utils.viz import viz_lmk +from .live_portrait_wrapper import LivePortraitWrapper + + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + + +class LivePortraitPipeline(object): + + def __init__(self, inference_cfg: InferenceConfig, crop_cfg: CropConfig): + self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(inference_cfg=inference_cfg) + self.cropper: Cropper = Cropper(crop_cfg=crop_cfg) + + def make_motion_template(self, I_lst, c_eyes_lst, c_lip_lst, **kwargs): + n_frames = I_lst.shape[0] + template_dct = { + 'n_frames': n_frames, + 'output_fps': kwargs.get('output_fps', 25), + 'motion': [], + 'c_eyes_lst': [], + 'c_lip_lst': [], + } + + for i in track(range(n_frames), description='Making motion templates...', total=n_frames): + # collect s, R, ฮด and t for inference + I_i = I_lst[i] + x_i_info = self.live_portrait_wrapper.get_kp_info(I_i) + x_s = self.live_portrait_wrapper.transform_keypoint(x_i_info) + R_i = get_rotation_matrix(x_i_info['pitch'], x_i_info['yaw'], x_i_info['roll']) + + item_dct = { + 'scale': x_i_info['scale'].cpu().numpy().astype(np.float32), + 'R': R_i.cpu().numpy().astype(np.float32), + 'exp': x_i_info['exp'].cpu().numpy().astype(np.float32), + 't': x_i_info['t'].cpu().numpy().astype(np.float32), + 'kp': x_i_info['kp'].cpu().numpy().astype(np.float32), + 'x_s': x_s.cpu().numpy().astype(np.float32), + } + + template_dct['motion'].append(item_dct) + + c_eyes = c_eyes_lst[i].astype(np.float32) + template_dct['c_eyes_lst'].append(c_eyes) + + c_lip = c_lip_lst[i].astype(np.float32) + template_dct['c_lip_lst'].append(c_lip) + + return template_dct + + def execute(self, args: ArgumentConfig): + # for convenience + inf_cfg = self.live_portrait_wrapper.inference_cfg + device = self.live_portrait_wrapper.device + crop_cfg = self.cropper.crop_cfg + + ######## load source input ######## + flag_is_source_video = False + source_fps = None + if is_image(args.source): + flag_is_source_video = False + img_rgb = load_image_rgb(args.source) + img_rgb = resize_to_limit(img_rgb, inf_cfg.source_max_dim, inf_cfg.source_division) + log(f"Load source image from {args.source}") + source_rgb_lst = [img_rgb] + elif is_video(args.source): + flag_is_source_video = True + source_rgb_lst = load_video(args.source) + source_rgb_lst = [resize_to_limit(img, inf_cfg.source_max_dim, inf_cfg.source_division) for img in source_rgb_lst] + source_fps = int(get_fps(args.source)) + log(f"Load source video from {args.source}, FPS is {source_fps}") + else: # source input is an unknown format + raise Exception(f"Unknown source format: {args.source}") + + ######## process driving info ######## + flag_load_from_template = is_template(args.driving) + driving_rgb_crop_256x256_lst = None + wfp_template = None + + if flag_load_from_template: + # NOTE: load from template, it is fast, but the cropping video is None + log(f"Load from template: {args.driving}, NOT the video, so the cropping video and audio are both NULL.", style='bold green') + driving_template_dct = load(args.driving) + c_d_eyes_lst = driving_template_dct['c_eyes_lst'] if 'c_eyes_lst' in driving_template_dct.keys() else driving_template_dct['c_d_eyes_lst'] # compatible with previous keys + c_d_lip_lst = driving_template_dct['c_lip_lst'] if 'c_lip_lst' in driving_template_dct.keys() else driving_template_dct['c_d_lip_lst'] + driving_n_frames = driving_template_dct['n_frames'] + flag_is_driving_video = True if driving_n_frames > 1 else False + if flag_is_source_video and flag_is_driving_video: + n_frames = min(len(source_rgb_lst), driving_n_frames) # minimum number as the number of the animated frames + elif flag_is_source_video and not flag_is_driving_video: + n_frames = len(source_rgb_lst) + else: + n_frames = driving_n_frames + + # set output_fps + output_fps = driving_template_dct.get('output_fps', inf_cfg.output_fps) + log(f'The FPS of template: {output_fps}') + + if args.flag_crop_driving_video: + log("Warning: flag_crop_driving_video is True, but the driving info is a template, so it is ignored.") + + elif osp.exists(args.driving): + if is_video(args.driving): + flag_is_driving_video = True + # load from video file, AND make motion template + output_fps = int(get_fps(args.driving)) + log(f"Load driving video from: {args.driving}, FPS is {output_fps}") + driving_rgb_lst = load_video(args.driving) + elif is_image(args.driving): + flag_is_driving_video = False + driving_img_rgb = load_image_rgb(args.driving) + output_fps = 25 + log(f"Load driving image from {args.driving}") + driving_rgb_lst = [driving_img_rgb] + else: + raise Exception(f"{args.driving} is not a supported type!") + ######## make motion template ######## + log("Start making driving motion template...") + driving_n_frames = len(driving_rgb_lst) + if flag_is_source_video and flag_is_driving_video: + n_frames = min(len(source_rgb_lst), driving_n_frames) # minimum number as the number of the animated frames + driving_rgb_lst = driving_rgb_lst[:n_frames] + elif flag_is_source_video and not flag_is_driving_video: + n_frames = len(source_rgb_lst) + else: + n_frames = driving_n_frames + if inf_cfg.flag_crop_driving_video or (not is_square_video(args.driving)): + ret_d = self.cropper.crop_driving_video(driving_rgb_lst) + log(f'Driving video is cropped, {len(ret_d["frame_crop_lst"])} frames are processed.') + if len(ret_d["frame_crop_lst"]) is not n_frames and flag_is_driving_video: + n_frames = min(n_frames, len(ret_d["frame_crop_lst"])) + driving_rgb_crop_lst, driving_lmk_crop_lst = ret_d['frame_crop_lst'], ret_d['lmk_crop_lst'] + driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_crop_lst] + else: + driving_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(driving_rgb_lst) + driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst] # force to resize to 256x256 + ####################################### + + c_d_eyes_lst, c_d_lip_lst = self.live_portrait_wrapper.calc_ratio(driving_lmk_crop_lst) + # save the motion template + I_d_lst = self.live_portrait_wrapper.prepare_videos(driving_rgb_crop_256x256_lst) + driving_template_dct = self.make_motion_template(I_d_lst, c_d_eyes_lst, c_d_lip_lst, output_fps=output_fps) + + mkdir(args.template_dir) + wfp_template = osp.join(args.template_dir, remove_suffix(os.path.basename(args.driving)) + '.pkl') + + dump(wfp_template, driving_template_dct) + log(f"Dump motion template to {wfp_template}") + else: + raise Exception(f"{args.driving} does not exist!") + if not flag_is_driving_video: + c_d_eyes_lst = c_d_eyes_lst*n_frames + c_d_lip_lst = c_d_lip_lst*n_frames + + ######## prepare for pasteback ######## + I_p_pstbk_lst = None + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + I_p_pstbk_lst = [] + log("Prepared pasteback mask done.") + + I_p_lst = [] + R_d_0, x_d_0_info = None, None + flag_normalize_lip = inf_cfg.flag_normalize_lip # not overwrite + flag_source_video_eye_retargeting = inf_cfg.flag_source_video_eye_retargeting # not overwrite + lip_delta_before_animation, eye_delta_before_animation = None, None + + ######## process source info ######## + if flag_is_source_video: + log(f"Start making source motion template...") + + source_rgb_lst = source_rgb_lst[:n_frames] + if inf_cfg.flag_do_crop: + ret_s = self.cropper.crop_source_video(source_rgb_lst, crop_cfg) + log(f'Source video is cropped, {len(ret_s["frame_crop_lst"])} frames are processed.') + if len(ret_s["frame_crop_lst"]) is not n_frames: + n_frames = min(n_frames, len(ret_s["frame_crop_lst"])) + img_crop_256x256_lst, source_lmk_crop_lst, source_M_c2o_lst = ret_s['frame_crop_lst'], ret_s['lmk_crop_lst'], ret_s['M_c2o_lst'] + else: + source_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(source_rgb_lst) + img_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in source_rgb_lst] # force to resize to 256x256 + + c_s_eyes_lst, c_s_lip_lst = self.live_portrait_wrapper.calc_ratio(source_lmk_crop_lst) + # save the motion template + I_s_lst = self.live_portrait_wrapper.prepare_videos(img_crop_256x256_lst) + source_template_dct = self.make_motion_template(I_s_lst, c_s_eyes_lst, c_s_lip_lst, output_fps=source_fps) + + key_r = 'R' if 'R' in driving_template_dct['motion'][0].keys() else 'R_d' # compatible with previous keys + if inf_cfg.flag_relative_motion: + if flag_is_driving_video: + x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + driving_template_dct['motion'][i]['exp'] - driving_template_dct['motion'][0]['exp'] for i in range(n_frames)] + x_d_exp_lst_smooth = smooth(x_d_exp_lst, source_template_dct['motion'][0]['exp'].shape, device, inf_cfg.driving_smooth_observation_variance) + else: + x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + (driving_template_dct['motion'][0]['exp'] - inf_cfg.lip_array) for i in range(n_frames)] + x_d_exp_lst_smooth = [torch.tensor(x_d_exp[0], dtype=torch.float32, device=device) for x_d_exp in x_d_exp_lst] + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + if flag_is_driving_video: + x_d_r_lst = [(np.dot(driving_template_dct['motion'][i][key_r], driving_template_dct['motion'][0][key_r].transpose(0, 2, 1))) @ source_template_dct['motion'][i]['R'] for i in range(n_frames)] + x_d_r_lst_smooth = smooth(x_d_r_lst, source_template_dct['motion'][0]['R'].shape, device, inf_cfg.driving_smooth_observation_variance) + else: + x_d_r_lst = [source_template_dct['motion'][i]['R'] for i in range(n_frames)] + x_d_r_lst_smooth = [torch.tensor(x_d_r[0], dtype=torch.float32, device=device) for x_d_r in x_d_r_lst] + else: + if flag_is_driving_video: + x_d_exp_lst = [driving_template_dct['motion'][i]['exp'] for i in range(n_frames)] + x_d_exp_lst_smooth = smooth(x_d_exp_lst, source_template_dct['motion'][0]['exp'].shape, device, inf_cfg.driving_smooth_observation_variance) + else: + x_d_exp_lst = [driving_template_dct['motion'][0]['exp']] + x_d_exp_lst_smooth = [torch.tensor(x_d_exp[0], dtype=torch.float32, device=device) for x_d_exp in x_d_exp_lst]*n_frames + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + if flag_is_driving_video: + x_d_r_lst = [driving_template_dct['motion'][i][key_r] for i in range(n_frames)] + x_d_r_lst_smooth = smooth(x_d_r_lst, source_template_dct['motion'][0]['R'].shape, device, inf_cfg.driving_smooth_observation_variance) + else: + x_d_r_lst = [driving_template_dct['motion'][0][key_r]] + x_d_r_lst_smooth = [torch.tensor(x_d_r[0], dtype=torch.float32, device=device) for x_d_r in x_d_r_lst]*n_frames + + else: # if the input is a source image, process it only once + if inf_cfg.flag_do_crop: + crop_info = self.cropper.crop_source_image(source_rgb_lst[0], crop_cfg) + if crop_info is None: + raise Exception("No face detected in the source image!") + source_lmk = crop_info['lmk_crop'] + img_crop_256x256 = crop_info['img_crop_256x256'] + else: + source_lmk = self.cropper.calc_lmk_from_cropped_image(source_rgb_lst[0]) + img_crop_256x256 = cv2.resize(source_rgb_lst[0], (256, 256)) # force to resize to 256x256 + I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256) + x_s_info = self.live_portrait_wrapper.get_kp_info(I_s) + x_c_s = x_s_info['kp'] + R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll']) + f_s = self.live_portrait_wrapper.extract_feature_3d(I_s) + x_s = self.live_portrait_wrapper.transform_keypoint(x_s_info) + + # let lip-open scalar to be 0 at first + if flag_normalize_lip and inf_cfg.flag_relative_motion and source_lmk is not None: + c_d_lip_before_animation = [0.] + combined_lip_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, source_lmk) + if combined_lip_ratio_tensor_before_animation[0][0] >= inf_cfg.lip_normalize_threshold: + lip_delta_before_animation = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor_before_animation) + + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + mask_ori_float = prepare_paste_back(inf_cfg.mask_crop, crop_info['M_c2o'], dsize=(source_rgb_lst[0].shape[1], source_rgb_lst[0].shape[0])) + + ######## animate ######## + if flag_is_driving_video or (flag_is_source_video and not flag_is_driving_video): + log(f"The animated video consists of {n_frames} frames.") + else: + log(f"The output of image-driven portrait animation is an image.") + for i in track(range(n_frames), description='๐Ÿš€Animating...', total=n_frames): + if flag_is_source_video: # source video + x_s_info = source_template_dct['motion'][i] + x_s_info = dct2device(x_s_info, device) + + source_lmk = source_lmk_crop_lst[i] + img_crop_256x256 = img_crop_256x256_lst[i] + I_s = I_s_lst[i] + f_s = self.live_portrait_wrapper.extract_feature_3d(I_s) + + x_c_s = x_s_info['kp'] + R_s = x_s_info['R'] + x_s =x_s_info['x_s'] + + # let lip-open scalar to be 0 at first if the input is a video + if flag_normalize_lip and inf_cfg.flag_relative_motion and source_lmk is not None: + c_d_lip_before_animation = [0.] + combined_lip_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, source_lmk) + if combined_lip_ratio_tensor_before_animation[0][0] >= inf_cfg.lip_normalize_threshold: + lip_delta_before_animation = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor_before_animation) + else: + lip_delta_before_animation = None + + # let eye-open scalar to be the same as the first frame if the latter is eye-open state + if flag_source_video_eye_retargeting and source_lmk is not None: + if i == 0: + combined_eye_ratio_tensor_frame_zero = c_s_eyes_lst[0] + c_d_eye_before_animation_frame_zero = [[combined_eye_ratio_tensor_frame_zero[0][:2].mean()]] + if c_d_eye_before_animation_frame_zero[0][0] < inf_cfg.source_video_eye_retargeting_threshold: + c_d_eye_before_animation_frame_zero = [[0.39]] + combined_eye_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_eye_ratio(c_d_eye_before_animation_frame_zero, source_lmk) + eye_delta_before_animation = self.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor_before_animation) + + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: # prepare for paste back + mask_ori_float = prepare_paste_back(inf_cfg.mask_crop, source_M_c2o_lst[i], dsize=(source_rgb_lst[i].shape[1], source_rgb_lst[i].shape[0])) + if flag_is_source_video and not flag_is_driving_video: + x_d_i_info = driving_template_dct['motion'][0] + else: + x_d_i_info = driving_template_dct['motion'][i] + x_d_i_info = dct2device(x_d_i_info, device) + R_d_i = x_d_i_info['R'] if 'R' in x_d_i_info.keys() else x_d_i_info['R_d'] # compatible with previous keys + + if i == 0: # cache the first frame + R_d_0 = R_d_i + x_d_0_info = x_d_i_info.copy() + + delta_new = x_s_info['exp'].clone() + if inf_cfg.flag_relative_motion: + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + R_new = x_d_r_lst_smooth[i] if flag_is_source_video else (R_d_i @ R_d_0.permute(0, 2, 1)) @ R_s + else: + R_new = R_s + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "exp": + if flag_is_source_video: + for idx in [1,2,6,11,12,13,14,15,16,17,18,19,20]: + delta_new[:, idx, :] = x_d_exp_lst_smooth[i][idx, :] + delta_new[:, 3:5, 1] = x_d_exp_lst_smooth[i][3:5, 1] + delta_new[:, 5, 2] = x_d_exp_lst_smooth[i][5, 2] + delta_new[:, 8, 2] = x_d_exp_lst_smooth[i][8, 2] + delta_new[:, 9, 1:] = x_d_exp_lst_smooth[i][9, 1:] + else: + if flag_is_driving_video: + delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']) + else: + delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - torch.from_numpy(inf_cfg.lip_array).to(dtype=torch.float32, device=device)) + elif inf_cfg.animation_region == "lip": + for lip_idx in [6, 12, 14, 17, 19, 20]: + if flag_is_source_video: + delta_new[:, lip_idx, :] = x_d_exp_lst_smooth[i][lip_idx, :] + elif flag_is_driving_video: + delta_new[:, lip_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']))[:, lip_idx, :] + else: + delta_new[:, lip_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - torch.from_numpy(inf_cfg.lip_array).to(dtype=torch.float32, device=device)))[:, lip_idx, :] + elif inf_cfg.animation_region == "eyes": + for eyes_idx in [11, 13, 15, 16, 18]: + if flag_is_source_video: + delta_new[:, eyes_idx, :] = x_d_exp_lst_smooth[i][eyes_idx, :] + elif flag_is_driving_video: + delta_new[:, eyes_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']))[:, eyes_idx, :] + else: + delta_new[:, eyes_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - 0))[:, eyes_idx, :] + if inf_cfg.animation_region == "all": + scale_new = x_s_info['scale'] if flag_is_source_video else x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale']) + else: + scale_new = x_s_info['scale'] + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + t_new = x_s_info['t'] if flag_is_source_video else x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t']) + else: + t_new = x_s_info['t'] + else: + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + R_new = x_d_r_lst_smooth[i] if flag_is_source_video else R_d_i + else: + R_new = R_s + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "exp": + for idx in [1,2,6,11,12,13,14,15,16,17,18,19,20]: + delta_new[:, idx, :] = x_d_exp_lst_smooth[i][idx, :] if flag_is_source_video else x_d_i_info['exp'][:, idx, :] + delta_new[:, 3:5, 1] = x_d_exp_lst_smooth[i][3:5, 1] if flag_is_source_video else x_d_i_info['exp'][:, 3:5, 1] + delta_new[:, 5, 2] = x_d_exp_lst_smooth[i][5, 2] if flag_is_source_video else x_d_i_info['exp'][:, 5, 2] + delta_new[:, 8, 2] = x_d_exp_lst_smooth[i][8, 2] if flag_is_source_video else x_d_i_info['exp'][:, 8, 2] + delta_new[:, 9, 1:] = x_d_exp_lst_smooth[i][9, 1:] if flag_is_source_video else x_d_i_info['exp'][:, 9, 1:] + elif inf_cfg.animation_region == "lip": + for lip_idx in [6, 12, 14, 17, 19, 20]: + delta_new[:, lip_idx, :] = x_d_exp_lst_smooth[i][lip_idx, :] if flag_is_source_video else x_d_i_info['exp'][:, lip_idx, :] + elif inf_cfg.animation_region == "eyes": + for eyes_idx in [11, 13, 15, 16, 18]: + delta_new[:, eyes_idx, :] = x_d_exp_lst_smooth[i][eyes_idx, :] if flag_is_source_video else x_d_i_info['exp'][:, eyes_idx, :] + scale_new = x_s_info['scale'] + if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose": + t_new = x_d_i_info['t'] + else: + t_new = x_s_info['t'] + + t_new[..., 2].fill_(0) # zero tz + x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new + + if inf_cfg.flag_relative_motion and inf_cfg.driving_option == "expression-friendly" and not flag_is_source_video and flag_is_driving_video: + if i == 0: + x_d_0_new = x_d_i_new + motion_multiplier = calc_motion_multiplier(x_s, x_d_0_new) + # motion_multiplier *= inf_cfg.driving_multiplier + x_d_diff = (x_d_i_new - x_d_0_new) * motion_multiplier + x_d_i_new = x_d_diff + x_s + + # Algorithm 1: + if not inf_cfg.flag_stitching and not inf_cfg.flag_eye_retargeting and not inf_cfg.flag_lip_retargeting: + # without stitching or retargeting + if flag_normalize_lip and lip_delta_before_animation is not None: + x_d_i_new += lip_delta_before_animation + if flag_source_video_eye_retargeting and eye_delta_before_animation is not None: + x_d_i_new += eye_delta_before_animation + else: + pass + elif inf_cfg.flag_stitching and not inf_cfg.flag_eye_retargeting and not inf_cfg.flag_lip_retargeting: + # with stitching and without retargeting + if flag_normalize_lip and lip_delta_before_animation is not None: + x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + lip_delta_before_animation + else: + x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + if flag_source_video_eye_retargeting and eye_delta_before_animation is not None: + x_d_i_new += eye_delta_before_animation + else: + eyes_delta, lip_delta = None, None + if inf_cfg.flag_eye_retargeting and source_lmk is not None: + c_d_eyes_i = c_d_eyes_lst[i] + combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio(c_d_eyes_i, source_lmk) + # โˆ†_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i) + eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor) + if inf_cfg.flag_lip_retargeting and source_lmk is not None: + c_d_lip_i = c_d_lip_lst[i] + combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_i, source_lmk) + # โˆ†_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i) + lip_delta = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor) + + if inf_cfg.flag_relative_motion: # use x_s + x_d_i_new = x_s + \ + (eyes_delta if eyes_delta is not None else 0) + \ + (lip_delta if lip_delta is not None else 0) + else: # use x_d,i + x_d_i_new = x_d_i_new + \ + (eyes_delta if eyes_delta is not None else 0) + \ + (lip_delta if lip_delta is not None else 0) + + if inf_cfg.flag_stitching: + x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + + x_d_i_new = x_s + (x_d_i_new - x_s) * inf_cfg.driving_multiplier + out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new) + I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0] + I_p_lst.append(I_p_i) + + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + # TODO: the paste back procedure is slow, considering optimize it using multi-threading or GPU + if flag_is_source_video: + I_p_pstbk = paste_back(I_p_i, source_M_c2o_lst[i], source_rgb_lst[i], mask_ori_float) + else: + I_p_pstbk = paste_back(I_p_i, crop_info['M_c2o'], source_rgb_lst[0], mask_ori_float) + I_p_pstbk_lst.append(I_p_pstbk) + + mkdir(args.output_dir) + wfp_concat = None + ######### build the final concatenation result ######### + # driving frame | source frame | generation + + # save the animated result + wfp = osp.join(args.output_dir, f'{args.output_name}.mp4') + if I_p_pstbk_lst is not None and len(I_p_pstbk_lst) > 0: + images2video(I_p_pstbk_lst, wfp=wfp, fps=output_fps) + else: + images2video(I_p_lst, wfp=wfp, fps=output_fps) + + # final log + if wfp_template not in (None, ''): + log(f'Animated template: {wfp_template}, you can specify `-d` argument with this template path next time to avoid cropping video, motion making and protecting privacy.', style='bold green') + log(f'Animated video: {wfp}') + + return wfp, wfp_concat diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline_animal.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline_animal.py new file mode 100644 index 0000000..9e82278 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_pipeline_animal.py @@ -0,0 +1,237 @@ +# coding: utf-8 + +""" +Pipeline of LivePortrait (Animal) +""" + +import warnings +warnings.filterwarnings("ignore", message="torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.") +warnings.filterwarnings("ignore", message="torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly.") +warnings.filterwarnings("ignore", message="None of the inputs have requires_grad=True. Gradients will be None") + +import torch +torch.backends.cudnn.benchmark = True # disable CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR warning + +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) +import numpy as np +import os +import os.path as osp +from rich.progress import track + +from .config.argument_config import ArgumentConfig +from .config.inference_config import InferenceConfig +from .config.crop_config import CropConfig +from .utils.cropper import Cropper +from .utils.camera import get_rotation_matrix +from .utils.video import images2video, concat_frames, get_fps, add_audio_to_video, has_audio_stream, video2gif +from .utils.crop import _transform_img, prepare_paste_back, paste_back +from .utils.io import load_image_rgb, load_video, resize_to_limit, dump, load +from .utils.helper import mkdir, basename, dct2device, is_video, is_template, remove_suffix, is_image, calc_motion_multiplier +from .utils.rprint import rlog as log +# from .utils.viz import viz_lmk +from .live_portrait_wrapper import LivePortraitWrapperAnimal + + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + +class LivePortraitPipelineAnimal(object): + + def __init__(self, inference_cfg: InferenceConfig, crop_cfg: CropConfig): + self.live_portrait_wrapper_animal: LivePortraitWrapperAnimal = LivePortraitWrapperAnimal(inference_cfg=inference_cfg) + self.cropper: Cropper = Cropper(crop_cfg=crop_cfg, image_type='animal_face', flag_use_half_precision=inference_cfg.flag_use_half_precision) + + def make_motion_template(self, I_lst, **kwargs): + n_frames = I_lst.shape[0] + template_dct = { + 'n_frames': n_frames, + 'output_fps': kwargs.get('output_fps', 25), + 'motion': [], + } + + for i in track(range(n_frames), description='Making driving motion templates...', total=n_frames): + # collect s, R, ฮด and t for inference + I_i = I_lst[i] + x_i_info = self.live_portrait_wrapper_animal.get_kp_info(I_i) + R_i = get_rotation_matrix(x_i_info['pitch'], x_i_info['yaw'], x_i_info['roll']) + + item_dct = { + 'scale': x_i_info['scale'].cpu().numpy().astype(np.float32), + 'R': R_i.cpu().numpy().astype(np.float32), + 'exp': x_i_info['exp'].cpu().numpy().astype(np.float32), + 't': x_i_info['t'].cpu().numpy().astype(np.float32), + } + + template_dct['motion'].append(item_dct) + + return template_dct + + def execute(self, args: ArgumentConfig): + # for convenience + inf_cfg = self.live_portrait_wrapper_animal.inference_cfg + device = self.live_portrait_wrapper_animal.device + crop_cfg = self.cropper.crop_cfg + + ######## load source input ######## + if is_image(args.source): + img_rgb = load_image_rgb(args.source) + img_rgb = resize_to_limit(img_rgb, inf_cfg.source_max_dim, inf_cfg.source_division) + log(f"Load source image from {args.source}") + else: # source input is an unknown format + raise Exception(f"Unknown source format: {args.source}") + + ######## process driving info ######## + flag_load_from_template = is_template(args.driving) + driving_rgb_crop_256x256_lst = None + wfp_template = None + + if flag_load_from_template: + # NOTE: load from template, it is fast, but the cropping video is None + log(f"Load from template: {args.driving}, NOT the video, so the cropping video and audio are both NULL.", style='bold green') + driving_template_dct = load(args.driving) + n_frames = driving_template_dct['n_frames'] + + # set output_fps + output_fps = driving_template_dct.get('output_fps', inf_cfg.output_fps) + log(f'The FPS of template: {output_fps}') + + if args.flag_crop_driving_video: + log("Warning: flag_crop_driving_video is True, but the driving info is a template, so it is ignored.") + + elif osp.exists(args.driving) and is_video(args.driving): + # load from video file, AND make motion template + output_fps = int(get_fps(args.driving)) + log(f"Load driving video from: {args.driving}, FPS is {output_fps}") + + driving_rgb_lst = load_video(args.driving) + n_frames = len(driving_rgb_lst) + + ######## make motion template ######## + log("Start making driving motion template...") + if inf_cfg.flag_crop_driving_video: + ret_d = self.cropper.crop_driving_video(driving_rgb_lst) + log(f'Driving video is cropped, {len(ret_d["frame_crop_lst"])} frames are processed.') + if len(ret_d["frame_crop_lst"]) is not n_frames: + n_frames = min(n_frames, len(ret_d["frame_crop_lst"])) + driving_rgb_crop_lst = ret_d['frame_crop_lst'] + driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_crop_lst] + else: + driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst] # force to resize to 256x256 + ####################################### + + # save the motion template + I_d_lst = self.live_portrait_wrapper_animal.prepare_videos(driving_rgb_crop_256x256_lst) + driving_template_dct = self.make_motion_template(I_d_lst, output_fps=output_fps) + + wfp_template = remove_suffix(args.driving) + '.pkl' + dump(wfp_template, driving_template_dct) + log(f"Dump motion template to {wfp_template}") + + else: + raise Exception(f"{args.driving} not exists or unsupported driving info types!") + + ######## prepare for pasteback ######## + I_p_pstbk_lst = None + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + I_p_pstbk_lst = [] + log("Prepared pasteback mask done.") + + ######## process source info ######## + if inf_cfg.flag_do_crop: + crop_info = self.cropper.crop_source_image(img_rgb, crop_cfg) + if crop_info is None: + raise Exception("No animal face detected in the source image!") + img_crop_256x256 = crop_info['img_crop_256x256'] + else: + img_crop_256x256 = cv2.resize(img_rgb, (256, 256)) # force to resize to 256x256 + I_s = self.live_portrait_wrapper_animal.prepare_source(img_crop_256x256) + x_s_info = self.live_portrait_wrapper_animal.get_kp_info(I_s) + x_c_s = x_s_info['kp'] + R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll']) + f_s = self.live_portrait_wrapper_animal.extract_feature_3d(I_s) + x_s = self.live_portrait_wrapper_animal.transform_keypoint(x_s_info) + + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + mask_ori_float = prepare_paste_back(inf_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0])) + + ######## animate ######## + I_p_lst = [] + for i in track(range(n_frames), description='๐Ÿš€Animating...', total=n_frames): + + x_d_i_info = driving_template_dct['motion'][i] + x_d_i_info = dct2device(x_d_i_info, device) + + R_d_i = x_d_i_info['R'] if 'R' in x_d_i_info.keys() else x_d_i_info['R_d'] # compatible with previous keys + delta_new = x_d_i_info['exp'] + t_new = x_d_i_info['t'] + t_new[..., 2].fill_(0) # zero tz + scale_new = x_s_info['scale'] + + x_d_i = scale_new * (x_c_s @ R_d_i + delta_new) + t_new + + if i == 0: + x_d_0 = x_d_i + motion_multiplier = calc_motion_multiplier(x_s, x_d_0) + + x_d_diff = (x_d_i - x_d_0) * motion_multiplier + x_d_i = x_d_diff + x_s + + if not inf_cfg.flag_stitching: + pass + else: + x_d_i = self.live_portrait_wrapper_animal.stitching(x_s, x_d_i) + + x_d_i = x_s + (x_d_i - x_s) * inf_cfg.driving_multiplier + out = self.live_portrait_wrapper_animal.warp_decode(f_s, x_s, x_d_i) + I_p_i = self.live_portrait_wrapper_animal.parse_output(out['out'])[0] + I_p_lst.append(I_p_i) + + if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: + I_p_pstbk = paste_back(I_p_i, crop_info['M_c2o'], img_rgb, mask_ori_float) + I_p_pstbk_lst.append(I_p_pstbk) + + mkdir(args.output_dir) + wfp_concat = None + flag_driving_has_audio = (not flag_load_from_template) and has_audio_stream(args.driving) + + ######### build the final concatenation result ######### + # driving frame | source image | generation + frames_concatenated = concat_frames(driving_rgb_crop_256x256_lst, [img_crop_256x256], I_p_lst) + wfp_concat = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_concat.mp4') + images2video(frames_concatenated, wfp=wfp_concat, fps=output_fps) + + if flag_driving_has_audio: + # final result with concatenation + wfp_concat_with_audio = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_concat_with_audio.mp4') + audio_from_which_video = args.driving + add_audio_to_video(wfp_concat, audio_from_which_video, wfp_concat_with_audio) + os.replace(wfp_concat_with_audio, wfp_concat) + log(f"Replace {wfp_concat_with_audio} with {wfp_concat}") + + # save the animated result + wfp = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}.mp4') + if I_p_pstbk_lst is not None and len(I_p_pstbk_lst) > 0: + images2video(I_p_pstbk_lst, wfp=wfp, fps=output_fps) + else: + images2video(I_p_lst, wfp=wfp, fps=output_fps) + + ######### build the final result ######### + if flag_driving_has_audio: + wfp_with_audio = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_with_audio.mp4') + audio_from_which_video = args.driving + add_audio_to_video(wfp, audio_from_which_video, wfp_with_audio) + os.replace(wfp_with_audio, wfp) + log(f"Replace {wfp_with_audio} with {wfp}") + + # final log + if wfp_template not in (None, ''): + log(f'Animated template: {wfp_template}, you can specify `-d` argument with this template path next time to avoid cropping video, motion making and protecting privacy.', style='bold green') + log(f'Animated video: {wfp}') + log(f'Animated video with concat: {wfp_concat}') + + # build the gif + wfp_gif = video2gif(wfp) + log(f'Animated gif: {wfp_gif}') + + + return wfp, wfp_concat, wfp_gif diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_wrapper.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_wrapper.py new file mode 100644 index 0000000..af95812 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/live_portrait_wrapper.py @@ -0,0 +1,384 @@ +# coding: utf-8 + +""" +Wrappers for LivePortrait core functions +""" + +import contextlib +import os.path as osp +import numpy as np +import cv2 +import torch +import yaml + +from .utils.timer import Timer +from .utils.helper import load_model, concat_feat +from .utils.camera import headpose_pred_to_degree, get_rotation_matrix +from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio +from .config.inference_config import InferenceConfig +from .utils.rprint import rlog as log + + +class LivePortraitWrapper(object): + """ + Wrapper for Human + """ + + def __init__(self, inference_cfg: InferenceConfig): + + self.inference_cfg = inference_cfg + self.device_id = inference_cfg.device_id + self.compile = inference_cfg.flag_do_torch_compile + if inference_cfg.flag_force_cpu: + self.device = 'cpu' + else: + try: + if torch.backends.mps.is_available(): + self.device = 'mps' + else: + self.device = 'cuda:' + str(self.device_id) + except: + self.device = 'cuda:' + str(self.device_id) + + model_config = yaml.load(open(inference_cfg.models_config, 'r'), Loader=yaml.SafeLoader) + # init F + self.appearance_feature_extractor = load_model(inference_cfg.checkpoint_F, model_config, self.device, 'appearance_feature_extractor') + log(f'Load appearance_feature_extractor from {osp.realpath(inference_cfg.checkpoint_F)} done.') + # init M + self.motion_extractor = load_model(inference_cfg.checkpoint_M, model_config, self.device, 'motion_extractor') + log(f'Load motion_extractor from {osp.realpath(inference_cfg.checkpoint_M)} done.') + # init W + self.warping_module = load_model(inference_cfg.checkpoint_W, model_config, self.device, 'warping_module') + log(f'Load warping_module from {osp.realpath(inference_cfg.checkpoint_W)} done.') + # init G + self.spade_generator = load_model(inference_cfg.checkpoint_G, model_config, self.device, 'spade_generator') + log(f'Load spade_generator from {osp.realpath(inference_cfg.checkpoint_G)} done.') + # init S and R + if inference_cfg.checkpoint_S is not None and osp.exists(inference_cfg.checkpoint_S): + self.stitching_retargeting_module = load_model(inference_cfg.checkpoint_S, model_config, self.device, 'stitching_retargeting_module') + log(f'Load stitching_retargeting_module from {osp.realpath(inference_cfg.checkpoint_S)} done.') + else: + self.stitching_retargeting_module = None + # Optimize for inference + if self.compile: + torch._dynamo.config.suppress_errors = True # Suppress errors and fall back to eager execution + self.warping_module = torch.compile(self.warping_module, mode='max-autotune') + self.spade_generator = torch.compile(self.spade_generator, mode='max-autotune') + + self.timer = Timer() + + def inference_ctx(self): + if self.device == "mps": + ctx = contextlib.nullcontext() + else: + ctx = torch.autocast(device_type=self.device[:4], dtype=torch.float16, + enabled=self.inference_cfg.flag_use_half_precision) + return ctx + + def update_config(self, user_args): + for k, v in user_args.items(): + if hasattr(self.inference_cfg, k): + setattr(self.inference_cfg, k, v) + + def prepare_source(self, img: np.ndarray) -> torch.Tensor: + """ construct the input as standard + img: HxWx3, uint8, 256x256 + """ + h, w = img.shape[:2] + if h != self.inference_cfg.input_shape[0] or w != self.inference_cfg.input_shape[1]: + x = cv2.resize(img, (self.inference_cfg.input_shape[0], self.inference_cfg.input_shape[1])) + else: + x = img.copy() + + if x.ndim == 3: + x = x[np.newaxis].astype(np.float32) / 255. # HxWx3 -> 1xHxWx3, normalized to 0~1 + elif x.ndim == 4: + x = x.astype(np.float32) / 255. # BxHxWx3, normalized to 0~1 + else: + raise ValueError(f'img ndim should be 3 or 4: {x.ndim}') + x = np.clip(x, 0, 1) # clip to 0~1 + x = torch.from_numpy(x).permute(0, 3, 1, 2) # 1xHxWx3 -> 1x3xHxW + x = x.to(self.device) + return x + + def prepare_videos(self, imgs) -> torch.Tensor: + """ construct the input as standard + imgs: NxBxHxWx3, uint8 + """ + if isinstance(imgs, list): + _imgs = np.array(imgs)[..., np.newaxis] # TxHxWx3x1 + elif isinstance(imgs, np.ndarray): + _imgs = imgs + else: + raise ValueError(f'imgs type error: {type(imgs)}') + + y = _imgs.astype(np.float32) / 255. + y = np.clip(y, 0, 1) # clip to 0~1 + y = torch.from_numpy(y).permute(0, 4, 3, 1, 2) # TxHxWx3x1 -> Tx1x3xHxW + y = y.to(self.device) + + return y + + def extract_feature_3d(self, x: torch.Tensor) -> torch.Tensor: + """ get the appearance feature of the image by F + x: Bx3xHxW, normalized to 0~1 + """ + with torch.no_grad(), self.inference_ctx(): + feature_3d = self.appearance_feature_extractor(x) + + return feature_3d.float() + + def get_kp_info(self, x: torch.Tensor, **kwargs) -> dict: + """ get the implicit keypoint information + x: Bx3xHxW, normalized to 0~1 + flag_refine_info: whether to trandform the pose to degrees and the dimention of the reshape + return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp' + """ + with torch.no_grad(), self.inference_ctx(): + kp_info = self.motion_extractor(x) + + if self.inference_cfg.flag_use_half_precision: + # float the dict + for k, v in kp_info.items(): + if isinstance(v, torch.Tensor): + kp_info[k] = v.float() + + flag_refine_info: bool = kwargs.get('flag_refine_info', True) + if flag_refine_info: + bs = kp_info['kp'].shape[0] + kp_info['pitch'] = headpose_pred_to_degree(kp_info['pitch'])[:, None] # Bx1 + kp_info['yaw'] = headpose_pred_to_degree(kp_info['yaw'])[:, None] # Bx1 + kp_info['roll'] = headpose_pred_to_degree(kp_info['roll'])[:, None] # Bx1 + kp_info['kp'] = kp_info['kp'].reshape(bs, -1, 3) # BxNx3 + kp_info['exp'] = kp_info['exp'].reshape(bs, -1, 3) # BxNx3 + + return kp_info + + def get_pose_dct(self, kp_info: dict) -> dict: + pose_dct = dict( + pitch=headpose_pred_to_degree(kp_info['pitch']).item(), + yaw=headpose_pred_to_degree(kp_info['yaw']).item(), + roll=headpose_pred_to_degree(kp_info['roll']).item(), + ) + return pose_dct + + def get_fs_and_kp_info(self, source_prepared, driving_first_frame): + + # get the canonical keypoints of source image by M + source_kp_info = self.get_kp_info(source_prepared, flag_refine_info=True) + source_rotation = get_rotation_matrix(source_kp_info['pitch'], source_kp_info['yaw'], source_kp_info['roll']) + + # get the canonical keypoints of first driving frame by M + driving_first_frame_kp_info = self.get_kp_info(driving_first_frame, flag_refine_info=True) + driving_first_frame_rotation = get_rotation_matrix( + driving_first_frame_kp_info['pitch'], + driving_first_frame_kp_info['yaw'], + driving_first_frame_kp_info['roll'] + ) + + # get feature volume by F + source_feature_3d = self.extract_feature_3d(source_prepared) + + return source_kp_info, source_rotation, source_feature_3d, driving_first_frame_kp_info, driving_first_frame_rotation + + def transform_keypoint(self, kp_info: dict): + """ + transform the implicit keypoints with the pose, shift, and expression deformation + kp: BxNx3 + """ + kp = kp_info['kp'] # (bs, k, 3) + pitch, yaw, roll = kp_info['pitch'], kp_info['yaw'], kp_info['roll'] + + t, exp = kp_info['t'], kp_info['exp'] + scale = kp_info['scale'] + + pitch = headpose_pred_to_degree(pitch) + yaw = headpose_pred_to_degree(yaw) + roll = headpose_pred_to_degree(roll) + + bs = kp.shape[0] + if kp.ndim == 2: + num_kp = kp.shape[1] // 3 # Bx(num_kpx3) + else: + num_kp = kp.shape[1] # Bxnum_kpx3 + + rot_mat = get_rotation_matrix(pitch, yaw, roll) # (bs, 3, 3) + + # Eqn.2: s * (R * x_c,s + exp) + t + kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat + exp.view(bs, num_kp, 3) + kp_transformed *= scale[..., None] # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3) + kp_transformed[:, :, 0:2] += t[:, None, 0:2] # remove z, only apply tx ty + + return kp_transformed + + def retarget_eye(self, kp_source: torch.Tensor, eye_close_ratio: torch.Tensor) -> torch.Tensor: + """ + kp_source: BxNx3 + eye_close_ratio: Bx3 + Return: Bx(3*num_kp) + """ + feat_eye = concat_feat(kp_source, eye_close_ratio) + + with torch.no_grad(): + delta = self.stitching_retargeting_module['eye'](feat_eye) + + return delta.reshape(-1, kp_source.shape[1], 3) + + def retarget_lip(self, kp_source: torch.Tensor, lip_close_ratio: torch.Tensor) -> torch.Tensor: + """ + kp_source: BxNx3 + lip_close_ratio: Bx2 + Return: Bx(3*num_kp) + """ + feat_lip = concat_feat(kp_source, lip_close_ratio) + + with torch.no_grad(): + delta = self.stitching_retargeting_module['lip'](feat_lip) + + return delta.reshape(-1, kp_source.shape[1], 3) + + def stitch(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor: + """ + kp_source: BxNx3 + kp_driving: BxNx3 + Return: Bx(3*num_kp+2) + """ + feat_stiching = concat_feat(kp_source, kp_driving) + + with torch.no_grad(): + delta = self.stitching_retargeting_module['stitching'](feat_stiching) + + return delta + + def stitching(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor: + """ conduct the stitching + kp_source: Bxnum_kpx3 + kp_driving: Bxnum_kpx3 + """ + + if self.stitching_retargeting_module is not None: + + bs, num_kp = kp_source.shape[:2] + + kp_driving_new = kp_driving.clone() + delta = self.stitch(kp_source, kp_driving_new) + + delta_exp = delta[..., :3*num_kp].reshape(bs, num_kp, 3) # 1x20x3 + delta_tx_ty = delta[..., 3*num_kp:3*num_kp+2].reshape(bs, 1, 2) # 1x1x2 + + kp_driving_new += delta_exp + kp_driving_new[..., :2] += delta_tx_ty + + return kp_driving_new + + return kp_driving + + def warp_decode(self, feature_3d: torch.Tensor, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor: + """ get the image after the warping of the implicit keypoints + feature_3d: Bx32x16x64x64, feature volume + kp_source: BxNx3 + kp_driving: BxNx3 + """ + # The line 18 in Algorithm 1: D(W(f_s; x_s, xโ€ฒ_d,i)๏ผ‰ + with torch.no_grad(), self.inference_ctx(): + if self.compile: + # Mark the beginning of a new CUDA Graph step + torch.compiler.cudagraph_mark_step_begin() + # get decoder input + ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving) + # decode + ret_dct['out'] = self.spade_generator(feature=ret_dct['out']) + + # float the dict + if self.inference_cfg.flag_use_half_precision: + for k, v in ret_dct.items(): + if isinstance(v, torch.Tensor): + ret_dct[k] = v.float() + + return ret_dct + + def parse_output(self, out: torch.Tensor) -> np.ndarray: + """ construct the output as standard + return: 1xHxWx3, uint8 + """ + out = np.transpose(out.data.cpu().numpy(), [0, 2, 3, 1]) # 1x3xHxW -> 1xHxWx3 + out = np.clip(out, 0, 1) # clip to 0~1 + out = np.clip(out * 255, 0, 255).astype(np.uint8) # 0~1 -> 0~255 + + return out + + def calc_ratio(self, lmk_lst): + input_eye_ratio_lst = [] + input_lip_ratio_lst = [] + for lmk in lmk_lst: + # for eyes retargeting + input_eye_ratio_lst.append(calc_eye_close_ratio(lmk[None])) + # for lip retargeting + input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None])) + return input_eye_ratio_lst, input_lip_ratio_lst + + def calc_combined_eye_ratio(self, c_d_eyes_i, source_lmk): + c_s_eyes = calc_eye_close_ratio(source_lmk[None]) + c_s_eyes_tensor = torch.from_numpy(c_s_eyes).float().to(self.device) + c_d_eyes_i_tensor = torch.Tensor([c_d_eyes_i[0][0]]).reshape(1, 1).to(self.device) + # [c_s,eyes, c_d,eyes,i] + combined_eye_ratio_tensor = torch.cat([c_s_eyes_tensor, c_d_eyes_i_tensor], dim=1) + return combined_eye_ratio_tensor + + def calc_combined_lip_ratio(self, c_d_lip_i, source_lmk): + c_s_lip = calc_lip_close_ratio(source_lmk[None]) + c_s_lip_tensor = torch.from_numpy(c_s_lip).float().to(self.device) + c_d_lip_i_tensor = torch.Tensor([c_d_lip_i[0]]).to(self.device).reshape(1, 1) # 1x1 + # [c_s,lip, c_d,lip,i] + combined_lip_ratio_tensor = torch.cat([c_s_lip_tensor, c_d_lip_i_tensor], dim=1) # 1x2 + return combined_lip_ratio_tensor + + +class LivePortraitWrapperAnimal(LivePortraitWrapper): + """ + Wrapper for Animal + """ + def __init__(self, inference_cfg: InferenceConfig): + # super().__init__(inference_cfg) # ่ฐƒ็”จ็ˆถ็ฑป็š„ๅˆๅง‹ๅŒ–ๆ–นๆณ• + + self.inference_cfg = inference_cfg + self.device_id = inference_cfg.device_id + self.compile = inference_cfg.flag_do_torch_compile + if inference_cfg.flag_force_cpu: + self.device = 'cpu' + else: + try: + if torch.backends.mps.is_available(): + self.device = 'mps' + else: + self.device = 'cuda:' + str(self.device_id) + except: + self.device = 'cuda:' + str(self.device_id) + + model_config = yaml.load(open(inference_cfg.models_config, 'r'), Loader=yaml.SafeLoader) + # init F + self.appearance_feature_extractor = load_model(inference_cfg.checkpoint_F_animal, model_config, self.device, 'appearance_feature_extractor') + log(f'Load appearance_feature_extractor from {osp.realpath(inference_cfg.checkpoint_F_animal)} done.') + # init M + self.motion_extractor = load_model(inference_cfg.checkpoint_M_animal, model_config, self.device, 'motion_extractor') + log(f'Load motion_extractor from {osp.realpath(inference_cfg.checkpoint_M_animal)} done.') + # init W + self.warping_module = load_model(inference_cfg.checkpoint_W_animal, model_config, self.device, 'warping_module') + log(f'Load warping_module from {osp.realpath(inference_cfg.checkpoint_W_animal)} done.') + # init G + self.spade_generator = load_model(inference_cfg.checkpoint_G_animal, model_config, self.device, 'spade_generator') + log(f'Load spade_generator from {osp.realpath(inference_cfg.checkpoint_G_animal)} done.') + # init S and R + if inference_cfg.checkpoint_S_animal is not None and osp.exists(inference_cfg.checkpoint_S_animal): + self.stitching_retargeting_module = load_model(inference_cfg.checkpoint_S_animal, model_config, self.device, 'stitching_retargeting_module') + log(f'Load stitching_retargeting_module from {osp.realpath(inference_cfg.checkpoint_S_animal)} done.') + else: + self.stitching_retargeting_module = None + + # Optimize for inference + if self.compile: + torch._dynamo.config.suppress_errors = True # Suppress errors and fall back to eager execution + self.warping_module = torch.compile(self.warping_module, mode='max-autotune') + self.spade_generator = torch.compile(self.spade_generator, mode='max-autotune') + + self.timer = Timer() diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/appearance_feature_extractor.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/appearance_feature_extractor.py new file mode 100644 index 0000000..8d89e4f --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/appearance_feature_extractor.py @@ -0,0 +1,48 @@ +# coding: utf-8 + +""" +Appearance extractor(F) defined in paper, which maps the source image s to a 3D appearance feature volume. +""" + +import torch +from torch import nn +from .util import SameBlock2d, DownBlock2d, ResBlock3d + + +class AppearanceFeatureExtractor(nn.Module): + + def __init__(self, image_channel, block_expansion, num_down_blocks, max_features, reshape_channel, reshape_depth, num_resblocks): + super(AppearanceFeatureExtractor, self).__init__() + self.image_channel = image_channel + self.block_expansion = block_expansion + self.num_down_blocks = num_down_blocks + self.max_features = max_features + self.reshape_channel = reshape_channel + self.reshape_depth = reshape_depth + + self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1)) + + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1) + + self.resblocks_3d = torch.nn.Sequential() + for i in range(num_resblocks): + self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1)) + + def forward(self, source_image): + out = self.first(source_image) # Bx3x256x256 -> Bx64x256x256 + + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + out = self.second(out) + bs, c, h, w = out.shape # ->Bx512x64x64 + + f_s = out.view(bs, self.reshape_channel, self.reshape_depth, h, w) # ->Bx32x16x64x64 + f_s = self.resblocks_3d(f_s) # ->Bx32x16x64x64 + return f_s diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/convnextv2.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/convnextv2.py new file mode 100644 index 0000000..83ea126 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/convnextv2.py @@ -0,0 +1,149 @@ +# coding: utf-8 + +""" +This moudle is adapted to the ConvNeXtV2 version for the extraction of implicit keypoints, poses, and expression deformation. +""" + +import torch +import torch.nn as nn +# from timm.models.layers import trunc_normal_, DropPath +from .util import LayerNorm, DropPath, trunc_normal_, GRN + +__all__ = ['convnextv2_tiny'] + + +class Block(nn.Module): + """ ConvNeXtV2 Block. + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__(self, dim, drop_path=0.): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.grn = GRN(4 * dim) + self.pwconv2 = nn.Linear(4 * dim, dim) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.grn(x) + x = self.pwconv2(x) + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + + +class ConvNeXtV2(nn.Module): + """ ConvNeXt V2 + + Args: + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] + dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] + drop_path_rate (float): Stochastic depth rate. Default: 0. + head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. + """ + + def __init__( + self, + in_chans=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0., + **kwargs + ): + super().__init__() + self.depths = depths + self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers + stem = nn.Sequential( + nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first") + ) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), + ) + self.downsample_layers.append(downsample_layer) + + self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks + dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + cur = 0 + for i in range(4): + stage = nn.Sequential( + *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer + + # NOTE: the output semantic items + num_bins = kwargs.get('num_bins', 66) + num_kp = kwargs.get('num_kp', 24) # the number of implicit keypoints + self.fc_kp = nn.Linear(dims[-1], 3 * num_kp) # implicit keypoints + + # print('dims[-1]: ', dims[-1]) + self.fc_scale = nn.Linear(dims[-1], 1) # scale + self.fc_pitch = nn.Linear(dims[-1], num_bins) # pitch bins + self.fc_yaw = nn.Linear(dims[-1], num_bins) # yaw bins + self.fc_roll = nn.Linear(dims[-1], num_bins) # roll bins + self.fc_t = nn.Linear(dims[-1], 3) # translation + self.fc_exp = nn.Linear(dims[-1], 3 * num_kp) # expression / delta + + def _init_weights(self, m): + if isinstance(m, (nn.Conv2d, nn.Linear)): + trunc_normal_(m.weight, std=.02) + nn.init.constant_(m.bias, 0) + + def forward_features(self, x): + for i in range(4): + x = self.downsample_layers[i](x) + x = self.stages[i](x) + return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C) + + def forward(self, x): + x = self.forward_features(x) + + # implicit keypoints + kp = self.fc_kp(x) + + # pose and expression deformation + pitch = self.fc_pitch(x) + yaw = self.fc_yaw(x) + roll = self.fc_roll(x) + t = self.fc_t(x) + exp = self.fc_exp(x) + scale = self.fc_scale(x) + + ret_dct = { + 'pitch': pitch, + 'yaw': yaw, + 'roll': roll, + 't': t, + 'exp': exp, + 'scale': scale, + + 'kp': kp, # canonical keypoint + } + + return ret_dct + + +def convnextv2_tiny(**kwargs): + model = ConvNeXtV2(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs) + return model diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/dense_motion.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/dense_motion.py new file mode 100644 index 0000000..c1a7f9a --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/dense_motion.py @@ -0,0 +1,104 @@ +# coding: utf-8 + +""" +The module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving +""" + +from torch import nn +import torch.nn.functional as F +import torch +from .util import Hourglass, make_coordinate_grid, kp2gaussian + + +class DenseMotionNetwork(nn.Module): + def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress, estimate_occlusion_map=True): + super(DenseMotionNetwork, self).__init__() + self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks) # ~60+G + + self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3) # 65G! NOTE: computation cost is large + self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1) # 0.8G + self.norm = nn.BatchNorm3d(compress, affine=True) + self.num_kp = num_kp + self.flag_estimate_occlusion_map = estimate_occlusion_map + + if self.flag_estimate_occlusion_map: + self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3) + else: + self.occlusion = None + + def create_sparse_motions(self, feature, kp_driving, kp_source): + bs, _, d, h, w = feature.shape # (bs, 4, 16, 64, 64) + identity_grid = make_coordinate_grid((d, h, w), ref=kp_source) # (16, 64, 64, 3) + identity_grid = identity_grid.view(1, 1, d, h, w, 3) # (1, 1, d=16, h=64, w=64, 3) + coordinate_grid = identity_grid - kp_driving.view(bs, self.num_kp, 1, 1, 1, 3) + + k = coordinate_grid.shape[1] + + # NOTE: there lacks an one-order flow + driving_to_source = coordinate_grid + kp_source.view(bs, self.num_kp, 1, 1, 1, 3) # (bs, num_kp, d, h, w, 3) + + # adding background feature + identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1) + sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) # (bs, 1+num_kp, d, h, w, 3) + return sparse_motions + + def create_deformed_feature(self, feature, sparse_motions): + bs, _, d, h, w = feature.shape + feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1) # (bs, num_kp+1, 1, c, d, h, w) + feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w) # (bs*(num_kp+1), c, d, h, w) + sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1)) # (bs*(num_kp+1), d, h, w, 3) + sparse_deformed = F.grid_sample(feature_repeat, sparse_motions, align_corners=False) + sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w)) # (bs, num_kp+1, c, d, h, w) + + return sparse_deformed + + def create_heatmap_representations(self, feature, kp_driving, kp_source): + spatial_size = feature.shape[3:] # (d=16, h=64, w=64) + gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01) # (bs, num_kp, d, h, w) + gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01) # (bs, num_kp, d, h, w) + heatmap = gaussian_driving - gaussian_source # (bs, num_kp, d, h, w) + + # adding background feature + zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.dtype).to(heatmap.device) + heatmap = torch.cat([zeros, heatmap], dim=1) + heatmap = heatmap.unsqueeze(2) # (bs, 1+num_kp, 1, d, h, w) + return heatmap + + def forward(self, feature, kp_driving, kp_source): + bs, _, d, h, w = feature.shape # (bs, 32, 16, 64, 64) + + feature = self.compress(feature) # (bs, 4, 16, 64, 64) + feature = self.norm(feature) # (bs, 4, 16, 64, 64) + feature = F.relu(feature) # (bs, 4, 16, 64, 64) + + out_dict = dict() + + # 1. deform 3d feature + sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source) # (bs, 1+num_kp, d, h, w, 3) + deformed_feature = self.create_deformed_feature(feature, sparse_motion) # (bs, 1+num_kp, c=4, d=16, h=64, w=64) + + # 2. (bs, 1+num_kp, d, h, w) + heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source) # (bs, 1+num_kp, 1, d, h, w) + + input = torch.cat([heatmap, deformed_feature], dim=2) # (bs, 1+num_kp, c=5, d=16, h=64, w=64) + input = input.view(bs, -1, d, h, w) # (bs, (1+num_kp)*c=105, d=16, h=64, w=64) + + prediction = self.hourglass(input) + + mask = self.mask(prediction) + mask = F.softmax(mask, dim=1) # (bs, 1+num_kp, d=16, h=64, w=64) + out_dict['mask'] = mask + mask = mask.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w) + sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4) # (bs, num_kp+1, 3, d, h, w) + deformation = (sparse_motion * mask).sum(dim=1) # (bs, 3, d, h, w) mask take effect in this place + deformation = deformation.permute(0, 2, 3, 4, 1) # (bs, d, h, w, 3) + + out_dict['deformation'] = deformation + + if self.flag_estimate_occlusion_map: + bs, _, d, h, w = prediction.shape + prediction_reshape = prediction.view(bs, -1, h, w) + occlusion_map = torch.sigmoid(self.occlusion(prediction_reshape)) # Bx1x64x64 + out_dict['occlusion_map'] = occlusion_map + + return out_dict diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/motion_extractor.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/motion_extractor.py new file mode 100644 index 0000000..b2982e5 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/motion_extractor.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +""" +Motion extractor(M), which directly predicts the canonical keypoints, head pose and expression deformation of the input image +""" + +from torch import nn +import torch + +from .convnextv2 import convnextv2_tiny +from .util import filter_state_dict + +model_dict = { + 'convnextv2_tiny': convnextv2_tiny, +} + + +class MotionExtractor(nn.Module): + def __init__(self, **kwargs): + super(MotionExtractor, self).__init__() + + # default is convnextv2_base + backbone = kwargs.get('backbone', 'convnextv2_tiny') + self.detector = model_dict.get(backbone)(**kwargs) + + def load_pretrained(self, init_path: str): + if init_path not in (None, ''): + state_dict = torch.load(init_path, map_location=lambda storage, loc: storage)['model'] + state_dict = filter_state_dict(state_dict, remove_name='head') + ret = self.detector.load_state_dict(state_dict, strict=False) + print(f'Load pretrained model from {init_path}, ret: {ret}') + + def forward(self, x): + out = self.detector(x) + return out diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/spade_generator.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/spade_generator.py new file mode 100644 index 0000000..147a9ae --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/spade_generator.py @@ -0,0 +1,59 @@ +# coding: utf-8 + +""" +Spade decoder(G) defined in the paper, which input the warped feature to generate the animated image. +""" + +import torch +from torch import nn +import torch.nn.functional as F +from .util import SPADEResnetBlock + + +class SPADEDecoder(nn.Module): + def __init__(self, upscale=1, max_features=256, block_expansion=64, out_channels=64, num_down_blocks=2): + for i in range(num_down_blocks): + input_channels = min(max_features, block_expansion * (2 ** (i + 1))) + self.upscale = upscale + super().__init__() + norm_G = 'spadespectralinstance' + label_num_channels = input_channels # 256 + + self.fc = nn.Conv2d(input_channels, 2 * input_channels, 3, padding=1) + self.G_middle_0 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.G_middle_1 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.G_middle_2 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.G_middle_3 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.G_middle_4 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.G_middle_5 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels) + self.up_0 = SPADEResnetBlock(2 * input_channels, input_channels, norm_G, label_num_channels) + self.up_1 = SPADEResnetBlock(input_channels, out_channels, norm_G, label_num_channels) + self.up = nn.Upsample(scale_factor=2) + + if self.upscale is None or self.upscale <= 1: + self.conv_img = nn.Conv2d(out_channels, 3, 3, padding=1) + else: + self.conv_img = nn.Sequential( + nn.Conv2d(out_channels, 3 * (2 * 2), kernel_size=3, padding=1), + nn.PixelShuffle(upscale_factor=2) + ) + + def forward(self, feature): + seg = feature # Bx256x64x64 + x = self.fc(feature) # Bx512x64x64 + x = self.G_middle_0(x, seg) + x = self.G_middle_1(x, seg) + x = self.G_middle_2(x, seg) + x = self.G_middle_3(x, seg) + x = self.G_middle_4(x, seg) + x = self.G_middle_5(x, seg) + + x = self.up(x) # Bx512x64x64 -> Bx512x128x128 + x = self.up_0(x, seg) # Bx512x128x128 -> Bx256x128x128 + x = self.up(x) # Bx256x128x128 -> Bx256x256x256 + x = self.up_1(x, seg) # Bx256x256x256 -> Bx64x256x256 + + x = self.conv_img(F.leaky_relu(x, 2e-1)) # Bx64x256x256 -> Bx3xHxW + x = torch.sigmoid(x) # Bx3xHxW + + return x \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/stitching_retargeting_network.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/stitching_retargeting_network.py new file mode 100644 index 0000000..5f50b7c --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/stitching_retargeting_network.py @@ -0,0 +1,38 @@ +# coding: utf-8 + +""" +Stitching module(S) and two retargeting modules(R) defined in the paper. + +- The stitching module pastes the animated portrait back into the original image space without pixel misalignment, such as in +the stitching region. + +- The eyes retargeting module is designed to address the issue of incomplete eye closure during cross-id reenactment, especially +when a person with small eyes drives a person with larger eyes. + +- The lip retargeting module is designed similarly to the eye retargeting module, and can also normalize the input by ensuring that +the lips are in a closed state, which facilitates better animation driving. +""" +from torch import nn + + +class StitchingRetargetingNetwork(nn.Module): + def __init__(self, input_size, hidden_sizes, output_size): + super(StitchingRetargetingNetwork, self).__init__() + layers = [] + for i in range(len(hidden_sizes)): + if i == 0: + layers.append(nn.Linear(input_size, hidden_sizes[i])) + else: + layers.append(nn.Linear(hidden_sizes[i - 1], hidden_sizes[i])) + layers.append(nn.ReLU(inplace=True)) + layers.append(nn.Linear(hidden_sizes[-1], output_size)) + self.mlp = nn.Sequential(*layers) + + def initialize_weights_to_zero(self): + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.zeros_(m.weight) + nn.init.zeros_(m.bias) + + def forward(self, x): + return self.mlp(x) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/util.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/util.py new file mode 100644 index 0000000..dc6b925 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/util.py @@ -0,0 +1,452 @@ +# coding: utf-8 + +""" +This file defines various neural network modules and utility functions, including convolutional and residual blocks, +normalizations, and functions for spatial transformation and tensor manipulation. +""" + +from torch import nn +import torch.nn.functional as F +import torch +import torch.nn.utils.spectral_norm as spectral_norm +import math +import warnings +import collections.abc +from itertools import repeat + +def kp2gaussian(kp, spatial_size, kp_variance): + """ + Transform a keypoint into gaussian like representation + """ + mean = kp + + coordinate_grid = make_coordinate_grid(spatial_size, mean) + number_of_leading_dimensions = len(mean.shape) - 1 + shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape + coordinate_grid = coordinate_grid.view(*shape) + repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1) + coordinate_grid = coordinate_grid.repeat(*repeats) + + # Preprocess kp shape + shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3) + mean = mean.view(*shape) + + mean_sub = (coordinate_grid - mean) + + out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) + + return out + + +def make_coordinate_grid(spatial_size, ref, **kwargs): + d, h, w = spatial_size + x = torch.arange(w).type(ref.dtype).to(ref.device) + y = torch.arange(h).type(ref.dtype).to(ref.device) + z = torch.arange(d).type(ref.dtype).to(ref.device) + + # NOTE: must be right-down-in + x = (2 * (x / (w - 1)) - 1) # the x axis faces to the right + y = (2 * (y / (h - 1)) - 1) # the y axis faces to the bottom + z = (2 * (z / (d - 1)) - 1) # the z axis faces to the inner + + yy = y.view(1, -1, 1).repeat(d, 1, w) + xx = x.view(1, 1, -1).repeat(d, h, 1) + zz = z.view(-1, 1, 1).repeat(1, h, w) + + meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3) + + return meshed + + +class ConvT2d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, stride=2, padding=1, output_padding=1): + super(ConvT2d, self).__init__() + + self.convT = nn.ConvTranspose2d(in_features, out_features, kernel_size=kernel_size, stride=stride, + padding=padding, output_padding=output_padding) + self.norm = nn.InstanceNorm2d(out_features) + + def forward(self, x): + out = self.convT(x) + out = self.norm(out) + out = F.leaky_relu(out) + return out + + +class ResBlock3d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock3d, self).__init__() + self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) + self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) + self.norm1 = nn.BatchNorm3d(in_features, affine=True) + self.norm2 = nn.BatchNorm3d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + + +class UpBlock3d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock3d, self).__init__() + + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = nn.BatchNorm3d(out_features, affine=True) + + def forward(self, x): + out = F.interpolate(x, scale_factor=(1, 2, 2)) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class DownBlock2d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = nn.BatchNorm2d(out_features, affine=True) + self.pool = nn.AvgPool2d(kernel_size=(2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class DownBlock3d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock3d, self).__init__() + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups, stride=(1, 2, 2)) + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = nn.BatchNorm3d(out_features, affine=True) + self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class SameBlock2d(nn.Module): + """ + Simple block, preserve spatial resolution. + """ + + def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = nn.BatchNorm2d(out_features, affine=True) + if lrelu: + self.ac = nn.LeakyReLU() + else: + self.ac = nn.ReLU() + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.ac(out) + return out + + +class Encoder(nn.Module): + """ + Hourglass Encoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Encoder, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1)) + self.down_blocks = nn.ModuleList(down_blocks) + + def forward(self, x): + outs = [x] + for down_block in self.down_blocks: + outs.append(down_block(outs[-1])) + return outs + + +class Decoder(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1) + self.norm = nn.BatchNorm3d(self.out_filters, affine=True) + + def forward(self, x): + out = x.pop() + for up_block in self.up_blocks: + out = up_block(out) + skip = x.pop() + out = torch.cat([out, skip], dim=1) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class Hourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + + def forward(self, x): + return self.decoder(self.encoder(x)) + + +class SPADE(nn.Module): + def __init__(self, norm_nc, label_nc): + super().__init__() + + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + nhidden = 128 + + self.mlp_shared = nn.Sequential( + nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), + nn.ReLU()) + self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + + def forward(self, x, segmap): + normalized = self.param_free_norm(x) + segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') + actv = self.mlp_shared(segmap) + gamma = self.mlp_gamma(actv) + beta = self.mlp_beta(actv) + out = normalized * (1 + gamma) + beta + return out + + +class SPADEResnetBlock(nn.Module): + def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1): + super().__init__() + # Attributes + self.learned_shortcut = (fin != fout) + fmiddle = min(fin, fout) + self.use_se = use_se + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + # apply spectral norm if specified + if 'spectral' in norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + # define normalization layers + self.norm_0 = SPADE(fin, label_nc) + self.norm_1 = SPADE(fmiddle, label_nc) + if self.learned_shortcut: + self.norm_s = SPADE(fin, label_nc) + + def forward(self, x, seg1): + x_s = self.shortcut(x, seg1) + dx = self.conv_0(self.actvn(self.norm_0(x, seg1))) + dx = self.conv_1(self.actvn(self.norm_1(dx, seg1))) + out = x_s + dx + return out + + def shortcut(self, x, seg1): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, seg1)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +def filter_state_dict(state_dict, remove_name='fc'): + new_state_dict = {} + for key in state_dict: + if remove_name in key: + continue + new_state_dict[key] = state_dict[key] + return new_state_dict + + +class GRN(nn.Module): + """ GRN (Global Response Normalization) layer + """ + + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim)) + self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +class LayerNorm(nn.Module): + r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape, ) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def drop_path(x, drop_prob=0., training=False, scale_by_keep=True): + """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=None, scale_by_keep=True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + return parse + +to_2tuple = _ntuple(2) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/warping_network.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/warping_network.py new file mode 100644 index 0000000..9191a19 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/modules/warping_network.py @@ -0,0 +1,77 @@ +# coding: utf-8 + +""" +Warping field estimator(W) defined in the paper, which generates a warping field using the implicit +keypoint representations x_s and x_d, and employs this flow field to warp the source feature volume f_s. +""" + +from torch import nn +import torch.nn.functional as F +from .util import SameBlock2d +from .dense_motion import DenseMotionNetwork + + +class WarpingNetwork(nn.Module): + def __init__( + self, + num_kp, + block_expansion, + max_features, + num_down_blocks, + reshape_channel, + estimate_occlusion_map=False, + dense_motion_params=None, + **kwargs + ): + super(WarpingNetwork, self).__init__() + + self.upscale = kwargs.get('upscale', 1) + self.flag_use_occlusion_map = kwargs.get('flag_use_occlusion_map', True) + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork( + num_kp=num_kp, + feature_channel=reshape_channel, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params + ) + else: + self.dense_motion_network = None + + self.third = SameBlock2d(max_features, block_expansion * (2 ** num_down_blocks), kernel_size=(3, 3), padding=(1, 1), lrelu=True) + self.fourth = nn.Conv2d(in_channels=block_expansion * (2 ** num_down_blocks), out_channels=block_expansion * (2 ** num_down_blocks), kernel_size=1, stride=1) + + self.estimate_occlusion_map = estimate_occlusion_map + + def deform_input(self, inp, deformation): + return F.grid_sample(inp, deformation, align_corners=False) + + def forward(self, feature_3d, kp_driving, kp_source): + if self.dense_motion_network is not None: + # Feature warper, Transforming feature representation according to deformation and occlusion + dense_motion = self.dense_motion_network( + feature=feature_3d, kp_driving=kp_driving, kp_source=kp_source + ) + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] # Bx1x64x64 + else: + occlusion_map = None + + deformation = dense_motion['deformation'] # Bx16x64x64x3 + out = self.deform_input(feature_3d, deformation) # Bx32x16x64x64 + + bs, c, d, h, w = out.shape # Bx32x16x64x64 + out = out.view(bs, c * d, h, w) # -> Bx512x64x64 + out = self.third(out) # -> Bx256x64x64 + out = self.fourth(out) # -> Bx256x64x64 + + if self.flag_use_occlusion_map and (occlusion_map is not None): + out = out * occlusion_map + + ret_dct = { + 'occlusion_map': occlusion_map, + 'deformation': deformation, + 'out': out, + } + + return ret_dct diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/animal_landmark_runner.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/animal_landmark_runner.py new file mode 100644 index 0000000..d7caafc --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/animal_landmark_runner.py @@ -0,0 +1,138 @@ +# coding: utf-8 + +""" +face detectoin and alignment using XPose +""" + +import os +import pickle +import torch +import numpy as np +from PIL import Image +from torchvision.ops import nms + +from .timer import Timer +from .rprint import rlog as log +from .helper import clean_state_dict + +from .dependencies.XPose import transforms as T +from .dependencies.XPose.models import build_model +from .dependencies.XPose.predefined_keypoints import * +from .dependencies.XPose.util import box_ops +from .dependencies.XPose.util.config import Config + + +class XPoseRunner(object): + def __init__(self, model_config_path, model_checkpoint_path, embeddings_cache_path=None, cpu_only=False, **kwargs): + self.device_id = kwargs.get("device_id", 0) + self.flag_use_half_precision = kwargs.get("flag_use_half_precision", True) + self.device = f"cuda:{self.device_id}" if not cpu_only else "cpu" + self.model = self.load_animal_model(model_config_path, model_checkpoint_path, self.device) + self.timer = Timer() + # Load cached embeddings if available + try: + with open(f'{embeddings_cache_path}_9.pkl', 'rb') as f: # nosec B301 + self.ins_text_embeddings_9, self.kpt_text_embeddings_9 = pickle.load(f) # nosec B301 + with open(f'{embeddings_cache_path}_68.pkl', 'rb') as f: # nosec B301 + self.ins_text_embeddings_68, self.kpt_text_embeddings_68 = pickle.load(f) # nosec B301 + print("Loaded cached embeddings from file.") + except Exception: + raise ValueError("Could not load clip embeddings from file, please check your file path.") + + def load_animal_model(self, model_config_path, model_checkpoint_path, device): + args = Config.fromfile(model_config_path) + args.device = device + model = build_model(args) + checkpoint = torch.load(model_checkpoint_path, map_location=lambda storage, loc: storage) + load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) + model.eval() + return model + + def load_image(self, input_image): + image_pil = input_image.convert("RGB") + transform = T.Compose([ + T.RandomResize([800], max_size=1333), # NOTE: fixed size to 800 + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + image, _ = transform(image_pil, None) + return image_pil, image + + def get_unipose_output(self, image, instance_text_prompt, keypoint_text_prompt, box_threshold, IoU_threshold): + instance_list = instance_text_prompt.split(',') + + if len(keypoint_text_prompt) == 9: + # torch.Size([1, 512]) torch.Size([9, 512]) + ins_text_embeddings, kpt_text_embeddings = self.ins_text_embeddings_9, self.kpt_text_embeddings_9 + elif len(keypoint_text_prompt) ==68: + # torch.Size([1, 512]) torch.Size([68, 512]) + ins_text_embeddings, kpt_text_embeddings = self.ins_text_embeddings_68, self.kpt_text_embeddings_68 + else: + raise ValueError("Invalid number of keypoint embeddings.") + target = { + "instance_text_prompt": instance_list, + "keypoint_text_prompt": keypoint_text_prompt, + "object_embeddings_text": ins_text_embeddings.float(), + "kpts_embeddings_text": torch.cat((kpt_text_embeddings.float(), torch.zeros(100 - kpt_text_embeddings.shape[0], 512, device=self.device)), dim=0), + "kpt_vis_text": torch.cat((torch.ones(kpt_text_embeddings.shape[0], device=self.device), torch.zeros(100 - kpt_text_embeddings.shape[0], device=self.device)), dim=0) + } + + self.model = self.model.to(self.device) + image = image.to(self.device) + + with torch.no_grad(): + with torch.autocast(device_type=self.device[:4], dtype=torch.float16, enabled=self.flag_use_half_precision): + outputs = self.model(image[None], [target]) + + logits = outputs["pred_logits"].sigmoid()[0] + boxes = outputs["pred_boxes"][0] + keypoints = outputs["pred_keypoints"][0][:, :2 * len(keypoint_text_prompt)] + + logits_filt = logits.cpu().clone() + boxes_filt = boxes.cpu().clone() + keypoints_filt = keypoints.cpu().clone() + filt_mask = logits_filt.max(dim=1)[0] > box_threshold + logits_filt = logits_filt[filt_mask] + boxes_filt = boxes_filt[filt_mask] + keypoints_filt = keypoints_filt[filt_mask] + + keep_indices = nms(box_ops.box_cxcywh_to_xyxy(boxes_filt), logits_filt.max(dim=1)[0], iou_threshold=IoU_threshold) + + filtered_boxes = boxes_filt[keep_indices] + filtered_keypoints = keypoints_filt[keep_indices] + + return filtered_boxes, filtered_keypoints + + def run(self, input_image, instance_text_prompt, keypoint_text_example, box_threshold, IoU_threshold): + if keypoint_text_example in globals(): + keypoint_dict = globals()[keypoint_text_example] + elif instance_text_prompt in globals(): + keypoint_dict = globals()[instance_text_prompt] + else: + keypoint_dict = globals()["animal"] + + keypoint_text_prompt = keypoint_dict.get("keypoints") + keypoint_skeleton = keypoint_dict.get("skeleton") + + image_pil, image = self.load_image(input_image) + boxes_filt, keypoints_filt = self.get_unipose_output(image, instance_text_prompt, keypoint_text_prompt, box_threshold, IoU_threshold) + + size = image_pil.size + H, W = size[1], size[0] + keypoints_filt = keypoints_filt[0].squeeze(0) + kp = np.array(keypoints_filt.cpu()) + num_kpts = len(keypoint_text_prompt) + Z = kp[:num_kpts * 2] * np.array([W, H] * num_kpts) + Z = Z.reshape(num_kpts * 2) + x = Z[0::2] + y = Z[1::2] + return np.stack((x, y), axis=1) + + def warmup(self): + self.timer.tic() + + img_rgb = Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)) + self.run(img_rgb, 'face', 'face', box_threshold=0.0, IoU_threshold=0.0) + + elapse = self.timer.toc() + log(f'XPoseRunner warmup time: {elapse:.3f}s') diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/camera.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/camera.py new file mode 100644 index 0000000..a3dd942 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/camera.py @@ -0,0 +1,73 @@ +# coding: utf-8 + +""" +functions for processing and transforming 3D facial keypoints +""" + +import numpy as np +import torch +import torch.nn.functional as F + +PI = np.pi + + +def headpose_pred_to_degree(pred): + """ + pred: (bs, 66) or (bs, 1) or others + """ + if pred.ndim > 1 and pred.shape[1] == 66: + # NOTE: note that the average is modified to 97.5 + device = pred.device + idx_tensor = [idx for idx in range(0, 66)] + idx_tensor = torch.FloatTensor(idx_tensor).to(device) + pred = F.softmax(pred, dim=1) + degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 97.5 + + return degree + + return pred + + +def get_rotation_matrix(pitch_, yaw_, roll_): + """ the input is in degree + """ + # transform to radian + pitch = pitch_ / 180 * PI + yaw = yaw_ / 180 * PI + roll = roll_ / 180 * PI + + device = pitch.device + + if pitch.ndim == 1: + pitch = pitch.unsqueeze(1) + if yaw.ndim == 1: + yaw = yaw.unsqueeze(1) + if roll.ndim == 1: + roll = roll.unsqueeze(1) + + # calculate the euler matrix + bs = pitch.shape[0] + ones = torch.ones([bs, 1]).to(device) + zeros = torch.zeros([bs, 1]).to(device) + x, y, z = pitch, yaw, roll + + rot_x = torch.cat([ + ones, zeros, zeros, + zeros, torch.cos(x), -torch.sin(x), + zeros, torch.sin(x), torch.cos(x) + ], dim=1).reshape([bs, 3, 3]) + + rot_y = torch.cat([ + torch.cos(y), zeros, torch.sin(y), + zeros, ones, zeros, + -torch.sin(y), zeros, torch.cos(y) + ], dim=1).reshape([bs, 3, 3]) + + rot_z = torch.cat([ + torch.cos(z), -torch.sin(z), zeros, + torch.sin(z), torch.cos(z), zeros, + zeros, zeros, ones + ], dim=1).reshape([bs, 3, 3]) + + rot = rot_z @ rot_y @ rot_x + return rot.permute(0, 2, 1) # transpose diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/check_windows_port.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/check_windows_port.py new file mode 100644 index 0000000..c2f9728 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/check_windows_port.py @@ -0,0 +1,18 @@ +import socket +import sys + +if len(sys.argv) != 2: + print("Usage: python check_port.py ") + sys.exit(1) + +port = int(sys.argv[1]) + +sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +sock.settimeout(1) +result = sock.connect_ex(('127.0.0.1', port)) + +if result == 0: + print("LISTENING") +else: + print("NOT LISTENING") +sock.close diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/crop.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/crop.py new file mode 100644 index 0000000..fd8b8a4 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/crop.py @@ -0,0 +1,423 @@ +# coding: utf-8 + +""" +cropping function and the related preprocess functions for cropping +""" + +import numpy as np +import os.path as osp +from math import sin, cos, acos, degrees +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) # NOTE: enforce single thread +from .rprint import rprint as print + +DTYPE = np.float32 +CV2_INTERP = cv2.INTER_LINEAR + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + +def _transform_img(img, M, dsize, flags=CV2_INTERP, borderMode=None): + """ conduct similarity or affine transformation to the image, do not do border operation! + img: + M: 2x3 matrix or 3x3 matrix + dsize: target shape (width, height) + """ + if isinstance(dsize, tuple) or isinstance(dsize, list): + _dsize = tuple(dsize) + else: + _dsize = (dsize, dsize) + + if borderMode is not None: + return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags, borderMode=borderMode, borderValue=(0, 0, 0)) + else: + return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags) + + +def _transform_pts(pts, M): + """ conduct similarity or affine transformation to the pts + pts: Nx2 ndarray + M: 2x3 matrix or 3x3 matrix + return: Nx2 + """ + return pts @ M[:2, :2].T + M[:2, 2] + + +def parse_pt2_from_pt101(pt101, use_lip=True): + """ + parsing the 2 points according to the 101 points, which cancels the roll + """ + # the former version use the eye center, but it is not robust, now use interpolation + pt_left_eye = np.mean(pt101[[39, 42, 45, 48]], axis=0) # left eye center + pt_right_eye = np.mean(pt101[[51, 54, 57, 60]], axis=0) # right eye center + + if use_lip: + # use lip + pt_center_eye = (pt_left_eye + pt_right_eye) / 2 + pt_center_lip = (pt101[75] + pt101[81]) / 2 + pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0) + else: + pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0) + return pt2 + + +def parse_pt2_from_pt106(pt106, use_lip=True): + """ + parsing the 2 points according to the 106 points, which cancels the roll + """ + pt_left_eye = np.mean(pt106[[33, 35, 40, 39]], axis=0) # left eye center + pt_right_eye = np.mean(pt106[[87, 89, 94, 93]], axis=0) # right eye center + + if use_lip: + # use lip + pt_center_eye = (pt_left_eye + pt_right_eye) / 2 + pt_center_lip = (pt106[52] + pt106[61]) / 2 + pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0) + else: + pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0) + return pt2 + + +def parse_pt2_from_pt203(pt203, use_lip=True): + """ + parsing the 2 points according to the 203 points, which cancels the roll + """ + pt_left_eye = np.mean(pt203[[0, 6, 12, 18]], axis=0) # left eye center + pt_right_eye = np.mean(pt203[[24, 30, 36, 42]], axis=0) # right eye center + if use_lip: + # use lip + pt_center_eye = (pt_left_eye + pt_right_eye) / 2 + pt_center_lip = (pt203[48] + pt203[66]) / 2 + pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0) + else: + pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0) + return pt2 + + +def parse_pt2_from_pt68(pt68, use_lip=True): + """ + parsing the 2 points according to the 68 points, which cancels the roll + """ + lm_idx = np.array([31, 37, 40, 43, 46, 49, 55], dtype=np.int32) - 1 + if use_lip: + pt5 = np.stack([ + np.mean(pt68[lm_idx[[1, 2]], :], 0), # left eye + np.mean(pt68[lm_idx[[3, 4]], :], 0), # right eye + pt68[lm_idx[0], :], # nose + pt68[lm_idx[5], :], # lip + pt68[lm_idx[6], :] # lip + ], axis=0) + + pt2 = np.stack([ + (pt5[0] + pt5[1]) / 2, + (pt5[3] + pt5[4]) / 2 + ], axis=0) + else: + pt2 = np.stack([ + np.mean(pt68[lm_idx[[1, 2]], :], 0), # left eye + np.mean(pt68[lm_idx[[3, 4]], :], 0), # right eye + ], axis=0) + + return pt2 + + +def parse_pt2_from_pt5(pt5, use_lip=True): + """ + parsing the 2 points according to the 5 points, which cancels the roll + """ + if use_lip: + pt2 = np.stack([ + (pt5[0] + pt5[1]) / 2, + (pt5[3] + pt5[4]) / 2 + ], axis=0) + else: + pt2 = np.stack([ + pt5[0], + pt5[1] + ], axis=0) + return pt2 + +def parse_pt2_from_pt9(pt9, use_lip=True): + ''' + parsing the 2 points according to the 9 points, which cancels the roll + ['right eye right', 'right eye left', 'left eye right', 'left eye left', 'nose tip', 'lip right', 'lip left', 'upper lip', 'lower lip'] + ''' + if use_lip: + pt9 = np.stack([ + (pt9[2] + pt9[3]) / 2, # left eye + (pt9[0] + pt9[1]) / 2, # right eye + pt9[4], + (pt9[5] + pt9[6] ) / 2 # lip + ], axis=0) + pt2 = np.stack([ + (pt9[0] + pt9[1]) / 2, # eye + pt9[3] # lip + ], axis=0) + else: + pt2 = np.stack([ + (pt9[2] + pt9[3]) / 2, + (pt9[0] + pt9[1]) / 2, + ], axis=0) + + return pt2 + +def parse_pt2_from_pt_x(pts, use_lip=True): + if pts.shape[0] == 101: + pt2 = parse_pt2_from_pt101(pts, use_lip=use_lip) + elif pts.shape[0] == 106: + pt2 = parse_pt2_from_pt106(pts, use_lip=use_lip) + elif pts.shape[0] == 68: + pt2 = parse_pt2_from_pt68(pts, use_lip=use_lip) + elif pts.shape[0] == 5: + pt2 = parse_pt2_from_pt5(pts, use_lip=use_lip) + elif pts.shape[0] == 203: + pt2 = parse_pt2_from_pt203(pts, use_lip=use_lip) + elif pts.shape[0] > 101: + # take the first 101 points + pt2 = parse_pt2_from_pt101(pts[:101], use_lip=use_lip) + elif pts.shape[0] == 9: + pt2 = parse_pt2_from_pt9(pts, use_lip=use_lip) + else: + raise Exception(f'Unknow shape: {pts.shape}') + + if not use_lip: + # NOTE: to compile with the latter code, need to rotate the pt2 90 degrees clockwise manually + v = pt2[1] - pt2[0] + pt2[1, 0] = pt2[0, 0] - v[1] + pt2[1, 1] = pt2[0, 1] + v[0] + + return pt2 + + +def parse_rect_from_landmark( + pts, + scale=1.5, + need_square=True, + vx_ratio=0, + vy_ratio=0, + use_deg_flag=False, + **kwargs +): + """parsing center, size, angle from 101/68/5/x landmarks + vx_ratio: the offset ratio along the pupil axis x-axis, multiplied by size + vy_ratio: the offset ratio along the pupil axis y-axis, multiplied by size, which is used to contain more forehead area + + judge with pts.shape + """ + pt2 = parse_pt2_from_pt_x(pts, use_lip=kwargs.get('use_lip', True)) + + uy = pt2[1] - pt2[0] + l = np.linalg.norm(uy) + if l <= 1e-3: + uy = np.array([0, 1], dtype=DTYPE) + else: + uy /= l + ux = np.array((uy[1], -uy[0]), dtype=DTYPE) + + # the rotation degree of the x-axis, the clockwise is positive, the counterclockwise is negative (image coordinate system) + # print(uy) + # print(ux) + angle = acos(ux[0]) + if ux[1] < 0: + angle = -angle + + # rotation matrix + M = np.array([ux, uy]) + + # calculate the size which contains the angle degree of the bbox, and the center + center0 = np.mean(pts, axis=0) + rpts = (pts - center0) @ M.T # (M @ P.T).T = P @ M.T + lt_pt = np.min(rpts, axis=0) + rb_pt = np.max(rpts, axis=0) + center1 = (lt_pt + rb_pt) / 2 + + size = rb_pt - lt_pt + if need_square: + m = max(size[0], size[1]) + size[0] = m + size[1] = m + + size *= scale # scale size + center = center0 + ux * center1[0] + uy * center1[1] # counterclockwise rotation, equivalent to M.T @ center1.T + center = center + ux * (vx_ratio * size) + uy * \ + (vy_ratio * size) # considering the offset in vx and vy direction + + if use_deg_flag: + angle = degrees(angle) + + return center, size, angle + + +def parse_bbox_from_landmark(pts, **kwargs): + center, size, angle = parse_rect_from_landmark(pts, **kwargs) + cx, cy = center + w, h = size + + # calculate the vertex positions before rotation + bbox = np.array([ + [cx-w/2, cy-h/2], # left, top + [cx+w/2, cy-h/2], + [cx+w/2, cy+h/2], # right, bottom + [cx-w/2, cy+h/2] + ], dtype=DTYPE) + + # construct rotation matrix + bbox_rot = bbox.copy() + R = np.array([ + [np.cos(angle), -np.sin(angle)], + [np.sin(angle), np.cos(angle)] + ], dtype=DTYPE) + + # calculate the relative position of each vertex from the rotation center, then rotate these positions, and finally add the coordinates of the rotation center + bbox_rot = (bbox_rot - center) @ R.T + center + + return { + 'center': center, # 2x1 + 'size': size, # scalar + 'angle': angle, # rad, counterclockwise + 'bbox': bbox, # 4x2 + 'bbox_rot': bbox_rot, # 4x2 + } + + +def crop_image_by_bbox(img, bbox, lmk=None, dsize=512, angle=None, flag_rot=False, **kwargs): + left, top, right, bot = bbox + if int(right - left) != int(bot - top): + print(f'right-left {right-left} != bot-top {bot-top}') + size = right - left + + src_center = np.array([(left + right) / 2, (top + bot) / 2], dtype=DTYPE) + tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE) + + s = dsize / size # scale + if flag_rot and angle is not None: + costheta, sintheta = cos(angle), sin(angle) + cx, cy = src_center[0], src_center[1] # ori center + tcx, tcy = tgt_center[0], tgt_center[1] # target center + # need to infer + M_o2c = np.array( + [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)], + [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]], + dtype=DTYPE + ) + else: + M_o2c = np.array( + [[s, 0, tgt_center[0] - s * src_center[0]], + [0, s, tgt_center[1] - s * src_center[1]]], + dtype=DTYPE + ) + + # if flag_rot and angle is None: + # print('angle is None, but flag_rotate is True', style="bold yellow") + + img_crop = _transform_img(img, M_o2c, dsize=dsize, borderMode=kwargs.get('borderMode', None)) + lmk_crop = _transform_pts(lmk, M_o2c) if lmk is not None else None + + M_o2c = np.vstack([M_o2c, np.array([0, 0, 1], dtype=DTYPE)]) + M_c2o = np.linalg.inv(M_o2c) + + # cv2.imwrite('crop.jpg', img_crop) + + return { + 'img_crop': img_crop, + 'lmk_crop': lmk_crop, + 'M_o2c': M_o2c, + 'M_c2o': M_c2o, + } + + +def _estimate_similar_transform_from_pts( + pts, + dsize, + scale=1.5, + vx_ratio=0, + vy_ratio=-0.1, + flag_do_rot=True, + **kwargs +): + """ calculate the affine matrix of the cropped image from sparse points, the original image to the cropped image, the inverse is the cropped image to the original image + pts: landmark, 101 or 68 points or other points, Nx2 + scale: the larger scale factor, the smaller face ratio + vx_ratio: x shift + vy_ratio: y shift, the smaller the y shift, the lower the face region + rot_flag: if it is true, conduct correction + """ + center, size, angle = parse_rect_from_landmark( + pts, scale=scale, vx_ratio=vx_ratio, vy_ratio=vy_ratio, + use_lip=kwargs.get('use_lip', True) + ) + + s = dsize / size[0] # scale + tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE) # center of dsize + + if flag_do_rot: + costheta, sintheta = cos(angle), sin(angle) + cx, cy = center[0], center[1] # ori center + tcx, tcy = tgt_center[0], tgt_center[1] # target center + # need to infer + M_INV = np.array( + [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)], + [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]], + dtype=DTYPE + ) + else: + M_INV = np.array( + [[s, 0, tgt_center[0] - s * center[0]], + [0, s, tgt_center[1] - s * center[1]]], + dtype=DTYPE + ) + + M_INV_H = np.vstack([M_INV, np.array([0, 0, 1])]) + M = np.linalg.inv(M_INV_H) + + # M_INV is from the original image to the cropped image, M is from the cropped image to the original image + return M_INV, M[:2, ...] + + +def crop_image(img, pts: np.ndarray, **kwargs): + dsize = kwargs.get('dsize', 224) + scale = kwargs.get('scale', 1.5) # 1.5 | 1.6 + vy_ratio = kwargs.get('vy_ratio', -0.1) # -0.0625 | -0.1 + + M_INV, _ = _estimate_similar_transform_from_pts( + pts, + dsize=dsize, + scale=scale, + vy_ratio=vy_ratio, + flag_do_rot=kwargs.get('flag_do_rot', True), + ) + + img_crop = _transform_img(img, M_INV, dsize) # origin to crop + pt_crop = _transform_pts(pts, M_INV) + + M_o2c = np.vstack([M_INV, np.array([0, 0, 1], dtype=DTYPE)]) + M_c2o = np.linalg.inv(M_o2c) + + ret_dct = { + 'M_o2c': M_o2c, # from the original image to the cropped image 3x3 + 'M_c2o': M_c2o, # from the cropped image to the original image 3x3 + 'img_crop': img_crop, # the cropped image + 'pt_crop': pt_crop, # the landmarks of the cropped image + } + + return ret_dct + +def average_bbox_lst(bbox_lst): + if len(bbox_lst) == 0: + return None + bbox_arr = np.array(bbox_lst) + return np.mean(bbox_arr, axis=0).tolist() + +def prepare_paste_back(mask_crop, crop_M_c2o, dsize): + """prepare mask for later image paste back + """ + mask_ori = _transform_img(mask_crop, crop_M_c2o, dsize) + mask_ori = mask_ori.astype(np.float32) / 255. + return mask_ori + +def paste_back(img_crop, M_c2o, img_ori, mask_ori): + """paste back the image + """ + dsize = (img_ori.shape[1], img_ori.shape[0]) + result = _transform_img(img_crop, M_c2o, dsize=dsize) + result = np.clip(mask_ori * result + (1 - mask_ori) * img_ori, 0, 255).astype(np.uint8) + return result diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/cropper.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/cropper.py new file mode 100644 index 0000000..97e26c7 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/cropper.py @@ -0,0 +1,313 @@ +# coding: utf-8 + +import os.path as osp +import torch +import numpy as np +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) + +from PIL import Image +from typing import List, Tuple, Union +from dataclasses import dataclass, field + +from ..config.crop_config import CropConfig +from .crop import ( + average_bbox_lst, + crop_image, + crop_image_by_bbox, + parse_bbox_from_landmark, +) +from .io import contiguous +from .rprint import rlog as log +from .face_analysis_diy import FaceAnalysisDIY +from .human_landmark_runner import LandmarkRunner as HumanLandmark + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + + +@dataclass +class Trajectory: + start: int = -1 # start frame + end: int = -1 # end frame + lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list + bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # bbox list + M_c2o_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # M_c2o list + + frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame list + lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list + frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame crop list + + +class Cropper(object): + def __init__(self, **kwargs) -> None: + self.crop_cfg: CropConfig = kwargs.get("crop_cfg", None) + self.image_type = kwargs.get("image_type", 'human_face') + device_id = kwargs.get("device_id", 0) + flag_force_cpu = kwargs.get("flag_force_cpu", False) + if flag_force_cpu: + device = "cpu" + face_analysis_wrapper_provider = ["CPUExecutionProvider"] + else: + try: + if torch.backends.mps.is_available(): + # Shape inference currently fails with CoreMLExecutionProvider + # for the retinaface model + device = "mps" + face_analysis_wrapper_provider = ["CPUExecutionProvider"] + else: + device = "cuda" + face_analysis_wrapper_provider = ["CUDAExecutionProvider"] + except: + device = "cuda" + face_analysis_wrapper_provider = ["CUDAExecutionProvider"] + self.face_analysis_wrapper = FaceAnalysisDIY( + name="buffalo_l", + root=self.crop_cfg.insightface_root, + providers=face_analysis_wrapper_provider, + ) + self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512), det_thresh=self.crop_cfg.det_thresh) + self.face_analysis_wrapper.warmup() + + self.human_landmark_runner = HumanLandmark( + ckpt_path=self.crop_cfg.landmark_ckpt_path, + onnx_provider=device, + device_id=device_id, + ) + self.human_landmark_runner.warmup() + + if self.image_type == "animal_face": + from .animal_landmark_runner import XPoseRunner as AnimalLandmarkRunner + self.animal_landmark_runner = AnimalLandmarkRunner( + model_config_path=self.crop_cfg.xpose_config_file_path, + model_checkpoint_path=self.crop_cfg.xpose_ckpt_path, + embeddings_cache_path=self.crop_cfg.xpose_embedding_cache_path, + flag_use_half_precision=kwargs.get("flag_use_half_precision", True), + ) + self.animal_landmark_runner.warmup() + + def update_config(self, user_args): + for k, v in user_args.items(): + if hasattr(self.crop_cfg, k): + setattr(self.crop_cfg, k, v) + + def crop_source_image(self, img_rgb_: np.ndarray, crop_cfg: CropConfig): + # crop a source image and get neccessary information + img_rgb = img_rgb_.copy() # copy it + img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR) + + if self.image_type == "human_face": + src_face = self.face_analysis_wrapper.get( + img_bgr, + flag_do_landmark_2d_106=True, + direction=crop_cfg.direction, + max_face_num=crop_cfg.max_face_num, + ) + + if len(src_face) == 0: + log("No face detected in the source image.") + return None + elif len(src_face) > 1: + log(f"More than one face detected in the image, only pick one face by rule {crop_cfg.direction}.") + + # NOTE: temporarily only pick the first face, to support multiple face in the future + src_face = src_face[0] + lmk = src_face.landmark_2d_106 # this is the 106 landmarks from insightface + else: + tmp_dct = { + 'animal_face_9': 'animal_face', + 'animal_face_68': 'face' + } + + img_rgb_pil = Image.fromarray(img_rgb) + lmk = self.animal_landmark_runner.run( + img_rgb_pil, + 'face', + tmp_dct[crop_cfg.animal_face_type], + 0, + 0 + ) + + # crop the face + ret_dct = crop_image( + img_rgb, # ndarray + lmk, # 106x2 or Nx2 + dsize=crop_cfg.dsize, + scale=crop_cfg.scale, + vx_ratio=crop_cfg.vx_ratio, + vy_ratio=crop_cfg.vy_ratio, + flag_do_rot=crop_cfg.flag_do_rot, + ) + + # update a 256x256 version for network input + ret_dct["img_crop_256x256"] = cv2.resize(ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA) + if self.image_type == "human_face": + lmk = self.human_landmark_runner.run(img_rgb, lmk) + ret_dct["lmk_crop"] = lmk + ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize + else: + # 68x2 or 9x2 + ret_dct["lmk_crop"] = lmk + + return ret_dct + + def calc_lmk_from_cropped_image(self, img_rgb_, **kwargs): + direction = kwargs.get("direction", "large-small") + src_face = self.face_analysis_wrapper.get( + contiguous(img_rgb_[..., ::-1]), # convert to BGR + flag_do_landmark_2d_106=True, + direction=direction, + ) + if len(src_face) == 0: + log("No face detected in the source image.") + return None + elif len(src_face) > 1: + log(f"More than one face detected in the image, only pick one face by rule {direction}.") + src_face = src_face[0] + lmk = src_face.landmark_2d_106 + lmk = self.human_landmark_runner.run(img_rgb_, lmk) + + return lmk + + # TODO: support skipping frame with NO FACE + def crop_source_video(self, source_rgb_lst, crop_cfg: CropConfig, **kwargs): + """Tracking based landmarks/alignment and cropping""" + trajectory = Trajectory() + direction = kwargs.get("direction", "large-small") + for idx, frame_rgb in enumerate(source_rgb_lst): + if idx == 0 or trajectory.start == -1: + src_face = self.face_analysis_wrapper.get( + contiguous(frame_rgb[..., ::-1]), + flag_do_landmark_2d_106=True, + direction=crop_cfg.direction, + max_face_num=crop_cfg.max_face_num, + ) + if len(src_face) == 0: + log(f"No face detected in the frame #{idx}") + continue + elif len(src_face) > 1: + log(f"More than one face detected in the source frame_{idx}, only pick one face by rule {direction}.") + src_face = src_face[0] + lmk = src_face.landmark_2d_106 + lmk = self.human_landmark_runner.run(frame_rgb, lmk) + trajectory.start, trajectory.end = idx, idx + else: + # TODO: add IOU check for tracking + lmk = self.human_landmark_runner.run(frame_rgb, trajectory.lmk_lst[-1]) + trajectory.end = idx + + trajectory.lmk_lst.append(lmk) + + # crop the face + ret_dct = crop_image( + frame_rgb, # ndarray + lmk, # 106x2 or Nx2 + dsize=crop_cfg.dsize, + scale=crop_cfg.scale, + vx_ratio=crop_cfg.vx_ratio, + vy_ratio=crop_cfg.vy_ratio, + flag_do_rot=crop_cfg.flag_do_rot, + ) + lmk = self.human_landmark_runner.run(frame_rgb, lmk) + ret_dct["lmk_crop"] = lmk + + # update a 256x256 version for network input + ret_dct["img_crop_256x256"] = cv2.resize(ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA) + ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize + + trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop_256x256"]) + trajectory.lmk_crop_lst.append(ret_dct["lmk_crop_256x256"]) + trajectory.M_c2o_lst.append(ret_dct['M_c2o']) + + return { + "frame_crop_lst": trajectory.frame_rgb_crop_lst, + "lmk_crop_lst": trajectory.lmk_crop_lst, + "M_c2o_lst": trajectory.M_c2o_lst, + } + + def crop_driving_video(self, driving_rgb_lst, **kwargs): + """Tracking based landmarks/alignment and cropping""" + trajectory = Trajectory() + direction = kwargs.get("direction", "large-small") + for idx, frame_rgb in enumerate(driving_rgb_lst): + if idx == 0 or trajectory.start == -1: + src_face = self.face_analysis_wrapper.get( + contiguous(frame_rgb[..., ::-1]), + flag_do_landmark_2d_106=True, + direction=direction, + ) + if len(src_face) == 0: + log(f"No face detected in the frame #{idx}") + continue + elif len(src_face) > 1: + log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.") + src_face = src_face[0] + lmk = src_face.landmark_2d_106 + lmk = self.human_landmark_runner.run(frame_rgb, lmk) + trajectory.start, trajectory.end = idx, idx + else: + lmk = self.human_landmark_runner.run(frame_rgb, trajectory.lmk_lst[-1]) + trajectory.end = idx + + trajectory.lmk_lst.append(lmk) + ret_bbox = parse_bbox_from_landmark( + lmk, + scale=self.crop_cfg.scale_crop_driving_video, + vx_ratio_crop_driving_video=self.crop_cfg.vx_ratio_crop_driving_video, + vy_ratio=self.crop_cfg.vy_ratio_crop_driving_video, + )["bbox"] + bbox = [ + ret_bbox[0, 0], + ret_bbox[0, 1], + ret_bbox[2, 0], + ret_bbox[2, 1], + ] # 4, + trajectory.bbox_lst.append(bbox) # bbox + trajectory.frame_rgb_lst.append(frame_rgb) + + global_bbox = average_bbox_lst(trajectory.bbox_lst) + + for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)): + ret_dct = crop_image_by_bbox( + frame_rgb, + global_bbox, + lmk=lmk, + dsize=kwargs.get("dsize", 512), + flag_rot=False, + borderValue=(0, 0, 0), + ) + trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop"]) + trajectory.lmk_crop_lst.append(ret_dct["lmk_crop"]) + + return { + "frame_crop_lst": trajectory.frame_rgb_crop_lst, + "lmk_crop_lst": trajectory.lmk_crop_lst, + } + + + def calc_lmks_from_cropped_video(self, driving_rgb_crop_lst, **kwargs): + """Tracking based landmarks/alignment""" + trajectory = Trajectory() + direction = kwargs.get("direction", "large-small") + + for idx, frame_rgb_crop in enumerate(driving_rgb_crop_lst): + if idx == 0 or trajectory.start == -1: + src_face = self.face_analysis_wrapper.get( + contiguous(frame_rgb_crop[..., ::-1]), # convert to BGR + flag_do_landmark_2d_106=True, + direction=direction, + ) + if len(src_face) == 0: + log(f"No face detected in the frame #{idx}") + raise Exception(f"No face detected in the frame #{idx}") + elif len(src_face) > 1: + log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.") + src_face = src_face[0] + lmk = src_face.landmark_2d_106 + lmk = self.human_landmark_runner.run(frame_rgb_crop, lmk) + trajectory.start, trajectory.end = idx, idx + else: + lmk = self.human_landmark_runner.run(frame_rgb_crop, trajectory.lmk_lst[-1]) + trajectory.end = idx + + trajectory.lmk_lst.append(lmk) + return trajectory.lmk_lst diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/UniPose_SwinT.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/UniPose_SwinT.py new file mode 100644 index 0000000..707b359 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/UniPose_SwinT.py @@ -0,0 +1,125 @@ +_base_ = ['coco_transformer.py'] + +use_label_enc = True + +num_classes=2 + +lr = 0.0001 +param_dict_type = 'default' +lr_backbone = 1e-05 +lr_backbone_names = ['backbone.0'] +lr_linear_proj_names = ['reference_points', 'sampling_offsets'] +lr_linear_proj_mult = 0.1 +ddetr_lr_param = False +batch_size = 2 +weight_decay = 0.0001 +epochs = 12 +lr_drop = 11 +save_checkpoint_interval = 100 +clip_max_norm = 0.1 +onecyclelr = False +multi_step_lr = False +lr_drop_list = [33, 45] + + +modelname = 'UniPose' +frozen_weights = None +backbone = 'swin_T_224_1k' + + +dilation = False +position_embedding = 'sine' +pe_temperatureH = 20 +pe_temperatureW = 20 +return_interm_indices = [1, 2, 3] +backbone_freeze_keywords = None +enc_layers = 6 +dec_layers = 6 +unic_layers = 0 +pre_norm = False +dim_feedforward = 2048 +hidden_dim = 256 +dropout = 0.0 +nheads = 8 +num_queries = 900 +query_dim = 4 +num_patterns = 0 +pdetr3_bbox_embed_diff_each_layer = False +pdetr3_refHW = -1 +random_refpoints_xy = False +fix_refpoints_hw = -1 +dabdetr_yolo_like_anchor_update = False +dabdetr_deformable_encoder = False +dabdetr_deformable_decoder = False +use_deformable_box_attn = False +box_attn_type = 'roi_align' +dec_layer_number = None +num_feature_levels = 4 +enc_n_points = 4 +dec_n_points = 4 +decoder_layer_noise = False +dln_xy_noise = 0.2 +dln_hw_noise = 0.2 +add_channel_attention = False +add_pos_value = False +two_stage_type = 'standard' +two_stage_pat_embed = 0 +two_stage_add_query_num = 0 +two_stage_bbox_embed_share = False +two_stage_class_embed_share = False +two_stage_learn_wh = False +two_stage_default_hw = 0.05 +two_stage_keep_all_tokens = False +num_select = 50 +transformer_activation = 'relu' +batch_norm_type = 'FrozenBatchNorm2d' +masks = False + +decoder_sa_type = 'sa' # ['sa', 'ca_label', 'ca_content'] +matcher_type = 'HungarianMatcher' # or SimpleMinsumMatcher +decoder_module_seq = ['sa', 'ca', 'ffn'] +nms_iou_threshold = -1 + +dec_pred_bbox_embed_share = True +dec_pred_class_embed_share = True + + +use_dn = True +dn_number = 100 +dn_box_noise_scale = 1.0 +dn_label_noise_ratio = 0.5 +dn_label_coef=1.0 +dn_bbox_coef=1.0 +embed_init_tgt = True +dn_labelbook_size = 2000 + +match_unstable_error = True + +# for ema +use_ema = True +ema_decay = 0.9997 +ema_epoch = 0 + +use_detached_boxes_dec_out = False + +max_text_len = 256 +shuffle_type = None + +use_text_enhancer = True +use_fusion_layer = True + +use_checkpoint = False # True +use_transformer_ckpt = True +text_encoder_type = 'bert-base-uncased' + +use_text_cross_attention = True +text_dropout = 0.0 +fusion_dropout = 0.0 +fusion_droppath = 0.1 + +num_body_points=68 +binary_query_selection = False +use_cdn = True +ffn_extra_layernorm = False + +fix_size=False diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/coco_transformer.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/coco_transformer.py new file mode 100644 index 0000000..e7b3fee --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/config_model/coco_transformer.py @@ -0,0 +1,8 @@ +data_aug_scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800] +data_aug_max_size = 1333 +data_aug_scales2_resize = [400, 500, 600] +data_aug_scales2_crop = [384, 600] + + +data_aug_scale_overlap = None + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/__init__.py new file mode 100644 index 0000000..0765963 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/__init__.py @@ -0,0 +1,10 @@ +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +from .unipose import build_unipose diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/attention.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/attention.py new file mode 100644 index 0000000..103cf17 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/attention.py @@ -0,0 +1,373 @@ +# ------------------------------------------------------------------------ +# UniPose +# url: https://github.com/IDEA-Research/UniPose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from codes in torch.nn +# ------------------------------------------------------------------------ + +""" +MultiheadAttention that support query, key, and value to have different dimensions. +Query, key, and value projections are removed. + +Mostly copy-paste from https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/activation.py#L873 +and https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py#L4837 +""" + +import warnings +import torch +from torch.nn.modules.linear import Linear +from torch.nn.init import constant_ +from torch.nn.modules.module import Module +from torch._jit_internal import Optional, Tuple +try: + from torch.overrides import has_torch_function, handle_torch_function +except: + from torch._overrides import has_torch_function, handle_torch_function +from torch.nn.functional import linear, pad, softmax, dropout +Tensor = torch.Tensor + +class MultiheadAttention(Module): + r"""Allows the model to jointly attend to information + from different representation subspaces. + See reference: Attention Is All You Need + .. math:: + \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O + \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + bias: add bias as module parameter. Default: True. + add_bias_kv: add bias to the key and value sequences at dim=0. + add_zero_attn: add a new batch of zeros to the key and + value sequences at dim=1. + kdim: total number of features in key. Default: None. + vdim: total number of features in value. Default: None. + Note: if kdim and vdim are None, they will be set to embed_dim such that + query, key, and value have the same number of features. + Examples:: + >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value) + """ + bias_k: Optional[torch.Tensor] + bias_v: Optional[torch.Tensor] + + def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None): + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" + + vdim = vdim if vdim is not None else embed_dim + self.out_proj = Linear(vdim , vdim) + + self.in_proj_bias = None + self.in_proj_weight = None + self.bias_k = self.bias_v = None + self.q_proj_weight = None + self.k_proj_weight = None + self.v_proj_weight = None + + self.add_zero_attn = add_zero_attn + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.out_proj.bias, 0.) + + def __setstate__(self, state): + # Support loading old MultiheadAttention checkpoints generated by v1.1.0 + if '_qkv_same_embed_dim' not in state: + state['_qkv_same_embed_dim'] = True + + super(MultiheadAttention, self).__setstate__(state) + + def forward(self, query, key, value, key_padding_mask=None, + need_weights=True, attn_mask=None): + # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]] + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + See "Attention Is All You Need" for more details. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. When given a binary mask and a value is True, + the corresponding value on the attention layer will be ignored. When given + a byte mask and a value is non-zero, the corresponding value on the attention + layer will be ignored + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the position + with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*\text{num_heads}, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + if not self._qkv_same_embed_dim: + return multi_head_attention_forward( + query, key, value, self.embed_dim, self.num_heads, + self.in_proj_weight, self.in_proj_bias, + self.bias_k, self.bias_v, self.add_zero_attn, + self.dropout, self.out_proj.weight, self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, need_weights=need_weights, + attn_mask=attn_mask, use_separate_proj_weight=True, + q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, + v_proj_weight=self.v_proj_weight, out_dim=self.vdim) + else: + return multi_head_attention_forward( + query, key, value, self.embed_dim, self.num_heads, + self.in_proj_weight, self.in_proj_bias, + self.bias_k, self.bias_v, self.add_zero_attn, + self.dropout, self.out_proj.weight, self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, need_weights=need_weights, + attn_mask=attn_mask, out_dim=self.vdim) + + +def multi_head_attention_forward(query: Tensor, + key: Tensor, + value: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Tensor, + bias_k: Optional[Tensor], + bias_v: Optional[Tensor], + add_zero_attn: bool, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Tensor, + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + use_separate_proj_weight: bool = False, + q_proj_weight: Optional[Tensor] = None, + k_proj_weight: Optional[Tensor] = None, + v_proj_weight: Optional[Tensor] = None, + static_k: Optional[Tensor] = None, + static_v: Optional[Tensor] = None, + out_dim: Optional[Tensor] = None + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + See "Attention Is All You Need" for more details. + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + bias_k, bias_v: bias of the key and value sequences to be added at dim=0. + add_zero_attn: add a new batch of zeros to the key and + value sequences at dim=1. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + use_separate_proj_weight: the function accept the proj. weights for query, key, + and value in different forms. If false, in_proj_weight will be used, which is + a combination of q_proj_weight, k_proj_weight, v_proj_weight. + q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias. + static_k, static_v: static key and value used for attention operators. + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions + will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, + N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. + - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, + N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + if not torch.jit.is_scripting(): + tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, + out_proj_weight, out_proj_bias) + if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops): + return handle_torch_function( + multi_head_attention_forward, tens_ops, query, key, value, + embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, + bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight, + out_proj_bias, training=training, key_padding_mask=key_padding_mask, + need_weights=need_weights, attn_mask=attn_mask, + use_separate_proj_weight=use_separate_proj_weight, + q_proj_weight=q_proj_weight, k_proj_weight=k_proj_weight, + v_proj_weight=v_proj_weight, static_k=static_k, static_v=static_v) + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + # allow MHA to have different sizes for the feature dimension + assert key.size(0) == value.size(0) and key.size(1) == value.size(1) + + head_dim = embed_dim // num_heads + v_head_dim = out_dim // num_heads + assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads" + scaling = float(head_dim) ** -0.5 + + q = query * scaling + k = key + v = value + + if attn_mask is not None: + assert attn_mask.dtype == torch.float32 or attn_mask.dtype == torch.float64 or \ + attn_mask.dtype == torch.float16 or attn_mask.dtype == torch.uint8 or attn_mask.dtype == torch.bool, \ + 'Only float, byte, and bool types are supported for attn_mask, not {}'.format(attn_mask.dtype) + if attn_mask.dtype == torch.uint8: + warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") + attn_mask = attn_mask.to(torch.bool) + + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(0) + if list(attn_mask.size()) != [1, query.size(0), key.size(0)]: + raise RuntimeError('The size of the 2D attn_mask is not correct.') + elif attn_mask.dim() == 3: + if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]: + raise RuntimeError('The size of the 3D attn_mask is not correct.') + else: + raise RuntimeError("attn_mask's dimension {} is not supported".format(attn_mask.dim())) + # attn_mask's dim is 3 now. + + # convert ByteTensor key_padding_mask to bool + if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8: + warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") + key_padding_mask = key_padding_mask.to(torch.bool) + + if bias_k is not None and bias_v is not None: + if static_k is None and static_v is None: + k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + else: + assert static_k is None, "bias cannot be added to static key." + assert static_v is None, "bias cannot be added to static value." + else: + assert bias_k is None + assert bias_v is None + + q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) + if k is not None: + k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + if v is not None: + v = v.contiguous().view(-1, bsz * num_heads, v_head_dim).transpose(0, 1) + + if static_k is not None: + assert static_k.size(0) == bsz * num_heads + assert static_k.size(2) == head_dim + k = static_k + + if static_v is not None: + assert static_v.size(0) == bsz * num_heads + assert static_v.size(2) == v_head_dim + v = static_v + + src_len = k.size(1) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if add_zero_attn: + src_len += 1 + k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1) + v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len] + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_output_weights.masked_fill_(attn_mask, float('-inf')) + else: + attn_output_weights += attn_mask + + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float('-inf'), + ) + attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len) + + # attn_output_weights = softmax( + # attn_output_weights, dim=-1) + attn_output_weights = softmax( + attn_output_weights - attn_output_weights.max(dim=-1, keepdim=True)[0], dim=-1) + attn_output_weights = dropout(attn_output_weights, p=dropout_p, training=training) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, v_head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, out_dim) + attn_output = linear(attn_output, out_proj_weight, out_proj_bias) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/backbone.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/backbone.py new file mode 100644 index 0000000..b2f4c04 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/backbone.py @@ -0,0 +1,211 @@ +# ------------------------------------------------------------------------ +# UniPose +# url: https://github.com/IDEA-Research/UniPose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +""" +Backbone modules. +""" + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter +from typing import Dict, List + +from util.misc import NestedTensor, is_main_process + +from .position_encoding import build_position_encoding +from .swin_transformer import build_swin_transformer + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other models than torchvision.models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + def __init__( + self, + backbone: nn.Module, + train_backbone: bool, + num_channels: int, + return_interm_indices: list, + ): + super().__init__() + for name, parameter in backbone.named_parameters(): + if ( + not train_backbone + or "layer2" not in name + and "layer3" not in name + and "layer4" not in name + ): + parameter.requires_grad_(False) + + return_layers = {} + for idx, layer_index in enumerate(return_interm_indices): + return_layers.update( + {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} + ) + + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor_list: NestedTensor): + xs = self.body(tensor_list.tensors) + out: Dict[str, NestedTensor] = {} + for name, x in xs.items(): + m = tensor_list.mask + assert m is not None + mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + out[name] = NestedTensor(x, mask) + # import ipdb; ipdb.set_trace() + return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + + def __init__( + self, + name: str, + train_backbone: bool, + dilation: bool, + return_interm_indices: list, + batch_norm=FrozenBatchNorm2d, + ): + if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], + pretrained=is_main_process(), + norm_layer=batch_norm, + ) + else: + raise NotImplementedError("Why you can get here with name {}".format(name)) + # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 + assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." + assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] + num_channels_all = [256, 512, 1024, 2048] + num_channels = num_channels_all[4 - len(return_interm_indices) :] + super().__init__(backbone, train_backbone, num_channels, return_interm_indices) + + +class Joiner(nn.Sequential): + def __init__(self, backbone, position_embedding): + super().__init__(backbone, position_embedding) + + def forward(self, tensor_list: NestedTensor): + xs = self[0](tensor_list) + out: List[NestedTensor] = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self[1](x).to(x.tensors.dtype)) + + return out, pos + + +def build_backbone(args): + """ + Useful args: + - backbone: backbone name + - lr_backbone: + - dilation + - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] + - backbone_freeze_keywords: + - use_checkpoint: for swin only for now + + """ + position_embedding = build_position_encoding(args) + train_backbone = True + if not train_backbone: + raise ValueError("Please set lr_backbone > 0") + return_interm_indices = args.return_interm_indices + assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] + args.backbone_freeze_keywords + use_checkpoint = getattr(args, "use_checkpoint", False) + + if args.backbone in ["resnet50", "resnet101"]: + backbone = Backbone( + args.backbone, + train_backbone, + args.dilation, + return_interm_indices, + batch_norm=FrozenBatchNorm2d, + ) + bb_num_channels = backbone.num_channels + elif args.backbone in [ + "swin_T_224_1k", + "swin_B_224_22k", + "swin_B_384_22k", + "swin_L_224_22k", + "swin_L_384_22k", + ]: + pretrain_img_size = int(args.backbone.split("_")[-2]) + backbone = build_swin_transformer( + args.backbone, + pretrain_img_size=pretrain_img_size, + out_indices=tuple(return_interm_indices), + dilation=False, + use_checkpoint=use_checkpoint, + ) + + bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] + else: + raise NotImplementedError("Unknown backbone {}".format(args.backbone)) + + assert len(bb_num_channels) == len( + return_interm_indices + ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" + + model = Joiner(backbone, position_embedding) + model.num_channels = bb_num_channels + assert isinstance( + bb_num_channels, List + ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) + return model diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/deformable_transformer.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/deformable_transformer.py new file mode 100644 index 0000000..8f99779 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/deformable_transformer.py @@ -0,0 +1,1230 @@ +# ------------------------------------------------------------------------ +# UniPose +# url: https://github.com/IDEA-Research/UniPose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# DINO +# Copyright (c) 2022 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +import math +import copy +import torch +import torch.utils.checkpoint as checkpoint +from torch import nn, Tensor +from typing import Optional +from util.misc import inverse_sigmoid + +from .transformer_vanilla import TransformerEncoderLayer +from .fuse_modules import BiAttentionBlock +from .utils import gen_encoder_output_proposals, MLP, _get_activation_fn, gen_sineembed_for_position, get_sine_pos_embed +from .ops.modules import MSDeformAttn + + +class DeformableTransformer(nn.Module): + + def __init__(self, d_model=256, nhead=8, + num_queries=300, + num_encoder_layers=6, + num_unicoder_layers=0, + num_decoder_layers=6, + dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False, + return_intermediate_dec=False, query_dim=4, + num_patterns=0, + modulate_hw_attn=False, + # for deformable encoder + deformable_encoder=False, + deformable_decoder=False, + num_feature_levels=1, + enc_n_points=4, + dec_n_points=4, + use_deformable_box_attn=False, + box_attn_type='roi_align', + # init query + learnable_tgt_init=False, + decoder_query_perturber=None, + add_channel_attention=False, + add_pos_value=False, + random_refpoints_xy=False, + # two stage + two_stage_type='no', + two_stage_pat_embed=0, + two_stage_add_query_num=0, + two_stage_learn_wh=False, + two_stage_keep_all_tokens=False, + # evo of #anchors + dec_layer_number=None, + rm_enc_query_scale=True, + rm_dec_query_scale=True, + rm_self_attn_layers=None, + key_aware_type=None, + # layer share + layer_share_type=None, + # for detach + rm_detach=None, + decoder_sa_type='ca', + module_seq=['sa', 'ca', 'ffn'], + # for dn + embed_init_tgt=False, + + use_detached_boxes_dec_out=False, + use_text_enhancer=False, + use_fusion_layer=False, + use_checkpoint=False, + use_transformer_ckpt=False, + use_text_cross_attention=False, + text_dropout=0.1, + fusion_dropout=0.1, + fusion_droppath=0.0, + + binary_query_selection=False, + ffn_extra_layernorm=False, + ): + super().__init__() + self.num_feature_levels = num_feature_levels + self.num_encoder_layers = num_encoder_layers + self.num_unicoder_layers = num_unicoder_layers + self.num_decoder_layers = num_decoder_layers + self.deformable_encoder = deformable_encoder + self.deformable_decoder = deformable_decoder + self.two_stage_keep_all_tokens = two_stage_keep_all_tokens + self.num_queries = num_queries + self.random_refpoints_xy = random_refpoints_xy + self.use_detached_boxes_dec_out = use_detached_boxes_dec_out + self.ffn_extra_layernorm = ffn_extra_layernorm + assert query_dim == 4 + + self.binary_query_selection = binary_query_selection + if self.binary_query_selection: + self.binary_query_selection_layer = nn.Linear(d_model, 1) + # assert not binary_query_selection, 'binary_query_selection not implemented yet' + + if num_feature_levels > 1: + assert deformable_encoder, "only support deformable_encoder for num_feature_levels > 1" + if use_deformable_box_attn: + assert deformable_encoder or deformable_encoder + + assert layer_share_type in [None, 'encoder', 'decoder', 'both'] + if layer_share_type in ['encoder', 'both']: + enc_layer_share = True + else: + enc_layer_share = False + if layer_share_type in ['decoder', 'both']: + dec_layer_share = True + else: + dec_layer_share = False + assert layer_share_type is None + + self.decoder_sa_type = decoder_sa_type + assert decoder_sa_type in ['sa', 'ca_label', 'ca_content'] + + # choose encoder layer type + if deformable_encoder: + encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, enc_n_points, + add_channel_attention=add_channel_attention, + use_deformable_box_attn=use_deformable_box_attn, + box_attn_type=box_attn_type) + else: + raise NotImplementedError + + if use_text_enhancer: + text_enhance_layer = TransformerEncoderLayer( + d_model=d_model, + nhead=nhead // 2, + dim_feedforward=dim_feedforward // 2, + dropout=text_dropout + ) + else: + text_enhance_layer = None + + if use_fusion_layer: + feature_fusion_layer = BiAttentionBlock( + v_dim=d_model, + l_dim=d_model, + embed_dim=dim_feedforward // 2, + num_heads=nhead // 2, + dropout=fusion_dropout, + drop_path=fusion_droppath + ) + else: + feature_fusion_layer = None + + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + assert encoder_norm is None + self.encoder = TransformerEncoder( + encoder_layer, num_encoder_layers, d_model=d_model, + num_queries=num_queries, + enc_layer_share=enc_layer_share, + text_enhance_layer=text_enhance_layer, + feature_fusion_layer=feature_fusion_layer, + use_checkpoint=use_checkpoint, + use_transformer_ckpt=use_transformer_ckpt, + ) + + # choose decoder layer type + if deformable_decoder: + decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, dec_n_points, + use_text_cross_attention=use_text_cross_attention, + ffn_extra_layernorm=ffn_extra_layernorm, ) + + else: + raise NotImplementedError + + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, + return_intermediate=return_intermediate_dec, + d_model=d_model, query_dim=query_dim, + modulate_hw_attn=modulate_hw_attn, + num_feature_levels=num_feature_levels, + deformable_decoder=deformable_decoder, + decoder_query_perturber=decoder_query_perturber, + dec_layer_number=dec_layer_number, rm_dec_query_scale=rm_dec_query_scale, + dec_layer_share=dec_layer_share, + use_detached_boxes_dec_out=use_detached_boxes_dec_out + ) + + self.d_model = d_model + self.nhead = nhead + self.dec_layers = num_decoder_layers + self.num_queries = num_queries # useful for single stage model only + self.num_patterns = num_patterns + if not isinstance(num_patterns, int): + Warning("num_patterns should be int but {}".format(type(num_patterns))) + self.num_patterns = 0 + + if num_feature_levels > 1: + if self.num_encoder_layers > 0: + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + else: + self.level_embed = None + + self.learnable_tgt_init = learnable_tgt_init + assert learnable_tgt_init, "why not learnable_tgt_init" + self.embed_init_tgt = embed_init_tgt + if (two_stage_type != 'no' and embed_init_tgt) or (two_stage_type == 'no'): + self.tgt_embed = nn.Embedding(self.num_queries, d_model) + nn.init.normal_(self.tgt_embed.weight.data) + else: + self.tgt_embed = None + + # for two stage + self.two_stage_type = two_stage_type + self.two_stage_pat_embed = two_stage_pat_embed + self.two_stage_add_query_num = two_stage_add_query_num + self.two_stage_learn_wh = two_stage_learn_wh + assert two_stage_type in ['no', 'standard'], "unknown param {} of two_stage_type".format(two_stage_type) + if two_stage_type == 'standard': + # anchor selection at the output of encoder + self.enc_output = nn.Linear(d_model, d_model) + self.enc_output_norm = nn.LayerNorm(d_model) + + if two_stage_pat_embed > 0: + self.pat_embed_for_2stage = nn.Parameter(torch.Tensor(two_stage_pat_embed, d_model)) + nn.init.normal_(self.pat_embed_for_2stage) + + if two_stage_add_query_num > 0: + self.tgt_embed = nn.Embedding(self.two_stage_add_query_num, d_model) + + if two_stage_learn_wh: + # import ipdb; ipdb.set_trace() + self.two_stage_wh_embedding = nn.Embedding(1, 2) + else: + self.two_stage_wh_embedding = None + + if two_stage_type == 'no': + self.init_ref_points(num_queries) # init self.refpoint_embed + + self.enc_out_class_embed = None + self.enc_out_bbox_embed = None + + # evolution of anchors + self.dec_layer_number = dec_layer_number + if dec_layer_number is not None: + if self.two_stage_type != 'no' or num_patterns == 0: + assert dec_layer_number[ + 0] == num_queries, f"dec_layer_number[0]({dec_layer_number[0]}) != num_queries({num_queries})" + else: + assert dec_layer_number[ + 0] == num_queries * num_patterns, f"dec_layer_number[0]({dec_layer_number[0]}) != num_queries({num_queries}) * num_patterns({num_patterns})" + + self._reset_parameters() + + self.rm_self_attn_layers = rm_self_attn_layers + if rm_self_attn_layers is not None: + # assert len(rm_self_attn_layers) == num_decoder_layers + print("Removing the self-attn in {} decoder layers".format(rm_self_attn_layers)) + for lid, dec_layer in enumerate(self.decoder.layers): + if lid in rm_self_attn_layers: + dec_layer.rm_self_attn_modules() + + self.rm_detach = rm_detach + if self.rm_detach: + assert isinstance(rm_detach, list) + assert any([i in ['enc_ref', 'enc_tgt', 'dec'] for i in rm_detach]) + self.decoder.rm_detach = rm_detach + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + if self.num_feature_levels > 1 and self.level_embed is not None: + nn.init.normal_(self.level_embed) + + if self.two_stage_learn_wh: + nn.init.constant_(self.two_stage_wh_embedding.weight, math.log(0.05 / (1 - 0.05))) + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def init_ref_points(self, use_num_queries): + self.refpoint_embed = nn.Embedding(use_num_queries, 4) + + if self.random_refpoints_xy: + # import ipdb; ipdb.set_trace() + self.refpoint_embed.weight.data[:, :2].uniform_(0, 1) + self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2]) + self.refpoint_embed.weight.data[:, :2].requires_grad = False + + def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, attn_mask2=None, text_dict=None, + dn_meta=None,targets=None,kpt_embed=None): + """ + Input: + - srcs: List of multi features [bs, ci, hi, wi] + - masks: List of multi masks [bs, hi, wi] + - refpoint_embed: [bs, num_dn, 4]. None in infer + - pos_embeds: List of multi pos embeds [bs, ci, hi, wi] + - tgt: [bs, num_dn, d_model]. None in infer + + """ + # if self.two_stage_type != 'no' and self.two_stage_add_query_num == 0: + # assert refpoint_embed is None + + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + src = src.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) # bs, hw + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + if self.num_feature_levels > 1 and self.level_embed is not None: + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + else: + lvl_pos_embed = pos_embed + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c + mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw} + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \sum{hxw}, c + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # two stage + enc_topk_proposals = enc_refpoint_embed = None + + ######################################################### + # Begin Encoder + ######################################################### + memory, memory_text = self.encoder( + src_flatten, + pos=lvl_pos_embed_flatten, + level_start_index=level_start_index, + spatial_shapes=spatial_shapes, + valid_ratios=valid_ratios, + key_padding_mask=mask_flatten, + memory_text=text_dict['encoded_text'], + text_attention_mask=~text_dict['text_token_mask'], + # we ~ the mask . False means use the token; True means pad the token + position_ids=text_dict['position_ids'], + text_self_attention_masks=text_dict['text_self_attention_masks'], + ) + ######################################################### + # End Encoder + # - memory: bs, \sum{hw}, c + # - mask_flatten: bs, \sum{hw} + # - lvl_pos_embed_flatten: bs, \sum{hw}, c + # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) + # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) + ######################################################### + text_dict['encoded_text'] = memory_text + + if self.two_stage_type == 'standard': + if self.two_stage_learn_wh: + input_hw = self.two_stage_wh_embedding.weight[0] + else: + input_hw = None + output_memory, output_proposals = gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes, + input_hw) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + + if self.two_stage_pat_embed > 0: + bs, nhw, _ = output_memory.shape + # output_memory: bs, n, 256; self.pat_embed_for_2stage: k, 256 + output_memory = output_memory.repeat(1, self.two_stage_pat_embed, 1) + _pats = self.pat_embed_for_2stage.repeat_interleave(nhw, 0) + output_memory = output_memory + _pats + output_proposals = output_proposals.repeat(1, self.two_stage_pat_embed, 1) + + if self.two_stage_add_query_num > 0: + assert refpoint_embed is not None + output_memory = torch.cat((output_memory, tgt), dim=1) + output_proposals = torch.cat((output_proposals, refpoint_embed), dim=1) + + if self.binary_query_selection: + topk_logits = self.binary_query_selection_layer(output_memory).squeeze(-1) + else: + if text_dict is not None: + enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict) + else: + enc_outputs_class_unselected = self.enc_out_class_embed(output_memory) + + topk_logits = enc_outputs_class_unselected.max(-1)[0] + enc_outputs_coord_unselected = self.enc_out_bbox_embed( + output_memory) + output_proposals # (bs, \sum{hw}, 4) unsigmoid + topk = self.num_queries + + topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq + + # gather boxes + refpoint_embed_undetach = torch.gather(enc_outputs_coord_unselected, 1, + topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) # unsigmoid + refpoint_embed_ = refpoint_embed_undetach.detach() + init_box_proposal = torch.gather(output_proposals, 1, + topk_proposals.unsqueeze(-1).repeat(1, 1, 4)).sigmoid() # sigmoid + + # gather tgt + tgt_undetach = torch.gather(output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)) + if self.embed_init_tgt: + tgt_ = self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) # nq, bs, d_model + else: + tgt_ = tgt_undetach.detach() + + if refpoint_embed is not None: + refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) + tgt = torch.cat([tgt, tgt_], dim=1) + else: + refpoint_embed, tgt = refpoint_embed_, tgt_ + + elif self.two_stage_type == 'no': + tgt_ = self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) # nq, bs, d_model + refpoint_embed_ = self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) # nq, bs, 4 + + if refpoint_embed is not None: + refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) + tgt = torch.cat([tgt, tgt_], dim=1) + else: + refpoint_embed, tgt = refpoint_embed_, tgt_ + + if self.num_patterns > 0: + tgt_embed = tgt.repeat(1, self.num_patterns, 1) + refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1) + tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(self.num_queries, + 1) # 1, n_q*n_pat, d_model + tgt = tgt_embed + tgt_pat + + init_box_proposal = refpoint_embed_.sigmoid() + + else: + raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type)) + ######################################################### + # End preparing tgt + # - tgt: bs, NQ, d_model + # - refpoint_embed(unsigmoid): bs, NQ, d_model + ######################################################### + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if refpoint_embed.isnan().any() | refpoint_embed.isinf().any(): + # import ipdb; ipdb.set_trace() + # if tgt.isnan().any() | tgt.isinf().any(): + # import ipdb; ipdb.set_trace() + + ######################################################### + # Begin Decoder + ######################################################### + hs, references = self.decoder( + tgt=tgt.transpose(0, 1), + memory=memory.transpose(0, 1), + memory_key_padding_mask=mask_flatten, + pos=lvl_pos_embed_flatten.transpose(0, 1), + refpoints_unsigmoid=refpoint_embed.transpose(0, 1), + level_start_index=level_start_index, + spatial_shapes=spatial_shapes, + valid_ratios=valid_ratios, tgt_mask=attn_mask, + tgt_mask2=attn_mask2, + memory_text=text_dict['encoded_text'], + text_attention_mask=~text_dict['text_token_mask'], + text_dict=text_dict, + dn_meta=dn_meta, + targets=targets, + kpt_embed=kpt_embed + # we ~ the mask . False means use the token; True means pad the token + ) + ######################################################### + # End Decoder + # hs: n_dec, bs, nq, d_model + # references: n_dec+1, bs, nq, query_dim + ######################################################### + + ######################################################### + # Begin postprocess + ######################################################### + if self.two_stage_type == 'standard': + if self.two_stage_keep_all_tokens: + hs_enc = output_memory.unsqueeze(0) + ref_enc = enc_outputs_coord_unselected.unsqueeze(0) + init_box_proposal = output_proposals + # import ipdb; ipdb.set_trace() + else: + hs_enc = tgt_undetach.unsqueeze(0) + ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0) + else: + hs_enc = ref_enc = None + ######################################################### + # End postprocess + # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None + # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None + ######################################################### + + return hs, references, hs_enc, ref_enc, init_box_proposal + # hs: (n_dec, bs, nq, d_model) + # references: sigmoid coordinates. (n_dec+1, bs, bq, 4) + # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None + # ref_enc: sigmoid coordinates. \ + # (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None + + +class TransformerEncoder(nn.Module): + + def __init__(self, + encoder_layer, num_layers, d_model=256, + num_queries=300, + enc_layer_share=False, + text_enhance_layer=None, + feature_fusion_layer=None, + use_checkpoint=False, + use_transformer_ckpt=False, + ): + """_summary_ + + Args: + encoder_layer (_type_): _description_ + num_layers (_type_): _description_ + norm (_type_, optional): _description_. Defaults to None. + d_model (int, optional): _description_. Defaults to 256. + num_queries (int, optional): _description_. Defaults to 300. + enc_layer_share (bool, optional): _description_. Defaults to False. + + """ + super().__init__() + # prepare layers + self.layers = [] + self.text_layers = [] + self.fusion_layers = [] + if num_layers > 0: + self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share) + + if text_enhance_layer is not None: + self.text_layers = _get_clones(text_enhance_layer, num_layers, layer_share=enc_layer_share) + if feature_fusion_layer is not None: + self.fusion_layers = _get_clones(feature_fusion_layer, num_layers, layer_share=enc_layer_share) + else: + self.layers = [] + del encoder_layer + + if text_enhance_layer is not None: + self.text_layers = [] + del text_enhance_layer + if feature_fusion_layer is not None: + self.fusion_layers = [] + del feature_fusion_layer + + self.query_scale = None + self.num_queries = num_queries + self.num_layers = num_layers + self.d_model = d_model + + self.use_checkpoint = use_checkpoint + self.use_transformer_ckpt = use_transformer_ckpt + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward(self, + # for images + src: Tensor, + pos: Tensor, + spatial_shapes: Tensor, + level_start_index: Tensor, + valid_ratios: Tensor, + key_padding_mask: Tensor, + # for texts + memory_text: Tensor = None, + text_attention_mask: Tensor = None, + pos_text: Tensor = None, + text_self_attention_masks: Tensor = None, + position_ids: Tensor = None, + ): + """ + Input: + - src: [bs, sum(hi*wi), 256] + - pos: pos embed for src. [bs, sum(hi*wi), 256] + - spatial_shapes: h,w of each level [num_level, 2] + - level_start_index: [num_level] start point of level in sum(hi*wi). + - valid_ratios: [bs, num_level, 2] + - key_padding_mask: [bs, sum(hi*wi)] + + - memory_text: bs, n_text, 256 + - text_attention_mask: bs, n_text + False for no padding; True for padding + - pos_text: bs, n_text, 256 + + - position_ids: bs, n_text + Intermedia: + - reference_points: [bs, sum(hi*wi), num_level, 2] + Outpus: + - output: [bs, sum(hi*wi), 256] + """ + + output = src + + # preparation and reshape + if self.num_layers > 0: + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) + + if self.text_layers: + # generate pos_text + bs, n_text, text_dim = memory_text.shape + if pos_text is None and position_ids is None: + pos_text = torch.arange(n_text, device=memory_text.device).float().unsqueeze(0).unsqueeze(-1).repeat(bs, + 1, + 1) + pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False) + if position_ids is not None: + pos_text = get_sine_pos_embed(position_ids[..., None], num_pos_feats=256, exchange_xy=False) + + # main process + for layer_id, layer in enumerate(self.layers): + # if output.isnan().any() or memory_text.isnan().any(): + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + if self.fusion_layers: + if self.use_checkpoint: + output, memory_text = checkpoint.checkpoint( + self.fusion_layers[layer_id], + output, + memory_text, + key_padding_mask, + text_attention_mask + ) + else: + output, memory_text = self.fusion_layers[layer_id](v=output, l=memory_text, + attention_mask_v=key_padding_mask, + attention_mask_l=text_attention_mask) + + if self.text_layers: + memory_text = self.text_layers[layer_id]( + src=memory_text.transpose(0, 1), + src_mask=~text_self_attention_masks, # note we use ~ for mask here + src_key_padding_mask=text_attention_mask, + pos=(pos_text.transpose(0, 1) if pos_text is not None else None) + ).transpose(0, 1) + + # main process + if self.use_transformer_ckpt: + output = checkpoint.checkpoint( + layer, + output, + pos, + reference_points, + spatial_shapes, + level_start_index, + key_padding_mask + ) + else: + output = layer(src=output, pos=pos, reference_points=reference_points, spatial_shapes=spatial_shapes, + level_start_index=level_start_index, key_padding_mask=key_padding_mask) + + return output, memory_text + + +class TransformerDecoder(nn.Module): + + def __init__(self, decoder_layer, num_layers, norm=None, + return_intermediate=False, + d_model=256, query_dim=4, + modulate_hw_attn=False, + num_feature_levels=1, + deformable_decoder=False, + decoder_query_perturber=None, + dec_layer_number=None, # number of queries each layer in decoder + rm_dec_query_scale=False, + dec_layer_share=False, + dec_layer_dropout_prob=None, + use_detached_boxes_dec_out=False, + num_box_decoder_layers=2, + num_body_points=68, + ): + super().__init__() + if num_layers > 0: + self.layers = _get_clones(decoder_layer, num_layers, layer_share=dec_layer_share) + else: + self.layers = [] + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + assert return_intermediate, "support return_intermediate only" + self.query_dim = query_dim + assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim) + self.num_feature_levels = num_feature_levels + self.use_detached_boxes_dec_out = use_detached_boxes_dec_out + + self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2) + if not deformable_decoder: + self.query_pos_sine_scale = MLP(d_model, d_model, d_model, 2) + else: + self.query_pos_sine_scale = None + + if rm_dec_query_scale: + self.query_scale = None + else: + raise NotImplementedError + self.query_scale = MLP(d_model, d_model, d_model, 2) + self.bbox_embed = None + self.class_embed = None + self.pose_embed = None + self.pose_hw_embed = None + self.d_model = d_model + self.modulate_hw_attn = modulate_hw_attn + self.deformable_decoder = deformable_decoder + + if not deformable_decoder and modulate_hw_attn: + self.ref_anchor_head = MLP(d_model, d_model, 2, 2) + else: + self.ref_anchor_head = None + + self.decoder_query_perturber = decoder_query_perturber + self.box_pred_damping = None + + self.dec_layer_number = dec_layer_number + if dec_layer_number is not None: + assert isinstance(dec_layer_number, list) + assert len(dec_layer_number) == num_layers + # assert dec_layer_number[0] == + + self.dec_layer_dropout_prob = dec_layer_dropout_prob + if dec_layer_dropout_prob is not None: + assert isinstance(dec_layer_dropout_prob, list) + assert len(dec_layer_dropout_prob) == num_layers + for i in dec_layer_dropout_prob: + assert 0.0 <= i <= 1.0 + + self.rm_detach = None + self.num_body_points = num_body_points + + self.hw = nn.Embedding(17, 2) + self.num_box_decoder_layers = num_box_decoder_layers + self.kpt_index = [x for x in range(50 * (self.num_body_points + 1)) if x % (self.num_body_points + 1) != 0] + self.hw_append = nn.Embedding(self.num_body_points-17, 2) + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + tgt_mask2: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2 + # for memory + level_start_index: Optional[Tensor] = None, # num_levels + spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + valid_ratios: Optional[Tensor] = None, + # for text + memory_text: Optional[Tensor] = None, + text_attention_mask: Optional[Tensor] = None, + text_dict: Optional[Tensor] = None, + dn_meta: Optional[Tensor] = None, + targets: Optional[Tensor] = None, + kpt_embed: Optional[Tensor] = None + ): + """ + Input: + - tgt: nq, bs, d_model + - memory: hw, bs, d_model + - pos: hw, bs, d_model + - refpoints_unsigmoid: nq, bs, 2/4 + - valid_ratios/spatial_shapes: bs, nlevel, 2 + """ + + output = tgt + output += self.hw.weight[0, 0] * 0.0 + + + intermediate = [] + reference_points = refpoints_unsigmoid.sigmoid() + ref_points = [reference_points] + effect_num_dn = dn_meta['pad_size'] if self.training else 0 + inter_select_number = 50 + for layer_id, layer in enumerate(self.layers): + + if reference_points.shape[-1] == 4: + reference_points_input = reference_points[:, :, None] \ + * torch.cat([valid_ratios, valid_ratios], -1)[None, :] # nq, bs, nlevel, 4 + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[None, :] + query_sine_embed = gen_sineembed_for_position(reference_points_input[:, :, 0, :]) # nq, bs, 256*2 + + # conditional query + raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256 + pos_scale = self.query_scale(output) if self.query_scale is not None else 1 + query_pos = pos_scale * raw_query_pos + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if query_pos.isnan().any() | query_pos.isinf().any(): + # import ipdb; ipdb.set_trace() + + # main process + output = layer( + tgt=output, + tgt_query_pos=query_pos, + tgt_query_sine_embed=query_sine_embed, + tgt_key_padding_mask=tgt_key_padding_mask, + tgt_reference_points=reference_points_input, + + memory_text=memory_text, + text_attention_mask=text_attention_mask, + + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + memory_level_start_index=level_start_index, + memory_spatial_shapes=spatial_shapes, + memory_pos=pos, + + self_attn_mask=tgt_mask, + cross_attn_mask=memory_mask + ) + if output.isnan().any() | output.isinf().any(): + print(f"output layer_id {layer_id} is nan") + try: + num_nan = output.isnan().sum().item() + num_inf = output.isinf().sum().item() + print(f"num_nan {num_nan}, num_inf {num_inf}") + except Exception as e: + print(e) + + + + + intermediate.append(self.norm(output)) + # iter update + if layer_id < self.num_box_decoder_layers: + reference_before_sigmoid = inverse_sigmoid(reference_points) + delta_unsig = self.bbox_embed[layer_id](output) + outputs_unsig = delta_unsig + reference_before_sigmoid + new_reference_points = outputs_unsig.sigmoid() + + # select # ref points as anchors + if layer_id == self.num_box_decoder_layers - 1: + dn_output = output[:effect_num_dn] + dn_new_reference_points = new_reference_points[:effect_num_dn] + class_unselected = self.class_embed[layer_id](output.transpose(0, 1), text_dict)[:, + effect_num_dn:].transpose(0, 1) + topk_proposals = torch.topk(class_unselected.max(-1)[0], inter_select_number, dim=0)[1] + new_reference_points_for_box = torch.gather(new_reference_points[effect_num_dn:], 0, + topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) + new_output_for_box = torch.gather(output[effect_num_dn:], 0, + topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)) + keypoint_embed=kpt_embed.transpose(0, 1) + + new_output_for_keypoint = keypoint_embed[None, :, :, :].repeat(new_output_for_box.shape[0],1,1,1) + delta_xy = self.pose_embed[-1](new_output_for_keypoint)[..., :2] + keypoint_xy = (inverse_sigmoid(new_reference_points_for_box[..., :2][:, None]) + delta_xy).sigmoid() + num_queries, _, bs, _ = keypoint_xy.shape + aa = torch.cat((self.hw.weight,self.hw_append.weight),dim=0) + keypoint_wh_weight = aa.unsqueeze(0).unsqueeze(-2).repeat(num_queries, 1, bs, 1).sigmoid() + keypoint_wh = keypoint_wh_weight * new_reference_points_for_box[..., 2:][:, None] + new_reference_points_for_keypoint = torch.cat((keypoint_xy, keypoint_wh), dim=-1) + new_reference_points = torch.cat( + (new_reference_points_for_box.unsqueeze(1), new_reference_points_for_keypoint), dim=1).flatten(0, 1) + output = torch.cat((new_output_for_box.unsqueeze(1), new_output_for_keypoint), dim=1).flatten(0, 1) + new_reference_points = torch.cat((dn_new_reference_points, new_reference_points), dim=0) + output = torch.cat((dn_output, output), dim=0) + tgt_mask = tgt_mask2 + + if layer_id >= self.num_box_decoder_layers: + reference_before_sigmoid = inverse_sigmoid(reference_points) + output_bbox_dn = output[:effect_num_dn] + output_bbox_norm = output[effect_num_dn:][0::(self.num_body_points + 1)] + reference_before_sigmoid_bbox_dn = reference_before_sigmoid[:effect_num_dn] + reference_before_sigmoid_bbox_norm = reference_before_sigmoid[effect_num_dn:][ + 0::(self.num_body_points + 1)] + delta_unsig_dn = self.bbox_embed[layer_id](output_bbox_dn) + delta_unsig_norm = self.bbox_embed[layer_id](output_bbox_norm) + outputs_unsig_dn = delta_unsig_dn + reference_before_sigmoid_bbox_dn + outputs_unsig_norm = delta_unsig_norm + reference_before_sigmoid_bbox_norm + new_reference_points_for_box_dn = outputs_unsig_dn.sigmoid() + new_reference_points_for_box_norm = outputs_unsig_norm.sigmoid() + output_kpt = output[effect_num_dn:].index_select(0, torch.tensor(self.kpt_index, device=output.device)) + delta_xy_unsig = self.pose_embed[layer_id - self.num_box_decoder_layers](output_kpt) + outputs_unsig = reference_before_sigmoid[effect_num_dn:].index_select(0, torch.tensor(self.kpt_index, + device=output.device)).clone() ## + delta_hw_unsig = self.pose_hw_embed[layer_id - self.num_box_decoder_layers](output_kpt) + outputs_unsig[..., :2] += delta_xy_unsig[..., :2] + outputs_unsig[..., 2:] += delta_hw_unsig + new_reference_points_for_keypoint = outputs_unsig.sigmoid() + bs = new_reference_points_for_box_norm.shape[1] + new_reference_points_norm = torch.cat((new_reference_points_for_box_norm.unsqueeze(1), + new_reference_points_for_keypoint.view(-1, self.num_body_points, + bs, 4)), dim=1).flatten(0, + 1) + new_reference_points = torch.cat((new_reference_points_for_box_dn, new_reference_points_norm), dim=0) + + if self.rm_detach and 'dec' in self.rm_detach: + reference_points = new_reference_points + else: + reference_points = new_reference_points.detach() + + # if layer_id != self.num_layers - 1: + if self.use_detached_boxes_dec_out: + ref_points.append(reference_points) + else: + ref_points.append(new_reference_points) + + return [ + [itm_out.transpose(0, 1) for itm_out in intermediate], + [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points] + ] + + +class DeformableTransformerEncoderLayer(nn.Module): + def __init__(self, + d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4, + add_channel_attention=False, + use_deformable_box_attn=False, + box_attn_type='roi_align', + ): + super().__init__() + + # self attention + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + # channel attention + self.add_channel_attention = add_channel_attention + if add_channel_attention: + self.activ_channel = _get_activation_fn('dyrelu', d_model=d_model) + self.norm_channel = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None): + # self attention + # import ipdb; ipdb.set_trace() + src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, + key_padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + # channel attn + if self.add_channel_attention: + src = self.norm_channel(src + self.activ_channel(src)) + + return src + + +class DeformableTransformerDecoderLayer(nn.Module): + def __init__(self, d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4, + use_text_feat_guide=False, + use_text_cross_attention=False, + ffn_extra_layernorm=False + ): + super().__init__() + + # cross attention + # self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm1 = nn.LayerNorm(d_model) + + # cross attention text + if use_text_cross_attention: + self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) + self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.catext_norm = nn.LayerNorm(d_model) + + # self attention + self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) + self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm2 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1) + self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm3 = nn.LayerNorm(d_model) + if ffn_extra_layernorm: + raise NotImplementedError('ffn_extra_layernorm not implemented') + self.norm_ext = nn.LayerNorm(d_ffn) + else: + self.norm_ext = None + + self.key_aware_proj = None + self.use_text_feat_guide = use_text_feat_guide + assert not use_text_feat_guide + self.use_text_cross_attention = use_text_cross_attention + + def rm_self_attn_modules(self): + self.self_attn = None + self.dropout2 = None + self.norm2 = None + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, tgt, ipdb_flag=False): + + with torch.cuda.amp.autocast(enabled=False): + tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) + + tgt = tgt + self.dropout4(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward(self, + # for tgt + tgt: Optional[Tensor], # nq, bs, d_model + tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) + tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) + tgt_key_padding_mask: Optional[Tensor] = None, + tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 + + memory_text: Optional[Tensor] = None, # bs, num_token, d_model + text_attention_mask: Optional[Tensor] = None, # bs, num_token + + # for memory + memory: Optional[Tensor] = None, # hw, bs, d_model + memory_key_padding_mask: Optional[Tensor] = None, + memory_level_start_index: Optional[Tensor] = None, # num_levels + memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + memory_pos: Optional[Tensor] = None, # pos for memory + + # sa + self_attn_mask: Optional[Tensor] = None, # mask used for self-attention + cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention + ): + """ + Input: + - tgt/tgt_query_pos: nq, bs, d_model + - + """ + assert cross_attn_mask is None + + # self attention + if self.self_attn is not None: + # import ipdb; ipdb.set_trace() + q = k = self.with_pos_embed(tgt, tgt_query_pos) + tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if tgt.isnan().any() | tgt.isinf().any() : + # import ipdb; ipdb.set_trace() + + if self.use_text_cross_attention: + tgt2 = self.ca_text(self.with_pos_embed(tgt, tgt_query_pos), memory_text.transpose(0, 1), + memory_text.transpose(0, 1), key_padding_mask=text_attention_mask)[0] + tgt = tgt + self.catext_dropout(tgt2) + tgt = self.catext_norm(tgt) + + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + + # if tgt.isnan().any() | tgt.isinf().any() : + # import ipdb; ipdb.set_trace() + + tgt2 = self.cross_attn(self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1), + tgt_reference_points.transpose(0, 1).contiguous(), + memory.transpose(0, 1), memory_spatial_shapes, memory_level_start_index, + memory_key_padding_mask).transpose(0, 1) + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # tgtk = tgt.clone() + # if tgt.isnan().any() | tgt.isinf().any() : + # import ipdb; ipdb.set_trace() + + # ffn + tgt = self.forward_ffn(tgt) + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if tgt.isnan().any() | tgt.isinf().any() : + # tgtk = self.forward_ffn(tgtk, ipdb_flag=True) + # import ipdb; ipdb.set_trace() + + return tgt + + +def _get_clones(module, N, layer_share=False): + # import ipdb; ipdb.set_trace() + if layer_share: + return nn.ModuleList([module for i in range(N)]) + else: + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def build_deformable_transformer(args): + decoder_query_perturber = None + if args.decoder_layer_noise: + from .utils import RandomBoxPerturber + decoder_query_perturber = RandomBoxPerturber( + x_noise_scale=args.dln_xy_noise, y_noise_scale=args.dln_xy_noise, + w_noise_scale=args.dln_hw_noise, h_noise_scale=args.dln_hw_noise) + + use_detached_boxes_dec_out = False + try: + use_detached_boxes_dec_out = args.use_detached_boxes_dec_out + except: + use_detached_boxes_dec_out = False + + binary_query_selection = False + try: + binary_query_selection = args.binary_query_selection + except: + binary_query_selection = False + + ffn_extra_layernorm = False + try: + ffn_extra_layernorm = args.ffn_extra_layernorm + except: + print('ffn_extra_layernorm not found, set to False') + ffn_extra_layernorm = False + + return DeformableTransformer( + d_model=args.hidden_dim, + dropout=args.dropout, + nhead=args.nheads, + num_queries=args.num_queries, + dim_feedforward=args.dim_feedforward, + num_encoder_layers=args.enc_layers, + num_unicoder_layers=args.unic_layers, + num_decoder_layers=args.dec_layers, + normalize_before=args.pre_norm, + return_intermediate_dec=True, + query_dim=args.query_dim, + activation=args.transformer_activation, + num_patterns=args.num_patterns, + modulate_hw_attn=True, + + deformable_encoder=True, + deformable_decoder=True, + num_feature_levels=args.num_feature_levels, + enc_n_points=args.enc_n_points, + dec_n_points=args.dec_n_points, + use_deformable_box_attn=args.use_deformable_box_attn, + box_attn_type=args.box_attn_type, + + learnable_tgt_init=True, + decoder_query_perturber=decoder_query_perturber, + + add_channel_attention=args.add_channel_attention, + add_pos_value=args.add_pos_value, + random_refpoints_xy=args.random_refpoints_xy, + + # two stage + two_stage_type=args.two_stage_type, # ['no', 'standard', 'early'] + two_stage_pat_embed=args.two_stage_pat_embed, + two_stage_add_query_num=args.two_stage_add_query_num, + two_stage_learn_wh=args.two_stage_learn_wh, + two_stage_keep_all_tokens=args.two_stage_keep_all_tokens, + dec_layer_number=args.dec_layer_number, + rm_self_attn_layers=None, + key_aware_type=None, + layer_share_type=None, + + rm_detach=None, + decoder_sa_type=args.decoder_sa_type, + module_seq=args.decoder_module_seq, + + embed_init_tgt=args.embed_init_tgt, + use_detached_boxes_dec_out=use_detached_boxes_dec_out, + use_text_enhancer=args.use_text_enhancer, + use_fusion_layer=args.use_fusion_layer, + use_checkpoint=args.use_checkpoint, + use_transformer_ckpt=args.use_transformer_ckpt, + use_text_cross_attention=args.use_text_cross_attention, + + text_dropout=args.text_dropout, + fusion_dropout=args.fusion_dropout, + fusion_droppath=args.fusion_droppath, + + binary_query_selection=binary_query_selection, + ffn_extra_layernorm=ffn_extra_layernorm, + ) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/fuse_modules.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/fuse_modules.py new file mode 100644 index 0000000..3e8edb6 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/fuse_modules.py @@ -0,0 +1,274 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +# from timm.models.layers import DropPath +from src.modules.util import DropPath + +class FeatureResizer(nn.Module): + """ + This class takes as input a set of embeddings of dimension C1 and outputs a set of + embedding of dimension C2, after a linear transformation, dropout and normalization (LN). + """ + + def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): + super().__init__() + self.do_ln = do_ln + # Object feature encoding + self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) + self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) + self.dropout = nn.Dropout(dropout) + + def forward(self, encoder_features): + x = self.fc(encoder_features) + if self.do_ln: + x = self.layer_norm(x) + output = self.dropout(x) + return output + + + + +def l1norm(X, dim, eps=1e-8): + """L1-normalize columns of X + """ + norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps + X = torch.div(X, norm) + return X + + +def l2norm(X, dim, eps=1e-8): + """L2-normalize columns of X + """ + norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps + X = torch.div(X, norm) + return X + + +def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): + """ + query: (n_context, queryL, d) + context: (n_context, sourceL, d) + """ + batch_size_q, queryL = query.size(0), query.size(1) + batch_size, sourceL = context.size(0), context.size(1) + + # Get attention + # --> (batch, d, queryL) + queryT = torch.transpose(query, 1, 2) + + # (batch, sourceL, d)(batch, d, queryL) + # --> (batch, sourceL, queryL) + attn = torch.bmm(context, queryT) + if raw_feature_norm == "softmax": + # --> (batch*sourceL, queryL) + attn = attn.view(batch_size * sourceL, queryL) + attn = nn.Softmax()(attn) + # --> (batch, sourceL, queryL) + attn = attn.view(batch_size, sourceL, queryL) + elif raw_feature_norm == "l2norm": + attn = l2norm(attn, 2) + elif raw_feature_norm == "clipped_l2norm": + attn = nn.LeakyReLU(0.1)(attn) + attn = l2norm(attn, 2) + else: + raise ValueError("unknown first norm type:", raw_feature_norm) + # --> (batch, queryL, sourceL) + attn = torch.transpose(attn, 1, 2).contiguous() + # --> (batch*queryL, sourceL) + attn = attn.view(batch_size * queryL, sourceL) + attn = nn.Softmax()(attn * smooth) + # --> (batch, queryL, sourceL) + attn = attn.view(batch_size, queryL, sourceL) + # --> (batch, sourceL, queryL) + attnT = torch.transpose(attn, 1, 2).contiguous() + + # --> (batch, d, sourceL) + contextT = torch.transpose(context, 1, 2) + # (batch x d x sourceL)(batch x sourceL x queryL) + # --> (batch, d, queryL) + weightedContext = torch.bmm(contextT, attnT) + # --> (batch, queryL, d) + weightedContext = torch.transpose(weightedContext, 1, 2) + + return weightedContext, attnT + + +class BiMultiHeadAttention(nn.Module): + def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): + super(BiMultiHeadAttention, self).__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.v_dim = v_dim + self.l_dim = l_dim + + assert ( + self.head_dim * self.num_heads == self.embed_dim + ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + self.scale = self.head_dim ** (-0.5) + self.dropout = dropout + + self.v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.l_proj = nn.Linear(self.l_dim, self.embed_dim) + self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) + + self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) + self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) + + self.stable_softmax_2d = True + self.clamp_min_for_underflow = True + self.clamp_max_for_overflow = True + + self._reset_parameters() + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _reset_parameters(self): + nn.init.xavier_uniform_(self.v_proj.weight) + self.v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.l_proj.weight) + self.l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_v_proj.weight) + self.values_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_l_proj.weight) + self.values_l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_v_proj.weight) + self.out_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_l_proj.weight) + self.out_l_proj.bias.data.fill_(0) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + """_summary_ + + Args: + v (_type_): bs, n_img, dim + l (_type_): bs, n_text, dim + attention_mask_v (_type_, optional): _description_. bs, n_img + attention_mask_l (_type_, optional): _description_. bs, n_text + + Returns: + _type_: _description_ + """ + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + bsz, tgt_len, _ = v.size() + + query_states = self.v_proj(v) * self.scale + key_states = self._shape(self.l_proj(l), -1, bsz) + value_v_states = self._shape(self.values_v_proj(v), -1, bsz) + value_l_states = self._shape(self.values_l_proj(l), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_v_states = value_v_states.view(*proj_shape) + value_l_states = value_l_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" + ) + + if self.stable_softmax_2d: + attn_weights = attn_weights - attn_weights.max() + + if self.clamp_min_for_underflow: + attn_weights = torch.clamp(attn_weights, min=-50000) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights = torch.clamp(attn_weights, max=50000) # Do not increase 50000, data type half has quite limited range + + attn_weights_T = attn_weights.transpose(1, 2) + attn_weights_l = (attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[ + 0]) + if self.clamp_min_for_underflow: + attn_weights_l = torch.clamp(attn_weights_l, min=-50000) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights_l = torch.clamp(attn_weights_l, max=50000) # Do not increase 50000, data type half has quite limited range + + # mask vison for language + if attention_mask_v is not None: + attention_mask_v = attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + attn_weights_l.masked_fill_(attention_mask_v, float('-inf')) + + attn_weights_l = attn_weights_l.softmax(dim=-1) + + # mask language for vision + if attention_mask_l is not None: + attention_mask_l = attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + attn_weights.masked_fill_(attention_mask_l, float('-inf')) + attn_weights_v = attn_weights.softmax(dim=-1) + + attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) + attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) + + attn_output_v = torch.bmm(attn_probs_v, value_l_states) + attn_output_l = torch.bmm(attn_probs_l, value_v_states) + + + if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" + ) + + if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): + raise ValueError( + f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" + ) + + attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output_v = attn_output_v.transpose(1, 2) + attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) + + attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) + attn_output_l = attn_output_l.transpose(1, 2) + attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) + + attn_output_v = self.out_v_proj(attn_output_v) + attn_output_l = self.out_l_proj(attn_output_l) + + return attn_output_v, attn_output_l + + +# Bi-Direction MHA (text->image, image->text) +class BiAttentionBlock(nn.Module): + def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, + drop_path=.0, init_values=1e-4, cfg=None): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + hidden_dim - Dimensionality of hidden layer in feed-forward network + (usually 2-4x larger than embed_dim) + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(BiAttentionBlock, self).__init__() + + # pre layer norm + self.layer_norm_v = nn.LayerNorm(v_dim) + self.layer_norm_l = nn.LayerNorm(l_dim) + self.attn = BiMultiHeadAttention(v_dim=v_dim, + l_dim=l_dim, + embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout) + + # add layer scale for training stability + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=False) + self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=False) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + v = self.layer_norm_v(v) + l = self.layer_norm_l(l) + delta_v, delta_l = self.attn(v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l) + # v, l = v + delta_v, l + delta_l + v = v + self.drop_path(self.gamma_v * delta_v) + l = l + self.drop_path(self.gamma_l * delta_l) + return v, l diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/mask_generate.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/mask_generate.py new file mode 100644 index 0000000..ed79e74 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/mask_generate.py @@ -0,0 +1,56 @@ +import torch + + +def prepare_for_mask(kpt_mask): + + + tgt_size2 = 50 * 69 + attn_mask2 = torch.ones(kpt_mask.shape[0], 8, tgt_size2, tgt_size2).to('cuda') < 0 + group_bbox_kpt = 69 + num_group=50 + for matchj in range(num_group * group_bbox_kpt): + sj = (matchj // group_bbox_kpt) * group_bbox_kpt + ej = (matchj // group_bbox_kpt + 1)*group_bbox_kpt + if sj > 0: + attn_mask2[:,:,matchj, :sj] = True + if ej < num_group * group_bbox_kpt: + attn_mask2[:,:,matchj, ej:] = True + + + bs, length = kpt_mask.shape + equal_mask = kpt_mask[:, :, None] == kpt_mask[:, None, :] + equal_mask= equal_mask.unsqueeze(1).repeat(1,8,1,1) + for idx in range(num_group): + start_idx = idx * length + end_idx = (idx + 1) * length + attn_mask2[:, :,start_idx:end_idx, start_idx:end_idx][equal_mask] = False + attn_mask2[:, :,start_idx:end_idx, start_idx:end_idx][~equal_mask] = True + + + + + input_query_label = None + input_query_bbox = None + attn_mask = None + dn_meta = None + + return input_query_label, input_query_bbox, attn_mask, attn_mask2.flatten(0,1), dn_meta + + +def post_process(outputs_class, outputs_coord, dn_meta, aux_loss, _set_aux_loss): + + if dn_meta and dn_meta['pad_size'] > 0: + + output_known_class = [outputs_class_i[:, :dn_meta['pad_size'], :] for outputs_class_i in outputs_class] + output_known_coord = [outputs_coord_i[:, :dn_meta['pad_size'], :] for outputs_coord_i in outputs_coord] + + outputs_class = [outputs_class_i[:, dn_meta['pad_size']:, :] for outputs_class_i in outputs_class] + outputs_coord = [outputs_coord_i[:, dn_meta['pad_size']:, :] for outputs_coord_i in outputs_coord] + + out = {'pred_logits': output_known_class[-1], 'pred_boxes': output_known_coord[-1]} + if aux_loss: + out['aux_outputs'] = _set_aux_loss(output_known_class, output_known_coord) + dn_meta['output_known_lbs_bboxes'] = out + return outputs_class, outputs_coord + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/__init__.py new file mode 100644 index 0000000..8a2197b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/__init__.py @@ -0,0 +1,10 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from .ms_deform_attn_func import MSDeformAttnFunction + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/ms_deform_attn_func.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/ms_deform_attn_func.py new file mode 100644 index 0000000..8c5df8c --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/functions/ms_deform_attn_func.py @@ -0,0 +1,61 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +import MultiScaleDeformableAttention as MSDA + + +class MSDeformAttnFunction(Function): + @staticmethod + def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): + ctx.im2col_step = im2col_step + output = MSDA.ms_deform_attn_forward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) + ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = \ + MSDA.ms_deform_attn_backward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): + # for debug and test only, + # need to use cuda version instead + N_, S_, M_, D_ = value.shape + _, Lq_, M_, L_, P_, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for lid_, (H_, W_) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/__init__.py new file mode 100644 index 0000000..f82cb1a --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/__init__.py @@ -0,0 +1,9 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from .ms_deform_attn import MSDeformAttn diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn.py new file mode 100644 index 0000000..cd48d27 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn.py @@ -0,0 +1,142 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math, os +import sys +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +from src.utils.dependencies.XPose.models.UniPose.ops.functions.ms_deform_attn_func import MSDeformAttnFunction + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, use_4D_normalizer=False): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 64 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self.use_4D_normalizer = use_4D_normalizer + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + + # if os.environ.get('IPDB_DEBUG_SHILONG', False) == 'INFO': + # import ipdb; ipdb.set_trace() + + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + if self.use_4D_normalizer: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + + + # if os.environ.get('IPDB_DEBUG_SHILONG', False) == 'INFO': + # import ipdb; ipdb.set_trace() + + # for amp + if value.dtype == torch.float16: + # for mixed precision + output = MSDeformAttnFunction.apply( + value.to(torch.float32), input_spatial_shapes, input_level_start_index, sampling_locations.to(torch.float32), attention_weights, self.im2col_step) + output = output.to(torch.float16) + output = self.output_proj(output) + return output + + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + output = self.output_proj(output) + return output diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn_key_aware.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn_key_aware.py new file mode 100644 index 0000000..b737ba6 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/modules/ms_deform_attn_key_aware.py @@ -0,0 +1,130 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math, os + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +try: + from src.utils.dependencies.XPose.models.UniPose.ops.functions import MSDeformAttnFunction +except: + warnings.warn('Failed to import MSDeformAttnFunction.') + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, use_4D_normalizer=False): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 64 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self.use_4D_normalizer = use_4D_normalizer + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, key, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param key (N, 1, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + + # if os.environ.get('IPDB_DEBUG_SHILONG', False) == 'INFO': + # import ipdb; ipdb.set_trace() + + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + if self.use_4D_normalizer: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + output = self.output_proj(output) + return output diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/setup.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/setup.py new file mode 100644 index 0000000..049f923 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/setup.py @@ -0,0 +1,73 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +import os +import glob + +import torch + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +from setuptools import find_packages +from setuptools import setup + +requirements = ["torch", "torchvision"] + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + # import ipdb; ipdb.set_trace() + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + raise NotImplementedError('Cuda is not availabel') + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "MultiScaleDeformableAttention", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + +setup( + name="MultiScaleDeformableAttention", + version="1.0", + author="Weijie Su", + url="https://github.com/fundamentalvision/Deformable-DETR", + description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention", + packages=find_packages(exclude=("configs", "tests",)), + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.cpp b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.cpp new file mode 100644 index 0000000..e1bf854 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.cpp @@ -0,0 +1,41 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include + +#include +#include + + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.h b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.h new file mode 100644 index 0000000..81b7b58 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cpu/ms_deform_attn_cpu.h @@ -0,0 +1,33 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.cu b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.cu new file mode 100644 index 0000000..d6d5836 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.cu @@ -0,0 +1,153 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include +#include "cuda/ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.h b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.h new file mode 100644 index 0000000..c7ae53f --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_attn_cuda.h @@ -0,0 +1,30 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_im2col_cuda.cuh b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_im2col_cuda.cuh new file mode 100644 index 0000000..6bc2acb --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/cuda/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1327 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/ms_deform_attn.h b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/ms_deform_attn.h new file mode 100644 index 0000000..ac0ef2e --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/ms_deform_attn.h @@ -0,0 +1,62 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once + +#include "cpu/ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "cuda/ms_deform_attn_cuda.h" +#endif + + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/vision.cpp b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/vision.cpp new file mode 100644 index 0000000..2201f63 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/src/vision.cpp @@ -0,0 +1,16 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include "ms_deform_attn.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); +} diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/test.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/test.py new file mode 100644 index 0000000..8dbf6d5 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/ops/test.py @@ -0,0 +1,89 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch + + +N, M, D = 1, 2, 2 +Lq, L, P = 2, 2, 2 +shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda() +level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1])) +S = sum([(H*W).item() for H, W in shapes]) + + +torch.manual_seed(3) + + +@torch.no_grad() +def check_forward_equal_with_pytorch_double(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +@torch.no_grad() +def check_forward_equal_with_pytorch_float(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True): + + value = torch.rand(N, S, M, channels).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + func = MSDeformAttnFunction.apply + + value.requires_grad = grad_value + sampling_locations.requires_grad = grad_sampling_loc + attention_weights.requires_grad = grad_attn_weight + + gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step)) + + print(f'* {gradok} check_gradient_numerical(D={channels})') + + +if __name__ == '__main__': + check_forward_equal_with_pytorch_double() + check_forward_equal_with_pytorch_float() + + for channels in [30, 32, 64, 71, 1025, 2048, 3096]: + check_gradient_numerical(channels, True, True, True) + + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/position_encoding.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/position_encoding.py new file mode 100644 index 0000000..e0db9cc --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/position_encoding.py @@ -0,0 +1,157 @@ +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +""" +Various positional encodings for the transformer. +""" +import math +import torch +from torch import nn + +from util.misc import NestedTensor + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + mask = tensor_list.mask + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + # if os.environ.get("SHILONG_AMP", None) == '1': + # eps = 1e-4 + # else: + # eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + +class PositionEmbeddingSineHW(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + def __init__(self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperatureH = temperatureH + self.temperatureW = temperatureW + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + mask = tensor_list.mask + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + + # import ipdb; ipdb.set_trace() + + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_tx = self.temperatureW ** (2 * (dim_tx // 2) / self.num_pos_feats) + pos_x = x_embed[:, :, :, None] / dim_tx + + dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_ty = self.temperatureH ** (2 * (dim_ty // 2) / self.num_pos_feats) + pos_y = y_embed[:, :, :, None] / dim_ty + + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + + # import ipdb; ipdb.set_trace() + + return pos + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = torch.cat([ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) + return pos + + +def build_position_encoding(args): + N_steps = args.hidden_dim // 2 + if args.position_embedding in ('v2', 'sine'): + # TODO find a better way of exposing other arguments + position_embedding = PositionEmbeddingSineHW( + N_steps, + temperatureH=args.pe_temperatureH, + temperatureW=args.pe_temperatureW, + normalize=True + ) + elif args.position_embedding in ('v3', 'learned'): + position_embedding = PositionEmbeddingLearned(N_steps) + else: + raise ValueError(f"not supported {args.position_embedding}") + + return position_embedding diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/swin_transformer.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/swin_transformer.py new file mode 100644 index 0000000..8f42508 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/swin_transformer.py @@ -0,0 +1,701 @@ + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np + +from util.misc import NestedTensor +# from timm.models.layers import DropPath, to_2tuple, trunc_normal_ +from src.modules.util import DropPath, to_2tuple, trunc_normal_ + + + +class Mlp(nn.Module): + """ Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """ Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """ Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """ Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """ Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + dilation (bool): if True, the output size if 16x downsample, ow 32x downsample. + """ + + def __init__(self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + dilation=False, + use_checkpoint=False): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.dilation = dilation + + # if use_checkpoint: + # print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!") + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + # prepare downsample list + downsamplelist = [PatchMerging for i in range(self.num_layers)] + downsamplelist[-1] = None + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + if self.dilation: + downsamplelist[-2] = None + num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2 + for i_layer in range(self.num_layers): + layer = BasicLayer( + # dim=int(embed_dim * 2 ** i_layer), + dim=num_features[i_layer], + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + downsample=downsamplelist[i_layer], + use_checkpoint=use_checkpoint) + self.layers.append(layer) + + # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + + + def forward_raw(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + # import ipdb; ipdb.set_trace() + + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + # in: + # torch.Size([2, 3, 1024, 1024]) + # outs: + # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ + # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] + return tuple(outs) + + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + # in: + # torch.Size([2, 3, 1024, 1024]) + # out: + # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ + # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] + + # collect for nesttensors + outs_dict = {} + for idx, out_i in enumerate(outs): + m = tensor_list.mask + assert m is not None + mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0] + outs_dict[idx] = NestedTensor(out_i, mask) + + return outs_dict + + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + + +def build_swin_transformer(modelname, pretrain_img_size, **kw): + assert modelname in ['swin_T_224_1k', 'swin_B_224_22k', 'swin_B_384_22k', 'swin_L_224_22k', 'swin_L_384_22k'] + + model_para_dict = { + 'swin_T_224_1k': dict( + embed_dim=96, + depths=[ 2, 2, 6, 2 ], + num_heads=[ 3, 6, 12, 24], + window_size=7 + ), + 'swin_B_224_22k': dict( + embed_dim=128, + depths=[ 2, 2, 18, 2 ], + num_heads=[ 4, 8, 16, 32 ], + window_size=7 + ), + 'swin_B_384_22k': dict( + embed_dim=128, + depths=[ 2, 2, 18, 2 ], + num_heads=[ 4, 8, 16, 32 ], + window_size=12 + ), + 'swin_L_224_22k': dict( + embed_dim=192, + depths=[ 2, 2, 18, 2 ], + num_heads=[ 6, 12, 24, 48 ], + window_size=7 + ), + 'swin_L_384_22k': dict( + embed_dim=192, + depths=[ 2, 2, 18, 2 ], + num_heads=[ 6, 12, 24, 48 ], + window_size=12 + ), + } + kw_cgf = model_para_dict[modelname] + kw_cgf.update(kw) + model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf) + return model + +if __name__ == "__main__": + model = build_swin_transformer('swin_L_384_22k', 384, dilation=True) + x = torch.rand(2, 3, 1024, 1024) + y = model.forward_raw(x) + import ipdb; ipdb.set_trace() + x = torch.rand(2, 3, 384, 384) + y = model.forward_raw(x) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_deformable.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_deformable.py new file mode 100644 index 0000000..1d441e8 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_deformable.py @@ -0,0 +1,595 @@ +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# ------------------------------------------------------------------------ + +import copy +import math +import torch +from torch import nn, Tensor +from torch.nn.init import xavier_uniform_, constant_, normal_ +from typing import Optional + +from util.misc import inverse_sigmoid +from .ops.modules import MSDeformAttn +from .utils import MLP, _get_activation_fn, gen_sineembed_for_position + +class DeformableTransformer(nn.Module): + def __init__(self, d_model=256, nhead=8, + num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1, + activation="relu", return_intermediate_dec=False, + num_feature_levels=4, dec_n_points=4, enc_n_points=4, + two_stage=False, two_stage_num_proposals=300, + use_dab=False, high_dim_query_update=False, no_sine_embed=False): + super().__init__() + + self.d_model = d_model + self.nhead = nhead + self.two_stage = two_stage + self.two_stage_num_proposals = two_stage_num_proposals + self.use_dab = use_dab + + encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, enc_n_points) + self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers) + + decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, dec_n_points) + self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec, + use_dab=use_dab, d_model=d_model, high_dim_query_update=high_dim_query_update, no_sine_embed=no_sine_embed) + + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + + if two_stage: + self.enc_output = nn.Linear(d_model, d_model) + self.enc_output_norm = nn.LayerNorm(d_model) + self.pos_trans = nn.Linear(d_model * 2, d_model * 2) + self.pos_trans_norm = nn.LayerNorm(d_model * 2) + else: + if not self.use_dab: + self.reference_points = nn.Linear(d_model, 2) + + self.high_dim_query_update = high_dim_query_update + if high_dim_query_update: + assert not self.use_dab, "use_dab must be True" + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + if not self.two_stage and not self.use_dab: + xavier_uniform_(self.reference_points.weight.data, gain=1.0) + constant_(self.reference_points.bias.data, 0.) + normal_(self.level_embed) + + def get_proposal_pos_embed(self, proposals): + num_pos_feats = 128 + temperature = 10000 + scale = 2 * math.pi + + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats) + # N, L, 4 + proposals = proposals.sigmoid() * scale + # N, L, 4, 128 + pos = proposals[:, :, :, None] / dim_t + # N, L, 4, 64, 2 + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) + return pos + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): + N_, S_, C_ = memory.shape + base_scale = 4.0 + proposals = [] + _cur = 0 + for lvl, (H_, W_) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), + torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device)) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl) + proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) + proposals.append(proposal) + _cur += (H_ * W_) + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf')) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf')) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + return output_memory, output_proposals + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def forward(self, srcs, masks, pos_embeds, query_embed=None): + """ + Input: + - srcs: List([bs, c, h, w]) + - masks: List([bs, h, w]) + """ + assert self.two_stage or query_embed is not None + + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + src = src.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) # bs, hw + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c + mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw} + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # encoder + memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten) + # import ipdb; ipdb.set_trace() + + # prepare input for decoder + bs, _, c = memory.shape + if self.two_stage: + output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes) + + # hack implementation for two-stage Deformable DETR + enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) + enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals + + topk = self.two_stage_num_proposals + topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] + topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) + query_embed, tgt = torch.split(pos_trans_out, c, dim=2) + elif self.use_dab: + reference_points = query_embed[..., self.d_model:].sigmoid() + tgt = query_embed[..., :self.d_model] + tgt = tgt.unsqueeze(0).expand(bs, -1, -1) + init_reference_out = reference_points + else: + query_embed, tgt = torch.split(query_embed, c, dim=1) + query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1) + tgt = tgt.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_embed).sigmoid() + # bs, num_quires, 2 + init_reference_out = reference_points + + # decoder + # import ipdb; ipdb.set_trace() + hs, inter_references = self.decoder(tgt, reference_points, memory, + spatial_shapes, level_start_index, valid_ratios, + query_pos=query_embed if not self.use_dab else None, + src_padding_mask=mask_flatten) + + inter_references_out = inter_references + if self.two_stage: + return hs, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact + return hs, init_reference_out, inter_references_out, None, None + + +class DeformableTransformerEncoderLayer(nn.Module): + def __init__(self, + d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4, + add_channel_attention=False, + use_deformable_box_attn=False, + box_attn_type='roi_align', + ): + super().__init__() + + # self attention + if use_deformable_box_attn: + self.self_attn = MSDeformableBoxAttention(d_model, n_levels, n_heads, n_boxes=n_points, used_func=box_attn_type) + else: + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + # channel attention + self.add_channel_attention = add_channel_attention + if add_channel_attention: + self.activ_channel = _get_activation_fn('dyrelu', d_model=d_model) + self.norm_channel = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None): + # self attention + # import ipdb; ipdb.set_trace() + src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, key_padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + # channel attn + if self.add_channel_attention: + src = self.norm_channel(src + self.activ_channel(src)) + + return src + + +class DeformableTransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + if num_layers > 0: + self.layers = _get_clones(encoder_layer, num_layers) + else: + self.layers = [] + del encoder_layer + self.num_layers = num_layers + self.norm = norm + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + + ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None): + """ + Input: + - src: [bs, sum(hi*wi), 256] + - spatial_shapes: h,w of each level [num_level, 2] + - level_start_index: [num_level] start point of level in sum(hi*wi). + - valid_ratios: [bs, num_level, 2] + - pos: pos embed for src. [bs, sum(hi*wi), 256] + - padding_mask: [bs, sum(hi*wi)] + Intermedia: + - reference_points: [bs, sum(hi*wi), num_lebel, 2] + """ + output = src + # bs, sum(hi*wi), 256 + # import ipdb; ipdb.set_trace() + if self.num_layers > 0: + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) + for _, layer in enumerate(self.layers): + output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class DeformableTransformerDecoderLayer(nn.Module): + def __init__(self, d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4, + use_deformable_box_attn=False, + box_attn_type='roi_align', + key_aware_type=None, + decoder_sa_type='ca', + module_seq=['sa', 'ca', 'ffn'], + ): + super().__init__() + self.module_seq = module_seq + assert sorted(module_seq) == ['ca', 'ffn', 'sa'] + + # cross attention + # self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + if use_deformable_box_attn: + self.cross_attn = MSDeformableBoxAttention(d_model, n_levels, n_heads, n_boxes=n_points, used_func=box_attn_type) + else: + self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # self attention + self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) + self.dropout2 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1) + self.dropout3 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout4 = nn.Dropout(dropout) + self.norm3 = nn.LayerNorm(d_model) + + self.key_aware_type = key_aware_type + self.key_aware_proj = None + self.decoder_sa_type = decoder_sa_type + assert decoder_sa_type in ['sa', 'ca_label', 'ca_content'] + + if decoder_sa_type == 'ca_content': + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + + + + + def rm_self_attn_modules(self): + self.self_attn = None + self.dropout2 = None + self.norm2 = None + + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, tgt): + tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout4(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_sa(self, + # for tgt + tgt: Optional[Tensor], # nq, bs, d_model + tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) + tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) + tgt_key_padding_mask: Optional[Tensor] = None, + tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 + + # for memory + memory: Optional[Tensor] = None, # hw, bs, d_model + memory_key_padding_mask: Optional[Tensor] = None, + memory_level_start_index: Optional[Tensor] = None, # num_levels + memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + memory_pos: Optional[Tensor] = None, # pos for memory + + # sa + self_attn_mask: Optional[Tensor] = None, # mask used for self-attention + cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention + ): + # self attention + if self.self_attn is not None: + # import ipdb; ipdb.set_trace() + if self.decoder_sa_type == 'sa': + q = k = self.with_pos_embed(tgt, tgt_query_pos) + tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + elif self.decoder_sa_type == 'ca_label': + # import ipdb; ipdb.set_trace() + # q = self.with_pos_embed(tgt, tgt_query_pos) + bs = tgt.shape[1] + k = v = self.label_embedding.weight[:, None, :].repeat(1, bs, 1) + tgt2 = self.self_attn(tgt, k, v, attn_mask=self_attn_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + elif self.decoder_sa_type == 'ca_content': + tgt2 = self.self_attn(self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1), + tgt_reference_points.transpose(0, 1).contiguous(), + memory.transpose(0, 1), memory_spatial_shapes, memory_level_start_index, memory_key_padding_mask).transpose(0, 1) + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + else: + raise NotImplementedError("Unknown decoder_sa_type {}".format(self.decoder_sa_type)) + + return tgt + + def forward_ca(self, + # for tgt + tgt: Optional[Tensor], # nq, bs, d_model + tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) + tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) + tgt_key_padding_mask: Optional[Tensor] = None, + tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 + + # for memory + memory: Optional[Tensor] = None, # hw, bs, d_model + memory_key_padding_mask: Optional[Tensor] = None, + memory_level_start_index: Optional[Tensor] = None, # num_levels + memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + memory_pos: Optional[Tensor] = None, # pos for memory + + # sa + self_attn_mask: Optional[Tensor] = None, # mask used for self-attention + cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention + ): + # cross attention + # import ipdb; ipdb.set_trace() + if self.key_aware_type is not None: + + if self.key_aware_type == 'mean': + tgt = tgt + memory.mean(0, keepdim=True) + elif self.key_aware_type == 'proj_mean': + tgt = tgt + self.key_aware_proj(memory).mean(0, keepdim=True) + else: + raise NotImplementedError("Unknown key_aware_type: {}".format(self.key_aware_type)) + tgt2 = self.cross_attn(self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1), + tgt_reference_points.transpose(0, 1).contiguous(), + memory.transpose(0, 1), memory_spatial_shapes, memory_level_start_index, memory_key_padding_mask).transpose(0, 1) + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + + return tgt + + def forward(self, + # for tgt + tgt: Optional[Tensor], # nq, bs, d_model + tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) + tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) + tgt_key_padding_mask: Optional[Tensor] = None, + tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 + + # for memory + memory: Optional[Tensor] = None, # hw, bs, d_model + memory_key_padding_mask: Optional[Tensor] = None, + memory_level_start_index: Optional[Tensor] = None, # num_levels + memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + memory_pos: Optional[Tensor] = None, # pos for memory + + # sa + self_attn_mask: Optional[Tensor] = None, # mask used for self-attention + cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention + ): + + for funcname in self.module_seq: + # if os.environ.get('IPDB_DEBUG_SHILONG') == 'INFO': + # import ipdb; ipdb.set_trace() + if funcname == 'ffn': + tgt = self.forward_ffn(tgt) + elif funcname == 'ca': + tgt = self.forward_ca(tgt, tgt_query_pos, tgt_query_sine_embed, \ + tgt_key_padding_mask, tgt_reference_points, \ + memory, memory_key_padding_mask, memory_level_start_index, \ + memory_spatial_shapes, memory_pos, self_attn_mask, cross_attn_mask) + elif funcname == 'sa': + tgt = self.forward_sa(tgt, tgt_query_pos, tgt_query_sine_embed, \ + tgt_key_padding_mask, tgt_reference_points, \ + memory, memory_key_padding_mask, memory_level_start_index, \ + memory_spatial_shapes, memory_pos, self_attn_mask, cross_attn_mask) + else: + raise ValueError('unknown funcname {}'.format(funcname)) + + return tgt + + + +class DeformableTransformerDecoder(nn.Module): + def __init__(self, decoder_layer, num_layers, return_intermediate=False, use_dab=False, d_model=256, query_dim=4): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.return_intermediate = return_intermediate + assert return_intermediate + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + self.use_dab = use_dab + self.d_model = d_model + self.query_dim = query_dim + if use_dab: + self.query_scale = MLP(d_model, d_model, d_model, 2) + self.ref_point_head = MLP(2 * d_model, d_model, d_model, 2) + + + def forward(self, tgt, reference_points, src, src_spatial_shapes, + src_level_start_index, src_valid_ratios, + query_pos=None, src_padding_mask=None): + output = tgt + if self.use_dab: + assert query_pos is None + + intermediate = [] + intermediate_reference_points = [reference_points] + for layer_id, layer in enumerate(self.layers): + # import ipdb; ipdb.set_trace() + if reference_points.shape[-1] == 4: + reference_points_input = reference_points[:, :, None] \ + * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None] # bs, nq, 4, 4 + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None] + + if self.use_dab: + # import ipdb; ipdb.set_trace() + query_sine_embed = gen_sineembed_for_position(reference_points_input[:, :, 0, :]) # bs, nq, 256*2 + raw_query_pos = self.ref_point_head(query_sine_embed) # bs, nq, 256 + pos_scale = self.query_scale(output) if layer_id != 0 else 1 + query_pos = pos_scale * raw_query_pos + + output = layer(output, query_pos, reference_points_input, src, src_spatial_shapes, src_level_start_index, src_padding_mask) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + box_holder = self.bbox_embed(output) + box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points) + new_reference_points = box_holder[..., :self.query_dim].sigmoid() + reference_points = new_reference_points.detach() + if layer_id != self.num_layers - 1: + intermediate_reference_points.append(new_reference_points) + + intermediate.append(output) + + return torch.stack(intermediate), torch.stack(intermediate_reference_points) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def build_deforamble_transformer(args): + return DeformableTransformer( + d_model=args.hidden_dim, + nhead=args.nheads, + num_encoder_layers=args.enc_layers, + num_decoder_layers=args.dec_layers, + dim_feedforward=args.dim_feedforward, + dropout=args.dropout, + activation="relu", + return_intermediate_dec=True, + num_feature_levels=args.ddetr_num_feature_levels, + dec_n_points=args.ddetr_dec_n_points, + enc_n_points=args.ddetr_enc_n_points, + two_stage=args.ddetr_two_stage, + two_stage_num_proposals=args.num_queries, + use_dab=args.ddetr_use_dab, + high_dim_query_update=args.ddetr_high_dim_query_update, + no_sine_embed=args.ddetr_no_sine_embed) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_vanilla.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_vanilla.py new file mode 100644 index 0000000..450885a --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/transformer_vanilla.py @@ -0,0 +1,102 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import torch +from torch import Tensor, nn +from typing import List, Optional + +from .utils import _get_activation_fn, _get_clones + + +class TextTransformer(nn.Module): + def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1): + super().__init__() + self.num_layers = num_layers + self.d_model = d_model + self.nheads = nheads + self.dim_feedforward = dim_feedforward + self.norm = None + + single_encoder_layer = TransformerEncoderLayer(d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout) + self.layers = _get_clones(single_encoder_layer, num_layers) + + + def forward(self, memory_text:torch.Tensor, text_attention_mask:torch.Tensor): + """ + + Args: + text_attention_mask: bs, num_token + memory_text: bs, num_token, d_model + + Raises: + RuntimeError: _description_ + + Returns: + output: bs, num_token, d_model + """ + + output = memory_text.transpose(0, 1) + + for layer in self.layers: + output = layer(output, src_key_padding_mask=text_attention_mask) + + if self.norm is not None: + output = self.norm(output) + + return output.transpose(0, 1) + + + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + self.nhead = nhead + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + # repeat attn mask + if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]: + # bs, num_q, num_k + src_mask = src_mask.repeat(self.nhead, 1, 1) + + q = k = self.with_pos_embed(src, pos) + + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0] + + # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/unipose.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/unipose.py new file mode 100644 index 0000000..d05c94c --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/unipose.py @@ -0,0 +1,621 @@ +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR) +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# ------------------------------------------------------------------------ +import os +import copy +import torch +import torch.nn.functional as F +from torch import nn +from typing import List + +from util.keypoint_ops import keypoint_xyzxyz_to_xyxyzz +from util.misc import NestedTensor, nested_tensor_from_tensor_list,inverse_sigmoid + +from .utils import MLP +from .backbone import build_backbone +from ..registry import MODULE_BUILD_FUNCS +from .mask_generate import prepare_for_mask, post_process +from .deformable_transformer import build_deformable_transformer + + +class UniPose(nn.Module): + """ This is the Cross-Attention Detector module that performs object detection """ + + def __init__(self, backbone, transformer, num_classes, num_queries, + aux_loss=False, iter_update=False, + query_dim=2, + random_refpoints_xy=False, + fix_refpoints_hw=-1, + num_feature_levels=1, + nheads=8, + # two stage + two_stage_type='no', # ['no', 'standard'] + two_stage_add_query_num=0, + dec_pred_class_embed_share=True, + dec_pred_bbox_embed_share=True, + two_stage_class_embed_share=True, + two_stage_bbox_embed_share=True, + decoder_sa_type='sa', + num_patterns=0, + dn_number=100, + dn_box_noise_scale=0.4, + dn_label_noise_ratio=0.5, + dn_labelbook_size=100, + use_label_enc=True, + + text_encoder_type='bert-base-uncased', + + binary_query_selection=False, + use_cdn=True, + sub_sentence_present=True, + num_body_points=68, + num_box_decoder_layers=2, + ): + """ Initializes the model. + Parameters: + backbone: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + num_classes: number of object classes + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + Conditional DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + + fix_refpoints_hw: -1(default): learn w and h for each box seperately + >0 : given fixed number + -2 : learn a shared w and h + """ + super().__init__() + self.num_queries = num_queries + self.transformer = transformer + self.num_classes = num_classes + self.hidden_dim = hidden_dim = transformer.d_model + self.num_feature_levels = num_feature_levels + self.nheads = nheads + self.use_label_enc = use_label_enc + if use_label_enc: + self.label_enc = nn.Embedding(dn_labelbook_size + 1, hidden_dim) + else: + raise NotImplementedError + self.label_enc = None + self.max_text_len = 256 + self.binary_query_selection = binary_query_selection + self.sub_sentence_present = sub_sentence_present + + # setting query dim + self.query_dim = query_dim + assert query_dim == 4 + self.random_refpoints_xy = random_refpoints_xy + self.fix_refpoints_hw = fix_refpoints_hw + + # for dn training + self.num_patterns = num_patterns + self.dn_number = dn_number + self.dn_box_noise_scale = dn_box_noise_scale + self.dn_label_noise_ratio = dn_label_noise_ratio + self.dn_labelbook_size = dn_labelbook_size + self.use_cdn = use_cdn + + + self.projection = MLP(512, hidden_dim, hidden_dim, 3) + + self.projection_kpt = MLP(512, hidden_dim, hidden_dim, 3) + + + device = "cuda" if torch.cuda.is_available() else "cpu" + # model, _ = clip.load("ViT-B/32", device=device) + # self.clip_model = model + # visual_parameters = list(self.clip_model.visual.parameters()) + # # + # for param in visual_parameters: + # param.requires_grad = False + + self.pos_proj = nn.Linear(hidden_dim, 768) + self.padding = nn.Embedding(1, 768) + + # prepare input projection layers + if num_feature_levels > 1: + num_backbone_outs = len(backbone.num_channels) + input_proj_list = [] + for _ in range(num_backbone_outs): + in_channels = backbone.num_channels[_] + input_proj_list.append(nn.Sequential( + nn.Conv2d(in_channels, hidden_dim, kernel_size=1), + nn.GroupNorm(32, hidden_dim), + )) + for _ in range(num_feature_levels - num_backbone_outs): + input_proj_list.append(nn.Sequential( + nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1), + nn.GroupNorm(32, hidden_dim), + )) + in_channels = hidden_dim + self.input_proj = nn.ModuleList(input_proj_list) + else: + assert two_stage_type == 'no', "two_stage_type should be no if num_feature_levels=1 !!!" + self.input_proj = nn.ModuleList([ + nn.Sequential( + nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1), + nn.GroupNorm(32, hidden_dim), + )]) + + self.backbone = backbone + self.aux_loss = aux_loss + self.box_pred_damping = box_pred_damping = None + + self.iter_update = iter_update + assert iter_update, "Why not iter_update?" + + # prepare pred layers + self.dec_pred_class_embed_share = dec_pred_class_embed_share + self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share + # prepare class & box embed + _class_embed = ContrastiveAssign() + + + + _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) + nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0) + nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0) + + _pose_embed = MLP(hidden_dim, hidden_dim, 2, 3) + _pose_hw_embed = MLP(hidden_dim, hidden_dim, 2, 3) + nn.init.constant_(_pose_embed.layers[-1].weight.data, 0) + nn.init.constant_(_pose_embed.layers[-1].bias.data, 0) + + if dec_pred_bbox_embed_share: + box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)] + else: + box_embed_layerlist = [copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)] + if dec_pred_class_embed_share: + class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)] + else: + class_embed_layerlist = [copy.deepcopy(_class_embed) for i in range(transformer.num_decoder_layers)] + + + if dec_pred_bbox_embed_share: + + pose_embed_layerlist = [_pose_embed for i in + range(transformer.num_decoder_layers - num_box_decoder_layers + 1)] + else: + pose_embed_layerlist = [copy.deepcopy(_pose_embed) for i in + range(transformer.num_decoder_layers - num_box_decoder_layers + 1)] + + pose_hw_embed_layerlist = [_pose_hw_embed for i in + range(transformer.num_decoder_layers - num_box_decoder_layers)] + + + self.num_box_decoder_layers = num_box_decoder_layers + self.bbox_embed = nn.ModuleList(box_embed_layerlist) + self.class_embed = nn.ModuleList(class_embed_layerlist) + self.num_body_points = num_body_points + self.pose_embed = nn.ModuleList(pose_embed_layerlist) + self.pose_hw_embed = nn.ModuleList(pose_hw_embed_layerlist) + + self.transformer.decoder.bbox_embed = self.bbox_embed + self.transformer.decoder.class_embed = self.class_embed + + self.transformer.decoder.pose_embed = self.pose_embed + self.transformer.decoder.pose_hw_embed = self.pose_hw_embed + + self.transformer.decoder.num_body_points = num_body_points + + + # two stage + self.two_stage_type = two_stage_type + self.two_stage_add_query_num = two_stage_add_query_num + assert two_stage_type in ['no', 'standard'], "unknown param {} of two_stage_type".format(two_stage_type) + if two_stage_type != 'no': + if two_stage_bbox_embed_share: + assert dec_pred_class_embed_share and dec_pred_bbox_embed_share + self.transformer.enc_out_bbox_embed = _bbox_embed + else: + self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed) + + if two_stage_class_embed_share: + assert dec_pred_class_embed_share and dec_pred_bbox_embed_share + self.transformer.enc_out_class_embed = _class_embed + else: + self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed) + + self.refpoint_embed = None + if self.two_stage_add_query_num > 0: + self.init_ref_points(two_stage_add_query_num) + + self.decoder_sa_type = decoder_sa_type + assert decoder_sa_type in ['sa', 'ca_label', 'ca_content'] + # self.replace_sa_with_double_ca = replace_sa_with_double_ca + if decoder_sa_type == 'ca_label': + self.label_embedding = nn.Embedding(num_classes, hidden_dim) + for layer in self.transformer.decoder.layers: + layer.label_embedding = self.label_embedding + else: + for layer in self.transformer.decoder.layers: + layer.label_embedding = None + self.label_embedding = None + + self._reset_parameters() + + def open_set_transfer_init(self): + for name, param in self.named_parameters(): + if 'fusion_layers' in name: + continue + if 'ca_text' in name: + continue + if 'catext_norm' in name: + continue + if 'catext_dropout' in name: + continue + if "text_layers" in name: + continue + if 'bert' in name: + continue + if 'bbox_embed' in name: + continue + if 'label_enc.weight' in name: + continue + if 'feat_map' in name: + continue + if 'enc_output' in name: + continue + + param.requires_grad_(False) + + # import ipdb; ipdb.set_trace() + + def _reset_parameters(self): + # init input_proj + for proj in self.input_proj: + nn.init.xavier_uniform_(proj[0].weight, gain=1) + nn.init.constant_(proj[0].bias, 0) + + def init_ref_points(self, use_num_queries): + self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim) + + if self.random_refpoints_xy: + # import ipdb; ipdb.set_trace() + self.refpoint_embed.weight.data[:, :2].uniform_(0, 1) + self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2]) + self.refpoint_embed.weight.data[:, :2].requires_grad = False + + if self.fix_refpoints_hw > 0: + print("fix_refpoints_hw: {}".format(self.fix_refpoints_hw)) + assert self.random_refpoints_xy + self.refpoint_embed.weight.data[:, 2:] = self.fix_refpoints_hw + self.refpoint_embed.weight.data[:, 2:] = inverse_sigmoid(self.refpoint_embed.weight.data[:, 2:]) + self.refpoint_embed.weight.data[:, 2:].requires_grad = False + elif int(self.fix_refpoints_hw) == -1: + pass + elif int(self.fix_refpoints_hw) == -2: + print('learn a shared h and w') + assert self.random_refpoints_xy + self.refpoint_embed = nn.Embedding(use_num_queries, 2) + self.refpoint_embed.weight.data[:, :2].uniform_(0, 1) + self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2]) + self.refpoint_embed.weight.data[:, :2].requires_grad = False + self.hw_embed = nn.Embedding(1, 1) + else: + raise NotImplementedError('Unknown fix_refpoints_hw {}'.format(self.fix_refpoints_hw)) + + def forward(self, samples: NestedTensor, targets: List = None, **kw): + """ The forward expects a NestedTensor, which consists of: + - samples.tensor: batched images, of shape [batch_size x 3 x H x W] + - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels + + It returns a dict with the following elements: + - "pred_logits": the classification logits (including no-object) for all queries. + Shape= [batch_size x num_queries x num_classes] + - "pred_boxes": The normalized boxes coordinates for all queries, represented as + (center_x, center_y, width, height). These values are normalized in [0, 1], + relative to the size of each individual image (disregarding possible padding). + See PostProcess for information on how to retrieve the unnormalized bounding box. + - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of + dictionnaries containing the two above keys for each decoder layer. + """ + + captions = [t['instance_text_prompt'] for t in targets] + bs=len(captions) + tensor_list = [tgt["object_embeddings_text"] for tgt in targets] + max_size = 350 + padded_tensors = [torch.cat([tensor, torch.zeros(max_size - tensor.size(0), tensor.size(1),device=tensor.device)]) if tensor.size(0) < max_size else tensor for tensor in tensor_list] + object_embeddings_text = torch.stack(padded_tensors) + + kpts_embeddings_text = torch.stack([tgt["kpts_embeddings_text"] for tgt in targets])[:, :self.num_body_points] + encoded_text=self.projection(object_embeddings_text) # bs, 81, 101, 256 + kpt_embeddings_specific=self.projection_kpt(kpts_embeddings_text) # bs, 81, 101, 256 + + + kpt_vis = torch.stack([tgt["kpt_vis_text"] for tgt in targets])[:, :self.num_body_points] + kpt_mask = torch.cat((torch.ones_like(kpt_vis, device=kpt_vis.device)[..., 0].unsqueeze(-1), kpt_vis), dim=-1) + + + num_classes = encoded_text.shape[1] # bs, 81, 101, 256 + text_self_attention_masks = torch.eye(num_classes).unsqueeze(0).expand(bs, -1, -1).bool().to(samples.device) + text_token_mask = torch.zeros(samples.shape[0],num_classes).to(samples.device)>0 + for i in range(bs): + text_token_mask[i,:len(captions[i])]=True + + position_ids = torch.zeros(samples.shape[0], num_classes).to(samples.device) + + for i in range(bs): + position_ids[i,:len(captions[i])]= 1 + + + text_dict = { + 'encoded_text': encoded_text, # bs, 195, d_model + 'text_token_mask': text_token_mask, # bs, 195 + 'position_ids': position_ids, # bs, 195 + 'text_self_attention_masks': text_self_attention_masks # bs, 195,195 + } + + + # import ipdb; ipdb.set_trace() + + if isinstance(samples, (list, torch.Tensor)): + samples = nested_tensor_from_tensor_list(samples) + features, poss = self.backbone(samples) + if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + import ipdb; + ipdb.set_trace() + + + srcs = [] + masks = [] + for l, feat in enumerate(features): + src, mask = feat.decompose() + srcs.append(self.input_proj[l](src)) + masks.append(mask) + assert mask is not None + + if self.num_feature_levels > len(srcs): + _len_srcs = len(srcs) + for l in range(_len_srcs, self.num_feature_levels): + if l == _len_srcs: + src = self.input_proj[l](features[-1].tensors) + else: + src = self.input_proj[l](srcs[-1]) + m = samples.mask + mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0] + pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype) + srcs.append(src) + masks.append(mask) + poss.append(pos_l) + + if self.label_enc is not None: + label_enc = self.label_enc + else: + raise NotImplementedError + label_enc = encoded_text + if self.dn_number > 0 or targets is not None: + input_query_label, input_query_bbox, attn_mask, attn_mask2, dn_meta = \ + prepare_for_mask(kpt_mask=kpt_mask) + else: + assert targets is None + input_query_bbox = input_query_label = attn_mask = attn_mask2 = dn_meta = None + + + hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(srcs, masks, input_query_bbox, poss, + input_query_label, attn_mask, attn_mask2, + text_dict, dn_meta,targets,kpt_embeddings_specific) + + # In case num object=0 + if self.label_enc is not None: + hs[0] += self.label_enc.weight[0, 0] * 0.0 + + hs[0] += self.pos_proj.weight[0, 0] * 0.0 + hs[0] += self.pos_proj.bias[0] * 0.0 + hs[0] += self.padding.weight[0, 0] * 0.0 + + num_group = 50 + effective_dn_number = dn_meta['pad_size'] if self.training else 0 + outputs_coord_list = [] + outputs_class = [] + + + for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_cls_embed, layer_hs) in enumerate( + zip(reference[:-1], self.bbox_embed, self.class_embed, hs)): + + + if dec_lid < self.num_box_decoder_layers: + layer_delta_unsig = layer_bbox_embed(layer_hs) + layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig) + layer_outputs_unsig = layer_outputs_unsig.sigmoid() + layer_cls = layer_cls_embed(layer_hs, text_dict) + outputs_coord_list.append(layer_outputs_unsig) + outputs_class.append(layer_cls) + + + else: + + layer_hs_bbox_dn = layer_hs[:, :effective_dn_number, :] + layer_hs_bbox_norm = layer_hs[:, effective_dn_number:, :][:, 0::(self.num_body_points + 1), :] + bs = layer_ref_sig.shape[0] + reference_before_sigmoid_bbox_dn = layer_ref_sig[:, :effective_dn_number, :] + reference_before_sigmoid_bbox_norm = layer_ref_sig[:, effective_dn_number:, :][:, + 0::(self.num_body_points + 1), :] + layer_delta_unsig_dn = layer_bbox_embed(layer_hs_bbox_dn) + layer_delta_unsig_norm = layer_bbox_embed(layer_hs_bbox_norm) + layer_outputs_unsig_dn = layer_delta_unsig_dn + inverse_sigmoid(reference_before_sigmoid_bbox_dn) + layer_outputs_unsig_dn = layer_outputs_unsig_dn.sigmoid() + layer_outputs_unsig_norm = layer_delta_unsig_norm + inverse_sigmoid(reference_before_sigmoid_bbox_norm) + layer_outputs_unsig_norm = layer_outputs_unsig_norm.sigmoid() + layer_outputs_unsig = torch.cat((layer_outputs_unsig_dn, layer_outputs_unsig_norm), dim=1) + layer_cls_dn = layer_cls_embed(layer_hs_bbox_dn, text_dict) + layer_cls_norm = layer_cls_embed(layer_hs_bbox_norm, text_dict) + layer_cls = torch.cat((layer_cls_dn, layer_cls_norm), dim=1) + outputs_class.append(layer_cls) + outputs_coord_list.append(layer_outputs_unsig) + + # update keypoints + outputs_keypoints_list = [] + outputs_keypoints_hw = [] + kpt_index = [x for x in range(num_group * (self.num_body_points + 1)) if x % (self.num_body_points + 1) != 0] + for dec_lid, (layer_ref_sig, layer_hs) in enumerate(zip(reference[:-1], hs)): + if dec_lid < self.num_box_decoder_layers: + assert isinstance(layer_hs, torch.Tensor) + bs = layer_hs.shape[0] + layer_res = layer_hs.new_zeros((bs, self.num_queries, self.num_body_points * 3)) + outputs_keypoints_list.append(layer_res) + else: + bs = layer_ref_sig.shape[0] + layer_hs_kpt = layer_hs[:, effective_dn_number:, :].index_select(1, torch.tensor(kpt_index, + device=layer_hs.device)) + delta_xy_unsig = self.pose_embed[dec_lid - self.num_box_decoder_layers](layer_hs_kpt) + layer_ref_sig_kpt = layer_ref_sig[:, effective_dn_number:, :].index_select(1, torch.tensor(kpt_index, + device=layer_hs.device)) + layer_outputs_unsig_keypoints = delta_xy_unsig + inverse_sigmoid(layer_ref_sig_kpt[..., :2]) + vis_xy_unsig = torch.ones_like(layer_outputs_unsig_keypoints, + device=layer_outputs_unsig_keypoints.device) + xyv = torch.cat((layer_outputs_unsig_keypoints, vis_xy_unsig[:, :, 0].unsqueeze(-1)), dim=-1) + xyv = xyv.sigmoid() + layer_res = xyv.reshape((bs, num_group, self.num_body_points, 3)).flatten(2, 3) + layer_hw = layer_ref_sig_kpt[..., 2:].reshape(bs, num_group, self.num_body_points, 2).flatten(2, 3) + layer_res = keypoint_xyzxyz_to_xyxyzz(layer_res) + outputs_keypoints_list.append(layer_res) + outputs_keypoints_hw.append(layer_hw) + + + if self.dn_number > 0 and dn_meta is not None: + outputs_class, outputs_coord_list = \ + post_process(outputs_class, outputs_coord_list, + dn_meta, self.aux_loss, self._set_aux_loss) + out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord_list[-1], + 'pred_keypoints': outputs_keypoints_list[-1]} + + return out + + +@MODULE_BUILD_FUNCS.registe_with_name(module_name='UniPose') +def build_unipose(args): + + num_classes = args.num_classes + device = torch.device(args.device) + + backbone = build_backbone(args) + + transformer = build_deformable_transformer(args) + + try: + match_unstable_error = args.match_unstable_error + dn_labelbook_size = args.dn_labelbook_size + except: + match_unstable_error = True + dn_labelbook_size = num_classes + + try: + dec_pred_class_embed_share = args.dec_pred_class_embed_share + except: + dec_pred_class_embed_share = True + try: + dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share + except: + dec_pred_bbox_embed_share = True + + binary_query_selection = False + try: + binary_query_selection = args.binary_query_selection + except: + binary_query_selection = False + + use_cdn = True + try: + use_cdn = args.use_cdn + except: + use_cdn = True + + sub_sentence_present = True + try: + sub_sentence_present = args.sub_sentence_present + except: + sub_sentence_present = True + # print('********* sub_sentence_present', sub_sentence_present) + + model = UniPose( + backbone, + transformer, + num_classes=num_classes, + num_queries=args.num_queries, + aux_loss=True, + iter_update=True, + query_dim=4, + random_refpoints_xy=args.random_refpoints_xy, + fix_refpoints_hw=args.fix_refpoints_hw, + num_feature_levels=args.num_feature_levels, + nheads=args.nheads, + dec_pred_class_embed_share=dec_pred_class_embed_share, + dec_pred_bbox_embed_share=dec_pred_bbox_embed_share, + # two stage + two_stage_type=args.two_stage_type, + # box_share + two_stage_bbox_embed_share=args.two_stage_bbox_embed_share, + two_stage_class_embed_share=args.two_stage_class_embed_share, + decoder_sa_type=args.decoder_sa_type, + num_patterns=args.num_patterns, + dn_number=args.dn_number if args.use_dn else 0, + dn_box_noise_scale=args.dn_box_noise_scale, + dn_label_noise_ratio=args.dn_label_noise_ratio, + dn_labelbook_size=dn_labelbook_size, + use_label_enc=args.use_label_enc, + + text_encoder_type=args.text_encoder_type, + + binary_query_selection=binary_query_selection, + use_cdn=use_cdn, + sub_sentence_present=sub_sentence_present + ) + + return model + + +class ContrastiveAssign(nn.Module): + def __init__(self, project=False, cal_bias=None, max_text_len=256): + """ + :param x: query + :param y: text embed + :param proj: + :return: + """ + super().__init__() + self.project = project + self.cal_bias = cal_bias + self.max_text_len = max_text_len + + def forward(self, x, text_dict): + """_summary_ + + Args: + x (_type_): _description_ + text_dict (_type_): _description_ + { + 'encoded_text': encoded_text, # bs, 195, d_model + 'text_token_mask': text_token_mask, # bs, 195 + # True for used tokens. False for padding tokens + } + Returns: + _type_: _description_ + """ + assert isinstance(text_dict, dict) + + y = text_dict['encoded_text'] + + + max_text_len = y.shape[1] + + + + text_token_mask = text_dict['text_token_mask'] + + if self.cal_bias is not None: + raise NotImplementedError + return x @ y.transpose(-1, -2) + self.cal_bias.weight.repeat(x.shape[0], x.shape[1], 1) + res = x @ y.transpose(-1, -2) + res.masked_fill_(~text_token_mask[:, None, :], float('-inf')) + + # padding to max_text_len + new_res = torch.full((*res.shape[:-1], max_text_len), float('-inf'), device=res.device) + new_res[..., :res.shape[-1]] = res + + return new_res diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/utils.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/utils.py new file mode 100644 index 0000000..350d831 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/UniPose/utils.py @@ -0,0 +1,348 @@ +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import copy +import torch +import random +from torch import nn, Tensor +import os +import numpy as np +import math +import torch.nn.functional as F +from torch import nn + + +def _get_clones(module, N, layer_share=False): + # import ipdb; ipdb.set_trace() + if layer_share: + return nn.ModuleList([module for i in range(N)]) + else: + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def get_sine_pos_embed( + pos_tensor: torch.Tensor, + num_pos_feats: int = 128, + temperature: int = 10000, + exchange_xy: bool = True, +): + """generate sine position embedding from a position tensor + Args: + pos_tensor (torch.Tensor): shape: [..., n]. + num_pos_feats (int): projected shape for each float in the tensor. + temperature (int): temperature in the sine/cosine function. + exchange_xy (bool, optional): exchange pos x and pos y. \ + For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True. + Returns: + pos_embed (torch.Tensor): shape: [..., n*num_pos_feats]. + """ + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device) + dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) + + def sine_func(x: torch.Tensor): + sin_x = x * scale / dim_t + sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2) + return sin_x + + pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)] + if exchange_xy: + pos_res[0], pos_res[1] = pos_res[1], pos_res[0] + pos_res = torch.cat(pos_res, dim=-1) + return pos_res + + +def gen_encoder_output_proposals(memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None): + """ + Input: + - memory: bs, \sum{hw}, d_model + - memory_padding_mask: bs, \sum{hw} + - spatial_shapes: nlevel, 2 + - learnedwh: 2 + Output: + - output_memory: bs, \sum{hw}, d_model + - output_proposals: bs, \sum{hw}, 4 + """ + N_, S_, C_ = memory.shape + base_scale = 4.0 + proposals = [] + _cur = 0 + for lvl, (H_, W_) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + # import ipdb; ipdb.set_trace() + + grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), + torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device)) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2 + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale + + if learnedwh is not None: + # import ipdb; ipdb.set_trace() + wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0 ** lvl) + else: + wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl) + + # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1) + # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale + # wh = torch.ones_like(grid) / scale + proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) + proposals.append(proposal) + _cur += (H_ * W_) + # import ipdb; ipdb.set_trace() + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid + output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf')) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf')) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + + # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf')) + # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf')) + + return output_memory, output_proposals + + +class RandomBoxPerturber(): + def __init__(self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2) -> None: + self.noise_scale = torch.Tensor([x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]) + + def __call__(self, refanchors: Tensor) -> Tensor: + nq, bs, query_dim = refanchors.shape + device = refanchors.device + + noise_raw = torch.rand_like(refanchors) + noise_scale = self.noise_scale.to(device)[:query_dim] + + new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale) + return new_refanchors.clamp_(0, 1) + + +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + if no_reduction: + return loss + + return loss.mean(1).sum() / num_boxes + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +def _get_activation_fn(activation, d_model=256, batch_dim=0): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + if activation == "prelu": + return nn.PReLU() + if activation == "selu": + return F.selu + + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +def gen_sineembed_for_position(pos_tensor): + # n_query, bs, _ = pos_tensor.size() + # sineembed_tensor = torch.zeros(n_query, bs, 256) + scale = 2 * math.pi + dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device) + dim_t = 10000 ** (2 * (dim_t // 2) / 128) + x_embed = pos_tensor[:, :, 0] * scale + y_embed = pos_tensor[:, :, 1] * scale + pos_x = x_embed[:, :, None] / dim_t + pos_y = y_embed[:, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) + pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) + if pos_tensor.size(-1) == 2: + pos = torch.cat((pos_y, pos_x), dim=2) + elif pos_tensor.size(-1) == 4: + w_embed = pos_tensor[:, :, 2] * scale + pos_w = w_embed[:, :, None] / dim_t + pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) + + h_embed = pos_tensor[:, :, 3] * scale + pos_h = h_embed[:, :, None] / dim_t + pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) + + pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) + else: + raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1))) + return pos + + +def oks_overlaps(kpt_preds, kpt_gts, kpt_valids, kpt_areas, sigmas): + sigmas = kpt_preds.new_tensor(sigmas) + variances = (sigmas * 2) ** 2 + + assert kpt_preds.size(0) == kpt_gts.size(0) + kpt_preds = kpt_preds.reshape(-1, kpt_preds.size(-1) // 2, 2) + kpt_gts = kpt_gts.reshape(-1, kpt_gts.size(-1) // 2, 2) + + squared_distance = (kpt_preds[:, :, 0] - kpt_gts[:, :, 0]) ** 2 + \ + (kpt_preds[:, :, 1] - kpt_gts[:, :, 1]) ** 2 + # import pdb + # pdb.set_trace() + # assert (kpt_valids.sum(-1) > 0).all() + squared_distance0 = squared_distance / (kpt_areas[:, None] * variances[None, :] * 2) + squared_distance1 = torch.exp(-squared_distance0) + squared_distance1 = squared_distance1 * kpt_valids + oks = squared_distance1.sum(dim=1) / (kpt_valids.sum(dim=1) + 1e-6) + + return oks + + +def oks_loss(pred, + target, + valid=None, + area=None, + linear=False, + sigmas=None, + eps=1e-6): + """Oks loss. + Computing the oks loss between a set of predicted poses and target poses. + The loss is calculated as negative log of oks. + Args: + pred (torch.Tensor): Predicted poses of format (x1, y1, x2, y2, ...), + shape (n, 2K). + target (torch.Tensor): Corresponding gt poses, shape (n, 2K). + linear (bool, optional): If True, use linear scale of loss instead of + log scale. Default: False. + eps (float): Eps to avoid log(0). + Return: + torch.Tensor: Loss tensor. + """ + oks = oks_overlaps(pred, target, valid, area, sigmas).clamp(min=eps) + if linear: + loss = 1 - oks + else: + loss = -oks.log() + return loss + + +class OKSLoss(nn.Module): + """IoULoss. + Computing the oks loss between a set of predicted poses and target poses. + Args: + linear (bool): If True, use linear scale of loss instead of log scale. + Default: False. + eps (float): Eps to avoid log(0). + reduction (str): Options are "none", "mean" and "sum". + loss_weight (float): Weight of loss. + """ + + def __init__(self, + linear=False, + num_keypoints=17, + eps=1e-6, + reduction='mean', + loss_weight=1.0): + super(OKSLoss, self).__init__() + self.linear = linear + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + if num_keypoints == 68: + self.sigmas = np.array([ + .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, + 1.07, .87, .87, .89, .89, .25, .25, .25, .25, .25, .25, .25, .25, + .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, + .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, + .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, .25, + ], dtype=np.float32) / 10.0 + else: + raise ValueError(f'Unsupported keypoints number {num_keypoints}') + + def forward(self, + pred, + target, + valid, + area, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + valid (torch.Tensor): The visible flag of the target pose. + area (torch.Tensor): The area of the target pose. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. Options are "none", "mean" and "sum". + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if (weight is not None) and (not torch.any(weight > 0)) and ( + reduction != 'none'): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + if weight is not None and weight.dim() > 1: + # TODO: remove this in the future + # reduce the weight of shape (n, 4) to (n,) to match the + # iou_loss of shape (n,) + assert weight.shape == pred.shape + weight = weight.mean(-1) + loss = self.loss_weight * oks_loss( + pred, + target, + valid=valid, + area=area, + linear=self.linear, + sigmas=self.sigmas, + eps=self.eps) + return loss diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/__init__.py new file mode 100644 index 0000000..ab6c39b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/__init__.py @@ -0,0 +1,16 @@ +# ------------------------------------------------------------------------ +# ED-Pose +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .UniPose.unipose import build_unipose + +def build_model(args): + # we use register to maintain models from catdet6 on. + from .registry import MODULE_BUILD_FUNCS + + assert args.modelname in MODULE_BUILD_FUNCS._module_dict + build_func = MODULE_BUILD_FUNCS.get(args.modelname) + model = build_func(args) + return model diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/registry.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/registry.py new file mode 100644 index 0000000..f438c6e --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/models/registry.py @@ -0,0 +1,58 @@ +# -*- coding: utf-8 -*- +# @Author: Yihao Chen +# @Date: 2021-08-16 16:03:17 +# @Last Modified by: Shilong Liu +# @Last Modified time: 2022-01-23 15:26 +# modified from mmcv + +import inspect +from functools import partial + + +class Registry(object): + + def __init__(self, name): + self._name = name + self._module_dict = dict() + + def __repr__(self): + format_str = self.__class__.__name__ + '(name={}, items={})'.format( + self._name, list(self._module_dict.keys())) + return format_str + + def __len__(self): + return len(self._module_dict) + + @property + def name(self): + return self._name + + @property + def module_dict(self): + return self._module_dict + + def get(self, key): + return self._module_dict.get(key, None) + + def registe_with_name(self, module_name=None, force=False): + return partial(self.register, module_name=module_name, force=force) + + def register(self, module_build_function, module_name=None, force=False): + """Register a module build function. + Args: + module (:obj:`nn.Module`): Module to be registered. + """ + if not inspect.isfunction(module_build_function): + raise TypeError('module_build_function must be a function, but got {}'.format( + type(module_build_function))) + if module_name is None: + module_name = module_build_function.__name__ + if not force and module_name in self._module_dict: + raise KeyError('{} is already registered in {}'.format( + module_name, self.name)) + self._module_dict[module_name] = module_build_function + + return module_build_function + +MODULE_BUILD_FUNCS = Registry('model build functions') + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/predefined_keypoints.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/predefined_keypoints.py new file mode 100644 index 0000000..c32c5ad --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/predefined_keypoints.py @@ -0,0 +1,56 @@ +person = {"keypoints":['nose', 'left eye', 'right eye', 'left ear', 'right ear', 'left shoulder', 'right shoulder', 'left elbow', 'right elbow', 'left wrist', 'right wrist', 'left hip', 'right hip', 'left knee', 'right knee', 'left ankle', 'right ankle'],"skeleton": [[16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13],[6,7],[6,8],[7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]]} + +face = {"keypoints": ['right cheekbone 1', 'right cheekbone 2', 'right cheek 1', 'right cheek 2', 'right cheek 3', 'right cheek 4', 'right cheek 5', 'right chin', 'chin center', 'left chin', 'left cheek 5', 'left cheek 4', 'left cheek 3', 'left cheek 2', 'left cheek 1', 'left cheekbone 2', 'left cheekbone 1', 'right eyebrow 1', 'right eyebrow 2', 'right eyebrow 3', 'right eyebrow 4', 'right eyebrow 5', 'left eyebrow 1', 'left eyebrow 2', 'left eyebrow 3', 'left eyebrow 4', 'left eyebrow 5', 'nasal bridge 1', 'nasal bridge 2', 'nasal bridge 3', 'nasal bridge 4', 'right nasal wing 1', 'right nasal wing 2', 'nasal wing center', 'left nasal wing 1', 'left nasal wing 2', 'right eye eye corner 1', 'right eye upper eyelid 1', 'right eye upper eyelid 2', 'right eye eye corner 2', 'right eye lower eyelid 2', 'right eye lower eyelid 1', 'left eye eye corner 1', 'left eye upper eyelid 1', 'left eye upper eyelid 2', 'left eye eye corner 2', 'left eye lower eyelid 2', 'left eye lower eyelid 1', 'right mouth corner', 'upper lip outer edge 1', 'upper lip outer edge 2', 'upper lip outer edge 3', 'upper lip outer edge 4', 'upper lip outer edge 5', 'left mouth corner', 'lower lip outer edge 5', 'lower lip outer edge 4', 'lower lip outer edge 3', 'lower lip outer edge 2', 'lower lip outer edge 1', 'upper lip inter edge 1', 'upper lip inter edge 2', 'upper lip inter edge 3', 'upper lip inter edge 4', 'upper lip inter edge 5', 'lower lip inter edge 3', 'lower lip inter edge 2', 'lower lip inter edge 1'], "skeleton": []} + +hand = {"keypoints":['wrist', 'thumb root', "thumb's third knuckle", "thumb's second knuckle", 'thumbโ€™s first knuckle', "forefinger's root", "forefinger's third knuckle", "forefinger's second knuckle", "forefinger's first knuckle", "middle finger's root", "middle finger's third knuckle", "middle finger's second knuckle", "middle finger's first knuckle", "ring finger's root", "ring finger's third knuckle", "ring finger's second knuckle", "ring finger's first knuckle", "pinky finger's root", "pinky finger's third knuckle", "pinky finger's second knuckle", "pinky finger's first knuckle"],"skeleton": []} + +animal_in_AnimalKindom = {"keypoints":['head mid top', 'eye left', 'eye right', 'mouth front top', 'mouth back left', 'mouth back right', 'mouth front bottom', 'shoulder left', 'shoulder right', 'elbow left', 'elbow right', 'wrist left', 'wrist right', 'torso mid back', 'hip left', 'hip right', 'knee left', 'knee right', 'ankle left ', 'ankle right', 'tail top back', 'tail mid back', 'tail end back'],"skeleton": [[1, 0], [2, 0], [3, 4], [3, 5], [4, 6], [5, 6], [0, 7], [0, 8], [7, 9], [8, 10], [9, 11], [10, 12], [0, 13], [13, 20], [20, 14], [20, 15], [14, 16], [15, 17], [16, 18], [17, 19], [20, 21], [21, 22]]} + +animal_in_AP10K = {"keypoints": ['left eye', 'right eye', 'nose', 'neck', 'root of tail', 'left shoulder', 'left elbow', 'left front paw', 'right shoulder', 'right elbow', 'right front paw', 'left hip', 'left knee', 'left back paw', 'right hip', 'right knee', 'right back paw'], "skeleton": [[1, 2], [1, 3], [2, 3], [3, 4], [4, 5], [4, 6], [6, 7], [7, 8], [4, 9], [9, 10], [10, 11], [5, 12], [12, 13], [13, 14], [5, 15], [15, 16], [16, 17]]} + +animal= {"keypoints": ['left eye', 'right eye', 'nose', 'neck', 'root of tail', 'left shoulder', 'left elbow', 'left front paw', 'right shoulder', 'right elbow', 'right front paw', 'left hip', 'left knee', 'left back paw', 'right hip', 'right knee', 'right back paw'], "skeleton": [[1, 2], [1, 3], [2, 3], [3, 4], [4, 5], [4, 6], [6, 7], [7, 8], [4, 9], [9, 10], [10, 11], [5, 12], [12, 13], [13, 14], [5, 15], [15, 16], [16, 17]]} + +animal_face = {"keypoints": ['right eye right', 'right eye left', 'left eye right', 'left eye left', 'nose tip', 'lip right', 'lip left', 'upper lip', 'lower lip'], "skeleton": []} + +fly = {"keypoints": ['head', 'eye left', 'eye right', 'neck', 'thorax', 'abdomen', 'foreleg right base', 'foreleg right first segment', 'foreleg right second segment', 'foreleg right tip', 'midleg right base', 'midleg right first segment', 'midleg right second segment', 'midleg right tip', 'hindleg right base', 'hindleg right first segment', 'hindleg right second segment', 'hindleg right tip', 'foreleg left base', 'foreleg left first segment', 'foreleg left second segment', 'foreleg left tip', 'midleg left base', 'midleg left first segment', 'midleg left second segment', 'midleg left tip', 'hindleg left base', 'hindleg left first segment', 'hindleg left second segment', 'hindleg left tip', 'wing left', 'wing right'], "skeleton": [[2, 1], [3, 1], [4, 1], [5, 4], [6, 5], [8, 7], [9, 8], [10, 9], [12, 11], [13, 12], [14, 13], [16, 15], [17, 16], [18, 17], [20, 19], [21, 20], [22, 21], [24, 23], [25, 24], [26, 25], [28, 27], [29, 28], [30, 29], [31, 4], [32, 4]]} + +locust = {"keypoints": ['head', 'neck', 'thorax', 'abdomen1', 'abdomen2', 'anttip left', 'antbase left', 'eye left', 'foreleg left base', 'foreleg left first segment', 'foreleg left second segment', 'foreleg left tip', 'midleg left base', 'midleg left first segment', 'midleg left second segment', 'midleg left tip', 'hindleg left base', 'hindleg left first segment', 'hindleg left second segment', 'hindleg left tip', 'anttip right', 'antbase right', 'eye right', 'foreleg right base', 'foreleg right first segment', 'foreleg right second segment', 'foreleg right tip', 'midleg right base', 'midleg right first segment', 'midleg right second segment', 'midleg right tip', 'hindleg right base', 'hindleg right first segment', 'hindleg right second segment', 'hindleg right tip'],"skeleton": [[2, 1], [3, 2], [4, 3], [5, 4], [7, 6], [8, 7], [10, 9], [11, 10], [12, 11], [14, 13], [15, 14],[16, 15], [18, 17], [19, 18], [20, 19], [22, 21], [23, 22], [25, 24], [26, 25], [27, 26],[29, 28], [30, 29], [31, 30], [33, 32], [34, 33], [35, 34]]} + +car ={"keypoints": ['right front wheel center', 'left front wheel center', 'right rear wheel center', 'left rear wheel center', 'front right', 'front left', 'back right', 'back left', 'none', 'roof front right', 'roof front left', 'roof back right', 'roof back left', 'none'],"skeleton": [[0, 2], [1, 3], [0, 1], [2, 3], [9, 11], [10, 12], [9, 10], [11, 12], [4, 0], [4, 9], [4, 5], [5, 1], [5, 10], [6, 2], [6, 11], [7, 3], [7, 12], [6, 7]]} + +short_sleeved_shirt = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right cuff outside', 'right cuff inside', 'right sleeve inside 2', 'right sleeve inside 1', 'right side 1', 'right side 2', 'right side 3', 'center hem', 'left side 3', 'left side 2', 'left side 1', 'left sleeve inside 1', 'left sleeve inside 2', 'left cuff inside', 'left cuff outside', 'left sleeve outside 2', 'left sleeve outside 1'], 'skeleton': []} + +long_sleeved_outwear={'keypoints': ['upper center neckline', 'lower right center neckline', 'lower right neckline', 'upper right neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right sleeve outside 3', 'right sleeve outside 4', 'right cuff outside', 'right cuff inside', 'right sleeve inside 1', 'right sleeve inside 2', 'right sleeve inside 3', 'right sleeve inside 4', 'right side outside 1', 'right side outside 2', 'right side outside 3', 'right side inside 3', 'left side outside 3', 'left side outside 2', 'left side outside 1', 'left sleeve inside 4', 'left sleeve inside 3', 'left sleeve inside 2', 'left sleeve inside 1', 'left cuff inside', 'left cuff outside', 'left sleeve outside 4', 'left sleeve outside 3', 'left sleeve outside 2', 'left sleeve outside 1', 'lower left center neckline', 'left side inside 1', 'left side inside 2', 'left side inside 3', 'right side inside 1', 'right side inside 2'], 'skeleton': []} + +short_sleeved_outwear={'keypoints': ['upper center neckline', 'lower right center neckline', 'lower right neckline', 'upper right neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right cuff outside', 'right cuff inside', 'right sleeve inside 2', 'right sleeve inside 1', 'right side outside 1', 'right side outside 2', 'right side outside 3', 'right side inside 3', 'left side outside 3', 'left side outside 2', 'left side outside 1', 'left sleeve inside 1', 'left sleeve inside 2', 'left cuff inside', 'left cuff outside', 'left sleeve outside 2', 'left sleeve outside 1', 'lower left center neckline', 'left side inside 1', 'left side inside 2', 'left side inside 3', 'right side inside 1', 'right side inside 2'], 'skeleton': []} + +sling={'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve', 'right side 1', 'right side 2', 'right side 3', 'center hem', 'left side 3', 'left side 2', 'left side 1', 'left sleeve'], 'skeleton': []} + +vest = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve', 'right side 1', 'right side 2', 'right side 3', 'center hem', 'left side 3', 'left side 2', 'left side 1', 'left sleeve'], 'skeleton': []} + +long_sleeved_dress={'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right sleeve outside 3', 'right sleeve outside 4', 'right cuff outside', 'right cuff inside', 'right sleeve inside 4', 'right sleeve inside 3', 'right sleeve inside 2', 'right sleeve inside 1', 'right side 1', 'right side 2', 'right side 3', 'right side 4', 'right side 5', 'center hem', 'left side 5', 'left side 4', 'left side 3', 'left side 2', 'left side 1', 'left sleeve inside 1', 'left sleeve inside 2', 'left sleeve inside 3', 'left sleeve inside 4', 'left cuff inside', 'left cuff outside', 'left sleeve outside 4', 'left sleeve outside 3', 'left sleeve outside 2', 'left sleeve outside 1'], 'skeleton': []} + +long_sleeved_shirt = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right sleeve outside 3', 'right sleeve outside 4', 'right cuff outside', 'right cuff inside', 'right sleeve inside 4', 'right sleeve inside 3', 'right sleeve inside 2', 'right sleeve inside 1', 'right side 1', 'right side 2', 'right side 3', 'center hem', 'left side 3', 'left side 2', 'left side 1', 'left sleeve inside 1', 'left sleeve inside 2', 'left sleeve inside 3', 'left sleeve inside 4', 'left cuff inside', 'left cuff outside', 'left sleeve outside 4', 'left sleeve outside 3', 'left sleeve outside 2', 'left sleeve outside 1'], 'skeleton': []} + +trousers = {'keypoints': ['right side outside 1', 'upper center', 'left side outside 1', 'right side outside 2', 'right side outside 3', 'right cuff outside', 'right cuff inside', 'right side inside 1', 'crotch', 'left side inside 1', 'left cuff inside', 'left cuff outside', 'left side outside 3', 'left side outside 2'], 'skeleton': []} + +sling_dress = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right side 1', 'right side 2', 'right side 3', 'right side 4', 'right side 5', 'right side 6', 'center hem', 'left side 6', 'left side 5', 'left side 4', 'left side 3', 'left side 2', 'left side 1'], 'skeleton': []} + +vest_dress = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right side 1', 'right side 2', 'right side 3', 'right side 4', 'right side 5', 'right side 6', 'center hem', 'left side 6', 'left side 5', 'left side 4', 'left side 3', 'left side 2', 'left side 1'], 'skeleton': []} + +skirt = {'keypoints': ['right side 1', 'upper center', 'left side 1', 'right side 2', 'right side 3', 'center hem', 'left side 3', 'left side 2'], 'skeleton': []} + +short_sleeved_dress = {'keypoints': ['upper center neckline', 'upper right neckline', 'lower right neckline', 'lower center neckline', 'lower left neckline', 'upper left neckline', 'right sleeve outside 1', 'right sleeve outside 2', 'right cuff outside', 'right cuff inside', 'right sleeve inside 1', 'right sleeve inside 2', 'left side 1', 'left side 2', 'left side 3', 'left side 4', 'left side 5', 'center hem', 'right side 5', 'right side 4', 'right side 3', 'right side 2', 'right side 1', 'left sleeve inside 2', 'left sleeve inside 1', 'left cuff inside', 'left cuff outside', 'left sleeve outside 2', 'left sleeve outside 1'], 'skeleton': []} + +shorts = {'keypoints': ['right side outside 1', 'upper center', 'left side outside 1', 'right side outside 2', 'right cuff outside', 'right cuff inside', 'crotch', 'left cuff inside', 'left cuff outside', 'left side outside 2'], 'skeleton': []} + +table = {'keypoints': ['desktop corner 1', 'desktop corner 2', 'desktop corner 3', 'desktop corner 4', 'table leg 1', 'table leg 2', 'table leg 3', 'table leg 4'], 'skeleton': []} + +chair = {'keypoints': ['legs righttopcorner', 'legs lefttopcorner', 'legs leftbottomcorner', 'legs rightbottomcorner', 'base righttop', 'base lefttop', 'base leftbottom', 'base rightbottom', 'headboard righttop', 'headboard lefttop'], 'skeleton': []} + +bed = {'keypoints': ['legs rightbottomcorner', 'legs righttopcorner', 'base rightbottom', 'base righttop', 'backrest righttop', 'legs leftbottomcorner', 'legs lefttopcorner', 'base leftbottom', 'base lefttop', 'backrest lefttop'], 'skeleton': []} + +sofa = {'keypoints': ['legs rightbottomcorner', 'legs righttopcorner', 'base rightbottom', 'base righttop', 'armrests rightbottomcorner', 'armrests righttopcorner', 'backrest righttop', 'legs leftbottomcorner', 'legs lefttopcorner', 'base leftbottom', 'base lefttop', 'armrests leftbottomcorner', 'armrests lefttopcorner', 'backrest lefttop'], 'skeleton': []} + +swivelchair = {'keypoints': ['rotatingbase 1', 'rotatingbase 2', 'rotatingbase 3', 'rotatingbase 4', 'rotatingbase 5', 'rotatingbase center', 'base center', 'base righttop', 'base lefttop', 'base leftbottom', 'base rightbottom', 'backrest righttop', 'backrest lefttop'], 'skeleton': []} + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/transforms.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/transforms.py new file mode 100644 index 0000000..9155913 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/transforms.py @@ -0,0 +1,394 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Transforms and data augmentation for both image + bbox. +""" +import os +import sys +import random + +import PIL +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as F + +sys.path.append(os.path.dirname(os.path.abspath(__file__))) +from util.box_ops import box_xyxy_to_cxcywh +from util.misc import interpolate + + +def crop(image, target, region): + cropped_image = F.crop(image, *region) + + if target is not None: + target = target.copy() + i, j, h, w = region + id2catname = target["id2catname"] + caption_list = target["caption_list"] + target["size"] = torch.tensor([h, w]) + + fields = ["labels", "area", "iscrowd", "positive_map","keypoints"] + + if "boxes" in target: + boxes = target["boxes"] + max_size = torch.as_tensor([w, h], dtype=torch.float32) + cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) + cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) + cropped_boxes = cropped_boxes.clamp(min=0) + area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) + target["boxes"] = cropped_boxes.reshape(-1, 4) + target["area"] = area + fields.append("boxes") + + if "masks" in target: + # FIXME should we update the area here if there are no boxes? + target['masks'] = target['masks'][:, i:i + h, j:j + w] + fields.append("masks") + + + # remove elements for which the boxes or masks that have zero area + if "boxes" in target or "masks" in target: + # favor boxes selection when defining which elements to keep + # this is compatible with previous implementation + if "boxes" in target: + cropped_boxes = target['boxes'].reshape(-1, 2, 2) + keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) + else: + keep = target['masks'].flatten(1).any(1) + + for field in fields: + if field in target: + target[field] = target[field][keep] + + if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # for debug and visualization only. + if 'strings_positive' in target: + target['strings_positive'] = [_i for _i, _j in zip(target['strings_positive'], keep) if _j] + + + if "keypoints" in target: + max_size = torch.as_tensor([w, h], dtype=torch.float32) + keypoints = target["keypoints"] + cropped_keypoints = keypoints.view(-1, 3)[:,:2] - torch.as_tensor([j, i]) + cropped_keypoints = torch.min(cropped_keypoints, max_size) + cropped_keypoints = cropped_keypoints.clamp(min=0) + cropped_keypoints = torch.cat([cropped_keypoints, keypoints.view(-1, 3)[:,2].unsqueeze(1)], dim=1) + target["keypoints"] = cropped_keypoints.view(target["keypoints"].shape[0], target["keypoints"].shape[1], 3) + + target["id2catname"] = id2catname + target["caption_list"] = caption_list + + return cropped_image, target + + +def hflip(image, target): + flipped_image = F.hflip(image) + + w, h = image.size + + if target is not None: + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0]) + target["boxes"] = boxes + + if "masks" in target: + target['masks'] = target['masks'].flip(-1) + + + if "keypoints" in target: + dataset_name=target["dataset_name"] + if dataset_name == "coco_person" or dataset_name == "macaque": + flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], + [9, 10], [11, 12], [13, 14], [15, 16]] + + elif dataset_name=="animalkindom_ak_P1_animal": + flip_pairs = [[1, 2], [4, 5],[7,8],[9,10],[11,12],[14,15],[16,17],[18,19]] + + elif dataset_name=="animalweb_animal": + flip_pairs = [[0, 3], [1, 2], [5, 6]] + + elif dataset_name=="face": + flip_pairs = [ + [0, 16], [1, 15], [2, 14], [3, 13], [4, 12], [5, 11], [6, 10], [7, 9], + [17, 26], [18, 25], [19, 24], [20, 23], [21, 22], + [31, 35], [32, 34], + [36, 45], [37, 44], [38, 43], [39, 42], [40, 47], [41, 46], + [48, 54], [49, 53], [50, 52], + [55, 59], [56, 58], + [60, 64], [61, 63], + [65, 67] + ] + + elif dataset_name=="hand": + flip_pairs = [] + + elif dataset_name=="foot": + flip_pairs = [] + + elif dataset_name=="locust": + flip_pairs = [[5, 20], [6, 21], [7, 22], [8, 23], [9, 24], [10, 25], [11, 26], [12, 27], [13, 28], [14, 29], [15, 30], [16, 31], [17, 32], [18, 33], [19, 34]] + + elif dataset_name=="fly": + flip_pairs = [[1, 2], [6, 18], [7, 19], [8, 20], [9, 21], [10, 22], [11, 23], [12, 24], [13, 25], [14, 26], [15, 27], [16, 28], [17, 29], [30, 31]] + + elif dataset_name == "ap_36k_animal" or dataset_name == "ap_10k_animal": + flip_pairs = [[0, 1],[5, 8], [6, 9], [7, 10], [11, 14], [12, 15], [13, 16]] + + + + keypoints = target["keypoints"] + keypoints[:,:,0] = w - keypoints[:,:, 0]-1 + for pair in flip_pairs: + keypoints[:,pair[0], :], keypoints[:,pair[1], :] = keypoints[:,pair[1], :], keypoints[:,pair[0], :].clone() + target["keypoints"] = keypoints + return flipped_image, target + + +def resize(image, target, size, max_size=None): + # size can be min_size (scalar) or (w, h) tuple + + def get_size_with_aspect_ratio(image_size, size, max_size=None): + w, h = image_size + if max_size is not None: + min_original_size = float(min((w, h))) + max_original_size = float(max((w, h))) + if max_original_size / min_original_size * size > max_size: + size = int(round(max_size * min_original_size / max_original_size)) + + if (w <= h and w == size) or (h <= w and h == size): + return (h, w) + + if w < h: + ow = size + oh = int(size * h / w) + else: + oh = size + ow = int(size * w / h) + + return (oh, ow) + + def get_size(image_size, size, max_size=None): + if isinstance(size, (list, tuple)): + return size[::-1] + else: + return get_size_with_aspect_ratio(image_size, size, max_size) + + size = get_size(image.size, size, max_size) + rescaled_image = F.resize(image, size) + + if target is None: + return rescaled_image, None + + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) + ratio_width, ratio_height = ratios + + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height]) + target["boxes"] = scaled_boxes + + if "area" in target: + area = target["area"] + scaled_area = area * (ratio_width * ratio_height) + target["area"] = scaled_area + + + if "keypoints" in target: + keypoints = target["keypoints"] + scaled_keypoints = keypoints * torch.as_tensor([ratio_width, ratio_height, 1]) + target["keypoints"] = scaled_keypoints + + h, w = size + target["size"] = torch.tensor([h, w]) + + if "masks" in target: + target['masks'] = interpolate( + target['masks'][:, None].float(), size, mode="nearest")[:, 0] > 0.5 + + return rescaled_image, target + + +def pad(image, target, padding): + # assumes that we only pad on the bottom right corners + padded_image = F.pad(image, (0, 0, padding[0], padding[1])) + if target is None: + return padded_image, None + target = target.copy() + # should we do something wrt the original size? + target["size"] = torch.tensor(padded_image.size[::-1]) + if "masks" in target: + target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1])) + return padded_image, target + + +class ResizeDebug(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + return resize(img, target, self.size) + + +class RandomCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + region = T.RandomCrop.get_params(img, self.size) + return crop(img, target, region) + + +class RandomSizeCrop(object): + def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False): + # respect_boxes: True to keep all boxes + # False to tolerence box filter + self.min_size = min_size + self.max_size = max_size + self.respect_boxes = respect_boxes + + def __call__(self, img: PIL.Image.Image, target: dict): + init_boxes = len(target["boxes"]) if (target is not None and "boxes" in target) else 0 + max_patience = 10 + for i in range(max_patience): + w = random.randint(self.min_size, min(img.width, self.max_size)) + h = random.randint(self.min_size, min(img.height, self.max_size)) + region = T.RandomCrop.get_params(img, [h, w]) + result_img, result_target = crop(img, target, region) + if target is not None: + if not self.respect_boxes or len(result_target["boxes"]) == init_boxes or i == max_patience - 1: + return result_img, result_target + return result_img, result_target + + +class CenterCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + image_width, image_height = img.size + crop_height, crop_width = self.size + crop_top = int(round((image_height - crop_height) / 2.)) + crop_left = int(round((image_width - crop_width) / 2.)) + return crop(img, target, (crop_top, crop_left, crop_height, crop_width)) + + +class RandomHorizontalFlip(object): + def __init__(self, p=0.5): + self.p = p + + def __call__(self, img, target): + if random.random() < self.p: + return hflip(img, target) + return img, target + + +class RandomResize(object): + def __init__(self, sizes, max_size=None): + assert isinstance(sizes, (list, tuple)) + self.sizes = sizes + self.max_size = max_size + + def __call__(self, img, target=None): + size = random.choice(self.sizes) + return resize(img, target, size, self.max_size) + + +class RandomPad(object): + def __init__(self, max_pad): + self.max_pad = max_pad + + def __call__(self, img, target): + pad_x = random.randint(0, self.max_pad) + pad_y = random.randint(0, self.max_pad) + return pad(img, target, (pad_x, pad_y)) + + +class RandomSelect(object): + """ + Randomly selects between transforms1 and transforms2, + with probability p for transforms1 and (1 - p) for transforms2 + """ + def __init__(self, transforms1, transforms2, p=0.5): + self.transforms1 = transforms1 + self.transforms2 = transforms2 + self.p = p + + def __call__(self, img, target): + if random.random() < self.p: + return self.transforms1(img, target) + return self.transforms2(img, target) + + +class ToTensor(object): + def __call__(self, img, target): + return F.to_tensor(img), target + + +class RandomErasing(object): + + def __init__(self, *args, **kwargs): + self.eraser = T.RandomErasing(*args, **kwargs) + + def __call__(self, img, target): + return self.eraser(img), target + + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, image, target=None): + image = F.normalize(image, mean=self.mean, std=self.std) + if target is None: + return image, None + target = target.copy() + h, w = image.shape[-2:] + if "boxes" in target: + boxes = target["boxes"] + boxes = box_xyxy_to_cxcywh(boxes) + boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32) + target["boxes"] = boxes + + if "area" in target: + area = target["area"] + area = area / (torch.tensor(w, dtype=torch.float32)*torch.tensor(h, dtype=torch.float32)) + target["area"] = area + + if "keypoints" in target: + keypoints = target["keypoints"] + V = keypoints[:, :, 2] + V[V == 2] = 1 + Z=keypoints[:, :, :2] + Z = Z.contiguous().view(-1, 2 * V.shape[-1]) + Z = Z / torch.tensor([w, h] * V.shape[-1], dtype=torch.float32) + target["valid_kpt_num"] = V.shape[1] + Z_pad = torch.zeros(Z.shape[0],68 * 2 - Z.shape[1]) + V_pad = torch.zeros(V.shape[0],68 - V.shape[1]) + V=torch.cat([V, V_pad], dim=1) + Z=torch.cat([Z, Z_pad], dim=1) + all_keypoints = torch.cat([Z, V], dim=1) + target["keypoints"] = all_keypoints + + + return image, target + + +class Compose(object): + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, image, target): + for t in self.transforms: + image, target = t(image, target) + return image, target + + def __repr__(self): + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\n)" + return format_string diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/addict.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/addict.py new file mode 100644 index 0000000..55e02d1 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/addict.py @@ -0,0 +1,159 @@ +import copy + + +class Dict(dict): + + def __init__(__self, *args, **kwargs): + object.__setattr__(__self, '__parent', kwargs.pop('__parent', None)) + object.__setattr__(__self, '__key', kwargs.pop('__key', None)) + object.__setattr__(__self, '__frozen', False) + for arg in args: + if not arg: + continue + elif isinstance(arg, dict): + for key, val in arg.items(): + __self[key] = __self._hook(val) + elif isinstance(arg, tuple) and (not isinstance(arg[0], tuple)): + __self[arg[0]] = __self._hook(arg[1]) + else: + for key, val in iter(arg): + __self[key] = __self._hook(val) + + for key, val in kwargs.items(): + __self[key] = __self._hook(val) + + def __setattr__(self, name, value): + if hasattr(self.__class__, name): + raise AttributeError("'Dict' object attribute " + "'{0}' is read-only".format(name)) + else: + self[name] = value + + def __setitem__(self, name, value): + isFrozen = (hasattr(self, '__frozen') and + object.__getattribute__(self, '__frozen')) + if isFrozen and name not in super(Dict, self).keys(): + raise KeyError(name) + super(Dict, self).__setitem__(name, value) + try: + p = object.__getattribute__(self, '__parent') + key = object.__getattribute__(self, '__key') + except AttributeError: + p = None + key = None + if p is not None: + p[key] = self + object.__delattr__(self, '__parent') + object.__delattr__(self, '__key') + + def __add__(self, other): + if not self.keys(): + return other + else: + self_type = type(self).__name__ + other_type = type(other).__name__ + msg = "unsupported operand type(s) for +: '{}' and '{}'" + raise TypeError(msg.format(self_type, other_type)) + + @classmethod + def _hook(cls, item): + if isinstance(item, dict): + return cls(item) + elif isinstance(item, (list, tuple)): + return type(item)(cls._hook(elem) for elem in item) + return item + + def __getattr__(self, item): + return self.__getitem__(item) + + def __missing__(self, name): + if object.__getattribute__(self, '__frozen'): + raise KeyError(name) + return self.__class__(__parent=self, __key=name) + + def __delattr__(self, name): + del self[name] + + def to_dict(self): + base = {} + for key, value in self.items(): + if isinstance(value, type(self)): + base[key] = value.to_dict() + elif isinstance(value, (list, tuple)): + base[key] = type(value)( + item.to_dict() if isinstance(item, type(self)) else + item for item in value) + else: + base[key] = value + return base + + def copy(self): + return copy.copy(self) + + def deepcopy(self): + return copy.deepcopy(self) + + def __deepcopy__(self, memo): + other = self.__class__() + memo[id(self)] = other + for key, value in self.items(): + other[copy.deepcopy(key, memo)] = copy.deepcopy(value, memo) + return other + + def update(self, *args, **kwargs): + other = {} + if args: + if len(args) > 1: + raise TypeError() + other.update(args[0]) + other.update(kwargs) + for k, v in other.items(): + if ((k not in self) or + (not isinstance(self[k], dict)) or + (not isinstance(v, dict))): + self[k] = v + else: + self[k].update(v) + + def __getnewargs__(self): + return tuple(self.items()) + + def __getstate__(self): + return self + + def __setstate__(self, state): + self.update(state) + + def __or__(self, other): + if not isinstance(other, (Dict, dict)): + return NotImplemented + new = Dict(self) + new.update(other) + return new + + def __ror__(self, other): + if not isinstance(other, (Dict, dict)): + return NotImplemented + new = Dict(other) + new.update(self) + return new + + def __ior__(self, other): + self.update(other) + return self + + def setdefault(self, key, default=None): + if key in self: + return self[key] + else: + self[key] = default + return default + + def freeze(self, shouldFreeze=True): + object.__setattr__(self, '__frozen', shouldFreeze) + for key, val in self.items(): + if isinstance(val, Dict): + val.freeze(shouldFreeze) + + def unfreeze(self): + self.freeze(False) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/box_ops.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/box_ops.py new file mode 100644 index 0000000..fff6624 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/box_ops.py @@ -0,0 +1,139 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch, os +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), + (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, + (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + # import ipdb; ipdb.set_trace() + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / (union + 1e-6) + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + # except: + # import ipdb; ipdb.set_trace() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / (area + 1e-6) + + + +# modified from torchvision to also return the union +def box_iou_pairwise(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2] + rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2] + + wh = (rb - lt).clamp(min=0) # [N,2] + inter = wh[:, 0] * wh[:, 1] # [N] + + union = area1 + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou_pairwise(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + Input: + - boxes1, boxes2: N,4 + Output: + - giou: N, 4 + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + assert boxes1.shape == boxes2.shape + iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4 + + lt = torch.min(boxes1[:, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,2] + area = wh[:, 0] * wh[:, 1] + + return iou - (area - union) / area + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = (masks * x.unsqueeze(0)) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = (masks * y.unsqueeze(0)) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) + +if __name__ == '__main__': + x = torch.rand(5, 4) + y = torch.rand(3, 4) + iou, union = box_iou(x, y) + import ipdb; ipdb.set_trace() diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/config.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/config.py new file mode 100644 index 0000000..2255307 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/config.py @@ -0,0 +1,428 @@ +# ========================================================== +# Modified from mmcv +# ========================================================== +import sys +import os.path as osp +import ast +import tempfile +import shutil +from importlib import import_module +from argparse import Action + +from .addict import Dict + +BASE_KEY = '_base_' +DELETE_KEY = '_delete_' +RESERVED_KEYS = ['filename', 'text', 'pretty_text', 'get', 'dump', 'merge_from_dict'] + + +def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): + if not osp.isfile(filename): + raise FileNotFoundError(msg_tmpl.format(filename)) + +class ConfigDict(Dict): + + def __missing__(self, name): + raise KeyError(name) + + def __getattr__(self, name): + try: + value = super(ConfigDict, self).__getattr__(name) + except KeyError: + ex = AttributeError(f"'{self.__class__.__name__}' object has no " + f"attribute '{name}'") + except Exception as e: + ex = e + else: + return value + raise ex + + +class Config(object): + """ + config files. + only support .py file as config now. + + ref: mmcv.utils.config + + Example: + >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) + >>> cfg.a + 1 + >>> cfg.b + {'b1': [0, 1]} + >>> cfg.b.b1 + [0, 1] + >>> cfg = Config.fromfile('tests/data/config/a.py') + >>> cfg.filename + "/home/kchen/projects/mmcv/tests/data/config/a.py" + >>> cfg.item4 + 'test' + >>> cfg + "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " + "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" + """ + @staticmethod + def _validate_py_syntax(filename): + with open(filename) as f: + content = f.read() + try: + ast.parse(content) + except SyntaxError: + raise SyntaxError('There are syntax errors in config ' + f'file {filename}') + + @staticmethod + def _file2dict(filename): + filename = osp.abspath(osp.expanduser(filename)) + check_file_exist(filename) + if filename.lower().endswith('.py'): + with tempfile.TemporaryDirectory() as temp_config_dir: + temp_config_file = tempfile.NamedTemporaryFile( + dir=temp_config_dir, suffix='.py') + temp_config_name = osp.basename(temp_config_file.name) + # close temp file before copy + temp_config_file.close() + shutil.copyfile(filename, + osp.join(temp_config_dir, temp_config_name)) + temp_module_name = osp.splitext(temp_config_name)[0] + sys.path.insert(0, temp_config_dir) + Config._validate_py_syntax(filename) + mod = import_module(temp_module_name) + sys.path.pop(0) + cfg_dict = { + name: value + for name, value in mod.__dict__.items() + if not name.startswith('__') + } + # delete imported module + del sys.modules[temp_module_name] + + + elif filename.lower().endswith(('.yml', '.yaml', '.json')): + from .slio import slload + cfg_dict = slload(filename) + else: + raise IOError('Only py/yml/yaml/json type are supported now!') + + cfg_text = filename + '\n' + with open(filename, 'r') as f: + cfg_text += f.read() + + # parse the base file + if BASE_KEY in cfg_dict: + cfg_dir = osp.dirname(filename) + base_filename = cfg_dict.pop(BASE_KEY) + base_filename = base_filename if isinstance( + base_filename, list) else [base_filename] + + cfg_dict_list = list() + cfg_text_list = list() + for f in base_filename: + _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f)) + cfg_dict_list.append(_cfg_dict) + cfg_text_list.append(_cfg_text) + + base_cfg_dict = dict() + for c in cfg_dict_list: + if len(base_cfg_dict.keys() & c.keys()) > 0: + raise KeyError('Duplicate key is not allowed among bases') + # TODO Allow the duplicate key while warnning user + base_cfg_dict.update(c) + + base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict) + cfg_dict = base_cfg_dict + + # merge cfg_text + cfg_text_list.append(cfg_text) + cfg_text = '\n'.join(cfg_text_list) + + return cfg_dict, cfg_text + + @staticmethod + def _merge_a_into_b(a, b): + """merge dict `a` into dict `b` (non-inplace). + values in `a` will overwrite `b`. + copy first to avoid inplace modification + + Args: + a ([type]): [description] + b ([type]): [description] + + Returns: + [dict]: [description] + """ + # import ipdb; ipdb.set_trace() + if not isinstance(a, dict): + return a + + b = b.copy() + for k, v in a.items(): + if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): + + if not isinstance(b[k], dict) and not isinstance(b[k], list): + # if : + # import ipdb; ipdb.set_trace() + raise TypeError( + f'{k}={v} in child config cannot inherit from base ' + f'because {k} is a dict in the child config but is of ' + f'type {type(b[k])} in base config. You may set ' + f'`{DELETE_KEY}=True` to ignore the base config') + b[k] = Config._merge_a_into_b(v, b[k]) + elif isinstance(b, list): + try: + _ = int(k) + except: + raise TypeError( + f'b is a list, ' + f'index {k} should be an int when input but {type(k)}' + ) + b[int(k)] = Config._merge_a_into_b(v, b[int(k)]) + else: + b[k] = v + + return b + + @staticmethod + def fromfile(filename): + cfg_dict, cfg_text = Config._file2dict(filename) + return Config(cfg_dict, cfg_text=cfg_text, filename=filename) + + + def __init__(self, cfg_dict=None, cfg_text=None, filename=None): + if cfg_dict is None: + cfg_dict = dict() + elif not isinstance(cfg_dict, dict): + raise TypeError('cfg_dict must be a dict, but ' + f'got {type(cfg_dict)}') + for key in cfg_dict: + if key in RESERVED_KEYS: + raise KeyError(f'{key} is reserved for config file') + + super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict)) + super(Config, self).__setattr__('_filename', filename) + if cfg_text: + text = cfg_text + elif filename: + with open(filename, 'r') as f: + text = f.read() + else: + text = '' + super(Config, self).__setattr__('_text', text) + + + @property + def filename(self): + return self._filename + + @property + def text(self): + return self._text + + @property + def pretty_text(self): + + indent = 4 + + def _indent(s_, num_spaces): + s = s_.split('\n') + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * ' ') + line for line in s] + s = '\n'.join(s) + s = first + '\n' + s + return s + + def _format_basic_types(k, v, use_mapping=False): + if isinstance(v, str): + v_str = f"'{v}'" + else: + v_str = str(v) + + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: {v_str}' + else: + attr_str = f'{str(k)}={v_str}' + attr_str = _indent(attr_str, indent) + + return attr_str + + def _format_list(k, v, use_mapping=False): + # check if all items in the list are dict + if all(isinstance(_, dict) for _ in v): + v_str = '[\n' + v_str += '\n'.join( + f'dict({_indent(_format_dict(v_), indent)}),' + for v_ in v).rstrip(',') + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: {v_str}' + else: + attr_str = f'{str(k)}={v_str}' + attr_str = _indent(attr_str, indent) + ']' + else: + attr_str = _format_basic_types(k, v, use_mapping) + return attr_str + + def _contain_invalid_identifier(dict_str): + contain_invalid_identifier = False + for key_name in dict_str: + contain_invalid_identifier |= \ + (not str(key_name).isidentifier()) + return contain_invalid_identifier + + def _format_dict(input_dict, outest_level=False): + r = '' + s = [] + + use_mapping = _contain_invalid_identifier(input_dict) + if use_mapping: + r += '{' + for idx, (k, v) in enumerate(input_dict.items()): + is_last = idx >= len(input_dict) - 1 + end = '' if outest_level or is_last else ',' + if isinstance(v, dict): + v_str = '\n' + _format_dict(v) + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f'{k_str}: dict({v_str}' + else: + attr_str = f'{str(k)}=dict({v_str}' + attr_str = _indent(attr_str, indent) + ')' + end + elif isinstance(v, list): + attr_str = _format_list(k, v, use_mapping) + end + else: + attr_str = _format_basic_types(k, v, use_mapping) + end + + s.append(attr_str) + r += '\n'.join(s) + if use_mapping: + r += '}' + return r + + cfg_dict = self._cfg_dict.to_dict() + text = _format_dict(cfg_dict, outest_level=True) + return text + + + def __repr__(self): + return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}' + + def __len__(self): + return len(self._cfg_dict) + + def __getattr__(self, name): + # # debug + # print('+'*15) + # print('name=%s' % name) + # print("addr:", id(self)) + # # print('type(self):', type(self)) + # print(self.__dict__) + # print('+'*15) + # if self.__dict__ == {}: + # raise ValueError + + return getattr(self._cfg_dict, name) + + def __getitem__(self, name): + return self._cfg_dict.__getitem__(name) + + def __setattr__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setattr__(name, value) + + def __setitem__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setitem__(name, value) + + def __iter__(self): + return iter(self._cfg_dict) + + def dump(self, file=None): + # import ipdb; ipdb.set_trace() + if file is None: + return self.pretty_text + else: + with open(file, 'w') as f: + f.write(self.pretty_text) + + def merge_from_dict(self, options): + """Merge list into cfg_dict + + Merge the dict parsed by MultipleKVAction into this cfg. + + Examples: + >>> options = {'model.backbone.depth': 50, + ... 'model.backbone.with_cp':True} + >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) + >>> cfg.merge_from_dict(options) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict( + ... model=dict(backbone=dict(depth=50, with_cp=True))) + + Args: + options (dict): dict of configs to merge from. + """ + option_cfg_dict = {} + for full_key, v in options.items(): + d = option_cfg_dict + key_list = full_key.split('.') + for subkey in key_list[:-1]: + d.setdefault(subkey, ConfigDict()) + d = d[subkey] + subkey = key_list[-1] + d[subkey] = v + + cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + super(Config, self).__setattr__( + '_cfg_dict', Config._merge_a_into_b(option_cfg_dict, cfg_dict)) + + # for multiprocess + def __setstate__(self, state): + self.__init__(state) + + + def copy(self): + return Config(self._cfg_dict.copy()) + + def deepcopy(self): + return Config(self._cfg_dict.deepcopy()) + + +class DictAction(Action): + """ + argparse action to split an argument into KEY=VALUE form + on the first = and append to a dictionary. List options should + be passed as comma separated values, i.e KEY=V1,V2,V3 + """ + + @staticmethod + def _parse_int_float_bool(val): + try: + return int(val) + except ValueError: + pass + try: + return float(val) + except ValueError: + pass + if val.lower() in ['true', 'false']: + return True if val.lower() == 'true' else False + if val.lower() in ['none', 'null']: + return None + return val + + def __call__(self, parser, namespace, values, option_string=None): + options = {} + for kv in values: + key, val = kv.split('=', maxsplit=1) + val = [self._parse_int_float_bool(v) for v in val.split(',')] + if len(val) == 1: + val = val[0] + options[key] = val + setattr(namespace, self.dest, options) + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/keypoint_ops.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/keypoint_ops.py new file mode 100644 index 0000000..036d813 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/keypoint_ops.py @@ -0,0 +1,29 @@ +import torch, os + +def keypoint_xyxyzz_to_xyzxyz(keypoints: torch.Tensor): + """_summary_ + + Args: + keypoints (torch.Tensor): ..., 51 + """ + res = torch.zeros_like(keypoints) + num_points = keypoints.shape[-1] // 3 + Z = keypoints[..., :2*num_points] + V = keypoints[..., 2*num_points:] + res[...,0::3] = Z[..., 0::2] + res[...,1::3] = Z[..., 1::2] + res[...,2::3] = V[...] + return res + +def keypoint_xyzxyz_to_xyxyzz(keypoints: torch.Tensor): + """_summary_ + + Args: + keypoints (torch.Tensor): ..., 51 + """ + res = torch.zeros_like(keypoints) + num_points = keypoints.shape[-1] // 3 + res[...,0:2*num_points:2] = keypoints[..., 0::3] + res[...,1:2*num_points:2] = keypoints[..., 1::3] + res[...,2*num_points:] = keypoints[..., 2::3] + return res \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/misc.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/misc.py new file mode 100644 index 0000000..a36413b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/XPose/util/misc.py @@ -0,0 +1,701 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import functools +import io +import os +import random +import subprocess +import time +from collections import OrderedDict, defaultdict, deque +import datetime +import pickle +from typing import Optional, List + +import json, time +import numpy as np +import torch +import torch.distributed as dist +from torch import Tensor + +import colorsys + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +__torchvision_need_compat_flag = float(torchvision.__version__.split('.')[1]) < 7 +if __torchvision_need_compat_flag: + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + if d.shape[0] == 0: + return 0 + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + if os.environ.get("SHILONG_AMP", None) == '1': + eps = 1e-4 + else: + eps = 1e-6 + return self.total / (self.count + eps) + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + + return dist.group.WORLD + +def all_gather_cpu(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + world_size = get_world_size() + if world_size == 1: + return [data] + + cpu_group = _get_global_gloo_group() + + buffer = io.BytesIO() + torch.save(data, buffer) + data_view = buffer.getbuffer() + device = "cuda" if cpu_group is None else "cpu" + tensor = torch.ByteTensor(data_view).to(device) + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) + size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] + if cpu_group is None: + dist.all_gather(size_list, local_size) + else: + print("gathering on cpu") + dist.all_gather(size_list, local_size, group=cpu_group) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + assert isinstance(local_size.item(), int) + local_size = int(local_size.item()) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) + tensor = torch.cat((tensor, padding), dim=0) + if cpu_group is None: + dist.all_gather(tensor_list, tensor) + else: + dist.all_gather(tensor_list, tensor, group=cpu_group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] + buffer = io.BytesIO(tensor.cpu().numpy()) + obj = torch.load(buffer) + data_list.append(obj) + + return data_list + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + if os.getenv("CPU_REDUCE") == "1": + return all_gather_cpu(data) + + + + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) # nosec B301 + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + # print(name, str(meter)) + # import ipdb;ipdb.set_trace() + if meter.count > 0: + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None, logger=None): + if logger is None: + print_func = print + else: + print_func = logger.info + + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + # import ipdb; ipdb.set_trace() + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print_func(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print_func(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print_func('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + # import ipdb; ipdb.set_trace() + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + if mask == 'auto': + self.mask = torch.zeros_like(tensors).to(tensors.device) + if self.mask.dim() == 3: + self.mask = self.mask.sum(0).to(bool) + elif self.mask.dim() == 4: + self.mask = self.mask.sum(1).to(bool) + else: + raise ValueError("tensors dim must be 3 or 4 but {}({})".format(self.tensors.dim(), self.tensors.shape)) + + def imgsize(self): + res = [] + for i in range(self.tensors.shape[0]): + mask = self.mask[i] + maxH = (~mask).sum(0).max() + maxW = (~mask).sum(1).max() + res.append(torch.Tensor([maxH, maxW])) + return res + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def to_img_list_single(self, tensor, mask): + assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim()) + maxH = (~mask).sum(0).max() + maxW = (~mask).sum(1).max() + img = tensor[:, :maxH, :maxW] + return img + + def to_img_list(self): + """remove the padding and convert to img list + + Returns: + [type]: [description] + """ + if self.tensors.dim() == 3: + return self.to_img_list_single(self.tensors, self.mask) + else: + res = [] + for i in range(self.tensors.shape[0]): + tensor_i = self.tensors[i] + mask_i = self.mask[i] + res.append(self.to_img_list_single(tensor_i, mask_i)) + return res + + @property + def device(self): + return self.tensors.device + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + @property + def shape(self): + return { + 'tensors.shape': self.tensors.shape, + 'mask.shape': self.mask.shape + } + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], :img.shape[2]] = False + else: + raise ValueError('not supported') + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + +def init_distributed_mode(args): + if 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != '': # 'RANK' in os.environ and + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = args.local_rank = int(os.environ['LOCAL_RANK']) + + # launch by torch.distributed.launch + # Single node + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ... + # Multi nodes + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... + # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK')) + # local_world_size = int(os.environ['GPU_PER_NODE_COUNT']) + # args.world_size = args.world_size * local_world_size + # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK']) + # args.rank = args.rank * local_world_size + args.local_rank + print('world size: {}, rank: {}, local rank: {}'.format(args.world_size, args.rank, args.local_rank)) + print(json.dumps(dict(os.environ), indent=2)) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.local_rank = int(os.environ['SLURM_LOCALID']) + args.world_size = int(os.environ['SLURM_NPROCS']) + + if os.environ.get('HAND_DEFINE_DIST_URL', 0) == '1': + pass + else: + import util.hostlist as uh + nodenames = uh.parse_nodelist(os.environ['SLURM_JOB_NODELIST']) + gpu_ids = [int(node[3:]) for node in nodenames] + fixid = int(os.environ.get('FIX_DISTRIBUTED_PORT_NUMBER', 0)) + # fixid += random.randint(0, 300) + port = str(3137 + int(min(gpu_ids)) + fixid) + args.dist_url = "tcp://{ip}:{port}".format(ip=uh.nodename_to_ip(nodenames[0]), port=port) + + print('world size: {}, world rank: {}, local rank: {}, device_count: {}'.format(args.world_size, args.rank, args.local_rank, torch.cuda.device_count())) + + + else: + print('Not using distributed mode') + args.distributed = False + args.world_size = 1 + args.rank = 0 + args.local_rank = 0 + return + + print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank)) + args.distributed = True + torch.cuda.set_device(args.local_rank) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}'.format(args.rank, args.dist_url), flush=True) + + torch.distributed.init_process_group( + backend=args.dist_backend, + world_size=args.world_size, + rank=args.rank, + init_method=args.dist_url, + ) + + print("Before torch.distributed.barrier()") + torch.distributed.barrier() + print("End torch.distributed.barrier()") + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + +@torch.no_grad() +def accuracy_onehot(pred, gt): + """_summary_ + + Args: + pred (_type_): n, c + gt (_type_): n, c + """ + tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum() + acc = tp / gt.shape[0] * 100 + return acc + + + + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if __torchvision_need_compat_flag < 0.7: + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners + ) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) + + + +class color_sys(): + def __init__(self, num_colors) -> None: + self.num_colors = num_colors + colors=[] + for i in np.arange(0., 360., 360. / num_colors): + hue = i/360. + lightness = (50 + np.random.rand() * 10)/100. + saturation = (90 + np.random.rand() * 10)/100. + colors.append(tuple([int(j*255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])) + self.colors = colors + + def __call__(self, idx): + return self.colors[idx] + +def inverse_sigmoid(x, eps=1e-3): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1/x2) + +def clean_state_dict(state_dict): + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k[:7] == 'module.': + k = k[7:] # remove `module.` + new_state_dict[k] = v + return new_state_dict \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/__init__.py new file mode 100644 index 0000000..1680083 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/__init__.py @@ -0,0 +1,20 @@ +# coding: utf-8 +# pylint: disable=wrong-import-position +"""InsightFace: A Face Analysis Toolkit.""" +from __future__ import absolute_import + +try: + #import mxnet as mx + import onnxruntime +except ImportError: + raise ImportError( + "Unable to import dependency onnxruntime. " + ) + +__version__ = '0.7.3' + +from . import model_zoo +from . import utils +from . import app +from . import data + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/__init__.py new file mode 100644 index 0000000..cc57461 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/__init__.py @@ -0,0 +1 @@ +from .face_analysis import * diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/common.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/common.py new file mode 100644 index 0000000..82ca987 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/common.py @@ -0,0 +1,49 @@ +import numpy as np +from numpy.linalg import norm as l2norm +#from easydict import EasyDict + +class Face(dict): + + def __init__(self, d=None, **kwargs): + if d is None: + d = {} + if kwargs: + d.update(**kwargs) + for k, v in d.items(): + setattr(self, k, v) + # Class attributes + #for k in self.__class__.__dict__.keys(): + # if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'): + # setattr(self, k, getattr(self, k)) + + def __setattr__(self, name, value): + if isinstance(value, (list, tuple)): + value = [self.__class__(x) + if isinstance(x, dict) else x for x in value] + elif isinstance(value, dict) and not isinstance(value, self.__class__): + value = self.__class__(value) + super(Face, self).__setattr__(name, value) + super(Face, self).__setitem__(name, value) + + __setitem__ = __setattr__ + + def __getattr__(self, name): + return None + + @property + def embedding_norm(self): + if self.embedding is None: + return None + return l2norm(self.embedding) + + @property + def normed_embedding(self): + if self.embedding is None: + return None + return self.embedding / self.embedding_norm + + @property + def sex(self): + if self.gender is None: + return None + return 'M' if self.gender==1 else 'F' diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/face_analysis.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/face_analysis.py new file mode 100644 index 0000000..aa5128b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/app/face_analysis.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-05-04 +# @Function : + + +from __future__ import division + +import glob +import os.path as osp + +import numpy as np +import onnxruntime +from numpy.linalg import norm + +from ..model_zoo import model_zoo +from ..utils import ensure_available +from .common import Face + + +DEFAULT_MP_NAME = 'buffalo_l' +__all__ = ['FaceAnalysis'] + +class FaceAnalysis: + def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs): + onnxruntime.set_default_logger_severity(3) + self.models = {} + self.model_dir = ensure_available('models', name, root=root) + onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx')) + onnx_files = sorted(onnx_files) + for onnx_file in onnx_files: + model = model_zoo.get_model(onnx_file, **kwargs) + if model is None: + print('model not recognized:', onnx_file) + elif allowed_modules is not None and model.taskname not in allowed_modules: + print('model ignore:', onnx_file, model.taskname) + del model + elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules): + # print('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std) + self.models[model.taskname] = model + else: + print('duplicated model task type, ignore:', onnx_file, model.taskname) + del model + assert 'detection' in self.models + self.det_model = self.models['detection'] + + + def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)): + self.det_thresh = det_thresh + assert det_size is not None + # print('set det-size:', det_size) + self.det_size = det_size + for taskname, model in self.models.items(): + if taskname=='detection': + model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh) + else: + model.prepare(ctx_id) + + def get(self, img, max_num=0): + bboxes, kpss = self.det_model.detect(img, + max_num=max_num, + metric='default') + if bboxes.shape[0] == 0: + return [] + ret = [] + for i in range(bboxes.shape[0]): + bbox = bboxes[i, 0:4] + det_score = bboxes[i, 4] + kps = None + if kpss is not None: + kps = kpss[i] + face = Face(bbox=bbox, kps=kps, det_score=det_score) + for taskname, model in self.models.items(): + if taskname=='detection': + continue + model.get(img, face) + ret.append(face) + return ret + + def draw_on(self, img, faces): + import cv2 + dimg = img.copy() + for i in range(len(faces)): + face = faces[i] + box = face.bbox.astype(np.int) + color = (0, 0, 255) + cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2) + if face.kps is not None: + kps = face.kps.astype(np.int) + #print(landmark.shape) + for l in range(kps.shape[0]): + color = (0, 0, 255) + if l == 0 or l == 3: + color = (0, 255, 0) + cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color, + 2) + if face.gender is not None and face.age is not None: + cv2.putText(dimg,'%s,%d'%(face.sex,face.age), (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) + + #for key, value in face.items(): + # if key.startswith('landmark_3d'): + # print(key, value.shape) + # print(value[0:10,:]) + # lmk = np.round(value).astype(np.int) + # for l in range(lmk.shape[0]): + # color = (255, 0, 0) + # cv2.circle(dimg, (lmk[l][0], lmk[l][1]), 1, color, + # 2) + return dimg diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/__init__.py new file mode 100644 index 0000000..665c59e --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/__init__.py @@ -0,0 +1,2 @@ +from .image import get_image +from .pickle_object import get_object diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/image.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/image.py new file mode 100644 index 0000000..6d32c4b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/image.py @@ -0,0 +1,27 @@ +import cv2 +import os +import os.path as osp +from pathlib import Path + +class ImageCache: + data = {} + +def get_image(name, to_rgb=False): + key = (name, to_rgb) + if key in ImageCache.data: + return ImageCache.data[key] + images_dir = osp.join(Path(__file__).parent.absolute(), 'images') + ext_names = ['.jpg', '.png', '.jpeg'] + image_file = None + for ext_name in ext_names: + _image_file = osp.join(images_dir, "%s%s"%(name, ext_name)) + if osp.exists(_image_file): + image_file = _image_file + break + assert image_file is not None, '%s not found'%name + img = cv2.imread(image_file) + if to_rgb: + img = img[:,:,::-1] + ImageCache.data[key] = img + return img + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/images/Tom_Hanks_54745.png b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/data/images/Tom_Hanks_54745.png new file mode 100644 index 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zGdk~p^qI8mnXz|4k^`jNU2Q$;mypQat|5EgP8XktEEAC5IfbR$hyi&{x8RLmN@MaQ zfI0?~axuWy)O8DH8QP{}%L;akw$;?0MHSSvne1S;kVjCon6mlU%ysew6`iGTq&T`K zu=YvHuVe7eod%flRA(u&#z{c(`Jxh#`4J>)326$!@e1Wfd~}U5Z3B#>2}mkEZ7J5H zL{5o}kT`Nv)J8Z1WlCeg2xuiB615!q;*Ng@toPP*GmTG^v!4 zz!vhTglNl}?t7;_#joR{Cd=x$#cIm|zRGZTPA~*FLg1{X=pcV6vC8%vYl!+47+6kg z(72`&n?<%hv5^e|>jT|b$F0F94voRfH@!KpdoD{$h)|X# zE=y~b(ROiM3Q!jip)Brw()LM7X;l)$x=UaRE2(uUM6oR)DM(0EqO55oro5+Jc1?Gv zt0j!Ld#0Q0o_I~AbW0VEBdT#Ao(gRx4IxS@&K6MuKofve!c1@}z?9VBmK~f*;CZFR za0(K`#nl}}VIZTl@T!YUaZI%9Oe2*=*~vNXmXlPXSm2x!EjS?!F$x>HQY*S46=?$5 zz$GMs!a#XT)fE9_NhhgX#I&5+3Q+~JloMbsmOG#`{-koLHqh`_E?Sk|_ZyMULcVpDv#xZQBAA~rCZO5Rh5jC@WBvgSb(C>uW|d{{0? z4s0)w(?z2*3cfxSQ}t&?YCBP;)MuNAJlDw@$0h^K6kfVaJaTQ|k=a65z* z1_#QC&Xzvq56_HuP>uVhkgtJHA!6Ldfk3CXEI8Gww4jmDtKf<9GR@L%=xsDT*VRH~ zt_bnTxT*L1mZGy@3SrvYWHHrwc&+?Nb-uK4@Jw7rY(*+4s;}u+j4A=mVG{U2Q*wq zmp1z@T#gO%LZ|$m5i~)vk$nmgZ4wtQT|{#k+d`S9JhLb_v$!jlA)oRdKj^uYv=l-) zT)AdO#v{d>LUdtrv0^ z(2W$bG2Vv|xpG|68y3?nF|{c{r+R{zCiAM=1+HAONmDuyZK3Ujt`?Z+h0^87NWr#Pa^++cuBFS7B7~&0xpIKBh+SMRT&)6j+fR{!yKC8UN?n zcHRN^T)9Gyb7?ZUT)9yN*e;VC&n zT&)z&XilZem4YGaxpL)bf`q$%YnLi4N~D)-mn$HITI!|Cm6UZZ0dnO;7LwZK$`ItM zmnd1wk1LldAksNWOP3-=GpTXtxpIJt-rqFp;FZgj7BzJ)T%iRi7cN#qlJs1;5G+6~ zJc=Jh%aE|q)Vh~0LRrJ2<;#_#n8)zyNO%jED?p9hkhu0-xl;&RG>{X0S1v*t2*YNP zgkXz3mo8LV1tw3Iz;8tYeMVA9XxsU&T#g`QdLce{xz=253GO9w<)#K1$=E9-^;}5@ zYURnuNS8^cms;h@%4|a8*$lX_TII?hSq^-M7Z9VFTzjruofINX*wCC8E=Dbi086!U zP~k)~;0(royc{4J!nwl25|}|6A%hqwXjfkL`Q!V?=ljVPD$_Dj(=!8< zDs^H&YDS9oh(fJaWQtPmm-m$o-kar5>MZPj&B=gis`RCQ*3>Yt@x~rq7)w7t1;_n_ppvy&U$e^WqNY0siGE#MY8*eVjs7@p1KD;z<+#-_DyTKmZZ|Q64BVljRl$jTcNk>jms& z$X{sN-fB!Xd$YH@U32V1Hq5pWR^wY9i2(llbh71E8o%_(i#5ii7+;Ul>PvVA4&52!>~nhloOrFLue zmqY@CMV_k>GzA{^5&=5?7fv R1g2ow-wIx== 0 + #assert label > self.last_label + assert len(imgs) > 0 + label = self.wlabel + for img in imgs: + idx = self.widx + image_meta = {'image_index': idx, 'image_classes': [label]} + header = mx.recordio.IRHeader(0, label, idx, 0) + if isinstance(img, np.ndarray): + s = mx.recordio.pack_img(header,img,quality=95,img_fmt='.jpg') + else: + s = mx.recordio.pack(header, img) + self.writer.write_idx(idx, s) + self.meta.append(image_meta) + self.widx += 1 + self.max_label = label + self.wlabel += 1 + + + def add_image(self, img, label): + #!!! img should be BGR!!!! + #assert label >= 0 + #assert label > self.last_label + idx = self.widx + header = mx.recordio.IRHeader(0, label, idx, 0) + if isinstance(label, list): + idlabel = label[0] + else: + idlabel = label + image_meta = {'image_index': idx, 'image_classes': [idlabel]} + if isinstance(img, np.ndarray): + s = mx.recordio.pack_img(header,img,quality=95,img_fmt='.jpg') + else: + s = mx.recordio.pack(header, img) + self.writer.write_idx(idx, s) + self.meta.append(image_meta) + self.widx += 1 + self.max_label = max(self.max_label, idlabel) + + def close(self): + with open(osp.join(self.path, 'train.meta'), 'wb') as pfile: + pickle.dump(self.meta, pfile, protocol=pickle.HIGHEST_PROTOCOL) + print('stat:', self.widx, self.wlabel) + with open(os.path.join(self.path, 'property'), 'w') as f: + f.write("%d,%d,%d\n" % (self.max_label+1, self.image_size[0], self.image_size[1])) + f.write("%d\n" % (self.widx)) + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/__init__.py new file mode 100644 index 0000000..225623d --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/__init__.py @@ -0,0 +1,6 @@ +from .model_zoo import get_model +from .arcface_onnx import ArcFaceONNX +from .retinaface import RetinaFace +from .scrfd import SCRFD +from .landmark import Landmark +from .attribute import Attribute diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/arcface_onnx.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/arcface_onnx.py new file mode 100644 index 0000000..b537ce2 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/arcface_onnx.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-05-04 +# @Function : + +from __future__ import division +import numpy as np +import cv2 +import onnx +import onnxruntime +from ..utils import face_align + +__all__ = [ + 'ArcFaceONNX', +] + + +class ArcFaceONNX: + def __init__(self, model_file=None, session=None): + assert model_file is not None + self.model_file = model_file + self.session = session + self.taskname = 'recognition' + find_sub = False + find_mul = False + model = onnx.load(self.model_file) + graph = model.graph + for nid, node in enumerate(graph.node[:8]): + #print(nid, node.name) + if node.name.startswith('Sub') or node.name.startswith('_minus'): + find_sub = True + if node.name.startswith('Mul') or node.name.startswith('_mul'): + find_mul = True + if find_sub and find_mul: + #mxnet arcface model + input_mean = 0.0 + input_std = 1.0 + else: + input_mean = 127.5 + input_std = 127.5 + self.input_mean = input_mean + self.input_std = input_std + #print('input mean and std:', self.input_mean, self.input_std) + if self.session is None: + self.session = onnxruntime.InferenceSession(self.model_file, None) + input_cfg = self.session.get_inputs()[0] + input_shape = input_cfg.shape + input_name = input_cfg.name + self.input_size = tuple(input_shape[2:4][::-1]) + self.input_shape = input_shape + outputs = self.session.get_outputs() + output_names = [] + for out in outputs: + output_names.append(out.name) + self.input_name = input_name + self.output_names = output_names + assert len(self.output_names)==1 + self.output_shape = outputs[0].shape + + def prepare(self, ctx_id, **kwargs): + if ctx_id<0: + self.session.set_providers(['CPUExecutionProvider']) + + def get(self, img, face): + aimg = face_align.norm_crop(img, landmark=face.kps, image_size=self.input_size[0]) + face.embedding = self.get_feat(aimg).flatten() + return face.embedding + + def compute_sim(self, feat1, feat2): + from numpy.linalg import norm + feat1 = feat1.ravel() + feat2 = feat2.ravel() + sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2)) + return sim + + def get_feat(self, imgs): + if not isinstance(imgs, list): + imgs = [imgs] + input_size = self.input_size + + blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size, + (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + net_out = self.session.run(self.output_names, {self.input_name: blob})[0] + return net_out + + def forward(self, batch_data): + blob = (batch_data - self.input_mean) / self.input_std + net_out = self.session.run(self.output_names, {self.input_name: blob})[0] + return net_out + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/attribute.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/attribute.py new file mode 100644 index 0000000..40c34de --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/attribute.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-06-19 +# @Function : + +from __future__ import division +import numpy as np +import cv2 +import onnx +import onnxruntime +from ..utils import face_align + +__all__ = [ + 'Attribute', +] + + +class Attribute: + def __init__(self, model_file=None, session=None): + assert model_file is not None + self.model_file = model_file + self.session = session + find_sub = False + find_mul = False + model = onnx.load(self.model_file) + graph = model.graph + for nid, node in enumerate(graph.node[:8]): + #print(nid, node.name) + if node.name.startswith('Sub') or node.name.startswith('_minus'): + find_sub = True + if node.name.startswith('Mul') or node.name.startswith('_mul'): + find_mul = True + if nid<3 and node.name=='bn_data': + find_sub = True + find_mul = True + if find_sub and find_mul: + #mxnet arcface model + input_mean = 0.0 + input_std = 1.0 + else: + input_mean = 127.5 + input_std = 128.0 + self.input_mean = input_mean + self.input_std = input_std + #print('input mean and std:', model_file, self.input_mean, self.input_std) + if self.session is None: + self.session = onnxruntime.InferenceSession(self.model_file, None) + input_cfg = self.session.get_inputs()[0] + input_shape = input_cfg.shape + input_name = input_cfg.name + self.input_size = tuple(input_shape[2:4][::-1]) + self.input_shape = input_shape + outputs = self.session.get_outputs() + output_names = [] + for out in outputs: + output_names.append(out.name) + self.input_name = input_name + self.output_names = output_names + assert len(self.output_names)==1 + output_shape = outputs[0].shape + #print('init output_shape:', output_shape) + if output_shape[1]==3: + self.taskname = 'genderage' + else: + self.taskname = 'attribute_%d'%output_shape[1] + + def prepare(self, ctx_id, **kwargs): + if ctx_id<0: + self.session.set_providers(['CPUExecutionProvider']) + + def get(self, img, face): + bbox = face.bbox + w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1]) + center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2 + rotate = 0 + _scale = self.input_size[0] / (max(w, h)*1.5) + #print('param:', img.shape, bbox, center, self.input_size, _scale, rotate) + aimg, M = face_align.transform(img, center, self.input_size[0], _scale, rotate) + input_size = tuple(aimg.shape[0:2][::-1]) + #assert input_size==self.input_size + blob = cv2.dnn.blobFromImage(aimg, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + pred = self.session.run(self.output_names, {self.input_name : blob})[0][0] + if self.taskname=='genderage': + assert len(pred)==3 + gender = np.argmax(pred[:2]) + age = int(np.round(pred[2]*100)) + face['gender'] = gender + face['age'] = age + return gender, age + else: + return pred + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/inswapper.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/inswapper.py new file mode 100644 index 0000000..f321c62 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/inswapper.py @@ -0,0 +1,114 @@ +import time +import numpy as np +import onnxruntime +import cv2 +import onnx +from onnx import numpy_helper +from ..utils import face_align + + + + +class INSwapper(): + def __init__(self, model_file=None, session=None): + self.model_file = model_file + self.session = session + model = onnx.load(self.model_file) + graph = model.graph + self.emap = numpy_helper.to_array(graph.initializer[-1]) + self.input_mean = 0.0 + self.input_std = 255.0 + #print('input mean and std:', model_file, self.input_mean, self.input_std) + if self.session is None: + self.session = onnxruntime.InferenceSession(self.model_file, None) + inputs = self.session.get_inputs() + self.input_names = [] + for inp in inputs: + self.input_names.append(inp.name) + outputs = self.session.get_outputs() + output_names = [] + for out in outputs: + output_names.append(out.name) + self.output_names = output_names + assert len(self.output_names)==1 + output_shape = outputs[0].shape + input_cfg = inputs[0] + input_shape = input_cfg.shape + self.input_shape = input_shape + # print('inswapper-shape:', self.input_shape) + self.input_size = tuple(input_shape[2:4][::-1]) + + def forward(self, img, latent): + img = (img - self.input_mean) / self.input_std + pred = self.session.run(self.output_names, {self.input_names[0]: img, self.input_names[1]: latent})[0] + return pred + + def get(self, img, target_face, source_face, paste_back=True): + face_mask = np.zeros((img.shape[0], img.shape[1]), np.uint8) + cv2.fillPoly(face_mask, np.array([target_face.landmark_2d_106[[1,9,10,11,12,13,14,15,16,2,3,4,5,6,7,8,0,24,23,22,21,20,19,18,32,31,30,29,28,27,26,25,17,101,105,104,103,51,49,48,43]].astype('int64')]), 1) + aimg, M = face_align.norm_crop2(img, target_face.kps, self.input_size[0]) + blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size, + (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + latent = source_face.normed_embedding.reshape((1,-1)) + latent = np.dot(latent, self.emap) + latent /= np.linalg.norm(latent) + pred = self.session.run(self.output_names, {self.input_names[0]: blob, self.input_names[1]: latent})[0] + #print(latent.shape, latent.dtype, pred.shape) + img_fake = pred.transpose((0,2,3,1))[0] + bgr_fake = np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:,:,::-1] + if not paste_back: + return bgr_fake, M + else: + target_img = img + fake_diff = bgr_fake.astype(np.float32) - aimg.astype(np.float32) + fake_diff = np.abs(fake_diff).mean(axis=2) + fake_diff[:2,:] = 0 + fake_diff[-2:,:] = 0 + fake_diff[:,:2] = 0 + fake_diff[:,-2:] = 0 + IM = cv2.invertAffineTransform(M) + img_white = np.full((aimg.shape[0],aimg.shape[1]), 255, dtype=np.float32) + bgr_fake = cv2.warpAffine(bgr_fake, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0) + img_white = cv2.warpAffine(img_white, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0) + fake_diff = cv2.warpAffine(fake_diff, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0) + img_white[img_white>20] = 255 + fthresh = 10 + fake_diff[fake_diff=fthresh] = 255 + img_mask = img_white + mask_h_inds, mask_w_inds = np.where(img_mask==255) + mask_h = np.max(mask_h_inds) - np.min(mask_h_inds) + mask_w = np.max(mask_w_inds) - np.min(mask_w_inds) + mask_size = int(np.sqrt(mask_h*mask_w)) + k = max(mask_size//10, 10) + #k = max(mask_size//20, 6) + #k = 6 + kernel = np.ones((k,k),np.uint8) + img_mask = cv2.erode(img_mask,kernel,iterations = 1) + kernel = np.ones((2,2),np.uint8) + fake_diff = cv2.dilate(fake_diff,kernel,iterations = 1) + + face_mask = cv2.erode(face_mask,np.ones((11,11),np.uint8),iterations = 1) + fake_diff[face_mask==1] = 255 + + k = max(mask_size//20, 5) + #k = 3 + #k = 3 + kernel_size = (k, k) + blur_size = tuple(2*i+1 for i in kernel_size) + img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) + k = 5 + kernel_size = (k, k) + blur_size = tuple(2*i+1 for i in kernel_size) + fake_diff = cv2.blur(fake_diff, (11,11), 0) + ##fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0) + # print('blur_size: ', blur_size) + # fake_diff = cv2.blur(fake_diff, (21, 21), 0) # blur_size + img_mask /= 255 + fake_diff /= 255 + # img_mask = fake_diff + img_mask = img_mask*fake_diff + img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1]) + fake_merged = img_mask * bgr_fake + (1-img_mask) * target_img.astype(np.float32) + fake_merged = fake_merged.astype(np.uint8) + return fake_merged diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/landmark.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/landmark.py new file mode 100644 index 0000000..598b4b2 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/landmark.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-05-04 +# @Function : + +from __future__ import division +import numpy as np +import cv2 +import onnx +import onnxruntime +from ..utils import face_align +from ..utils import transform +from ..data import get_object + +__all__ = [ + 'Landmark', +] + + +class Landmark: + def __init__(self, model_file=None, session=None): + assert model_file is not None + self.model_file = model_file + self.session = session + find_sub = False + find_mul = False + model = onnx.load(self.model_file) + graph = model.graph + for nid, node in enumerate(graph.node[:8]): + #print(nid, node.name) + if node.name.startswith('Sub') or node.name.startswith('_minus'): + find_sub = True + if node.name.startswith('Mul') or node.name.startswith('_mul'): + find_mul = True + if nid<3 and node.name=='bn_data': + find_sub = True + find_mul = True + if find_sub and find_mul: + #mxnet arcface model + input_mean = 0.0 + input_std = 1.0 + else: + input_mean = 127.5 + input_std = 128.0 + self.input_mean = input_mean + self.input_std = input_std + #print('input mean and std:', model_file, self.input_mean, self.input_std) + if self.session is None: + self.session = onnxruntime.InferenceSession(self.model_file, None) + input_cfg = self.session.get_inputs()[0] + input_shape = input_cfg.shape + input_name = input_cfg.name + self.input_size = tuple(input_shape[2:4][::-1]) + self.input_shape = input_shape + outputs = self.session.get_outputs() + output_names = [] + for out in outputs: + output_names.append(out.name) + self.input_name = input_name + self.output_names = output_names + assert len(self.output_names)==1 + output_shape = outputs[0].shape + self.require_pose = False + #print('init output_shape:', output_shape) + if output_shape[1]==3309: + self.lmk_dim = 3 + self.lmk_num = 68 + self.mean_lmk = get_object('meanshape_68.pkl') + self.require_pose = True + else: + self.lmk_dim = 2 + self.lmk_num = output_shape[1]//self.lmk_dim + self.taskname = 'landmark_%dd_%d'%(self.lmk_dim, self.lmk_num) + + def prepare(self, ctx_id, **kwargs): + if ctx_id<0: + self.session.set_providers(['CPUExecutionProvider']) + + def get(self, img, face): + bbox = face.bbox + w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1]) + center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2 + rotate = 0 + _scale = self.input_size[0] / (max(w, h)*1.5) + #print('param:', img.shape, bbox, center, self.input_size, _scale, rotate) + aimg, M = face_align.transform(img, center, self.input_size[0], _scale, rotate) + input_size = tuple(aimg.shape[0:2][::-1]) + #assert input_size==self.input_size + blob = cv2.dnn.blobFromImage(aimg, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + pred = self.session.run(self.output_names, {self.input_name : blob})[0][0] + if pred.shape[0] >= 3000: + pred = pred.reshape((-1, 3)) + else: + pred = pred.reshape((-1, 2)) + if self.lmk_num < pred.shape[0]: + pred = pred[self.lmk_num*-1:,:] + pred[:, 0:2] += 1 + pred[:, 0:2] *= (self.input_size[0] // 2) + if pred.shape[1] == 3: + pred[:, 2] *= (self.input_size[0] // 2) + + IM = cv2.invertAffineTransform(M) + pred = face_align.trans_points(pred, IM) + face[self.taskname] = pred + if self.require_pose: + P = transform.estimate_affine_matrix_3d23d(self.mean_lmk, pred) + s, R, t = transform.P2sRt(P) + rx, ry, rz = transform.matrix2angle(R) + pose = np.array( [rx, ry, rz], dtype=np.float32 ) + face['pose'] = pose #pitch, yaw, roll + return pred + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_store.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_store.py new file mode 100644 index 0000000..50bb85d --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_store.py @@ -0,0 +1,103 @@ +""" +This code file mainly comes from https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/model_store.py +""" +from __future__ import print_function + +__all__ = ['get_model_file'] +import os +import zipfile +import glob + +from ..utils import download, check_sha1 + +_model_sha1 = { + name: checksum + for checksum, name in [ + ('95be21b58e29e9c1237f229dae534bd854009ce0', 'arcface_r100_v1'), + ('', 'arcface_mfn_v1'), + ('39fd1e087a2a2ed70a154ac01fecaa86c315d01b', 'retinaface_r50_v1'), + ('2c9de8116d1f448fd1d4661f90308faae34c990a', 'retinaface_mnet025_v1'), + ('0db1d07921d005e6c9a5b38e059452fc5645e5a4', 'retinaface_mnet025_v2'), + ('7dd8111652b7aac2490c5dcddeb268e53ac643e6', 'genderage_v1'), + ] +} + +base_repo_url = 'https://insightface.ai/files/' +_url_format = '{repo_url}models/{file_name}.zip' + + +def short_hash(name): + if name not in _model_sha1: + raise ValueError( + 'Pretrained model for {name} is not available.'.format(name=name)) + return _model_sha1[name][:8] + + +def find_params_file(dir_path): + if not os.path.exists(dir_path): + return None + paths = glob.glob("%s/*.params" % dir_path) + if len(paths) == 0: + return None + paths = sorted(paths) + return paths[-1] + + +def get_model_file(name, root=os.path.join('~', '.insightface', 'models')): + r"""Return location for the pretrained on local file system. + + This function will download from online model zoo when model cannot be found or has mismatch. + The root directory will be created if it doesn't exist. + + Parameters + ---------- + name : str + Name of the model. + root : str, default '~/.mxnet/models' + Location for keeping the model parameters. + + Returns + ------- + file_path + Path to the requested pretrained model file. + """ + + file_name = name + root = os.path.expanduser(root) + dir_path = os.path.join(root, name) + file_path = find_params_file(dir_path) + #file_path = os.path.join(root, file_name + '.params') + sha1_hash = _model_sha1[name] + if file_path is not None: + if check_sha1(file_path, sha1_hash): + return file_path + else: + print( + 'Mismatch in the content of model file detected. Downloading again.' + ) + else: + print('Model file is not found. Downloading.') + + if not os.path.exists(root): + os.makedirs(root) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + + zip_file_path = os.path.join(root, file_name + '.zip') + repo_url = base_repo_url + if repo_url[-1] != '/': + repo_url = repo_url + '/' + download(_url_format.format(repo_url=repo_url, file_name=file_name), + path=zip_file_path, + overwrite=True) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(dir_path) + os.remove(zip_file_path) + file_path = find_params_file(dir_path) + + if check_sha1(file_path, sha1_hash): + return file_path + else: + raise ValueError( + 'Downloaded file has different hash. Please try again.') + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_zoo.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_zoo.py new file mode 100644 index 0000000..c90b791 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/model_zoo.py @@ -0,0 +1,97 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-05-04 +# @Function : + +import os +import os.path as osp +import glob +import onnxruntime +from .arcface_onnx import * +from .retinaface import * +#from .scrfd import * +from .landmark import * +from .attribute import Attribute +from .inswapper import INSwapper +from ..utils import download_onnx + +__all__ = ['get_model'] + + +class PickableInferenceSession(onnxruntime.InferenceSession): + # This is a wrapper to make the current InferenceSession class pickable. + def __init__(self, model_path, **kwargs): + super().__init__(model_path, **kwargs) + self.model_path = model_path + + def __getstate__(self): + return {'model_path': self.model_path} + + def __setstate__(self, values): + model_path = values['model_path'] + self.__init__(model_path) + +class ModelRouter: + def __init__(self, onnx_file): + self.onnx_file = onnx_file + + def get_model(self, **kwargs): + session = PickableInferenceSession(self.onnx_file, **kwargs) + # print(f'Applied providers: {session._providers}, with options: {session._provider_options}') + inputs = session.get_inputs() + input_cfg = inputs[0] + input_shape = input_cfg.shape + outputs = session.get_outputs() + + if len(outputs)>=5: + return RetinaFace(model_file=self.onnx_file, session=session) + elif input_shape[2]==192 and input_shape[3]==192: + return Landmark(model_file=self.onnx_file, session=session) + elif input_shape[2]==96 and input_shape[3]==96: + return Attribute(model_file=self.onnx_file, session=session) + elif len(inputs)==2 and input_shape[2]==128 and input_shape[3]==128: + return INSwapper(model_file=self.onnx_file, session=session) + elif input_shape[2]==input_shape[3] and input_shape[2]>=112 and input_shape[2]%16==0: + return ArcFaceONNX(model_file=self.onnx_file, session=session) + else: + #raise RuntimeError('error on model routing') + return None + +def find_onnx_file(dir_path): + if not os.path.exists(dir_path): + return None + paths = glob.glob("%s/*.onnx" % dir_path) + if len(paths) == 0: + return None + paths = sorted(paths) + return paths[-1] + +def get_default_providers(): + return ['CUDAExecutionProvider', 'CoreMLExecutionProvider', 'CPUExecutionProvider'] + +def get_default_provider_options(): + return None + +def get_model(name, **kwargs): + root = kwargs.get('root', '~/.insightface') + root = os.path.expanduser(root) + model_root = osp.join(root, 'models') + allow_download = kwargs.get('download', False) + download_zip = kwargs.get('download_zip', False) + if not name.endswith('.onnx'): + model_dir = os.path.join(model_root, name) + model_file = find_onnx_file(model_dir) + if model_file is None: + return None + else: + model_file = name + if not osp.exists(model_file) and allow_download: + model_file = download_onnx('models', model_file, root=root, download_zip=download_zip) + assert osp.exists(model_file), 'model_file %s should exist'%model_file + assert osp.isfile(model_file), 'model_file %s should be a file'%model_file + router = ModelRouter(model_file) + providers = kwargs.get('providers', get_default_providers()) + provider_options = kwargs.get('provider_options', get_default_provider_options()) + model = router.get_model(providers=providers, provider_options=provider_options) + return model diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/retinaface.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/retinaface.py new file mode 100644 index 0000000..fc4ad91 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/retinaface.py @@ -0,0 +1,301 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-09-18 +# @Function : + +from __future__ import division +import datetime +import numpy as np +import onnx +import onnxruntime +import os +import os.path as osp +import cv2 +import sys + +def softmax(z): + assert len(z.shape) == 2 + s = np.max(z, axis=1) + s = s[:, np.newaxis] # necessary step to do broadcasting + e_x = np.exp(z - s) + div = np.sum(e_x, axis=1) + div = div[:, np.newaxis] # dito + return e_x / div + +def distance2bbox(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). + max_shape (tuple): Shape of the image. + + Returns: + Tensor: Decoded bboxes. + """ + x1 = points[:, 0] - distance[:, 0] + y1 = points[:, 1] - distance[:, 1] + x2 = points[:, 0] + distance[:, 2] + y2 = points[:, 1] + distance[:, 3] + if max_shape is not None: + x1 = x1.clamp(min=0, max=max_shape[1]) + y1 = y1.clamp(min=0, max=max_shape[0]) + x2 = x2.clamp(min=0, max=max_shape[1]) + y2 = y2.clamp(min=0, max=max_shape[0]) + return np.stack([x1, y1, x2, y2], axis=-1) + +def distance2kps(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). + max_shape (tuple): Shape of the image. + + Returns: + Tensor: Decoded bboxes. + """ + preds = [] + for i in range(0, distance.shape[1], 2): + px = points[:, i%2] + distance[:, i] + py = points[:, i%2+1] + distance[:, i+1] + if max_shape is not None: + px = px.clamp(min=0, max=max_shape[1]) + py = py.clamp(min=0, max=max_shape[0]) + preds.append(px) + preds.append(py) + return np.stack(preds, axis=-1) + +class RetinaFace: + def __init__(self, model_file=None, session=None): + import onnxruntime + self.model_file = model_file + self.session = session + self.taskname = 'detection' + if self.session is None: + assert self.model_file is not None + assert osp.exists(self.model_file) + self.session = onnxruntime.InferenceSession(self.model_file, None) + self.center_cache = {} + self.nms_thresh = 0.4 + self.det_thresh = 0.5 + self._init_vars() + + def _init_vars(self): + input_cfg = self.session.get_inputs()[0] + input_shape = input_cfg.shape + #print(input_shape) + if isinstance(input_shape[2], str): + self.input_size = None + else: + self.input_size = tuple(input_shape[2:4][::-1]) + #print('image_size:', self.image_size) + input_name = input_cfg.name + self.input_shape = input_shape + outputs = self.session.get_outputs() + output_names = [] + for o in outputs: + output_names.append(o.name) + self.input_name = input_name + self.output_names = output_names + self.input_mean = 127.5 + self.input_std = 128.0 + #print(self.output_names) + #assert len(outputs)==10 or len(outputs)==15 + self.use_kps = False + self._anchor_ratio = 1.0 + self._num_anchors = 1 + if len(outputs)==6: + self.fmc = 3 + self._feat_stride_fpn = [8, 16, 32] + self._num_anchors = 2 + elif len(outputs)==9: + self.fmc = 3 + self._feat_stride_fpn = [8, 16, 32] + self._num_anchors = 2 + self.use_kps = True + elif len(outputs)==10: + self.fmc = 5 + self._feat_stride_fpn = [8, 16, 32, 64, 128] + self._num_anchors = 1 + elif len(outputs)==15: + self.fmc = 5 + self._feat_stride_fpn = [8, 16, 32, 64, 128] + self._num_anchors = 1 + self.use_kps = True + + def prepare(self, ctx_id, **kwargs): + if ctx_id<0: + self.session.set_providers(['CPUExecutionProvider']) + nms_thresh = kwargs.get('nms_thresh', None) + if nms_thresh is not None: + self.nms_thresh = nms_thresh + det_thresh = kwargs.get('det_thresh', None) + if det_thresh is not None: + self.det_thresh = det_thresh + input_size = kwargs.get('input_size', None) + if input_size is not None: + if self.input_size is not None: + print('warning: det_size is already set in detection model, ignore') + else: + self.input_size = input_size + + def forward(self, img, threshold): + scores_list = [] + bboxes_list = [] + kpss_list = [] + input_size = tuple(img.shape[0:2][::-1]) + blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + net_outs = self.session.run(self.output_names, {self.input_name : blob}) + + input_height = blob.shape[2] + input_width = blob.shape[3] + fmc = self.fmc + for idx, stride in enumerate(self._feat_stride_fpn): + scores = net_outs[idx] + bbox_preds = net_outs[idx+fmc] + bbox_preds = bbox_preds * stride + if self.use_kps: + kps_preds = net_outs[idx+fmc*2] * stride + height = input_height // stride + width = input_width // stride + K = height * width + key = (height, width, stride) + if key in self.center_cache: + anchor_centers = self.center_cache[key] + else: + #solution-1, c style: + #anchor_centers = np.zeros( (height, width, 2), dtype=np.float32 ) + #for i in range(height): + # anchor_centers[i, :, 1] = i + #for i in range(width): + # anchor_centers[:, i, 0] = i + + #solution-2: + #ax = np.arange(width, dtype=np.float32) + #ay = np.arange(height, dtype=np.float32) + #xv, yv = np.meshgrid(np.arange(width), np.arange(height)) + #anchor_centers = np.stack([xv, yv], axis=-1).astype(np.float32) + + #solution-3: + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + #print(anchor_centers.shape) + + anchor_centers = (anchor_centers * stride).reshape( (-1, 2) ) + if self._num_anchors>1: + anchor_centers = np.stack([anchor_centers]*self._num_anchors, axis=1).reshape( (-1,2) ) + if len(self.center_cache)<100: + self.center_cache[key] = anchor_centers + + pos_inds = np.where(scores>=threshold)[0] + bboxes = distance2bbox(anchor_centers, bbox_preds) + pos_scores = scores[pos_inds] + pos_bboxes = bboxes[pos_inds] + scores_list.append(pos_scores) + bboxes_list.append(pos_bboxes) + if self.use_kps: + kpss = distance2kps(anchor_centers, kps_preds) + #kpss = kps_preds + kpss = kpss.reshape( (kpss.shape[0], -1, 2) ) + pos_kpss = kpss[pos_inds] + kpss_list.append(pos_kpss) + return scores_list, bboxes_list, kpss_list + + def detect(self, img, input_size = None, max_num=0, metric='default'): + assert input_size is not None or self.input_size is not None + input_size = self.input_size if input_size is None else input_size + + im_ratio = float(img.shape[0]) / img.shape[1] + model_ratio = float(input_size[1]) / input_size[0] + if im_ratio>model_ratio: + new_height = input_size[1] + new_width = int(new_height / im_ratio) + else: + new_width = input_size[0] + new_height = int(new_width * im_ratio) + det_scale = float(new_height) / img.shape[0] + resized_img = cv2.resize(img, (new_width, new_height)) + det_img = np.zeros( (input_size[1], input_size[0], 3), dtype=np.uint8 ) + det_img[:new_height, :new_width, :] = resized_img + + scores_list, bboxes_list, kpss_list = self.forward(det_img, self.det_thresh) + + scores = np.vstack(scores_list) + scores_ravel = scores.ravel() + order = scores_ravel.argsort()[::-1] + bboxes = np.vstack(bboxes_list) / det_scale + if self.use_kps: + kpss = np.vstack(kpss_list) / det_scale + pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False) + pre_det = pre_det[order, :] + keep = self.nms(pre_det) + det = pre_det[keep, :] + if self.use_kps: + kpss = kpss[order,:,:] + kpss = kpss[keep,:,:] + else: + kpss = None + if max_num > 0 and det.shape[0] > max_num: + area = (det[:, 2] - det[:, 0]) * (det[:, 3] - + det[:, 1]) + img_center = img.shape[0] // 2, img.shape[1] // 2 + offsets = np.vstack([ + (det[:, 0] + det[:, 2]) / 2 - img_center[1], + (det[:, 1] + det[:, 3]) / 2 - img_center[0] + ]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + if metric=='max': + values = area + else: + values = area - offset_dist_squared * 2.0 # some extra weight on the centering + bindex = np.argsort( + values)[::-1] # some extra weight on the centering + bindex = bindex[0:max_num] + det = det[bindex, :] + if kpss is not None: + kpss = kpss[bindex, :] + return det, kpss + + def nms(self, dets): + thresh = self.nms_thresh + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + +def get_retinaface(name, download=False, root='~/.insightface/models', **kwargs): + if not download: + assert os.path.exists(name) + return RetinaFace(name) + else: + from .model_store import get_model_file + _file = get_model_file("retinaface_%s" % name, root=root) + return retinaface(_file) + + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/scrfd.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/scrfd.py new file mode 100644 index 0000000..674db4b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/model_zoo/scrfd.py @@ -0,0 +1,348 @@ +# -*- coding: utf-8 -*- +# @Organization : insightface.ai +# @Author : Jia Guo +# @Time : 2021-05-04 +# @Function : + +from __future__ import division +import datetime +import numpy as np +import onnx +import onnxruntime +import os +import os.path as osp +import cv2 +import sys + +def softmax(z): + assert len(z.shape) == 2 + s = np.max(z, axis=1) + s = s[:, np.newaxis] # necessary step to do broadcasting + e_x = np.exp(z - s) + div = np.sum(e_x, axis=1) + div = div[:, np.newaxis] # dito + return e_x / div + +def distance2bbox(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). + max_shape (tuple): Shape of the image. + + Returns: + Tensor: Decoded bboxes. + """ + x1 = points[:, 0] - distance[:, 0] + y1 = points[:, 1] - distance[:, 1] + x2 = points[:, 0] + distance[:, 2] + y2 = points[:, 1] + distance[:, 3] + if max_shape is not None: + x1 = x1.clamp(min=0, max=max_shape[1]) + y1 = y1.clamp(min=0, max=max_shape[0]) + x2 = x2.clamp(min=0, max=max_shape[1]) + y2 = y2.clamp(min=0, max=max_shape[0]) + return np.stack([x1, y1, x2, y2], axis=-1) + +def distance2kps(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). + max_shape (tuple): Shape of the image. + + Returns: + Tensor: Decoded bboxes. + """ + preds = [] + for i in range(0, distance.shape[1], 2): + px = points[:, i%2] + distance[:, i] + py = points[:, i%2+1] + distance[:, i+1] + if max_shape is not None: + px = px.clamp(min=0, max=max_shape[1]) + py = py.clamp(min=0, max=max_shape[0]) + preds.append(px) + preds.append(py) + return np.stack(preds, axis=-1) + +class SCRFD: + def __init__(self, model_file=None, session=None): + import onnxruntime + self.model_file = model_file + self.session = session + self.taskname = 'detection' + self.batched = False + if self.session is None: + assert self.model_file is not None + assert osp.exists(self.model_file) + self.session = onnxruntime.InferenceSession(self.model_file, None) + self.center_cache = {} + self.nms_thresh = 0.4 + self.det_thresh = 0.5 + self._init_vars() + + def _init_vars(self): + input_cfg = self.session.get_inputs()[0] + input_shape = input_cfg.shape + #print(input_shape) + if isinstance(input_shape[2], str): + self.input_size = None + else: + self.input_size = tuple(input_shape[2:4][::-1]) + #print('image_size:', self.image_size) + input_name = input_cfg.name + self.input_shape = input_shape + outputs = self.session.get_outputs() + if len(outputs[0].shape) == 3: + self.batched = True + output_names = [] + for o in outputs: + output_names.append(o.name) + self.input_name = input_name + self.output_names = output_names + self.input_mean = 127.5 + self.input_std = 128.0 + #print(self.output_names) + #assert len(outputs)==10 or len(outputs)==15 + self.use_kps = False + self._anchor_ratio = 1.0 + self._num_anchors = 1 + if len(outputs)==6: + self.fmc = 3 + self._feat_stride_fpn = [8, 16, 32] + self._num_anchors = 2 + elif len(outputs)==9: + self.fmc = 3 + self._feat_stride_fpn = [8, 16, 32] + self._num_anchors = 2 + self.use_kps = True + elif len(outputs)==10: + self.fmc = 5 + self._feat_stride_fpn = [8, 16, 32, 64, 128] + self._num_anchors = 1 + elif len(outputs)==15: + self.fmc = 5 + self._feat_stride_fpn = [8, 16, 32, 64, 128] + self._num_anchors = 1 + self.use_kps = True + + def prepare(self, ctx_id, **kwargs): + if ctx_id<0: + self.session.set_providers(['CPUExecutionProvider']) + nms_thresh = kwargs.get('nms_thresh', None) + if nms_thresh is not None: + self.nms_thresh = nms_thresh + det_thresh = kwargs.get('det_thresh', None) + if det_thresh is not None: + self.det_thresh = det_thresh + input_size = kwargs.get('input_size', None) + if input_size is not None: + if self.input_size is not None: + print('warning: det_size is already set in scrfd model, ignore') + else: + self.input_size = input_size + + def forward(self, img, threshold): + scores_list = [] + bboxes_list = [] + kpss_list = [] + input_size = tuple(img.shape[0:2][::-1]) + blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) + net_outs = self.session.run(self.output_names, {self.input_name : blob}) + + input_height = blob.shape[2] + input_width = blob.shape[3] + fmc = self.fmc + for idx, stride in enumerate(self._feat_stride_fpn): + # If model support batch dim, take first output + if self.batched: + scores = net_outs[idx][0] + bbox_preds = net_outs[idx + fmc][0] + bbox_preds = bbox_preds * stride + if self.use_kps: + kps_preds = net_outs[idx + fmc * 2][0] * stride + # If model doesn't support batching take output as is + else: + scores = net_outs[idx] + bbox_preds = net_outs[idx + fmc] + bbox_preds = bbox_preds * stride + if self.use_kps: + kps_preds = net_outs[idx + fmc * 2] * stride + + height = input_height // stride + width = input_width // stride + K = height * width + key = (height, width, stride) + if key in self.center_cache: + anchor_centers = self.center_cache[key] + else: + #solution-1, c style: + #anchor_centers = np.zeros( (height, width, 2), dtype=np.float32 ) + #for i in range(height): + # anchor_centers[i, :, 1] = i + #for i in range(width): + # anchor_centers[:, i, 0] = i + + #solution-2: + #ax = np.arange(width, dtype=np.float32) + #ay = np.arange(height, dtype=np.float32) + #xv, yv = np.meshgrid(np.arange(width), np.arange(height)) + #anchor_centers = np.stack([xv, yv], axis=-1).astype(np.float32) + + #solution-3: + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + #print(anchor_centers.shape) + + anchor_centers = (anchor_centers * stride).reshape( (-1, 2) ) + if self._num_anchors>1: + anchor_centers = np.stack([anchor_centers]*self._num_anchors, axis=1).reshape( (-1,2) ) + if len(self.center_cache)<100: + self.center_cache[key] = anchor_centers + + pos_inds = np.where(scores>=threshold)[0] + bboxes = distance2bbox(anchor_centers, bbox_preds) + pos_scores = scores[pos_inds] + pos_bboxes = bboxes[pos_inds] + scores_list.append(pos_scores) + bboxes_list.append(pos_bboxes) + if self.use_kps: + kpss = distance2kps(anchor_centers, kps_preds) + #kpss = kps_preds + kpss = kpss.reshape( (kpss.shape[0], -1, 2) ) + pos_kpss = kpss[pos_inds] + kpss_list.append(pos_kpss) + return scores_list, bboxes_list, kpss_list + + def detect(self, img, input_size = None, max_num=0, metric='default'): + assert input_size is not None or self.input_size is not None + input_size = self.input_size if input_size is None else input_size + + im_ratio = float(img.shape[0]) / img.shape[1] + model_ratio = float(input_size[1]) / input_size[0] + if im_ratio>model_ratio: + new_height = input_size[1] + new_width = int(new_height / im_ratio) + else: + new_width = input_size[0] + new_height = int(new_width * im_ratio) + det_scale = float(new_height) / img.shape[0] + resized_img = cv2.resize(img, (new_width, new_height)) + det_img = np.zeros( (input_size[1], input_size[0], 3), dtype=np.uint8 ) + det_img[:new_height, :new_width, :] = resized_img + + scores_list, bboxes_list, kpss_list = self.forward(det_img, self.det_thresh) + + scores = np.vstack(scores_list) + scores_ravel = scores.ravel() + order = scores_ravel.argsort()[::-1] + bboxes = np.vstack(bboxes_list) / det_scale + if self.use_kps: + kpss = np.vstack(kpss_list) / det_scale + pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False) + pre_det = pre_det[order, :] + keep = self.nms(pre_det) + det = pre_det[keep, :] + if self.use_kps: + kpss = kpss[order,:,:] + kpss = kpss[keep,:,:] + else: + kpss = None + if max_num > 0 and det.shape[0] > max_num: + area = (det[:, 2] - det[:, 0]) * (det[:, 3] - + det[:, 1]) + img_center = img.shape[0] // 2, img.shape[1] // 2 + offsets = np.vstack([ + (det[:, 0] + det[:, 2]) / 2 - img_center[1], + (det[:, 1] + det[:, 3]) / 2 - img_center[0] + ]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + if metric=='max': + values = area + else: + values = area - offset_dist_squared * 2.0 # some extra weight on the centering + bindex = np.argsort( + values)[::-1] # some extra weight on the centering + bindex = bindex[0:max_num] + det = det[bindex, :] + if kpss is not None: + kpss = kpss[bindex, :] + return det, kpss + + def nms(self, dets): + thresh = self.nms_thresh + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + +def get_scrfd(name, download=False, root='~/.insightface/models', **kwargs): + if not download: + assert os.path.exists(name) + return SCRFD(name) + else: + from .model_store import get_model_file + _file = get_model_file("scrfd_%s" % name, root=root) + return SCRFD(_file) + + +def scrfd_2p5gkps(**kwargs): + return get_scrfd("2p5gkps", download=True, **kwargs) + + +if __name__ == '__main__': + import glob + detector = SCRFD(model_file='./det.onnx') + detector.prepare(-1) + img_paths = ['tests/data/t1.jpg'] + for img_path in img_paths: + img = cv2.imread(img_path) + + for _ in range(1): + ta = datetime.datetime.now() + #bboxes, kpss = detector.detect(img, 0.5, input_size = (640, 640)) + bboxes, kpss = detector.detect(img, 0.5) + tb = datetime.datetime.now() + print('all cost:', (tb-ta).total_seconds()*1000) + print(img_path, bboxes.shape) + if kpss is not None: + print(kpss.shape) + for i in range(bboxes.shape[0]): + bbox = bboxes[i] + x1,y1,x2,y2,score = bbox.astype(np.int) + cv2.rectangle(img, (x1,y1) , (x2,y2) , (255,0,0) , 2) + if kpss is not None: + kps = kpss[i] + for kp in kps: + kp = kp.astype(np.int) + cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2) + filename = img_path.split('/')[-1] + print('output:', filename) + cv2.imwrite('./outputs/%s'%filename, img) + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/__init__.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/__init__.py new file mode 100644 index 0000000..6960431 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/__init__.py @@ -0,0 +1,6 @@ +from __future__ import absolute_import + +from .storage import download, ensure_available, download_onnx +from .filesystem import get_model_dir +from .filesystem import makedirs, try_import_dali +from .constant import * diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/constant.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/constant.py new file mode 100644 index 0000000..8860ff0 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/constant.py @@ -0,0 +1,3 @@ + +DEFAULT_MP_NAME = 'buffalo_l' + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/download.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/download.py new file mode 100644 index 0000000..01fe82f --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/download.py @@ -0,0 +1,67 @@ +""" +This code file mainly comes from https://github.com/dmlc/gluon-cv/blob/master/gluoncv/utils/download.py +""" +import os +import hashlib +import requests +from tqdm import tqdm + +def download_file(url, path=None, overwrite=False): + """Download an given URL + Parameters + ---------- + url : str + URL to download + path : str, optional + Destination path to store downloaded file. By default stores to the + current directory with same name as in url. + overwrite : bool, optional + Whether to overwrite destination file if already exists. + sha1_hash : str, optional + Expected sha1 hash in hexadecimal digits. Will ignore existing file when hash is specified + but doesn't match. + Returns + ------- + str + The file path of the downloaded file. + """ + if path is None: + fname = url.split('/')[-1] + else: + path = os.path.expanduser(path) + if os.path.isdir(path): + fname = os.path.join(path, url.split('/')[-1]) + else: + fname = path + + if overwrite or not os.path.exists(fname): + dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname))) + if not os.path.exists(dirname): + os.makedirs(dirname) + + print('Downloading %s from %s...' % (fname, url)) + r = requests.get(url, stream=True) + if r.status_code != 200: + raise RuntimeError("Failed downloading url %s" % url) + total_length = r.headers.get('content-length') + with open(fname, 'wb') as f: + if total_length is None: # no content length header + for chunk in r.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + f.write(chunk) + else: + total_length = int(total_length) + for chunk in tqdm(r.iter_content(chunk_size=1024), + total=int(total_length / 1024. + 0.5), + unit='KB', + unit_scale=False, + dynamic_ncols=True): + f.write(chunk) + + if sha1_hash and not check_sha1(fname, sha1_hash): + raise UserWarning('File {} is downloaded but the content hash does not match. ' \ + 'The repo may be outdated or download may be incomplete. ' \ + 'If the "repo_url" is overridden, consider switching to ' \ + 'the default repo.'.format(fname)) + + return fname diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/face_align.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/face_align.py new file mode 100644 index 0000000..226628b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/face_align.py @@ -0,0 +1,103 @@ +import cv2 +import numpy as np +from skimage import transform as trans + + +arcface_dst = np.array( + [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], + [41.5493, 92.3655], [70.7299, 92.2041]], + dtype=np.float32) + +def estimate_norm(lmk, image_size=112,mode='arcface'): + assert lmk.shape == (5, 2) + assert image_size%112==0 or image_size%128==0 + if image_size%112==0: + ratio = float(image_size)/112.0 + diff_x = 0 + else: + ratio = float(image_size)/128.0 + diff_x = 8.0*ratio + dst = arcface_dst * ratio + dst[:,0] += diff_x + tform = trans.SimilarityTransform() + tform.estimate(lmk, dst) + M = tform.params[0:2, :] + return M + +def norm_crop(img, landmark, image_size=112, mode='arcface'): + M = estimate_norm(landmark, image_size, mode) + warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) + return warped + +def norm_crop2(img, landmark, image_size=112, mode='arcface'): + M = estimate_norm(landmark, image_size, mode) + warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) + return warped, M + +def square_crop(im, S): + if im.shape[0] > im.shape[1]: + height = S + width = int(float(im.shape[1]) / im.shape[0] * S) + scale = float(S) / im.shape[0] + else: + width = S + height = int(float(im.shape[0]) / im.shape[1] * S) + scale = float(S) / im.shape[1] + resized_im = cv2.resize(im, (width, height)) + det_im = np.zeros((S, S, 3), dtype=np.uint8) + det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im + return det_im, scale + + +def transform(data, center, output_size, scale, rotation): + scale_ratio = scale + rot = float(rotation) * np.pi / 180.0 + #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio) + t1 = trans.SimilarityTransform(scale=scale_ratio) + cx = center[0] * scale_ratio + cy = center[1] * scale_ratio + t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy)) + t3 = trans.SimilarityTransform(rotation=rot) + t4 = trans.SimilarityTransform(translation=(output_size / 2, + output_size / 2)) + t = t1 + t2 + t3 + t4 + M = t.params[0:2] + cropped = cv2.warpAffine(data, + M, (output_size, output_size), + borderValue=0.0) + return cropped, M + + +def trans_points2d(pts, M): + new_pts = np.zeros(shape=pts.shape, dtype=np.float32) + for i in range(pts.shape[0]): + pt = pts[i] + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) + new_pt = np.dot(M, new_pt) + #print('new_pt', new_pt.shape, new_pt) + new_pts[i] = new_pt[0:2] + + return new_pts + + +def trans_points3d(pts, M): + scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1]) + #print(scale) + new_pts = np.zeros(shape=pts.shape, dtype=np.float32) + for i in range(pts.shape[0]): + pt = pts[i] + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) + new_pt = np.dot(M, new_pt) + #print('new_pt', new_pt.shape, new_pt) + new_pts[i][0:2] = new_pt[0:2] + new_pts[i][2] = pts[i][2] * scale + + return new_pts + + +def trans_points(pts, M): + if pts.shape[1] == 2: + return trans_points2d(pts, M) + else: + return trans_points3d(pts, M) + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/filesystem.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/filesystem.py new file mode 100644 index 0000000..01e3851 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/filesystem.py @@ -0,0 +1,157 @@ +""" +This code file mainly comes from https://github.com/dmlc/gluon-cv/blob/master/gluoncv/utils/filesystem.py +""" +import os +import os.path as osp +import errno + + +def get_model_dir(name, root='~/.insightface'): + root = os.path.expanduser(root) + model_dir = osp.join(root, 'models', name) + return model_dir + +def makedirs(path): + """Create directory recursively if not exists. + Similar to `makedir -p`, you can skip checking existence before this function. + + Parameters + ---------- + path : str + Path of the desired dir + """ + try: + os.makedirs(path) + except OSError as exc: + if exc.errno != errno.EEXIST: + raise + + +def try_import(package, message=None): + """Try import specified package, with custom message support. + + Parameters + ---------- + package : str + The name of the targeting package. + message : str, default is None + If not None, this function will raise customized error message when import error is found. + + + Returns + ------- + module if found, raise ImportError otherwise + + """ + try: + return __import__(package) + except ImportError as e: + if not message: + raise e + raise ImportError(message) + + +def try_import_cv2(): + """Try import cv2 at runtime. + + Returns + ------- + cv2 module if found. Raise ImportError otherwise + + """ + msg = "cv2 is required, you can install by package manager, e.g. 'apt-get', \ + or `pip install opencv-python --user` (note that this is unofficial PYPI package)." + + return try_import('cv2', msg) + + +def try_import_mmcv(): + """Try import mmcv at runtime. + + Returns + ------- + mmcv module if found. Raise ImportError otherwise + + """ + msg = "mmcv is required, you can install by first `pip install Cython --user` \ + and then `pip install mmcv --user` (note that this is unofficial PYPI package)." + + return try_import('mmcv', msg) + + +def try_import_rarfile(): + """Try import rarfile at runtime. + + Returns + ------- + rarfile module if found. Raise ImportError otherwise + + """ + msg = "rarfile is required, you can install by first `sudo apt-get install unrar` \ + and then `pip install rarfile --user` (note that this is unofficial PYPI package)." + + return try_import('rarfile', msg) + + +def import_try_install(package, extern_url=None): + """Try import the specified package. + If the package not installed, try use pip to install and import if success. + + Parameters + ---------- + package : str + The name of the package trying to import. + extern_url : str or None, optional + The external url if package is not hosted on PyPI. + For example, you can install a package using: + "pip install git+http://github.com/user/repo/tarball/master/egginfo=xxx". + In this case, you can pass the url to the extern_url. + + Returns + ------- + + The imported python module. + + """ + try: + return __import__(package) + except ImportError: + try: + from pip import main as pipmain + except ImportError: + from pip._internal import main as pipmain + + # trying to install package + url = package if extern_url is None else extern_url + pipmain(['install', '--user', + url]) # will raise SystemExit Error if fails + + # trying to load again + try: + return __import__(package) + except ImportError: + import sys + import site + user_site = site.getusersitepackages() + if user_site not in sys.path: + sys.path.append(user_site) + return __import__(package) + return __import__(package) + + +def try_import_dali(): + """Try import NVIDIA DALI at runtime. + """ + try: + dali = __import__('nvidia.dali', fromlist=['pipeline', 'ops', 'types']) + dali.Pipeline = dali.pipeline.Pipeline + except ImportError: + + class dali: + class Pipeline: + def __init__(self): + raise NotImplementedError( + "DALI not found, please check if you installed it correctly." + ) + + return dali diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/storage.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/storage.py new file mode 100644 index 0000000..5bf37e2 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/storage.py @@ -0,0 +1,52 @@ + +import os +import os.path as osp +import zipfile +from .download import download_file + +BASE_REPO_URL = 'https://github.com/deepinsight/insightface/releases/download/v0.7' + +def download(sub_dir, name, force=False, root='~/.insightface'): + _root = os.path.expanduser(root) + dir_path = os.path.join(_root, sub_dir, name) + if osp.exists(dir_path) and not force: + return dir_path + print('download_path:', dir_path) + zip_file_path = os.path.join(_root, sub_dir, name + '.zip') + model_url = "%s/%s.zip"%(BASE_REPO_URL, name) + download_file(model_url, + path=zip_file_path, + overwrite=True) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(dir_path) + #os.remove(zip_file_path) + return dir_path + +def ensure_available(sub_dir, name, root='~/.insightface'): + return download(sub_dir, name, force=False, root=root) + +def download_onnx(sub_dir, model_file, force=False, root='~/.insightface', download_zip=False): + _root = os.path.expanduser(root) + model_root = osp.join(_root, sub_dir) + new_model_file = osp.join(model_root, model_file) + if osp.exists(new_model_file) and not force: + return new_model_file + if not osp.exists(model_root): + os.makedirs(model_root) + print('download_path:', new_model_file) + if not download_zip: + model_url = "%s/%s"%(BASE_REPO_URL, model_file) + download_file(model_url, + path=new_model_file, + overwrite=True) + else: + model_url = "%s/%s.zip"%(BASE_REPO_URL, model_file) + zip_file_path = new_model_file+".zip" + download_file(model_url, + path=zip_file_path, + overwrite=True) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(model_root) + return new_model_file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/transform.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/transform.py new file mode 100644 index 0000000..06531d2 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/dependencies/insightface/utils/transform.py @@ -0,0 +1,116 @@ +import cv2 +import math +import numpy as np +from skimage import transform as trans + + +def transform(data, center, output_size, scale, rotation): + scale_ratio = scale + rot = float(rotation) * np.pi / 180.0 + #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio) + t1 = trans.SimilarityTransform(scale=scale_ratio) + cx = center[0] * scale_ratio + cy = center[1] * scale_ratio + t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy)) + t3 = trans.SimilarityTransform(rotation=rot) + t4 = trans.SimilarityTransform(translation=(output_size / 2, + output_size / 2)) + t = t1 + t2 + t3 + t4 + M = t.params[0:2] + cropped = cv2.warpAffine(data, + M, (output_size, output_size), + borderValue=0.0) + return cropped, M + + +def trans_points2d(pts, M): + new_pts = np.zeros(shape=pts.shape, dtype=np.float32) + for i in range(pts.shape[0]): + pt = pts[i] + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) + new_pt = np.dot(M, new_pt) + #print('new_pt', new_pt.shape, new_pt) + new_pts[i] = new_pt[0:2] + + return new_pts + + +def trans_points3d(pts, M): + scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1]) + #print(scale) + new_pts = np.zeros(shape=pts.shape, dtype=np.float32) + for i in range(pts.shape[0]): + pt = pts[i] + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) + new_pt = np.dot(M, new_pt) + #print('new_pt', new_pt.shape, new_pt) + new_pts[i][0:2] = new_pt[0:2] + new_pts[i][2] = pts[i][2] * scale + + return new_pts + + +def trans_points(pts, M): + if pts.shape[1] == 2: + return trans_points2d(pts, M) + else: + return trans_points3d(pts, M) + +def estimate_affine_matrix_3d23d(X, Y): + ''' Using least-squares solution + Args: + X: [n, 3]. 3d points(fixed) + Y: [n, 3]. corresponding 3d points(moving). Y = PX + Returns: + P_Affine: (3, 4). Affine camera matrix (the third row is [0, 0, 0, 1]). + ''' + X_homo = np.hstack((X, np.ones([X.shape[0],1]))) #n x 4 + P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4 + return P + +def P2sRt(P): + ''' decompositing camera matrix P + Args: + P: (3, 4). Affine Camera Matrix. + Returns: + s: scale factor. + R: (3, 3). rotation matrix. + t: (3,). translation. + ''' + t = P[:, 3] + R1 = P[0:1, :3] + R2 = P[1:2, :3] + s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2.0 + r1 = R1/np.linalg.norm(R1) + r2 = R2/np.linalg.norm(R2) + r3 = np.cross(r1, r2) + + R = np.concatenate((r1, r2, r3), 0) + return s, R, t + +def matrix2angle(R): + ''' get three Euler angles from Rotation Matrix + Args: + R: (3,3). rotation matrix + Returns: + x: pitch + y: yaw + z: roll + ''' + sy = math.sqrt(R[0,0] * R[0,0] + R[1,0] * R[1,0]) + + singular = sy < 1e-6 + + if not singular : + x = math.atan2(R[2,1] , R[2,2]) + y = math.atan2(-R[2,0], sy) + z = math.atan2(R[1,0], R[0,0]) + else : + x = math.atan2(-R[1,2], R[1,1]) + y = math.atan2(-R[2,0], sy) + z = 0 + + # rx, ry, rz = np.rad2deg(x), np.rad2deg(y), np.rad2deg(z) + rx, ry, rz = x*180/np.pi, y*180/np.pi, z*180/np.pi + return rx, ry, rz + diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/face_analysis_diy.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/face_analysis_diy.py new file mode 100644 index 0000000..f13a659 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/face_analysis_diy.py @@ -0,0 +1,79 @@ +# coding: utf-8 + +""" +face detectoin and alignment using InsightFace +""" + +import numpy as np +from .rprint import rlog as log +from .dependencies.insightface.app import FaceAnalysis +from .dependencies.insightface.app.common import Face +from .timer import Timer + + +def sort_by_direction(faces, direction: str = 'large-small', face_center=None): + if len(faces) <= 0: + return faces + + if direction == 'left-right': + return sorted(faces, key=lambda face: face['bbox'][0]) + if direction == 'right-left': + return sorted(faces, key=lambda face: face['bbox'][0], reverse=True) + if direction == 'top-bottom': + return sorted(faces, key=lambda face: face['bbox'][1]) + if direction == 'bottom-top': + return sorted(faces, key=lambda face: face['bbox'][1], reverse=True) + if direction == 'small-large': + return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1])) + if direction == 'large-small': + return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]), reverse=True) + if direction == 'distance-from-retarget-face': + return sorted(faces, key=lambda face: (((face['bbox'][2]+face['bbox'][0])/2-face_center[0])**2+((face['bbox'][3]+face['bbox'][1])/2-face_center[1])**2)**0.5) + return faces + + +class FaceAnalysisDIY(FaceAnalysis): + def __init__(self, name='buffalo_l', root='~/.insightface', allowed_modules=None, **kwargs): + super().__init__(name=name, root=root, allowed_modules=allowed_modules, **kwargs) + + self.timer = Timer() + + def get(self, img_bgr, **kwargs): + max_num = kwargs.get('max_face_num', 0) # the number of the detected faces, 0 means no limit + flag_do_landmark_2d_106 = kwargs.get('flag_do_landmark_2d_106', True) # whether to do 106-point detection + direction = kwargs.get('direction', 'large-small') # sorting direction + face_center = None + + bboxes, kpss = self.det_model.detect(img_bgr, max_num=max_num, metric='default') + if bboxes.shape[0] == 0: + return [] + ret = [] + for i in range(bboxes.shape[0]): + bbox = bboxes[i, 0:4] + det_score = bboxes[i, 4] + kps = None + if kpss is not None: + kps = kpss[i] + face = Face(bbox=bbox, kps=kps, det_score=det_score) + for taskname, model in self.models.items(): + if taskname == 'detection': + continue + + if (not flag_do_landmark_2d_106) and taskname == 'landmark_2d_106': + continue + + # print(f'taskname: {taskname}') + model.get(img_bgr, face) + ret.append(face) + + ret = sort_by_direction(ret, direction, face_center) + return ret + + def warmup(self): + self.timer.tic() + + img_bgr = np.zeros((512, 512, 3), dtype=np.uint8) + self.get(img_bgr) + + elapse = self.timer.toc() + log(f'FaceAnalysisDIY warmup time: {elapse:.3f}s') diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/filter.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/filter.py new file mode 100644 index 0000000..a8e27ca --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/filter.py @@ -0,0 +1,19 @@ +# coding: utf-8 + +import torch +import numpy as np +from pykalman import KalmanFilter + + +def smooth(x_d_lst, shape, device, observation_variance=3e-7, process_variance=1e-5): + x_d_lst_reshape = [x.reshape(-1) for x in x_d_lst] + x_d_stacked = np.vstack(x_d_lst_reshape) + kf = KalmanFilter( + initial_state_mean=x_d_stacked[0], + n_dim_obs=x_d_stacked.shape[1], + transition_covariance=process_variance * np.eye(x_d_stacked.shape[1]), + observation_covariance=observation_variance * np.eye(x_d_stacked.shape[1]) + ) + smoothed_state_means, _ = kf.smooth(x_d_stacked) + x_d_lst_smooth = [torch.tensor(state_mean.reshape(shape[-2:]), dtype=torch.float32, device=device) for state_mean in smoothed_state_means] + return x_d_lst_smooth diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/helper.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/helper.py new file mode 100644 index 0000000..dda9a69 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/helper.py @@ -0,0 +1,199 @@ +# coding: utf-8 + +""" +utility functions and classes to handle feature extraction and model loading +""" + +import os +import os.path as osp +import torch +from collections import OrderedDict +import numpy as np +from scipy.spatial import ConvexHull # pylint: disable=E0401,E0611 +from typing import Union +import cv2 + +from ..modules.spade_generator import SPADEDecoder +from ..modules.warping_network import WarpingNetwork +from ..modules.motion_extractor import MotionExtractor +from ..modules.appearance_feature_extractor import AppearanceFeatureExtractor +from ..modules.stitching_retargeting_network import StitchingRetargetingNetwork + + +def tensor_to_numpy(data: Union[np.ndarray, torch.Tensor]) -> np.ndarray: + """transform torch.Tensor into numpy.ndarray""" + if isinstance(data, torch.Tensor): + return data.data.cpu().numpy() + return data + +def calc_motion_multiplier( + kp_source: Union[np.ndarray, torch.Tensor], + kp_driving_initial: Union[np.ndarray, torch.Tensor] +) -> float: + """calculate motion_multiplier based on the source image and the first driving frame""" + kp_source_np = tensor_to_numpy(kp_source) + kp_driving_initial_np = tensor_to_numpy(kp_driving_initial) + + source_area = ConvexHull(kp_source_np.squeeze(0)).volume + driving_area = ConvexHull(kp_driving_initial_np.squeeze(0)).volume + motion_multiplier = np.sqrt(source_area) / np.sqrt(driving_area) + # motion_multiplier = np.cbrt(source_area) / np.cbrt(driving_area) + + return motion_multiplier + +def suffix(filename): + """a.jpg -> jpg""" + pos = filename.rfind(".") + if pos == -1: + return "" + return filename[pos + 1:] + + +def prefix(filename): + """a.jpg -> a""" + pos = filename.rfind(".") + if pos == -1: + return filename + return filename[:pos] + + +def basename(filename): + """a/b/c.jpg -> c""" + return prefix(osp.basename(filename)) + + +def remove_suffix(filepath): + """a/b/c.jpg -> a/b/c""" + return osp.join(osp.dirname(filepath), basename(filepath)) + + +def is_image(file_path): + image_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp') + return file_path.lower().endswith(image_extensions) + + +def is_video(file_path): + if file_path.lower().endswith((".mp4", ".mov", ".avi", ".webm")) or osp.isdir(file_path): + return True + return False + + +def is_template(file_path): + if file_path.endswith(".pkl"): + return True + return False + + +def mkdir(d, log=False): + # return self-assined `d`, for one line code + if not osp.exists(d): + os.makedirs(d, exist_ok=True) + if log: + print(f"Make dir: {d}") + return d + + +def squeeze_tensor_to_numpy(tensor): + out = tensor.data.squeeze(0).cpu().numpy() + return out + + +def dct2device(dct: dict, device): + for key in dct: + if isinstance(dct[key], torch.Tensor): + dct[key] = dct[key].to(device) + else: + dct[key] = torch.tensor(dct[key]).to(device) + return dct + + +def concat_feat(kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor: + """ + kp_source: (bs, k, 3) + kp_driving: (bs, k, 3) + Return: (bs, 2k*3) + """ + bs_src = kp_source.shape[0] + bs_dri = kp_driving.shape[0] + assert bs_src == bs_dri, 'batch size must be equal' + + feat = torch.cat([kp_source.view(bs_src, -1), kp_driving.view(bs_dri, -1)], dim=1) + return feat + + +def remove_ddp_dumplicate_key(state_dict): + state_dict_new = OrderedDict() + for key in state_dict.keys(): + state_dict_new[key.replace('module.', '')] = state_dict[key] + return state_dict_new + + +def load_model(ckpt_path, model_config, device, model_type): + model_params = model_config['model_params'][f'{model_type}_params'] + + if model_type == 'appearance_feature_extractor': + model = AppearanceFeatureExtractor(**model_params).to(device) + elif model_type == 'motion_extractor': + model = MotionExtractor(**model_params).to(device) + elif model_type == 'warping_module': + model = WarpingNetwork(**model_params).to(device) + elif model_type == 'spade_generator': + model = SPADEDecoder(**model_params).to(device) + elif model_type == 'stitching_retargeting_module': + # Special handling for stitching and retargeting module + config = model_config['model_params']['stitching_retargeting_module_params'] + checkpoint = torch.load(ckpt_path, map_location=lambda storage, loc: storage) + + stitcher = StitchingRetargetingNetwork(**config.get('stitching')) + stitcher.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_shoulder'])) + stitcher = stitcher.to(device) + stitcher.eval() + + retargetor_lip = StitchingRetargetingNetwork(**config.get('lip')) + retargetor_lip.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_mouth'])) + retargetor_lip = retargetor_lip.to(device) + retargetor_lip.eval() + + retargetor_eye = StitchingRetargetingNetwork(**config.get('eye')) + retargetor_eye.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_eye'])) + retargetor_eye = retargetor_eye.to(device) + retargetor_eye.eval() + + return { + 'stitching': stitcher, + 'lip': retargetor_lip, + 'eye': retargetor_eye + } + else: + raise ValueError(f"Unknown model type: {model_type}") + + model.load_state_dict(torch.load(ckpt_path, map_location=lambda storage, loc: storage)) + model.eval() + return model + + +def load_description(fp): + with open(fp, 'r', encoding='utf-8') as f: + content = f.read() + return content + + +def is_square_video(video_path): + video = cv2.VideoCapture(video_path) + + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + video.release() + # if width != height: + # gr.Info(f"Uploaded video is not square, force do crop (driving) to be True") + + return width == height + +def clean_state_dict(state_dict): + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k[:7] == 'module.': + k = k[7:] # remove `module.` + new_state_dict[k] = v + return new_state_dict diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/human_landmark_runner.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/human_landmark_runner.py new file mode 100644 index 0000000..62c3b96 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/human_landmark_runner.py @@ -0,0 +1,95 @@ +# coding: utf-8 + +import os.path as osp +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) +import torch +import numpy as np +import onnxruntime +from .timer import Timer +from .rprint import rlog +from .crop import crop_image, _transform_pts + + +def make_abs_path(fn): + return osp.join(osp.dirname(osp.realpath(__file__)), fn) + + +def to_ndarray(obj): + if isinstance(obj, torch.Tensor): + return obj.cpu().numpy() + elif isinstance(obj, np.ndarray): + return obj + else: + return np.array(obj) + + +class LandmarkRunner(object): + """landmark runner""" + + def __init__(self, **kwargs): + ckpt_path = kwargs.get('ckpt_path') + onnx_provider = kwargs.get('onnx_provider', 'cuda') # ้ป˜่ฎค็”จcuda + device_id = kwargs.get('device_id', 0) + self.dsize = kwargs.get('dsize', 224) + self.timer = Timer() + + if onnx_provider.lower() == 'cuda': + self.session = onnxruntime.InferenceSession( + ckpt_path, providers=[ + ('CUDAExecutionProvider', {'device_id': device_id}) + ] + ) + elif onnx_provider.lower() == 'mps': + self.session = onnxruntime.InferenceSession( + ckpt_path, providers=[ + 'CoreMLExecutionProvider' + ] + ) + else: + opts = onnxruntime.SessionOptions() + opts.intra_op_num_threads = 4 # ้ป˜่ฎค็บฟ็จ‹ๆ•ฐไธบ 4 + self.session = onnxruntime.InferenceSession( + ckpt_path, providers=['CPUExecutionProvider'], + sess_options=opts + ) + + def _run(self, inp): + out = self.session.run(None, {'input': inp}) + return out + + def run(self, img_rgb: np.ndarray, lmk=None): + if lmk is not None: + crop_dct = crop_image(img_rgb, lmk, dsize=self.dsize, scale=1.5, vy_ratio=-0.1) + img_crop_rgb = crop_dct['img_crop'] + else: + # NOTE: force resize to 224x224, NOT RECOMMEND! + img_crop_rgb = cv2.resize(img_rgb, (self.dsize, self.dsize)) + scale = max(img_rgb.shape[:2]) / self.dsize + crop_dct = { + 'M_c2o': np.array([ + [scale, 0., 0.], + [0., scale, 0.], + [0., 0., 1.], + ], dtype=np.float32), + } + + inp = (img_crop_rgb.astype(np.float32) / 255.).transpose(2, 0, 1)[None, ...] # HxWx3 (BGR) -> 1x3xHxW (RGB!) + + out_lst = self._run(inp) + out_pts = out_lst[2] + + # 2d landmarks 203 points + lmk = to_ndarray(out_pts[0]).reshape(-1, 2) * self.dsize # scale to 0-224 + lmk = _transform_pts(lmk, M=crop_dct['M_c2o']) + + return lmk + + def warmup(self): + self.timer.tic() + + dummy_image = np.zeros((1, 3, self.dsize, self.dsize), dtype=np.float32) + + _ = self._run(dummy_image) + + elapse = self.timer.toc() + rlog(f'LandmarkRunner warmup time: {elapse:.3f}s') diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/io.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/io.py new file mode 100644 index 0000000..42b2d61 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/io.py @@ -0,0 +1,117 @@ +# coding: utf-8 + +import os.path as osp +import imageio +import numpy as np +import pickle +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) + +from .helper import mkdir, suffix + + +def load_image_rgb(image_path: str): + if not osp.exists(image_path): + raise FileNotFoundError(f"Image not found: {image_path}") + img = cv2.imread(image_path, cv2.IMREAD_COLOR) + return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + +def load_video(video_info, n_frames=-1): + reader = imageio.get_reader(video_info, "ffmpeg") + + ret = [] + for idx, frame_rgb in enumerate(reader): + if n_frames > 0 and idx >= n_frames: + break + ret.append(frame_rgb) + + reader.close() + return ret + + +def contiguous(obj): + if not obj.flags.c_contiguous: + obj = obj.copy(order="C") + return obj + + +def resize_to_limit(img: np.ndarray, max_dim=1920, division=2): + """ + ajust the size of the image so that the maximum dimension does not exceed max_dim, and the width and the height of the image are multiples of n. + :param img: the image to be processed. + :param max_dim: the maximum dimension constraint. + :param n: the number that needs to be multiples of. + :return: the adjusted image. + """ + h, w = img.shape[:2] + + # ajust the size of the image according to the maximum dimension + if max_dim > 0 and max(h, w) > max_dim: + if h > w: + new_h = max_dim + new_w = int(w * (max_dim / h)) + else: + new_w = max_dim + new_h = int(h * (max_dim / w)) + img = cv2.resize(img, (new_w, new_h)) + + # ensure that the image dimensions are multiples of n + division = max(division, 1) + new_h = img.shape[0] - (img.shape[0] % division) + new_w = img.shape[1] - (img.shape[1] % division) + + if new_h == 0 or new_w == 0: + # when the width or height is less than n, no need to process + return img + + if new_h != img.shape[0] or new_w != img.shape[1]: + img = img[:new_h, :new_w] + + return img + + +def load_img_online(obj, mode="bgr", **kwargs): + max_dim = kwargs.get("max_dim", 1920) + n = kwargs.get("n", 2) + if isinstance(obj, str): + if mode.lower() == "gray": + img = cv2.imread(obj, cv2.IMREAD_GRAYSCALE) + else: + img = cv2.imread(obj, cv2.IMREAD_COLOR) + else: + img = obj + + # Resize image to satisfy constraints + img = resize_to_limit(img, max_dim=max_dim, division=n) + + if mode.lower() == "bgr": + return contiguous(img) + elif mode.lower() == "rgb": + return contiguous(img[..., ::-1]) + else: + raise Exception(f"Unknown mode {mode}") + + +def load(fp): + suffix_ = suffix(fp) + + if suffix_ == "npy": + return np.load(fp) + elif suffix_ == "pkl": + return pickle.load(open(fp, "rb")) # nosec B301 + else: + raise Exception(f"Unknown type: {suffix}") + + +def dump(wfp, obj): + wd = osp.split(wfp)[0] + if wd != "" and not osp.exists(wd): + mkdir(wd) + + _suffix = suffix(wfp) + if _suffix == "npy": + np.save(wfp, obj) + elif _suffix == "pkl": + pickle.dump(obj, open(wfp, "wb")) + else: + raise Exception("Unknown type: {}".format(_suffix)) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/resources/clip_embedding_68.pkl b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/resources/clip_embedding_68.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d69ca7f8481d3d87ee079bfb51f96d98b68961cc GIT binary patch literal 71352 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b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/retargeting_utils.py @@ -0,0 +1,24 @@ + +""" +Functions to compute distance ratios between specific pairs of facial landmarks +""" + +import numpy as np + + +def calculate_distance_ratio(lmk: np.ndarray, idx1: int, idx2: int, idx3: int, idx4: int, eps: float = 1e-6) -> np.ndarray: + return (np.linalg.norm(lmk[:, idx1] - lmk[:, idx2], axis=1, keepdims=True) / + (np.linalg.norm(lmk[:, idx3] - lmk[:, idx4], axis=1, keepdims=True) + eps)) + + +def calc_eye_close_ratio(lmk: np.ndarray, target_eye_ratio: np.ndarray = None) -> np.ndarray: + lefteye_close_ratio = calculate_distance_ratio(lmk, 6, 18, 0, 12) + righteye_close_ratio = calculate_distance_ratio(lmk, 30, 42, 24, 36) + if target_eye_ratio is not None: + return np.concatenate([lefteye_close_ratio, righteye_close_ratio, target_eye_ratio], axis=1) + else: + return np.concatenate([lefteye_close_ratio, righteye_close_ratio], axis=1) + + +def calc_lip_close_ratio(lmk: np.ndarray) -> np.ndarray: + return calculate_distance_ratio(lmk, 90, 102, 48, 66) diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/rprint.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/rprint.py new file mode 100644 index 0000000..c43a42f --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/rprint.py @@ -0,0 +1,16 @@ +# coding: utf-8 + +""" +custom print and log functions +""" + +__all__ = ['rprint', 'rlog'] + +try: + from rich.console import Console + console = Console() + rprint = console.print + rlog = console.log +except: + rprint = print + rlog = print diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/timer.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/timer.py new file mode 100644 index 0000000..3570fa4 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/timer.py @@ -0,0 +1,29 @@ +# coding: utf-8 + +""" +tools to measure elapsed time +""" + +import time + +class Timer(object): + """A simple timer.""" + + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + return self.diff + + def clear(self): + self.start_time = 0. + self.diff = 0. diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/video.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/video.py new file mode 100644 index 0000000..2e34e6d --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/video.py @@ -0,0 +1,218 @@ +# coding: utf-8 + +""" +Functions for processing video + +ATTENTION: you need to install ffmpeg and ffprobe in your env! +""" + +import os.path as osp +import numpy as np +import subprocess +import imageio +import cv2 +from rich.progress import track + +from .rprint import rlog as log +from .rprint import rprint as print +from .helper import prefix + + +def exec_cmd(cmd): + return subprocess.run(cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + + +def images2video(images, wfp, **kwargs): + fps = kwargs.get('fps', 30) + video_format = kwargs.get('format', 'mp4') # default is mp4 format + codec = kwargs.get('codec', 'libx264') # default is libx264 encoding + quality = kwargs.get('quality') # video quality + pixelformat = kwargs.get('pixelformat', 'yuv420p') # video pixel format + image_mode = kwargs.get('image_mode', 'rgb') + macro_block_size = kwargs.get('macro_block_size', 2) + ffmpeg_params = ['-crf', str(kwargs.get('crf', 18))] + + writer = imageio.get_writer( + wfp, fps=fps, format=video_format, + codec=codec, quality=quality, ffmpeg_params=ffmpeg_params, pixelformat=pixelformat, macro_block_size=macro_block_size + ) + + n = len(images) + for i in track(range(n), description='Writing', transient=True): + if image_mode.lower() == 'bgr': + writer.append_data(images[i][..., ::-1]) + else: + writer.append_data(images[i]) + + writer.close() + + +def video2gif(video_fp, fps=30, size=256): + if osp.exists(video_fp): + d = osp.split(video_fp)[0] + fn = prefix(osp.basename(video_fp)) + palette_wfp = osp.join(d, 'palette.png') + gif_wfp = osp.join(d, f'{fn}.gif') + # generate the palette + cmd = f'ffmpeg -i "{video_fp}" -vf "fps={fps},scale={size}:-1:flags=lanczos,palettegen" "{palette_wfp}" -y' + exec_cmd(cmd) + # use the palette to generate the gif + cmd = f'ffmpeg -i "{video_fp}" -i "{palette_wfp}" -filter_complex "fps={fps},scale={size}:-1:flags=lanczos[x];[x][1:v]paletteuse" "{gif_wfp}" -y' + exec_cmd(cmd) + return gif_wfp + else: + raise FileNotFoundError(f"video_fp: {video_fp} not exists!") + + +def merge_audio_video(video_fp, audio_fp, wfp): + if osp.exists(video_fp) and osp.exists(audio_fp): + cmd = f'ffmpeg -i "{video_fp}" -i "{audio_fp}" -c:v copy -c:a aac "{wfp}" -y' + exec_cmd(cmd) + print(f'merge {video_fp} and {audio_fp} to {wfp}') + else: + print(f'video_fp: {video_fp} or audio_fp: {audio_fp} not exists!') + + +def blend(img: np.ndarray, mask: np.ndarray, background_color=(255, 255, 255)): + mask_float = mask.astype(np.float32) / 255. + background_color = np.array(background_color).reshape([1, 1, 3]) + bg = np.ones_like(img) * background_color + img = np.clip(mask_float * img + (1 - mask_float) * bg, 0, 255).astype(np.uint8) + return img + + +def concat_frames(driving_image_lst, source_image_lst, I_p_lst): + # TODO: add more concat style, e.g., left-down corner driving + out_lst = [] + h, w, _ = I_p_lst[0].shape + source_image_resized_lst = [cv2.resize(img, (w, h)) for img in source_image_lst] + + for idx, _ in track(enumerate(I_p_lst), total=len(I_p_lst), description='Concatenating result...'): + I_p = I_p_lst[idx] + source_image_resized = source_image_resized_lst[idx] if len(source_image_lst) > 1 else source_image_resized_lst[0] + + if driving_image_lst is None: + out = np.hstack((source_image_resized, I_p)) + else: + driving_image = driving_image_lst[idx] + driving_image_resized = cv2.resize(driving_image, (w, h)) + out = np.hstack((driving_image_resized, source_image_resized, I_p)) + + out_lst.append(out) + return out_lst + + +class VideoWriter: + def __init__(self, **kwargs): + self.fps = kwargs.get('fps', 30) + self.wfp = kwargs.get('wfp', 'video.mp4') + self.video_format = kwargs.get('format', 'mp4') + self.codec = kwargs.get('codec', 'libx264') + self.quality = kwargs.get('quality') + self.pixelformat = kwargs.get('pixelformat', 'yuv420p') + self.image_mode = kwargs.get('image_mode', 'rgb') + self.ffmpeg_params = kwargs.get('ffmpeg_params') + + self.writer = imageio.get_writer( + self.wfp, fps=self.fps, format=self.video_format, + codec=self.codec, quality=self.quality, + ffmpeg_params=self.ffmpeg_params, pixelformat=self.pixelformat + ) + + def write(self, image): + if self.image_mode.lower() == 'bgr': + self.writer.append_data(image[..., ::-1]) + else: + self.writer.append_data(image) + + def close(self): + if self.writer is not None: + self.writer.close() + + +def change_video_fps(input_file, output_file, fps=20, codec='libx264', crf=12): + cmd = f'ffmpeg -i "{input_file}" -c:v {codec} -crf {crf} -r {fps} "{output_file}" -y' + exec_cmd(cmd) + + +def get_fps(filepath, default_fps=25): + try: + fps = cv2.VideoCapture(filepath).get(cv2.CAP_PROP_FPS) + + if fps in (0, None): + fps = default_fps + except Exception as e: + log(e) + fps = default_fps + + return fps + + +def has_audio_stream(video_path: str) -> bool: + """ + Check if the video file contains an audio stream. + + :param video_path: Path to the video file + :return: True if the video contains an audio stream, False otherwise + """ + if osp.isdir(video_path): + return False + + cmd = [ + 'ffprobe', + '-v', 'error', + '-select_streams', 'a', + '-show_entries', 'stream=codec_type', + '-of', 'default=noprint_wrappers=1:nokey=1', + f'"{video_path}"' + ] + + try: + # result = subprocess.run(cmd, capture_output=True, text=True) + result = exec_cmd(' '.join(cmd)) + if result.returncode != 0: + log(f"Error occurred while probing video: {result.stderr}") + return False + + # Check if there is any output from ffprobe command + return bool(result.stdout.strip()) + except Exception as e: + log( + f"Error occurred while probing video: {video_path}, " + "you may need to install ffprobe! (https://ffmpeg.org/download.html) " + "Now set audio to false!", + style="bold red" + ) + return False + + +def add_audio_to_video(silent_video_path: str, audio_video_path: str, output_video_path: str): + cmd = [ + 'ffmpeg', + '-y', + '-i', f'"{silent_video_path}"', + '-i', f'"{audio_video_path}"', + '-map', '0:v', + '-map', '1:a', + '-c:v', 'copy', + '-shortest', + f'"{output_video_path}"' + ] + + try: + exec_cmd(' '.join(cmd)) + log(f"Video with audio generated successfully: {output_video_path}") + except subprocess.CalledProcessError as e: + log(f"Error occurred: {e}") + + +def bb_intersection_over_union(boxA, boxB): + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1) + boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) + boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) + iou = interArea / float(boxAArea + boxBArea - interArea) + return iou diff --git a/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/viz.py b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/viz.py new file mode 100644 index 0000000..59443cb --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/liveportrait/src/utils/viz.py @@ -0,0 +1,19 @@ +# coding: utf-8 + +import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) + + +def viz_lmk(img_, vps, **kwargs): + """ๅฏ่ง†ๅŒ–็‚น""" + lineType = kwargs.get("lineType", cv2.LINE_8) # cv2.LINE_AA + img_for_viz = img_.copy() + for pt in vps: + cv2.circle( + img_for_viz, + (int(pt[0]), int(pt[1])), + radius=kwargs.get("radius", 1), + color=(0, 255, 0), + thickness=kwargs.get("thickness", 1), + lineType=lineType, + ) + return img_for_viz diff --git a/usecases/ai/digital-avatar/backend/liveportrait/main.py b/usecases/ai/digital-avatar/backend/liveportrait/main.py new file mode 100644 index 0000000..7ca59f9 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/main.py @@ -0,0 +1,92 @@ +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager +from fastapi import FastAPI, UploadFile, File, APIRouter +import uvicorn +import os +import logging + +import tyro +from liveportrait.src.config.argument_config import ArgumentConfig +from liveportrait.src.config.inference_config import InferenceConfig +from liveportrait.src.config.crop_config import CropConfig +from liveportrait.src.live_portrait_pipeline import LivePortraitPipeline +from pydantic import BaseModel +from typing import Optional + +import torch +import json + +from liveportrait.intel_xpu.xpu_override import xpu_override +xpu_override() + +logger = logging.getLogger('uvicorn.error') +LIVEPORTRAIT=None + +def partial_fields(target_class, kwargs): + return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)}) + +@asynccontextmanager +async def lifespan(app: FastAPI): + global LIVEPORTRAIT + args = tyro.cli(ArgumentConfig) + # specify configs for inference + inference_cfg = partial_fields(InferenceConfig, args.__dict__) + crop_cfg = partial_fields(CropConfig, args.__dict__) + LIVEPORTRAIT = LivePortraitPipeline( + inference_cfg=inference_cfg, + crop_cfg=crop_cfg + ) + args.source="assets/image.png" + args.driving="assets/idle.mp4" + args.output_dir="assets" + + LIVEPORTRAIT.execute(args) + yield + + +allowed_cors = json.loads(os.getenv("ALLOWED_CORS", '["http://localhost"]')) +app = FastAPI(lifespan=lifespan) +app.add_middleware( + CORSMiddleware, + allow_origins=allowed_cors, + allow_credentials=True, + allow_methods=['*'], + allow_headers=['*'], +) + +@app.get('/healthcheck', status_code=200) +def get_healthcheck(): + return 'OK' + +class InferenceRequest(BaseModel): + source: str = "assets/image.png" + driving: str = "assets/idle.mp4" + +router = APIRouter(prefix="/v1", + responses={404: {"description": "Unable to find route"}}) + +@router.post("/inference") +async def inference(data: Optional[InferenceRequest]= None): + global LIVEPORTRAIT + args = tyro.cli(ArgumentConfig) + + if data is not None: + args.source=data.source + args.driving=data.driving + else: + args.source="assets/image.png" + args.driving="assets/idle.mp4" + args.output_dir="assets" + args.output_name="video" + + LIVEPORTRAIT.execute(args) + +app.include_router(router) + +if __name__ == "__main__": + uvicorn.run( + "main:app", + host=os.environ.get('SERVER_HOST', "0.0.0.0"), + port=int(os.environ.get('SERVER_PORT', "8012")), + reload=os.environ.get('SERVER_RELOAD', False) + ) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/liveportrait/requirements.txt b/usecases/ai/digital-avatar/backend/liveportrait/requirements.txt new file mode 100644 index 0000000..66921bd --- /dev/null +++ b/usecases/ai/digital-avatar/backend/liveportrait/requirements.txt @@ -0,0 +1,21 @@ +--pre +--extra-index-url https://download.pytorch.org/whl/nightly/xpu +torch==2.6.0.dev20241115+xpu +torchvision +torchaudio +transformers==4.46.2 + +tyro==0.8.14 +opencv-python==4.10.* +imageio==2.36.0 +scipy==1.14.1 +onnxruntime-gpu==1.20.0 +onnx==1.17.0 +requests==2.32.3 +tqdm==4.67.0 +scikit-image==0.24.0 +pykalman==0.9.7 +pyyaml==6.0.2 +imageio[ffmpeg]==2.36.0 + +fastapi[standard] \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/.dockerignore b/usecases/ai/digital-avatar/backend/wav2lip/.dockerignore new file mode 100644 index 0000000..0f31a95 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/.dockerignore @@ -0,0 +1,4 @@ +__pycache__ +*env +cache +checkpoints \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/Dockerfile b/usecases/ai/digital-avatar/backend/wav2lip/Dockerfile new file mode 100644 index 0000000..d34800d --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/Dockerfile @@ -0,0 +1,77 @@ +FROM debian:12-slim + +ARG DEBIAN_FRONTEND=noninteractive +ARG RENDER_GROUP_ID +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install --no-install-recommends -y \ + sudo \ + wget \ + ca-certificates \ + ffmpeg \ + libsm6 \ + libxext6 \ + curl \ + git \ + build-essential \ + libssl-dev \ + zlib1g-dev \ + libbz2-dev \ + libreadline-dev \ + libsqlite3-dev \ + llvm \ + libncursesw5-dev \ + xz-utils \ + tk-dev \ + libxml2-dev \ + libxmlsec1-dev \ + libffi-dev \ + liblzma-dev \ + && addgroup --system intel --gid 1000 \ + && adduser --system --ingroup intel --uid 1000 --home /home/intel intel \ + && echo "intel ALL=(ALL:ALL) NOPASSWD:ALL" > /etc/sudoers.d/intel \ + && groupadd -g ${RENDER_GROUP_ID} render \ + && usermod -aG render intel \ + && rm -rf /var/lib/apt/lists/* \ + && mkdir -p /usr/src \ + && chown -R intel:intel /usr/src + +# Intel GPU Driver +RUN apt-get update && apt-get install -y gnupg + +RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \ + gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \ + tee /etc/apt/sources.list.d/intel-gpu-jammy.list && \ + apt update && \ + apt-get install -y --no-install-recommends libze1 intel-level-zero-gpu intel-opencl-icd clinfo libze-dev intel-ocloc + + +USER intel +WORKDIR /usr/src/app + +# Set environment variables for pyenv +ENV PYENV_ROOT="/usr/src/app/.pyenv" +ENV PATH="$PYENV_ROOT/bin:$PYENV_ROOT/shims:$PATH" + +# Install pyenv +RUN curl https://pyenv.run | bash \ + && echo 'export PYENV_ROOT="$PYENV_ROOT"' >> ~/.bashrc \ + && echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc \ + && echo 'eval "$(pyenv init --path)"' >> ~/.bashrc \ + && echo 'eval "$(pyenv init -)"' >> ~/.bashrc \ + && . ~/.bashrc \ + && pyenv install 3.10.15 \ + && pyenv global 3.10.15 + +RUN python3 -m pip install --upgrade pip +COPY ./backend/wav2lip/requirements.txt . +RUN python3 -m pip install --no-cache-dir -r requirements.txt + +COPY --chown=intel ./backend/wav2lip . +COPY --chown=intel ./weights/wav2lip_gan.pth ./setup/ +COPY --chown=intel ./assets/*.png ./assets/*.mp4 ./wav2lip/assets/ +RUN python3 setup/download_model.py + +HEALTHCHECK --interval=30s --timeout=180s --start-period=60s --retries=5 \ + CMD sh -c 'PORT=${SERVER_PORT:-8011} && wget --no-verbose -O /dev/null --tries=1 http://localhost:$PORT/healthcheck || exit 1' \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/main.py b/usecases/ai/digital-avatar/backend/wav2lip/main.py new file mode 100644 index 0000000..15137ee --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/main.py @@ -0,0 +1,84 @@ +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager +from fastapi.responses import FileResponse +from fastapi import FastAPI, UploadFile, File, APIRouter +import tempfile +import uvicorn +import os +import shutil +import logging +from starlette.background import BackgroundTasks +from wav2lip.ov_wav2lip import OVWav2Lip +import wave +import numpy as np +import json + +logger = logging.getLogger('uvicorn.error') +WAV2LIP=None +@asynccontextmanager +async def lifespan(app: FastAPI): + global WAV2LIP + WAV2LIP = OVWav2Lip(device="CPU", avatar_path="assets/video.mp4") + + print("warming up") + temp_filename = "empty.wav" + with wave.open(temp_filename, "w") as wf: + wf.setnchannels(1) # mono + wf.setsampwidth(2) # 2 bytes per sample + wf.setframerate(16000) # 16 kHz + wf.writeframes(np.zeros(16000 * 2, dtype=np.int16).tobytes()) # 1 second of silence + + result= WAV2LIP.inference(temp_filename) + if os.path.exists(result): + os.remove(result) + os.remove(temp_filename) + yield + + +allowed_cors = json.loads(os.getenv("ALLOWED_CORS", '["http://localhost"]')) +app = FastAPI(lifespan=lifespan) +app.add_middleware( + CORSMiddleware, + allow_origins=allowed_cors, + allow_credentials=True, + allow_methods=['*'], + allow_headers=['*'], +) + +@app.get('/healthcheck', status_code=200) +def get_healthcheck(): + return 'OK' + +router = APIRouter(prefix="/v1", + responses={404: {"description": "Unable to find route"}}) + +@router.post("/inference") +async def inference(bg_task: BackgroundTasks, file: UploadFile=File(...) ): + async def remove_file(file_name): + if os.path.exists(file_name): + try: + os.remove(file_name) + except: + logger.error(f"File: {file_name} not available") + + global WAV2LIP + if not file.filename.endswith(".wav"): + return "Only .wav files are allowed" + + with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio: + temp_audio_path = temp_audio.name + with open(temp_audio_path, "wb") as f: + shutil.copyfileobj(file.file, f) + result = WAV2LIP.inference(temp_audio_path) + print(result, flush=True) + return FileResponse(result, media_type="video/mp4", background=bg_task.add_task(remove_file, result)) + +app.include_router(router) + +if __name__ == "__main__": + uvicorn.run( + "main:app", + host=os.environ.get('SERVER_HOST', "0.0.0.0"), + port=int(os.environ.get('SERVER_PORT', "8011")), + reload=True + ) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/requirements.txt b/usecases/ai/digital-avatar/backend/wav2lip/requirements.txt new file mode 100644 index 0000000..71b4e63 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/requirements.txt @@ -0,0 +1,8 @@ +numpy==1.26.4 +opencv-python==4.10.* +librosa==0.10.2 +tqdm==4.67.0 +torch==2.3.0 +openvino==2024.4.0 + +fastapi[standard]==0.115.4 \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/setup/download_model.py b/usecases/ai/digital-avatar/backend/wav2lip/setup/download_model.py new file mode 100644 index 0000000..bc241a4 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/setup/download_model.py @@ -0,0 +1,70 @@ +import os +import sys + +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'wav2lip'))) + +import requests +from tqdm import tqdm +import numpy as np +import openvino as ov +from pathlib import Path +import torch +from face_detection.detection.sfd.net_s3fd import s3fd +from models import Wav2Lip +from torch.utils.model_zoo import load_url +import shutil + +def _load(checkpoint_path): + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + +def load_model(path): + model = Wav2Lip() + print("Load checkpoint from: {}".format(path) ) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + + return model.eval() + +if __name__ == "__main__": + models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth'} + path_to_detector = "wav2lip/checkpoints/face_detection.pth" + path_to_wav2lip = "setup/wav2lip_gan.pth" + + OV_FACE_DETECTION_MODEL_PATH = Path("wav2lip/checkpoints/face_detection.xml") + OV_WAV2LIP_MODEL_PATH = Path("wav2lip/checkpoints/wav2lip_gan.xml") + + # Convert Face Detection Model + print("Convert Face Detection Model ...") + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + face_detector = s3fd() + face_detector.load_state_dict(model_weights) + + if not OV_FACE_DETECTION_MODEL_PATH.exists(): + face_detection_dummy_inputs = torch.FloatTensor(np.random.rand(1, 3, 768, 576)) + face_detection_ov_model = ov.convert_model(face_detector, example_input=face_detection_dummy_inputs) + ov.save_model(face_detection_ov_model, OV_FACE_DETECTION_MODEL_PATH) + print("Converted face detection OpenVINO model: ", OV_FACE_DETECTION_MODEL_PATH) + + print("Convert Wav2Lip Model ...") + if not OV_WAV2LIP_MODEL_PATH.exists(): + if not os.path.isfile(path_to_wav2lip): + raise Exception("Wav2Lip model not found") + wav2lip = load_model(path_to_wav2lip) + img_batch = torch.FloatTensor(np.random.rand(123, 6, 96, 96)) + mel_batch = torch.FloatTensor(np.random.rand(123, 1, 80, 16)) + + if not OV_WAV2LIP_MODEL_PATH.exists(): + example_inputs = {"audio_sequences": mel_batch, "face_sequences": img_batch} + wav2lip_ov_model = ov.convert_model(wav2lip, example_input=example_inputs) + ov.save_model(wav2lip_ov_model, OV_WAV2LIP_MODEL_PATH) + print("Converted face detection OpenVINO model: ", OV_FACE_DETECTION_MODEL_PATH) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/setup/wav2lip_gan.pth b/usecases/ai/digital-avatar/backend/wav2lip/setup/wav2lip_gan.pth new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/README.md b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/README.md new file mode 100644 index 0000000..170b618 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/README.md @@ -0,0 +1,188 @@ +# **Wav2Lip**: *Accurately Lip-syncing Videos In The Wild* +This sample based on orginal [wav2lip repo](https://github.com/Rudrabha/Wav2Lip) to introduce how to deploy wav2lip pipeline with OpenVINO as follow +- Support Pytorch model to OpenVINO model conversion +- Build wav2lip pipeline with OpenVINO +#### 1. Setup envirnoment +```bash +conda create -n wav2lip python=3.8 +conda activate wav2lip +pip install -r requirments.txt +sudo apt-get install ffmpeg +``` +#### 2. Download Wav2lip model from [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?e=TBFBVW) and move to checkpoints folder + +#### 3. Convert pytorch face detection model and wav2lip model to OpenVINO model +```python +python export_openvino.py +``` + +#### 4. Run wav2lip inference with OpenVINO +```python +python inference_ov.py --face_detection_path checkpoints/face_detection.xml --wav2lip_path checkpoints/wav2lip.xml --inference_device CPU --face data_video_sun_5s.mp4 --audio data_audio_sun_5s.wav +``` + +The generated video will be saved as `results/result_voice.mp4` +### Wav2Lip is hosted for free at [Sync Labs](https://synclabs.so/) + +Are you looking to integrate this into a product? We have a turn-key hosted API with new and improved lip-syncing models here: https://synclabs.so/ + +For any other commercial / enterprise requests, please contact us at pavan@synclabs.so and prady@synclabs.so + +To reach out to the authors directly you can reach us at prajwal@synclabs.so, rudrabha@synclabs.so. + +This code is part of the paper: _A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild_ published at ACM Multimedia 2020. + +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/a-lip-sync-expert-is-all-you-need-for-speech/lip-sync-on-lrs2)](https://paperswithcode.com/sota/lip-sync-on-lrs2?p=a-lip-sync-expert-is-all-you-need-for-speech) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/a-lip-sync-expert-is-all-you-need-for-speech/lip-sync-on-lrs3)](https://paperswithcode.com/sota/lip-sync-on-lrs3?p=a-lip-sync-expert-is-all-you-need-for-speech) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/a-lip-sync-expert-is-all-you-need-for-speech/lip-sync-on-lrw)](https://paperswithcode.com/sota/lip-sync-on-lrw?p=a-lip-sync-expert-is-all-you-need-for-speech) + +|๐Ÿ“‘ Original Paper|๐Ÿ“ฐ Project Page|๐ŸŒ€ Demo|โšก Live Testing|๐Ÿ“” Colab Notebook +|:-:|:-:|:-:|:-:|:-:| +[Paper](http://arxiv.org/abs/2008.10010) | [Project Page](http://cvit.iiit.ac.in/research/projects/cvit-projects/a-lip-sync-expert-is-all-you-need-for-speech-to-lip-generation-in-the-wild/) | [Demo Video](https://youtu.be/0fXaDCZNOJc) | [Interactive Demo](https://synclabs.so/) | [Colab Notebook](https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing) /[Updated Collab Notebook](https://colab.research.google.com/drive/1IjFW1cLevs6Ouyu4Yht4mnR4yeuMqO7Y#scrollTo=MH1m608OymLH) + +![Logo](https://drive.google.com/uc?export=view&id=1Wn0hPmpo4GRbCIJR8Tf20Akzdi1qjjG9) + +---------- +**Highlights** +---------- + - Weights of the visual quality disc has been updated in readme! + - Lip-sync videos to any target speech with high accuracy :100:. Try our [interactive demo](https://synclabs.so/). + - :sparkles: Works for any identity, voice, and language. Also works for CGI faces and synthetic voices. + - Complete training code, inference code, and pretrained models are available :boom: + - Or, quick-start with the Google Colab Notebook: [Link](https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing). Checkpoints and samples are available in a Google Drive [folder](https://drive.google.com/drive/folders/1I-0dNLfFOSFwrfqjNa-SXuwaURHE5K4k?usp=sharing) as well. There is also a [tutorial video](https://www.youtube.com/watch?v=Ic0TBhfuOrA) on this, courtesy of [What Make Art](https://www.youtube.com/channel/UCmGXH-jy0o2CuhqtpxbaQgA). Also, thanks to [Eyal Gruss](https://eyalgruss.com), there is a more accessible [Google Colab notebook](https://j.mp/wav2lip) with more useful features. A tutorial collab notebook is present at this [link](https://colab.research.google.com/drive/1IjFW1cLevs6Ouyu4Yht4mnR4yeuMqO7Y#scrollTo=MH1m608OymLH). + - :fire: :fire: Several new, reliable evaluation benchmarks and metrics [[`evaluation/` folder of this repo]](https://github.com/Rudrabha/Wav2Lip/tree/master/evaluation) released. Instructions to calculate the metrics reported in the paper are also present. + +-------- +**Disclaimer** +-------- +All results from this open-source code or our [demo website](https://bhaasha.iiit.ac.in/lipsync) should only be used for research/academic/personal purposes only. As the models are trained on the LRS2 dataset, any form of commercial use is strictly prohibited. For commercial requests please contact us directly! + +Prerequisites +------------- +- `Python 3.6` +- ffmpeg: `sudo apt-get install ffmpeg` +- Install necessary packages using `pip install -r requirements.txt`. Alternatively, instructions for using a docker image is provided [here](https://gist.github.com/xenogenesi/e62d3d13dadbc164124c830e9c453668). Have a look at [this comment](https://github.com/Rudrabha/Wav2Lip/issues/131#issuecomment-725478562) and comment on [the gist](https://gist.github.com/xenogenesi/e62d3d13dadbc164124c830e9c453668) if you encounter any issues. +- Face detection [pre-trained model](https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth) should be downloaded to `face_detection/detection/sfd/s3fd.pth`. Alternative [link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/prajwal_k_research_iiit_ac_in/EZsy6qWuivtDnANIG73iHjIBjMSoojcIV0NULXV-yiuiIg?e=qTasa8) if the above does not work. + +Getting the weights +---------- +| Model | Description | Link to the model | +| :-------------: | :---------------: | :---------------: | +| Wav2Lip | Highly accurate lip-sync | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?e=TBFBVW) | +| Wav2Lip + GAN | Slightly inferior lip-sync, but better visual quality | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA?e=n9ljGW) | +| Expert Discriminator | Weights of the expert discriminator | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EQRvmiZg-HRAjvI6zqN9eTEBP74KefynCwPWVmF57l-AYA?e=ZRPHKP) | +| Visual Quality Discriminator | Weights of the visual disc trained in a GAN setup | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EQVqH88dTm1HjlK11eNba5gBbn15WMS0B0EZbDBttqrqkg?e=ic0ljo) | + +Lip-syncing videos using the pre-trained models (Inference) +------- +You can lip-sync any video to any audio: +```bash +python inference.py --checkpoint_path --face --audio +``` +The result is saved (by default) in `results/result_voice.mp4`. You can specify it as an argument, similar to several other available options. The audio source can be any file supported by `FFMPEG` containing audio data: `*.wav`, `*.mp3` or even a video file, from which the code will automatically extract the audio. + +##### Tips for better results: +- Experiment with the `--pads` argument to adjust the detected face bounding box. Often leads to improved results. You might need to increase the bottom padding to include the chin region. E.g. `--pads 0 20 0 0`. +- If you see the mouth position dislocated or some weird artifacts such as two mouths, then it can be because of over-smoothing the face detections. Use the `--nosmooth` argument and give it another try. +- Experiment with the `--resize_factor` argument, to get a lower-resolution video. Why? The models are trained on faces that were at a lower resolution. You might get better, visually pleasing results for 720p videos than for 1080p videos (in many cases, the latter works well too). +- The Wav2Lip model without GAN usually needs more experimenting with the above two to get the most ideal results, and sometimes, can give you a better result as well. + +Preparing LRS2 for training +---------- +Our models are trained on LRS2. See [here](#training-on-datasets-other-than-lrs2) for a few suggestions regarding training on other datasets. +##### LRS2 dataset folder structure + +``` +data_root (mvlrs_v1) +โ”œโ”€โ”€ main, pretrain (we use only main folder in this work) +| โ”œโ”€โ”€ list of folders +| โ”‚ โ”œโ”€โ”€ five-digit numbered video IDs ending with (.mp4) +``` + +Place the LRS2 filelists (train, val, test) `.txt` files in the `filelists/` folder. + +##### Preprocess the dataset for fast training + +```bash +python preprocess.py --data_root data_root/main --preprocessed_root lrs2_preprocessed/ +``` +Additional options like `batch_size` and the number of GPUs to use in parallel to use can also be set. + +##### Preprocessed LRS2 folder structure +``` +preprocessed_root (lrs2_preprocessed) +โ”œโ”€โ”€ list of folders +| โ”œโ”€โ”€ Folders with five-digit numbered video IDs +| โ”‚ โ”œโ”€โ”€ *.jpg +| โ”‚ โ”œโ”€โ”€ audio.wav +``` + +Train! +---------- +There are two major steps: (i) Train the expert lip-sync discriminator, (ii) Train the Wav2Lip model(s). + +##### Training the expert discriminator +You can download [the pre-trained weights](#getting-the-weights) if you want to skip this step. To train it: +```bash +python color_syncnet_train.py --data_root lrs2_preprocessed/ --checkpoint_dir +``` +##### Training the Wav2Lip models +You can either train the model without the additional visual quality discriminator (< 1 day of training) or use the discriminator (~2 days). For the former, run: +```bash +python wav2lip_train.py --data_root lrs2_preprocessed/ --checkpoint_dir --syncnet_checkpoint_path +``` + +To train with the visual quality discriminator, you should run `hq_wav2lip_train.py` instead. The arguments for both files are similar. In both cases, you can resume training as well. Look at `python wav2lip_train.py --help` for more details. You can also set additional less commonly-used hyper-parameters at the bottom of the `hparams.py` file. + +Training on datasets other than LRS2 +------------------------------------ +Training on other datasets might require modifications to the code. Please read the following before you raise an issue: + +- You might not get good results by training/fine-tuning on a few minutes of a single speaker. This is a separate research problem, to which we do not have a solution yet. Thus, we would most likely not be able to resolve your issue. +- You must train the expert discriminator for your own dataset before training Wav2Lip. +- If it is your own dataset downloaded from the web, in most cases, needs to be sync-corrected. +- Be mindful of the FPS of the videos of your dataset. Changes to FPS would need significant code changes. +- The expert discriminator's eval loss should go down to ~0.25 and the Wav2Lip eval sync loss should go down to ~0.2 to get good results. + +When raising an issue on this topic, please let us know that you are aware of all these points. + +We have an HD model trained on a dataset allowing commercial usage. The size of the generated face will be 192 x 288 in our new model. + +Evaluation +---------- +Please check the `evaluation/` folder for the instructions. + +License and Citation +---------- +This repository can only be used for personal/research/non-commercial purposes. However, for commercial requests, please contact us directly at rudrabha@synclabs.so or prajwal@synclabs.so. We have a turn-key hosted API with new and improved lip-syncing models here: https://synclabs.so/ +The size of the generated face will be 192 x 288 in our new models. Please cite the following paper if you use this repository: +``` +@inproceedings{10.1145/3394171.3413532, +author = {Prajwal, K R and Mukhopadhyay, Rudrabha and Namboodiri, Vinay P. and Jawahar, C.V.}, +title = {A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild}, +year = {2020}, +isbn = {9781450379885}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3394171.3413532}, +doi = {10.1145/3394171.3413532}, +booktitle = {Proceedings of the 28th ACM International Conference on Multimedia}, +pages = {484โ€“492}, +numpages = {9}, +keywords = {lip sync, talking face generation, video generation}, +location = {Seattle, WA, USA}, +series = {MM '20} +} +``` + + +Acknowledgments +---------- +Parts of the code structure are inspired by this [TTS repository](https://github.com/r9y9/deepvoice3_pytorch). We thank the author for this wonderful code. The code for Face Detection has been taken from the [face_alignment](https://github.com/1adrianb/face-alignment) repository. We thank the authors for releasing their code and models. We thank [zabique](https://github.com/zabique) for the tutorial collab notebook. + +## Acknowledgements + + - [Awesome Readme Templates](https://awesomeopensource.com/project/elangosundar/awesome-README-templates) + - [Awesome README](https://github.com/matiassingers/awesome-readme) + - [How to write a Good readme](https://bulldogjob.com/news/449-how-to-write-a-good-readme-for-your-github-project) + diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/__init__.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/audio.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/audio.py new file mode 100644 index 0000000..5f6491b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/audio.py @@ -0,0 +1,138 @@ +import librosa +import librosa.filters +import numpy as np +# import tensorflow as tf +from scipy import signal +from scipy.io import wavfile +from .hparams import hparams as hp + +def load_wav(path, sr): + wav = librosa.core.load(path, sr=sr)[0] + duration = librosa.get_duration(y=wav, sr=sr) + return wav, round(duration, 2) + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + #proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + +def get_hop_size(): + hop_size = hp.hop_size + if hop_size is None: + assert hp.frame_shift_ms is not None + hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) + return hop_size + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(np.abs(D)) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def melspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def _lws_processor(): + import lws + return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") + +def _stft(y): + if hp.use_lws: + return _lws_processor(hp).stft(y).T + else: + return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) + +########################################################## +#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram + """ + pad = (fsize - fshift) + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding + """ + M = num_frames(len(x), fsize, fshift) + pad = (fsize - fshift) + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r +########################################################## +#Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + +# Conversions +_mel_basis = None + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + +def _build_mel_basis(): + assert hp.fmax <= hp.sample_rate // 2 + return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, + fmin=hp.fmin, fmax=hp.fmax) + +def _amp_to_db(x): + min_level = np.exp(hp.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + +def _normalize(S): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, + -hp.max_abs_value, hp.max_abs_value) + else: + return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) + + assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 + if hp.symmetric_mels: + return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value + else: + return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) + +def _denormalize(D): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return (((np.clip(D, -hp.max_abs_value, + hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + + hp.min_level_db) + else: + return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) + + if hp.symmetric_mels: + return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) + else: + return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/bbox.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/bbox.py new file mode 100644 index 0000000..4bd7222 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/bbox.py @@ -0,0 +1,129 @@ +from __future__ import print_function +import os +import sys +import cv2 +import random +import datetime +import time +import math +import argparse +import numpy as np +import torch + +try: + from iou import IOU +except BaseException: + # IOU cython speedup 10x + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + +def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): + xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 + dx, dy = (xc - axc) / aww, (yc - ayc) / ahh + dw, dh = math.log(ww / aww), math.log(hh / ahh) + return dx, dy, dw, dh + + +def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): + xc, yc = dx * aww + axc, dy * ahh + ayc + ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh + x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 + return x1, y1, x2, y2 + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def batch_decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/color_syncnet_train.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/color_syncnet_train.py new file mode 100644 index 0000000..afa0054 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/color_syncnet_train.py @@ -0,0 +1,279 @@ +from os.path import dirname, join, basename, isfile +from tqdm import tqdm + +from models import SyncNet_color as SyncNet +import audio + +import torch +from torch import nn +from torch import optim +import torch.backends.cudnn as cudnn +from torch.utils import data as data_utils +import numpy as np + +from glob import glob + +import os, random, cv2, argparse +from hparams import hparams, get_image_list + +parser = argparse.ArgumentParser(description='Code to train the expert lip-sync discriminator') + +parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True) + +parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str) +parser.add_argument('--checkpoint_path', help='Resumed from this checkpoint', default=None, type=str) + +args = parser.parse_args() + + +global_step = 0 +global_epoch = 0 +use_cuda = torch.cuda.is_available() +print('use_cuda: {}'.format(use_cuda)) + +syncnet_T = 5 +syncnet_mel_step_size = 16 + +class Dataset(object): + def __init__(self, split): + self.all_videos = get_image_list(args.data_root, split) + + def get_frame_id(self, frame): + return int(basename(frame).split('.')[0]) + + def get_window(self, start_frame): + start_id = self.get_frame_id(start_frame) + vidname = dirname(start_frame) + + window_fnames = [] + for frame_id in range(start_id, start_id + syncnet_T): + frame = join(vidname, '{}.jpg'.format(frame_id)) + if not isfile(frame): + return None + window_fnames.append(frame) + return window_fnames + + def crop_audio_window(self, spec, start_frame): + # num_frames = (T x hop_size * fps) / sample_rate + start_frame_num = self.get_frame_id(start_frame) + start_idx = int(80. * (start_frame_num / float(hparams.fps))) + + end_idx = start_idx + syncnet_mel_step_size + + return spec[start_idx : end_idx, :] + + + def __len__(self): + return len(self.all_videos) + + def __getitem__(self, idx): + while 1: + idx = random.randint(0, len(self.all_videos) - 1) + vidname = self.all_videos[idx] + + img_names = list(glob(join(vidname, '*.jpg'))) + if len(img_names) <= 3 * syncnet_T: + continue + img_name = random.choice(img_names) + wrong_img_name = random.choice(img_names) + while wrong_img_name == img_name: + wrong_img_name = random.choice(img_names) + + if random.choice([True, False]): + y = torch.ones(1).float() + chosen = img_name + else: + y = torch.zeros(1).float() + chosen = wrong_img_name + + window_fnames = self.get_window(chosen) + if window_fnames is None: + continue + + window = [] + all_read = True + for fname in window_fnames: + img = cv2.imread(fname) + if img is None: + all_read = False + break + try: + img = cv2.resize(img, (hparams.img_size, hparams.img_size)) + except Exception as e: + all_read = False + break + + window.append(img) + + if not all_read: continue + + try: + wavpath = join(vidname, "audio.wav") + wav = audio.load_wav(wavpath, hparams.sample_rate) + + orig_mel = audio.melspectrogram(wav).T + except Exception as e: + continue + + mel = self.crop_audio_window(orig_mel.copy(), img_name) + + if (mel.shape[0] != syncnet_mel_step_size): + continue + + # H x W x 3 * T + x = np.concatenate(window, axis=2) / 255. + x = x.transpose(2, 0, 1) + x = x[:, x.shape[1]//2:] + + x = torch.FloatTensor(x) + mel = torch.FloatTensor(mel.T).unsqueeze(0) + + return x, mel, y + +logloss = nn.BCELoss() +def cosine_loss(a, v, y): + d = nn.functional.cosine_similarity(a, v) + loss = logloss(d.unsqueeze(1), y) + + return loss + +def train(device, model, train_data_loader, test_data_loader, optimizer, + checkpoint_dir=None, checkpoint_interval=None, nepochs=None): + + global global_step, global_epoch + resumed_step = global_step + + while global_epoch < nepochs: + running_loss = 0. + prog_bar = tqdm(enumerate(train_data_loader)) + for step, (x, mel, y) in prog_bar: + model.train() + optimizer.zero_grad() + + # Transform data to CUDA device + x = x.to(device) + + mel = mel.to(device) + + a, v = model(mel, x) + y = y.to(device) + + loss = cosine_loss(a, v, y) + loss.backward() + optimizer.step() + + global_step += 1 + cur_session_steps = global_step - resumed_step + running_loss += loss.item() + + if global_step == 1 or global_step % checkpoint_interval == 0: + save_checkpoint( + model, optimizer, global_step, checkpoint_dir, global_epoch) + + if global_step % hparams.syncnet_eval_interval == 0: + with torch.no_grad(): + eval_model(test_data_loader, global_step, device, model, checkpoint_dir) + + prog_bar.set_description('Loss: {}'.format(running_loss / (step + 1))) + + global_epoch += 1 + +def eval_model(test_data_loader, global_step, device, model, checkpoint_dir): + eval_steps = 1400 + print('Evaluating for {} steps'.format(eval_steps)) + losses = [] + while 1: + for step, (x, mel, y) in enumerate(test_data_loader): + + model.eval() + + # Transform data to CUDA device + x = x.to(device) + + mel = mel.to(device) + + a, v = model(mel, x) + y = y.to(device) + + loss = cosine_loss(a, v, y) + losses.append(loss.item()) + + if step > eval_steps: break + + averaged_loss = sum(losses) / len(losses) + print(averaged_loss) + + return + +def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch): + + checkpoint_path = join( + checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step)) + optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None + torch.save({ + "state_dict": model.state_dict(), + "optimizer": optimizer_state, + "global_step": step, + "global_epoch": epoch, + }, checkpoint_path) + print("Saved checkpoint:", checkpoint_path) + +def _load(checkpoint_path): + if use_cuda: + checkpoint = torch.load(checkpoint_path) + else: + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + +def load_checkpoint(path, model, optimizer, reset_optimizer=False): + global global_step + global global_epoch + + print("Load checkpoint from: {}".format(path)) + checkpoint = _load(path) + model.load_state_dict(checkpoint["state_dict"]) + if not reset_optimizer: + optimizer_state = checkpoint["optimizer"] + if optimizer_state is not None: + print("Load optimizer state from {}".format(path)) + optimizer.load_state_dict(checkpoint["optimizer"]) + global_step = checkpoint["global_step"] + global_epoch = checkpoint["global_epoch"] + + return model + +if __name__ == "__main__": + checkpoint_dir = args.checkpoint_dir + checkpoint_path = args.checkpoint_path + + if not os.path.exists(checkpoint_dir): os.mkdir(checkpoint_dir) + + # Dataset and Dataloader setup + train_dataset = Dataset('train') + test_dataset = Dataset('val') + + train_data_loader = data_utils.DataLoader( + train_dataset, batch_size=hparams.syncnet_batch_size, shuffle=True, + num_workers=hparams.num_workers) + + test_data_loader = data_utils.DataLoader( + test_dataset, batch_size=hparams.syncnet_batch_size, + num_workers=8) + + device = torch.device("cuda" if use_cuda else "cpu") + + # Model + model = SyncNet().to(device) + print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad))) + + optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad], + lr=hparams.syncnet_lr) + + if checkpoint_path is not None: + load_checkpoint(checkpoint_path, model, optimizer, reset_optimizer=False) + + train(device, model, train_data_loader, test_data_loader, optimizer, + checkpoint_dir=checkpoint_dir, + checkpoint_interval=hparams.syncnet_checkpoint_interval, + nepochs=hparams.nepochs) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/export_openvino.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/export_openvino.py new file mode 100644 index 0000000..fef31c8 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/export_openvino.py @@ -0,0 +1,82 @@ +import requests +from tqdm import tqdm +import numpy as np +import os +import openvino as ov +from pathlib import Path +import torch +from face_detection.detection.sfd.net_s3fd import s3fd +from models import Wav2Lip +from torch.utils.model_zoo import load_url + +def download(url, output_path): + response = requests.get(url, stream=True) + total_size = int(response.headers.get('content-length', 0)) + block_size = 1024 + progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True) + + with open(output_path, 'wb') as file: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + file.write(data) + progress_bar.close() + + if total_size != 0 and progress_bar.n != total_size: + print("ERROR: Something went wrong") + else: + print(f"Downloaded FFmpeg to {output_path}") + +def _load(checkpoint_path): + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + +def load_model(path): + model = Wav2Lip() + print("Load checkpoint from: {}".format(path) ) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + + return model.eval() + +if __name__ == "__main__": + models_urls = { + 'wav2lip': "https://iiitaphyd-my.sharepoint.com/:u:/r/personal/radrabha_m_research_iiit_ac_in/Documents/Wav2Lip_Models/wav2lip.pth?csf=1&web=1&e=HbccQs", + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth'} + path_to_detector = "checkpoints/face_detection.pth" + path_to_wav2lip = "checkpoints/wav2lip.pth" + + OV_FACE_DETECTION_MODEL_PATH = Path("checkpoints/face_detection.xml") + OV_WAV2LIP_MODEL_PATH = Path("checkpoints/wav2lip.xml") + + # Convert Face Detection Model + print("Convert Face Detection Model ...") + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + face_detector = s3fd() + face_detector.load_state_dict(model_weights) + + if not OV_FACE_DETECTION_MODEL_PATH.exists(): + face_detection_dummy_inputs = torch.FloatTensor(np.random.rand(1, 3, 768, 576)) + face_detection_ov_model = ov.convert_model(face_detector, example_input=face_detection_dummy_inputs) + ov.save_model(face_detection_ov_model, OV_FACE_DETECTION_MODEL_PATH) + print("Converted face detection OpenVINO model: ", OV_FACE_DETECTION_MODEL_PATH) + + # print("Convert Wav2Lip Model ...") + # if not OV_WAV2LIP_MODEL_PATH.exists(): + # download(models_urls['wav2lip'], "checkpoints/wav2lip.pth") + wav2lip = load_model(path_to_wav2lip) + img_batch = torch.FloatTensor(np.random.rand(123, 6, 96, 96)) + mel_batch = torch.FloatTensor(np.random.rand(123, 1, 80, 16)) + + if not OV_WAV2LIP_MODEL_PATH.exists(): + example_inputs = {"audio_sequences": mel_batch, "face_sequences": img_batch} + wav2lip_ov_model = ov.convert_model(wav2lip, example_input=example_inputs) + ov.save_model(wav2lip_ov_model, OV_WAV2LIP_MODEL_PATH) + print("Converted face detection OpenVINO model: ", OV_FACE_DETECTION_MODEL_PATH) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/README.md b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/README.md new file mode 100644 index 0000000..c073376 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/README.md @@ -0,0 +1 @@ +The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/__init__.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/__init__.py new file mode 100644 index 0000000..4bae29f --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- + +__author__ = """Adrian Bulat""" +__email__ = 'adrian.bulat@nottingham.ac.uk' +__version__ = '1.0.1' + +from .api import FaceAlignment, LandmarksType, NetworkSize diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/api.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/api.py new file mode 100644 index 0000000..cb02d52 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/api.py @@ -0,0 +1,79 @@ +from __future__ import print_function +import os +import torch +from torch.utils.model_zoo import load_url +from enum import Enum +import numpy as np +import cv2 +try: + import urllib.request as request_file +except BaseException: + import urllib as request_file + +from .models import FAN, ResNetDepth +from .utils import * + + +class LandmarksType(Enum): + """Enum class defining the type of landmarks to detect. + + ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face + ``_2halfD`` - this points represent the projection of the 3D points into 3D + ``_3D`` - detect the points ``(x,y,z)``` in a 3D space + + """ + _2D = 1 + _2halfD = 2 + _3D = 3 + + +class NetworkSize(Enum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + def __new__(cls, value): + member = object.__new__(cls) + member._value_ = value + return member + + def __int__(self): + return self.value + +ROOT = os.path.dirname(os.path.abspath(__file__)) + +class FaceAlignment: + def __init__(self, landmarks_type, network_size=NetworkSize.LARGE, + device='cuda', flip_input=False, face_detector='sfd', verbose=False): + self.device = device + self.flip_input = flip_input + self.landmarks_type = landmarks_type + self.verbose = verbose + + network_size = int(network_size) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + # Get the face detector + face_detector_module = __import__('face_detection.detection.' + face_detector, + globals(), locals(), [face_detector], 0) + self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose) + + def get_detections_for_batch(self, images): + images = images[..., ::-1] + detected_faces = self.face_detector.detect_from_batch(images.copy()) + results = [] + + for i, d in enumerate(detected_faces): + if len(d) == 0: + results.append(None) + continue + d = d[0] + d = np.clip(d, 0, None) + + x1, y1, x2, y2 = map(int, d[:-1]) + results.append((x1, y1, x2, y2)) + + return results \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/__init__.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/__init__.py new file mode 100644 index 0000000..1a6b040 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/__init__.py @@ -0,0 +1 @@ +from .core import FaceDetector \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/core.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/core.py new file mode 100644 index 0000000..0f8275e --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/core.py @@ -0,0 +1,130 @@ +import logging +import glob +from tqdm import tqdm +import numpy as np +import torch +import cv2 + + +class FaceDetector(object): + """An abstract class representing a face detector. + + Any other face detection implementation must subclass it. All subclasses + must implement ``detect_from_image``, that return a list of detected + bounding boxes. Optionally, for speed considerations detect from path is + recommended. + """ + + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + if verbose: + if 'cpu' in device: + logger = logging.getLogger(__name__) + logger.warning("Detection running on CPU, this may be potentially slow.") + + if 'cpu' not in device and 'cuda' not in device: + if verbose: + logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) + raise ValueError + + def detect_from_image(self, tensor_or_path): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path + to an image or the image itself. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + """Detects faces from all the images present in a given directory. + + Arguments: + path {string} -- a string containing a path that points to the folder containing the images + + Keyword Arguments: + extensions {list} -- list of string containing the extensions to be + consider in the following format: ``.extension_name`` (default: + {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the + folder recursively (default: {False}) show_progress_bar {bool} -- + display a progressbar (default: {True}) + + Example: + >>> directory = 'data' + ... detected_faces = detect_from_directory(directory) + {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} + + """ + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error("Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) + + if self.verbose: + logger.info("Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info("Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info("The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] + elif torch.is_tensor(tensor_or_path): + # Call cpu in case its coming from cuda + return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path + else: + raise TypeError diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/__init__.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/__init__.py new file mode 100644 index 0000000..5a63ecd --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/__init__.py @@ -0,0 +1 @@ +from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/bbox.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/bbox.py new file mode 100644 index 0000000..4bd7222 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/bbox.py @@ -0,0 +1,129 @@ +from __future__ import print_function +import os +import sys +import cv2 +import random +import datetime +import time +import math +import argparse +import numpy as np +import torch + +try: + from iou import IOU +except BaseException: + # IOU cython speedup 10x + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + +def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): + xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 + dx, dy = (xc - axc) / aww, (yc - ayc) / ahh + dw, dh = math.log(ww / aww), math.log(hh / ahh) + return dx, dy, dw, dh + + +def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): + xc, yc = dx * aww + axc, dy * ahh + ayc + ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh + x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 + return x1, y1, x2, y2 + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def batch_decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/detect.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/detect.py new file mode 100644 index 0000000..efef627 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/detect.py @@ -0,0 +1,112 @@ +import torch +import torch.nn.functional as F + +import os +import sys +import cv2 +import random +import datetime +import math +import argparse +import numpy as np + +import scipy.io as sio +import zipfile +from .net_s3fd import s3fd +from .bbox import * + + +def detect(net, img, device): + img = img - np.array([104, 117, 123]) + img = img.transpose(2, 0, 1) + img = img.reshape((1,) + img.shape) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + img = torch.from_numpy(img).float().to(device) + BB, CC, HH, WW = img.size() + with torch.no_grad(): + olist = net(img) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[0, 1, hindex, windex] + loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + variances = [0.1, 0.2] + box = decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append([x1, y1, x2, y2, score]) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, 5)) + + return bboxlist + +def batch_detect(net, imgs, device): + imgs = imgs - np.array([104, 117, 123]) + imgs = imgs.transpose(0, 3, 1, 2) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + imgs = torch.from_numpy(imgs).float().to(device) + BB, CC, HH, WW = imgs.size() + with torch.no_grad(): + olist = net(imgs) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[:, 1, hindex, windex] + loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) + variances = [0.1, 0.2] + box = batch_decode(loc, priors, variances) + box = box[:, 0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, BB, 5)) + + return bboxlist + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/net_s3fd.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/net_s3fd.py new file mode 100644 index 0000000..fc64313 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/net_s3fd.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class L2Norm(nn.Module): + def __init__(self, n_channels, scale=1.0): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.scale = scale + self.eps = 1e-10 + self.weight = nn.Parameter(torch.Tensor(self.n_channels)) + self.weight.data *= 0.0 + self.weight.data += self.scale + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = x / norm * self.weight.view(1, -1, 1, 1) + return x + + +class s3fd(nn.Module): + def __init__(self): + super(s3fd, self).__init__() + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) + + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) + + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) + self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) + + self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) + self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) + + self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) + self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + + self.conv3_3_norm = L2Norm(256, scale=10) + self.conv4_3_norm = L2Norm(512, scale=8) + self.conv5_3_norm = L2Norm(512, scale=5) + + self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) + self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + h = F.relu(self.conv1_1(x)) + h = F.relu(self.conv1_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv2_1(h)) + h = F.relu(self.conv2_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv3_1(h)) + h = F.relu(self.conv3_2(h)) + h = F.relu(self.conv3_3(h)) + f3_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv4_1(h)) + h = F.relu(self.conv4_2(h)) + h = F.relu(self.conv4_3(h)) + f4_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv5_1(h)) + h = F.relu(self.conv5_2(h)) + h = F.relu(self.conv5_3(h)) + f5_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.fc6(h)) + h = F.relu(self.fc7(h)) + ffc7 = h + h = F.relu(self.conv6_1(h)) + h = F.relu(self.conv6_2(h)) + f6_2 = h + h = F.relu(self.conv7_1(h)) + h = F.relu(self.conv7_2(h)) + f7_2 = h + + f3_3 = self.conv3_3_norm(f3_3) + f4_3 = self.conv4_3_norm(f4_3) + f5_3 = self.conv5_3_norm(f5_3) + + cls1 = self.conv3_3_norm_mbox_conf(f3_3) + reg1 = self.conv3_3_norm_mbox_loc(f3_3) + cls2 = self.conv4_3_norm_mbox_conf(f4_3) + reg2 = self.conv4_3_norm_mbox_loc(f4_3) + cls3 = self.conv5_3_norm_mbox_conf(f5_3) + reg3 = self.conv5_3_norm_mbox_loc(f5_3) + cls4 = self.fc7_mbox_conf(ffc7) + reg4 = self.fc7_mbox_loc(ffc7) + cls5 = self.conv6_2_mbox_conf(f6_2) + reg5 = self.conv6_2_mbox_loc(f6_2) + cls6 = self.conv7_2_mbox_conf(f7_2) + reg6 = self.conv7_2_mbox_loc(f7_2) + + # max-out background label + chunk = torch.chunk(cls1, 4, 1) + bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) + cls1 = torch.cat([bmax, chunk[3]], dim=1) + + return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/sfd_detector.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/sfd_detector.py new file mode 100644 index 0000000..8fbce15 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/detection/sfd/sfd_detector.py @@ -0,0 +1,59 @@ +import os +import cv2 +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector + +from .net_s3fd import s3fd +from .bbox import * +from .detect import * + +models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', +} + + +class SFDDetector(FaceDetector): + def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False): + super(SFDDetector, self).__init__(device, verbose) + + # Initialise the face detector + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + + self.face_detector = s3fd() + self.face_detector.load_state_dict(model_weights) + self.face_detector.to(device) + self.face_detector.eval() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device) + keep = nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > 0.5] + + return bboxlist + + def detect_from_batch(self, images): + bboxlists = batch_detect(self.face_detector, images, device=self.device) + keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])] + bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] + bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists] + + return bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/models.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/models.py new file mode 100644 index 0000000..ee2dde3 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/models.py @@ -0,0 +1,261 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class Bottleneck(nn.Module): + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + + # Lower branch + low1 = F.avg_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + + up2 = F.interpolate(low3, scale_factor=2, mode='nearest') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + + +class FAN(nn.Module): + + def __init__(self, num_modules=1): + super(FAN, self).__init__() + self.num_modules = num_modules + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + for hg_module in range(self.num_modules): + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module('conv_last' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + self.add_module('l' + str(hg_module), nn.Conv2d(256, + 68, kernel_size=1, stride=1, padding=0)) + + if hg_module < self.num_modules - 1: + self.add_module( + 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('al' + str(hg_module), nn.Conv2d(68, + 256, kernel_size=1, stride=1, padding=0)) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x)), True) + x = F.avg_pool2d(self.conv2(x), 2, stride=2) + x = self.conv3(x) + x = self.conv4(x) + + previous = x + + outputs = [] + for i in range(self.num_modules): + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = F.relu(self._modules['bn_end' + str(i)] + (self._modules['conv_last' + str(i)](ll)), True) + + # Predict heatmaps + tmp_out = self._modules['l' + str(i)](ll) + outputs.append(tmp_out) + + if i < self.num_modules - 1: + ll = self._modules['bl' + str(i)](ll) + tmp_out_ = self._modules['al' + str(i)](tmp_out) + previous = previous + ll + tmp_out_ + + return outputs + + +class ResNetDepth(nn.Module): + + def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): + self.inplanes = 64 + super(ResNetDepth, self).__init__() + self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/utils.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/utils.py new file mode 100644 index 0000000..3dc4cf3 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/face_detection/utils.py @@ -0,0 +1,313 @@ +from __future__ import print_function +import os +import sys +import time +import torch +import math +import numpy as np +import cv2 + + +def _gaussian( + size=3, sigma=0.25, amplitude=1, normalize=False, width=None, + height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, + mean_vert=0.5): + # handle some defaults + if width is None: + width = size + if height is None: + height = size + if sigma_horz is None: + sigma_horz = sigma + if sigma_vert is None: + sigma_vert = sigma + center_x = mean_horz * width + 0.5 + center_y = mean_vert * height + 0.5 + gauss = np.empty((height, width), dtype=np.float32) + # generate kernel + for i in range(height): + for j in range(width): + gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( + sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) + if normalize: + gauss = gauss / np.sum(gauss) + return gauss + + +def draw_gaussian(image, point, sigma): + # Check if the gaussian is inside + ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] + br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] + if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): + return image + size = 6 * sigma + 1 + g = _gaussian(size) + g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] + g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] + img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] + img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] + assert (g_x[0] > 0 and g_y[1] > 0) + image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] + ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] + image[image > 1] = 1 + return image + + +def transform(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {torch.tensor} -- the input 2D point + center {torch.tensor or numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = torch.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = torch.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = torch.inverse(t) + + new_point = (torch.matmul(t, _pt))[0:2] + + return new_point.int() + + +def crop(image, center, scale, resolution=256.0): + """Center crops an image or set of heatmaps + + Arguments: + image {numpy.array} -- an rgb image + center {numpy.array} -- the center of the object, usually the same as of the bounding box + scale {float} -- scale of the face + + Keyword Arguments: + resolution {float} -- the size of the output cropped image (default: {256.0}) + + Returns: + [type] -- [description] + """ # Crop around the center point + """ Crops the image around the center. Input is expected to be an np.ndarray """ + ul = transform([1, 1], center, scale, resolution, True) + br = transform([resolution, resolution], center, scale, resolution, True) + # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) + if image.ndim > 2: + newDim = np.array([br[1] - ul[1], br[0] - ul[0], + image.shape[2]], dtype=np.int32) + newImg = np.zeros(newDim, dtype=np.uint8) + else: + newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) + newImg = np.zeros(newDim, dtype=np.uint8) + ht = image.shape[0] + wd = image.shape[1] + newX = np.array( + [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) + newY = np.array( + [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) + oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) + oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) + newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] + ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] + newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), + interpolation=cv2.INTER_LINEAR) + return newImg + + +def get_preds_fromhm(hm, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if center is not None and scale is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], center, scale, hm.size(2), True) + + return preds, preds_orig + +def get_preds_fromhm_batch(hm, centers=None, scales=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the centers + and the scales is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + centers {torch.tensor} -- the centers of the bounding box (default: {None}) + scales {float} -- face scales (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if centers is not None and scales is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], centers[i], scales[i], hm.size(2), True) + + return preds, preds_orig + +def shuffle_lr(parts, pairs=None): + """Shuffle the points left-right according to the axis of symmetry + of the object. + + Arguments: + parts {torch.tensor} -- a 3D or 4D object containing the + heatmaps. + + Keyword Arguments: + pairs {list of integers} -- [order of the flipped points] (default: {None}) + """ + if pairs is None: + pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, + 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, + 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, + 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, + 62, 61, 60, 67, 66, 65] + if parts.ndimension() == 3: + parts = parts[pairs, ...] + else: + parts = parts[:, pairs, ...] + + return parts + + +def flip(tensor, is_label=False): + """Flip an image or a set of heatmaps left-right + + Arguments: + tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] + + Keyword Arguments: + is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) + """ + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + + if is_label: + tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) + else: + tensor = tensor.flip(tensor.ndimension() - 1) + + return tensor + +# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py) + + +def appdata_dir(appname=None, roaming=False): + """ appdata_dir(appname=None, roaming=False) + + Get the path to the application directory, where applications are allowed + to write user specific files (e.g. configurations). For non-user specific + data, consider using common_appdata_dir(). + If appname is given, a subdir is appended (and created if necessary). + If roaming is True, will prefer a roaming directory (Windows Vista/7). + """ + + # Define default user directory + userDir = os.getenv('FACEALIGNMENT_USERDIR', None) + if userDir is None: + userDir = os.path.expanduser('~') + if not os.path.isdir(userDir): # pragma: no cover + userDir = '/var/tmp' # issue #54 + + # Get system app data dir + path = None + if sys.platform.startswith('win'): + path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA') + path = (path2 or path1) if roaming else (path1 or path2) + elif sys.platform.startswith('darwin'): + path = os.path.join(userDir, 'Library', 'Application Support') + # On Linux and as fallback + if not (path and os.path.isdir(path)): + path = userDir + + # Maybe we should store things local to the executable (in case of a + # portable distro or a frozen application that wants to be portable) + prefix = sys.prefix + if getattr(sys, 'frozen', None): + prefix = os.path.abspath(os.path.dirname(sys.executable)) + for reldir in ('settings', '../settings'): + localpath = os.path.abspath(os.path.join(prefix, reldir)) + if os.path.isdir(localpath): # pragma: no cover + try: + open(os.path.join(localpath, 'test.write'), 'wb').close() + os.remove(os.path.join(localpath, 'test.write')) + except IOError: + pass # We cannot write in this directory + else: + path = localpath + break + + # Get path specific for this app + if appname: + if path == userDir: + appname = '.' + appname.lstrip('.') # Make it a hidden directory + path = os.path.join(path, appname) + if not os.path.isdir(path): # pragma: no cover + os.mkdir(path) + + # Done + return path diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/filelists/README.md b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/filelists/README.md new file mode 100644 index 0000000..e7d7e7b --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/filelists/README.md @@ -0,0 +1 @@ +Place LRS2 (and any other) filelists here for training. \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hparams.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hparams.py new file mode 100644 index 0000000..1c01904 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hparams.py @@ -0,0 +1,101 @@ +from glob import glob +import os + +def get_image_list(data_root, split): + filelist = [] + + with open('filelists/{}.txt'.format(split)) as f: + for line in f: + line = line.strip() + if ' ' in line: line = line.split()[0] + filelist.append(os.path.join(data_root, line)) + + return filelist + +class HParams: + def __init__(self, **kwargs): + self.data = {} + + for key, value in kwargs.items(): + self.data[key] = value + + def __getattr__(self, key): + if key not in self.data: + raise AttributeError("'HParams' object has no attribute %s" % key) + return self.data[key] + + def set_hparam(self, key, value): + self.data[key] = value + + +# Default hyperparameters +hparams = HParams( + num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality + # network + rescale=True, # Whether to rescale audio prior to preprocessing + rescaling_max=0.9, # Rescaling value + + # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction + # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder + # Does not work if n_ffit is not multiple of hop_size!! + use_lws=False, + + n_fft=800, # Extra window size is filled with 0 paddings to match this parameter + hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) + win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) + sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i ) + + frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) + + # Mel and Linear spectrograms normalization/scaling and clipping + signal_normalization=True, + # Whether to normalize mel spectrograms to some predefined range (following below parameters) + allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True + symmetric_mels=True, + # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, + # faster and cleaner convergence) + max_abs_value=4., + # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not + # be too big to avoid gradient explosion, + # not too small for fast convergence) + # Contribution by @begeekmyfriend + # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude + # levels. Also allows for better G&L phase reconstruction) + preemphasize=True, # whether to apply filter + preemphasis=0.97, # filter coefficient. + + # Limits + min_level_db=-100, + ref_level_db=20, + fmin=55, + # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To + # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) + fmax=7600, # To be increased/reduced depending on data. + + ###################### Our training parameters ################################# + img_size=96, + fps=25, + + batch_size=16, + initial_learning_rate=1e-4, + nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs + num_workers=16, + checkpoint_interval=3000, + eval_interval=3000, + save_optimizer_state=True, + + syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. + syncnet_batch_size=64, + syncnet_lr=1e-4, + syncnet_eval_interval=10000, + syncnet_checkpoint_interval=10000, + + disc_wt=0.07, + disc_initial_learning_rate=1e-4, +) + + +def hparams_debug_string(): + values = hparams.values() + hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] + return "Hyperparameters:\n" + "\n".join(hp) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hq_wav2lip_train.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hq_wav2lip_train.py new file mode 100644 index 0000000..c384ad9 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/hq_wav2lip_train.py @@ -0,0 +1,443 @@ +from os.path import dirname, join, basename, isfile +from tqdm import tqdm + +from models import SyncNet_color as SyncNet +from models import Wav2Lip, Wav2Lip_disc_qual +import audio + +import torch +from torch import nn +from torch.nn import functional as F +from torch import optim +import torch.backends.cudnn as cudnn +from torch.utils import data as data_utils +import numpy as np + +from glob import glob + +import os, random, cv2, argparse +from hparams import hparams, get_image_list + +parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model WITH the visual quality discriminator') + +parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True, type=str) + +parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str) +parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', required=True, type=str) + +parser.add_argument('--checkpoint_path', help='Resume generator from this checkpoint', default=None, type=str) +parser.add_argument('--disc_checkpoint_path', help='Resume quality disc from this checkpoint', default=None, type=str) + +args = parser.parse_args() + + +global_step = 0 +global_epoch = 0 +use_cuda = torch.cuda.is_available() +print('use_cuda: {}'.format(use_cuda)) + +syncnet_T = 5 +syncnet_mel_step_size = 16 + +class Dataset(object): + def __init__(self, split): + self.all_videos = get_image_list(args.data_root, split) + + def get_frame_id(self, frame): + return int(basename(frame).split('.')[0]) + + def get_window(self, start_frame): + start_id = self.get_frame_id(start_frame) + vidname = dirname(start_frame) + + window_fnames = [] + for frame_id in range(start_id, start_id + syncnet_T): + frame = join(vidname, '{}.jpg'.format(frame_id)) + if not isfile(frame): + return None + window_fnames.append(frame) + return window_fnames + + def read_window(self, window_fnames): + if window_fnames is None: return None + window = [] + for fname in window_fnames: + img = cv2.imread(fname) + if img is None: + return None + try: + img = cv2.resize(img, (hparams.img_size, hparams.img_size)) + except Exception as e: + return None + + window.append(img) + + return window + + def crop_audio_window(self, spec, start_frame): + if type(start_frame) == int: + start_frame_num = start_frame + else: + start_frame_num = self.get_frame_id(start_frame) + start_idx = int(80. * (start_frame_num / float(hparams.fps))) + + end_idx = start_idx + syncnet_mel_step_size + + return spec[start_idx : end_idx, :] + + def get_segmented_mels(self, spec, start_frame): + mels = [] + assert syncnet_T == 5 + start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing + if start_frame_num - 2 < 0: return None + for i in range(start_frame_num, start_frame_num + syncnet_T): + m = self.crop_audio_window(spec, i - 2) + if m.shape[0] != syncnet_mel_step_size: + return None + mels.append(m.T) + + mels = np.asarray(mels) + + return mels + + def prepare_window(self, window): + # 3 x T x H x W + x = np.asarray(window) / 255. + x = np.transpose(x, (3, 0, 1, 2)) + + return x + + def __len__(self): + return len(self.all_videos) + + def __getitem__(self, idx): + while 1: + idx = random.randint(0, len(self.all_videos) - 1) + vidname = self.all_videos[idx] + img_names = list(glob(join(vidname, '*.jpg'))) + if len(img_names) <= 3 * syncnet_T: + continue + + img_name = random.choice(img_names) + wrong_img_name = random.choice(img_names) + while wrong_img_name == img_name: + wrong_img_name = random.choice(img_names) + + window_fnames = self.get_window(img_name) + wrong_window_fnames = self.get_window(wrong_img_name) + if window_fnames is None or wrong_window_fnames is None: + continue + + window = self.read_window(window_fnames) + if window is None: + continue + + wrong_window = self.read_window(wrong_window_fnames) + if wrong_window is None: + continue + + try: + wavpath = join(vidname, "audio.wav") + wav = audio.load_wav(wavpath, hparams.sample_rate) + + orig_mel = audio.melspectrogram(wav).T + except Exception as e: + continue + + mel = self.crop_audio_window(orig_mel.copy(), img_name) + + if (mel.shape[0] != syncnet_mel_step_size): + continue + + indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name) + if indiv_mels is None: continue + + window = self.prepare_window(window) + y = window.copy() + window[:, :, window.shape[2]//2:] = 0. + + wrong_window = self.prepare_window(wrong_window) + x = np.concatenate([window, wrong_window], axis=0) + + x = torch.FloatTensor(x) + mel = torch.FloatTensor(mel.T).unsqueeze(0) + indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1) + y = torch.FloatTensor(y) + return x, indiv_mels, mel, y + +def save_sample_images(x, g, gt, global_step, checkpoint_dir): + x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + + refs, inps = x[..., 3:], x[..., :3] + folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step)) + if not os.path.exists(folder): os.mkdir(folder) + collage = np.concatenate((refs, inps, g, gt), axis=-2) + for batch_idx, c in enumerate(collage): + for t in range(len(c)): + cv2.imwrite('{}/{}_{}.jpg'.format(folder, batch_idx, t), c[t]) + +logloss = nn.BCELoss() +def cosine_loss(a, v, y): + d = nn.functional.cosine_similarity(a, v) + loss = logloss(d.unsqueeze(1), y) + + return loss + +device = torch.device("cuda" if use_cuda else "cpu") +syncnet = SyncNet().to(device) +for p in syncnet.parameters(): + p.requires_grad = False + +recon_loss = nn.L1Loss() +def get_sync_loss(mel, g): + g = g[:, :, :, g.size(3)//2:] + g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1) + # B, 3 * T, H//2, W + a, v = syncnet(mel, g) + y = torch.ones(g.size(0), 1).float().to(device) + return cosine_loss(a, v, y) + +def train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer, + checkpoint_dir=None, checkpoint_interval=None, nepochs=None): + global global_step, global_epoch + resumed_step = global_step + + while global_epoch < nepochs: + print('Starting Epoch: {}'.format(global_epoch)) + running_sync_loss, running_l1_loss, disc_loss, running_perceptual_loss = 0., 0., 0., 0. + running_disc_real_loss, running_disc_fake_loss = 0., 0. + prog_bar = tqdm(enumerate(train_data_loader)) + for step, (x, indiv_mels, mel, gt) in prog_bar: + disc.train() + model.train() + + x = x.to(device) + mel = mel.to(device) + indiv_mels = indiv_mels.to(device) + gt = gt.to(device) + + ### Train generator now. Remove ALL grads. + optimizer.zero_grad() + disc_optimizer.zero_grad() + + g = model(indiv_mels, x) + + if hparams.syncnet_wt > 0.: + sync_loss = get_sync_loss(mel, g) + else: + sync_loss = 0. + + if hparams.disc_wt > 0.: + perceptual_loss = disc.perceptual_forward(g) + else: + perceptual_loss = 0. + + l1loss = recon_loss(g, gt) + + loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \ + (1. - hparams.syncnet_wt - hparams.disc_wt) * l1loss + + loss.backward() + optimizer.step() + + ### Remove all gradients before Training disc + disc_optimizer.zero_grad() + + pred = disc(gt) + disc_real_loss = F.binary_cross_entropy(pred, torch.ones((len(pred), 1)).to(device)) + disc_real_loss.backward() + + pred = disc(g.detach()) + disc_fake_loss = F.binary_cross_entropy(pred, torch.zeros((len(pred), 1)).to(device)) + disc_fake_loss.backward() + + disc_optimizer.step() + + running_disc_real_loss += disc_real_loss.item() + running_disc_fake_loss += disc_fake_loss.item() + + if global_step % checkpoint_interval == 0: + save_sample_images(x, g, gt, global_step, checkpoint_dir) + + # Logs + global_step += 1 + cur_session_steps = global_step - resumed_step + + running_l1_loss += l1loss.item() + if hparams.syncnet_wt > 0.: + running_sync_loss += sync_loss.item() + else: + running_sync_loss += 0. + + if hparams.disc_wt > 0.: + running_perceptual_loss += perceptual_loss.item() + else: + running_perceptual_loss += 0. + + if global_step == 1 or global_step % checkpoint_interval == 0: + save_checkpoint( + model, optimizer, global_step, checkpoint_dir, global_epoch) + save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_') + + + if global_step % hparams.eval_interval == 0: + with torch.no_grad(): + average_sync_loss = eval_model(test_data_loader, global_step, device, model, disc) + + if average_sync_loss < .75: + hparams.set_hparam('syncnet_wt', 0.03) + + prog_bar.set_description('L1: {}, Sync: {}, Percep: {} | Fake: {}, Real: {}'.format(running_l1_loss / (step + 1), + running_sync_loss / (step + 1), + running_perceptual_loss / (step + 1), + running_disc_fake_loss / (step + 1), + running_disc_real_loss / (step + 1))) + + global_epoch += 1 + +def eval_model(test_data_loader, global_step, device, model, disc): + eval_steps = 300 + print('Evaluating for {} steps'.format(eval_steps)) + running_sync_loss, running_l1_loss, running_disc_real_loss, running_disc_fake_loss, running_perceptual_loss = [], [], [], [], [] + while 1: + for step, (x, indiv_mels, mel, gt) in enumerate((test_data_loader)): + model.eval() + disc.eval() + + x = x.to(device) + mel = mel.to(device) + indiv_mels = indiv_mels.to(device) + gt = gt.to(device) + + pred = disc(gt) + disc_real_loss = F.binary_cross_entropy(pred, torch.ones((len(pred), 1)).to(device)) + + g = model(indiv_mels, x) + pred = disc(g) + disc_fake_loss = F.binary_cross_entropy(pred, torch.zeros((len(pred), 1)).to(device)) + + running_disc_real_loss.append(disc_real_loss.item()) + running_disc_fake_loss.append(disc_fake_loss.item()) + + sync_loss = get_sync_loss(mel, g) + + if hparams.disc_wt > 0.: + perceptual_loss = disc.perceptual_forward(g) + else: + perceptual_loss = 0. + + l1loss = recon_loss(g, gt) + + loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \ + (1. - hparams.syncnet_wt - hparams.disc_wt) * l1loss + + running_l1_loss.append(l1loss.item()) + running_sync_loss.append(sync_loss.item()) + + if hparams.disc_wt > 0.: + running_perceptual_loss.append(perceptual_loss.item()) + else: + running_perceptual_loss.append(0.) + + if step > eval_steps: break + + print('L1: {}, Sync: {}, Percep: {} | Fake: {}, Real: {}'.format(sum(running_l1_loss) / len(running_l1_loss), + sum(running_sync_loss) / len(running_sync_loss), + sum(running_perceptual_loss) / len(running_perceptual_loss), + sum(running_disc_fake_loss) / len(running_disc_fake_loss), + sum(running_disc_real_loss) / len(running_disc_real_loss))) + return sum(running_sync_loss) / len(running_sync_loss) + + +def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch, prefix=''): + checkpoint_path = join( + checkpoint_dir, "{}checkpoint_step{:09d}.pth".format(prefix, global_step)) + optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None + torch.save({ + "state_dict": model.state_dict(), + "optimizer": optimizer_state, + "global_step": step, + "global_epoch": epoch, + }, checkpoint_path) + print("Saved checkpoint:", checkpoint_path) + +def _load(checkpoint_path): + if use_cuda: + checkpoint = torch.load(checkpoint_path) + else: + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + + +def load_checkpoint(path, model, optimizer, reset_optimizer=False, overwrite_global_states=True): + global global_step + global global_epoch + + print("Load checkpoint from: {}".format(path)) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + if not reset_optimizer: + optimizer_state = checkpoint["optimizer"] + if optimizer_state is not None: + print("Load optimizer state from {}".format(path)) + optimizer.load_state_dict(checkpoint["optimizer"]) + if overwrite_global_states: + global_step = checkpoint["global_step"] + global_epoch = checkpoint["global_epoch"] + + return model + +if __name__ == "__main__": + checkpoint_dir = args.checkpoint_dir + + # Dataset and Dataloader setup + train_dataset = Dataset('train') + test_dataset = Dataset('val') + + train_data_loader = data_utils.DataLoader( + train_dataset, batch_size=hparams.batch_size, shuffle=True, + num_workers=hparams.num_workers) + + test_data_loader = data_utils.DataLoader( + test_dataset, batch_size=hparams.batch_size, + num_workers=4) + + device = torch.device("cuda" if use_cuda else "cpu") + + # Model + model = Wav2Lip().to(device) + disc = Wav2Lip_disc_qual().to(device) + + print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad))) + print('total DISC trainable params {}'.format(sum(p.numel() for p in disc.parameters() if p.requires_grad))) + + optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad], + lr=hparams.initial_learning_rate, betas=(0.5, 0.999)) + disc_optimizer = optim.Adam([p for p in disc.parameters() if p.requires_grad], + lr=hparams.disc_initial_learning_rate, betas=(0.5, 0.999)) + + if args.checkpoint_path is not None: + load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False) + + if args.disc_checkpoint_path is not None: + load_checkpoint(args.disc_checkpoint_path, disc, disc_optimizer, + reset_optimizer=False, overwrite_global_states=False) + + load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True, + overwrite_global_states=False) + + if not os.path.exists(checkpoint_dir): + os.mkdir(checkpoint_dir) + + # Train! + train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer, + checkpoint_dir=checkpoint_dir, + checkpoint_interval=hparams.checkpoint_interval, + nepochs=hparams.nepochs) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/__init__.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/__init__.py new file mode 100644 index 0000000..4374370 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/__init__.py @@ -0,0 +1,2 @@ +from .wav2lip import Wav2Lip, Wav2Lip_disc_qual +from .syncnet import SyncNet_color \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/conv.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/conv.py new file mode 100644 index 0000000..ed83da0 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/conv.py @@ -0,0 +1,44 @@ +import torch +from torch import nn +from torch.nn import functional as F + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act(out) + +class nonorm_Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + ) + self.act = nn.LeakyReLU(0.01, inplace=True) + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2dTranspose(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/syncnet.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/syncnet.py new file mode 100644 index 0000000..e773cdc --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/syncnet.py @@ -0,0 +1,66 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from .conv import Conv2d + +class SyncNet_color(nn.Module): + def __init__(self): + super(SyncNet_color, self).__init__() + + self.face_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3), + + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=2, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=2, padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(512, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T) + face_embedding = self.face_encoder(face_sequences) + audio_embedding = self.audio_encoder(audio_sequences) + + audio_embedding = audio_embedding.view(audio_embedding.size(0), -1) + face_embedding = face_embedding.view(face_embedding.size(0), -1) + + audio_embedding = F.normalize(audio_embedding, p=2, dim=1) + face_embedding = F.normalize(face_embedding, p=2, dim=1) + + + return audio_embedding, face_embedding diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/wav2lip.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/wav2lip.py new file mode 100644 index 0000000..ae5d691 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/models/wav2lip.py @@ -0,0 +1,184 @@ +import torch +from torch import nn +from torch.nn import functional as F +import math + +from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d + +class Wav2Lip(nn.Module): + def __init__(self): + super(Wav2Lip, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 + + nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 24,24 + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # 12,12 + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 6,6 + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.face_decoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6 + + nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12 + + nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24 + + nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48 + + nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96 + + self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), + nn.Sigmoid()) + + def forward(self, audio_sequences, face_sequences): + # audio_sequences = (B, T, 1, 80, 16) + B = audio_sequences.size(0) + + input_dim_size = len(face_sequences.size()) + if input_dim_size > 4: + audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + + audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 + + feats = [] + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + feats.append(x) + + x = audio_embedding + for f in self.face_decoder_blocks: + x = f(x) + try: + x = torch.cat((x, feats[-1]), dim=1) + except Exception as e: + print(x.size()) + print(feats[-1].size()) + raise e + + feats.pop() + + x = self.output_block(x) + + if input_dim_size > 4: + x = torch.split(x, B, dim=0) # [(B, C, H, W)] + outputs = torch.stack(x, dim=2) # (B, C, T, H, W) + + else: + outputs = x + + return outputs + +class Wav2Lip_disc_qual(nn.Module): + def __init__(self): + super(Wav2Lip_disc_qual, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), # 48,96 + + nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48 + nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), # 24,24 + nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), # 12,12 + nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 6,6 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), # 3,3 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) + self.label_noise = .0 + + def get_lower_half(self, face_sequences): + return face_sequences[:, :, face_sequences.size(2)//2:] + + def to_2d(self, face_sequences): + B = face_sequences.size(0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + return face_sequences + + def perceptual_forward(self, false_face_sequences): + false_face_sequences = self.to_2d(false_face_sequences) + false_face_sequences = self.get_lower_half(false_face_sequences) + + false_feats = false_face_sequences + for f in self.face_encoder_blocks: + false_feats = f(false_feats) + + false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), + torch.ones((len(false_feats), 1)).cuda()) + + return false_pred_loss + + def forward(self, face_sequences): + face_sequences = self.to_2d(face_sequences) + face_sequences = self.get_lower_half(face_sequences) + + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + + return self.binary_pred(x).view(len(x), -1) diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/ov_wav2lip.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/ov_wav2lip.py new file mode 100644 index 0000000..40350b4 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/ov_wav2lip.py @@ -0,0 +1,707 @@ +import sys +import os + +script_folder = os.path.join(os.path.dirname(__file__), 'wav2lip') +sys.path.append(script_folder) + +import numpy as np +import cv2 +from .audio import load_wav, melspectrogram +import subprocess +from tqdm import tqdm +from glob import glob +import torch +import platform +import openvino as ov +import torch +from torch.utils.model_zoo import load_url +from enum import Enum +import torch.nn.functional as F +import openvino.properties.hint as hints +import openvino.properties as props +# from face_parsing import init_parser, swap_regions + +import time +import logging +import uuid +from pathlib import Path + +try: + import urllib.request as request_file +except BaseException: + import urllib as request_file + + +class LandmarksType(Enum): + """Enum class defining the type of landmarks to detect. + + ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face + ``_2halfD`` - this points represent the projection of the 3D points into 3D + ``_3D`` - detect the points ``(x,y,z)``` in a 3D space + + """ + _2D = 1 + _2halfD = 2 + _3D = 3 + + +class NetworkSize(Enum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + def __new__(cls, value): + member = object.__new__(cls) + member._value_ = value + return member + + def __int__(self): + return self.value + + +class OVFaceDetector(object): + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + def detect_from_image(self, tensor_or_path): + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error( + "Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + + extension, recursive=recursive)) + + if self.verbose: + logger.info( + "Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info( + "Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info( + "The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] + elif torch.is_tensor(tensor_or_path): + # Call cpu in case its coming from cuda + return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path + else: + raise TypeError + + +class OVSFDDetector(OVFaceDetector): + def __init__(self, device, face_detector, verbose=False): + super().__init__(device, verbose) + self.face_detector = face_detector + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = self.detect(self.face_detector, image, device="cpu") + keep = self.nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > 0.5] + + return bboxlist + + def detect_from_batch(self, images): + bboxlists = self.batch_detect(self.face_detector, images, device="cpu") + keeps = [self.nms(bboxlists[:, i, :], 0.3) + for i in range(bboxlists.shape[1])] + bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] + bboxlists = [[x for x in bboxlist if x[-1] > 0.5] + for bboxlist in bboxlists] + + return bboxlists + + def nms(self, dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, + 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum( + y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum( + y2[i], y2[order[1:]]) + + w, h = np.maximum( + 0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + def detect(self, net, img, device): + img = img - np.array([104, 117, 123]) + img = img.transpose(2, 0, 1) + img = img.reshape((1,) + img.shape) + + img = torch.from_numpy(img).float().to(device) + BB, CC, HH, WW = img.size() + + results = net({"x": img.numpy()}) + olist = [torch.Tensor(results[i]) for i in range(12)] + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[0, 1, hindex, windex] + loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) + priors = torch.Tensor( + [[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + variances = [0.1, 0.2] + box = self.decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append([x1, y1, x2, y2, score]) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, 5)) + + return bboxlist + + def decode(self, loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + + def batch_detect(self, net, imgs, device): + imgs = imgs - np.array([104, 117, 123]) + imgs = imgs.transpose(0, 3, 1, 2) + + imgs = torch.from_numpy(imgs).float().to(device) + BB, CC, HH, WW = imgs.size() + + results = net({"x": imgs.numpy()}) + olist = [torch.Tensor(results[i]) for i in range(12)] + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + # olist[i * 2] = (olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + # olist = [oelem for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[:, 1, hindex, windex] + loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) + priors = torch.Tensor( + [[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) + variances = [0.1, 0.2] + box = self.batch_decode(loc, priors, variances) + box = box[:, 0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append( + torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, BB, 5)) + + return bboxlist + + def batch_decode(self, loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 + + +class NetworkSize(Enum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + def __new__(cls, value): + member = object.__new__(cls) + member._value_ = value + return member + + def __int__(self): + return self.value + + +class OVFaceAlignment: + def __init__(self, landmarks_type, face_detector, network_size=NetworkSize.LARGE, + device='CPU', flip_input=False, verbose=False): + self.device = device + self.flip_input = flip_input + self.landmarks_type = landmarks_type + self.verbose = verbose + + network_size = int(network_size) + + self.face_detector = OVSFDDetector( + device=device, face_detector=face_detector, verbose=verbose) + + def get_detections_for_batch(self, images): + images = images[..., ::-1] + detected_faces = self.face_detector.detect_from_batch(images.copy()) + results = [] + + for i, d in enumerate(detected_faces): + if len(d) == 0: + results.append(None) + continue + d = d[0] + d = np.clip(d, 0, None) + + x1, y1, x2, y2 = map(int, d[:-1]) + results.append((x1, y1, x2, y2)) + + return results + + +class OVWav2Lip: + def __init__(self, avatar_path="assets/xy.png", device="GPU"): + # Paths to model checkpoints + self.face_detection_path = "wav2lip/checkpoints/face_detection.xml" + self.wav2lip_path = "wav2lip/checkpoints/wav2lip_gan.xml" + self.segmentation_path = 'face_segmentation.pth' + self.sr_path = 'esrgan_yunying.pth' + self.face_landmarks_detector_path = 'wav2lip/checkpoints/face_landmarker_v2_with_blendshapes.task' + + # Device configuration + self.inference_device = device + + # Input and output files + self.face = avatar_path + + # Video processing settings + self.static = False + self.fps = 25.0 + self.pads = [0, 30, 0, 0] + self.face_det_batch_size = 16 + self.wav2lip_batch_size = 128 + self.resize_factor = 1 + self.crop = [0, -1, 0, -1] + self.box = [-1, -1, -1, -1] + self.rotate = False + self.nosmooth = True + self.with_face_mask = False + self.no_segmentation = False + self.no_sr = False + self.img_size = 96 + + # Model URLs + self.models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', + } + + # Run the initialization process + self.run() + + def run(self): + core = ov.Core() + config = {hints.performance_mode: hints.PerformanceMode.LATENCY} + + # FIXME: A770 don't work for face detection + self.face_detector = core.compile_model( + self.face_detection_path, self.inference_device, config) + + # OV_WAV2LIP_MODEL_PATH = Path(args.wav2lip_path) + core.set_property( + {props.cache_dir: f'./cache/{self.inference_device}'}) + wav2_lip_model = core.read_model(model=self.wav2lip_path) + self.compiled_wav2lip_model = core.compile_model( + model=wav2_lip_model, device_name=self.inference_device, config=config) + + if os.path.isfile(self.face) and self.face.split('.')[1] in ['jpg', 'png', 'jpeg']: + self.static = True + # IOU cython speedup 10x + + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + self.full_frames = [] + os.makedirs("wav2lip/temp", exist_ok=True) + if not os.path.isfile(self.face): + raise ValueError( + '--face argument must be a valid path to video/image file') + + elif self.face.split('.')[1] in ['jpg', 'png', 'jpeg']: + self.full_frames = [cv2.imread(self.face)] + self.fps = self.fps + + else: + video_stream = cv2.VideoCapture(self.face) + self.fps = video_stream.get(cv2.CAP_PROP_FPS) + frame_count = video_stream.get(cv2.CAP_PROP_FRAME_COUNT) + self.duration = round(frame_count / self.fps, 2) + frames = [] + while 1: + still_reading, frame = video_stream.read() + if not still_reading: + video_stream.release() + break + if self.resize_factor > 1: + frame = cv2.resize( + frame, (frame.shape[1]//self.resize_factor, frame.shape[0]//self.resize_factor)) + + if self.rotate: + frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE) + + y1, y2, x1, x2 = self.crop + if x2 == -1: + x2 = frame.shape[1] + if y2 == -1: + y2 = frame.shape[0] + + frame = frame[y1:y2, x1:x2] + + frames.append(frame) + + self.full_frames=frames.copy() + + self.mel_step_size = 16 + + self.batch_size = self.wav2lip_batch_size + + self.face_det_results = None + + frames_temp = self.full_frames.copy() + + if self.box[0] == -1: + if not self.static: + self.face_det_results = self.face_detect_ov( + frames_temp, self.inference_device) + else: + self.face_det_results = self.face_detect_ov( + [frames_temp[0]], self.inference_device) + else: + print('Using the specified bounding box instead of face detection...') + y1, y2, x1, x2 = self.box + self.face_det_results = [ + [f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames_temp] + + def process_images_with_detector(self, images, detector, initial_batch_size): + batch_size = initial_batch_size + predictions = [] + + for _ in range(int(np.log2(initial_batch_size)) + 1): # Loop to halve the batch size + try: + for i in tqdm(range(0, len(images), batch_size)): + batch = np.array(images[i:i + batch_size]) + predictions.extend(detector.get_detections_for_batch(batch)) + return predictions # Return predictions if processing is successful + except RuntimeError as e: + import traceback + traceback.print_exc() + # if batch_size == 1: + # raise RuntimeError( + # 'Image too big to run face detection on GPU. Please use the --resize_factor argument') from e + # batch_size //= 2 + # print(f'Recovering from OOM error; New batch size: {batch_size}') + + # raise RuntimeError('Failed to process images even with the smallest batch size.') + + def face_detect_ov(self, images, device): + detector = OVFaceAlignment( + LandmarksType._2D, face_detector=self.face_detector, flip_input=False, device=device) + + predictions = self.process_images_with_detector(images, detector, self.face_det_batch_size) + + results = [] + pady1, pady2, padx1, padx2 = self.pads + + for rect, image in zip(predictions, images): + if rect is None: + # check this frame where the face was not detected. + cv2.imwrite('temp/faulty_frame.jpg', image) + raise ValueError( + 'Face not detected! Ensure the video contains a face in all the frames.') + + y1 = max(0, rect[1] - pady1) + y2 = min(image.shape[0], rect[3] + pady2) + x1 = max(0, rect[0] - padx1) + x2 = min(image.shape[1], rect[2] + padx2) + + results.append([x1, y1, x2, y2]) + + boxes = np.array(results) + if not self.nosmooth: + boxes = self.get_smoothened_boxes(boxes, T=5) + results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] + for image, (x1, y1, x2, y2) in zip(images, boxes)] + + del detector + return results + + def get_smoothened_boxes(boxes, T): + for i in range(len(boxes)): + if i + T > len(boxes): + window = boxes[len(boxes) - T:] + else: + window = boxes[i: i + T] + boxes[i] = np.mean(window, axis=0) + return boxes + + def get_full_frames_and_face_det_results(self, reverse=False, double=False): + if reverse: + full_frames = self.full_frames.copy()[::-1] + face_det_results = self.face_det_results.copy()[::-1] + else: + full_frames = self.full_frames.copy() + face_det_results = self.face_det_results.copy() + + if double: + full_frames = full_frames + full_frames[::-1] + face_det_results = face_det_results + face_det_results[::-1] + + return full_frames, face_det_results + + + def inference(self, audio_path, reversed=False): + file_id = str(uuid.uuid4()) + output_path = Path(f'wav2lip/results/{file_id}.mp4') + output_path.parent.mkdir(parents=True, exist_ok=True) + + wav, wav_duration = load_wav(audio_path, 16000) + mel = melspectrogram(wav) + + mel_chunks = [] + new_frames = [] + gen = None + mel_idx_multiplier = 80./self.fps + i = 0 + while 1: + start_idx = int(i * mel_idx_multiplier) + if start_idx + self.mel_step_size > len(mel[0]): + mel_chunks.append(mel[:, len(mel[0]) - self.mel_step_size:]) + break + mel_chunks.append( + mel[:, start_idx: start_idx + self.mel_step_size]) + i += 1 + + if not self.static: + if wav_duration > self.duration: + full_frames, face_det_results = self.get_full_frames_and_face_det_results(reverse=reversed, double=True) + else: + full_frames, face_det_results = self.get_full_frames_and_face_det_results(reverse=reversed) + else: + full_frames, face_det_results = self.get_full_frames_and_face_det_results() + + gen = self.datagen(full_frames.copy(), mel_chunks, face_det_results) + new_frames = full_frames.copy()[:len(mel_chunks)] + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + frames_generated = 0 + for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, + total=int(np.ceil(float(len(mel_chunks))/self.batch_size)))): + if i == 0: + img_batch = torch.FloatTensor( + np.transpose(img_batch, (0, 3, 1, 2))).to(device) + mel_batch = torch.FloatTensor( + np.transpose(mel_batch, (0, 3, 1, 2))).to(device) + + frame_h, frame_w = new_frames[0].shape[:-1] + out = cv2.VideoWriter('wav2lip/temp/result.avi', + cv2.VideoWriter_fourcc(*'DIVX'), self.fps, (frame_w, frame_h)) + pred_ov = self.compiled_wav2lip_model( + {"audio_sequences": mel_batch.numpy(), "face_sequences": img_batch.numpy()})[0] + else: + img_batch = np.transpose(img_batch, (0, 3, 1, 2)) + mel_batch = np.transpose(mel_batch, (0, 3, 1, 2)) + pred_ov = self.compiled_wav2lip_model( + {"audio_sequences": mel_batch, "face_sequences": img_batch})[0] + + # pred_ov = compiled_wav2lip_model({"audio_sequences": mel_batch, "face_sequences": img_batch})[0] + pred_ov = pred_ov.transpose(0, 2, 3, 1) * 255. + + for p, f, c in zip(pred_ov, frames, coords): + y1, y2, x1, x2 = c + p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1)) + f[y1:y2, x1:x2] = p + frames_generated += 1 + out.write(f) + + video_frames = len(self.full_frames) + + if not frames_generated == video_frames: + leftover_frames_index = frames_generated % video_frames + + if frames_generated > video_frames: + is_reversed = 0 if frames_generated // video_frames % 2 == 0 else 1 + else: + is_reversed = 0 + if (is_reversed and not reversed) or (not is_reversed and reversed): + leftover_frames = self.full_frames[:video_frames-leftover_frames_index][::-1].copy() + else: + leftover_frames = self.full_frames[leftover_frames_index:].copy() + + for f in leftover_frames: + out.write(f) + + out.release() + + command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {} > /dev/null 2>&1'.format( + audio_path, 'wav2lip/temp/result.avi', output_path) + subprocess.call(command, shell=platform.system() != 'Windows') + return output_path + + def datagen(self, frames, mels, face_det_results): + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + for i, m in enumerate(mels): + idx = 0 if self.static else i % len(frames) + frame_to_save = frames[idx].copy() + face, coords = face_det_results[idx].copy() + face = cv2.resize(face, (self.img_size, self.img_size)) + + img_batch.append(face) + mel_batch.append(m) + frame_batch.append(frame_to_save) + coords_batch.append(coords) + + if len(img_batch) >= self.wav2lip_batch_size: + img_batch, mel_batch = np.asarray( + img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, self.img_size//2:] = 0 + + img_batch = np.concatenate( + (img_masked, img_batch), axis=3) / 255. + mel_batch = np.reshape( + mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + + if len(img_batch) > 0: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, self.img_size//2:] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. + mel_batch = np.reshape( + mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + + +if __name__ == "__main__": + init_time = time.time() + wav2lip = OVWav2Lip(device="CPU", avatar_path="assets/image.png") + print("Init Time: ", time.time() - init_time) + start_time = time.time() + wav2lip.inference("assets/audio.wav") + print("Time taken: ", time.time() - start_time) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/preprocess.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/preprocess.py new file mode 100644 index 0000000..5322012 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/preprocess.py @@ -0,0 +1,113 @@ +import sys + +if sys.version_info[0] < 3 and sys.version_info[1] < 2: + raise Exception("Must be using >= Python 3.2") + +from os import listdir, path + +if not path.isfile('face_detection/detection/sfd/s3fd.pth'): + raise FileNotFoundError('Save the s3fd model to face_detection/detection/sfd/s3fd.pth \ + before running this script!') + +import multiprocessing as mp +from concurrent.futures import ThreadPoolExecutor, as_completed +import numpy as np +import argparse, os, cv2, traceback, subprocess +from tqdm import tqdm +from glob import glob +import audio +from hparams import hparams as hp + +import face_detection + +parser = argparse.ArgumentParser() + +parser.add_argument('--ngpu', help='Number of GPUs across which to run in parallel', default=1, type=int) +parser.add_argument('--batch_size', help='Single GPU Face detection batch size', default=32, type=int) +parser.add_argument("--data_root", help="Root folder of the LRS2 dataset", required=True) +parser.add_argument("--preprocessed_root", help="Root folder of the preprocessed dataset", required=True) + +args = parser.parse_args() + +fa = [face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, + device='cuda:{}'.format(id)) for id in range(args.ngpu)] + +template = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}' +# template2 = 'ffmpeg -hide_banner -loglevel panic -threads 1 -y -i {} -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 {}' + +def process_video_file(vfile, args, gpu_id): + video_stream = cv2.VideoCapture(vfile) + + frames = [] + while 1: + still_reading, frame = video_stream.read() + if not still_reading: + video_stream.release() + break + frames.append(frame) + + vidname = os.path.basename(vfile).split('.')[0] + dirname = vfile.split('/')[-2] + + fulldir = path.join(args.preprocessed_root, dirname, vidname) + os.makedirs(fulldir, exist_ok=True) + + batches = [frames[i:i + args.batch_size] for i in range(0, len(frames), args.batch_size)] + + i = -1 + for fb in batches: + preds = fa[gpu_id].get_detections_for_batch(np.asarray(fb)) + + for j, f in enumerate(preds): + i += 1 + if f is None: + continue + + x1, y1, x2, y2 = f + cv2.imwrite(path.join(fulldir, '{}.jpg'.format(i)), fb[j][y1:y2, x1:x2]) + +def process_audio_file(vfile, args): + vidname = os.path.basename(vfile).split('.')[0] + dirname = vfile.split('/')[-2] + + fulldir = path.join(args.preprocessed_root, dirname, vidname) + os.makedirs(fulldir, exist_ok=True) + + wavpath = path.join(fulldir, 'audio.wav') + + command = template.format(vfile, wavpath) + subprocess.call(command, shell=True) + + +def mp_handler(job): + vfile, args, gpu_id = job + try: + process_video_file(vfile, args, gpu_id) + except KeyboardInterrupt: + exit(0) + except: + traceback.print_exc() + +def main(args): + print('Started processing for {} with {} GPUs'.format(args.data_root, args.ngpu)) + + filelist = glob(path.join(args.data_root, '*/*.mp4')) + + jobs = [(vfile, args, i%args.ngpu) for i, vfile in enumerate(filelist)] + p = ThreadPoolExecutor(args.ngpu) + futures = [p.submit(mp_handler, j) for j in jobs] + _ = [r.result() for r in tqdm(as_completed(futures), total=len(futures))] + + print('Dumping audios...') + + for vfile in tqdm(filelist): + try: + process_audio_file(vfile, args) + except KeyboardInterrupt: + exit(0) + except: + traceback.print_exc() + continue + +if __name__ == '__main__': + main(args) \ No newline at end of file diff --git a/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/wav2lip_train.py b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/wav2lip_train.py new file mode 100644 index 0000000..6e08118 --- /dev/null +++ b/usecases/ai/digital-avatar/backend/wav2lip/wav2lip/wav2lip_train.py @@ -0,0 +1,374 @@ +from os.path import dirname, join, basename, isfile +from tqdm import tqdm + +from models import SyncNet_color as SyncNet +from models import Wav2Lip as Wav2Lip +import audio + +import torch +from torch import nn +from torch import optim +import torch.backends.cudnn as cudnn +from torch.utils import data as data_utils +import numpy as np + +from glob import glob + +import os, random, cv2, argparse +from hparams import hparams, get_image_list + +parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model without the visual quality discriminator') + +parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True, type=str) + +parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str) +parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', required=True, type=str) + +parser.add_argument('--checkpoint_path', help='Resume from this checkpoint', default=None, type=str) + +args = parser.parse_args() + + +global_step = 0 +global_epoch = 0 +use_cuda = torch.cuda.is_available() +print('use_cuda: {}'.format(use_cuda)) + +syncnet_T = 5 +syncnet_mel_step_size = 16 + +class Dataset(object): + def __init__(self, split): + self.all_videos = get_image_list(args.data_root, split) + + def get_frame_id(self, frame): + return int(basename(frame).split('.')[0]) + + def get_window(self, start_frame): + start_id = self.get_frame_id(start_frame) + vidname = dirname(start_frame) + + window_fnames = [] + for frame_id in range(start_id, start_id + syncnet_T): + frame = join(vidname, '{}.jpg'.format(frame_id)) + if not isfile(frame): + return None + window_fnames.append(frame) + return window_fnames + + def read_window(self, window_fnames): + if window_fnames is None: return None + window = [] + for fname in window_fnames: + img = cv2.imread(fname) + if img is None: + return None + try: + img = cv2.resize(img, (hparams.img_size, hparams.img_size)) + except Exception as e: + return None + + window.append(img) + + return window + + def crop_audio_window(self, spec, start_frame): + if type(start_frame) == int: + start_frame_num = start_frame + else: + start_frame_num = self.get_frame_id(start_frame) # 0-indexing ---> 1-indexing + start_idx = int(80. * (start_frame_num / float(hparams.fps))) + + end_idx = start_idx + syncnet_mel_step_size + + return spec[start_idx : end_idx, :] + + def get_segmented_mels(self, spec, start_frame): + mels = [] + assert syncnet_T == 5 + start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing + if start_frame_num - 2 < 0: return None + for i in range(start_frame_num, start_frame_num + syncnet_T): + m = self.crop_audio_window(spec, i - 2) + if m.shape[0] != syncnet_mel_step_size: + return None + mels.append(m.T) + + mels = np.asarray(mels) + + return mels + + def prepare_window(self, window): + # 3 x T x H x W + x = np.asarray(window) / 255. + x = np.transpose(x, (3, 0, 1, 2)) + + return x + + def __len__(self): + return len(self.all_videos) + + def __getitem__(self, idx): + while 1: + idx = random.randint(0, len(self.all_videos) - 1) + vidname = self.all_videos[idx] + img_names = list(glob(join(vidname, '*.jpg'))) + if len(img_names) <= 3 * syncnet_T: + continue + + img_name = random.choice(img_names) + wrong_img_name = random.choice(img_names) + while wrong_img_name == img_name: + wrong_img_name = random.choice(img_names) + + window_fnames = self.get_window(img_name) + wrong_window_fnames = self.get_window(wrong_img_name) + if window_fnames is None or wrong_window_fnames is None: + continue + + window = self.read_window(window_fnames) + if window is None: + continue + + wrong_window = self.read_window(wrong_window_fnames) + if wrong_window is None: + continue + + try: + wavpath = join(vidname, "audio.wav") + wav = audio.load_wav(wavpath, hparams.sample_rate) + + orig_mel = audio.melspectrogram(wav).T + except Exception as e: + continue + + mel = self.crop_audio_window(orig_mel.copy(), img_name) + + if (mel.shape[0] != syncnet_mel_step_size): + continue + + indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name) + if indiv_mels is None: continue + + window = self.prepare_window(window) + y = window.copy() + window[:, :, window.shape[2]//2:] = 0. + + wrong_window = self.prepare_window(wrong_window) + x = np.concatenate([window, wrong_window], axis=0) + + x = torch.FloatTensor(x) + mel = torch.FloatTensor(mel.T).unsqueeze(0) + indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1) + y = torch.FloatTensor(y) + return x, indiv_mels, mel, y + +def save_sample_images(x, g, gt, global_step, checkpoint_dir): + x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8) + + refs, inps = x[..., 3:], x[..., :3] + folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step)) + if not os.path.exists(folder): os.mkdir(folder) + collage = np.concatenate((refs, inps, g, gt), axis=-2) + for batch_idx, c in enumerate(collage): + for t in range(len(c)): + cv2.imwrite('{}/{}_{}.jpg'.format(folder, batch_idx, t), c[t]) + +logloss = nn.BCELoss() +def cosine_loss(a, v, y): + d = nn.functional.cosine_similarity(a, v) + loss = logloss(d.unsqueeze(1), y) + + return loss + +device = torch.device("cuda" if use_cuda else "cpu") +syncnet = SyncNet().to(device) +for p in syncnet.parameters(): + p.requires_grad = False + +recon_loss = nn.L1Loss() +def get_sync_loss(mel, g): + g = g[:, :, :, g.size(3)//2:] + g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1) + # B, 3 * T, H//2, W + a, v = syncnet(mel, g) + y = torch.ones(g.size(0), 1).float().to(device) + return cosine_loss(a, v, y) + +def train(device, model, train_data_loader, test_data_loader, optimizer, + checkpoint_dir=None, checkpoint_interval=None, nepochs=None): + + global global_step, global_epoch + resumed_step = global_step + + while global_epoch < nepochs: + print('Starting Epoch: {}'.format(global_epoch)) + running_sync_loss, running_l1_loss = 0., 0. + prog_bar = tqdm(enumerate(train_data_loader)) + for step, (x, indiv_mels, mel, gt) in prog_bar: + model.train() + optimizer.zero_grad() + + # Move data to CUDA device + x = x.to(device) + mel = mel.to(device) + indiv_mels = indiv_mels.to(device) + gt = gt.to(device) + + g = model(indiv_mels, x) + + if hparams.syncnet_wt > 0.: + sync_loss = get_sync_loss(mel, g) + else: + sync_loss = 0. + + l1loss = recon_loss(g, gt) + + loss = hparams.syncnet_wt * sync_loss + (1 - hparams.syncnet_wt) * l1loss + loss.backward() + optimizer.step() + + if global_step % checkpoint_interval == 0: + save_sample_images(x, g, gt, global_step, checkpoint_dir) + + global_step += 1 + cur_session_steps = global_step - resumed_step + + running_l1_loss += l1loss.item() + if hparams.syncnet_wt > 0.: + running_sync_loss += sync_loss.item() + else: + running_sync_loss += 0. + + if global_step == 1 or global_step % checkpoint_interval == 0: + save_checkpoint( + model, optimizer, global_step, checkpoint_dir, global_epoch) + + if global_step == 1 or global_step % hparams.eval_interval == 0: + with torch.no_grad(): + average_sync_loss = eval_model(test_data_loader, global_step, device, model, checkpoint_dir) + + if average_sync_loss < .75: + hparams.set_hparam('syncnet_wt', 0.01) # without image GAN a lesser weight is sufficient + + prog_bar.set_description('L1: {}, Sync Loss: {}'.format(running_l1_loss / (step + 1), + running_sync_loss / (step + 1))) + + global_epoch += 1 + + +def eval_model(test_data_loader, global_step, device, model, checkpoint_dir): + eval_steps = 700 + print('Evaluating for {} steps'.format(eval_steps)) + sync_losses, recon_losses = [], [] + step = 0 + while 1: + for x, indiv_mels, mel, gt in test_data_loader: + step += 1 + model.eval() + + # Move data to CUDA device + x = x.to(device) + gt = gt.to(device) + indiv_mels = indiv_mels.to(device) + mel = mel.to(device) + + g = model(indiv_mels, x) + + sync_loss = get_sync_loss(mel, g) + l1loss = recon_loss(g, gt) + + sync_losses.append(sync_loss.item()) + recon_losses.append(l1loss.item()) + + if step > eval_steps: + averaged_sync_loss = sum(sync_losses) / len(sync_losses) + averaged_recon_loss = sum(recon_losses) / len(recon_losses) + + print('L1: {}, Sync loss: {}'.format(averaged_recon_loss, averaged_sync_loss)) + + return averaged_sync_loss + +def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch): + + checkpoint_path = join( + checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step)) + optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None + torch.save({ + "state_dict": model.state_dict(), + "optimizer": optimizer_state, + "global_step": step, + "global_epoch": epoch, + }, checkpoint_path) + print("Saved checkpoint:", checkpoint_path) + + +def _load(checkpoint_path): + if use_cuda: + checkpoint = torch.load(checkpoint_path) + else: + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + +def load_checkpoint(path, model, optimizer, reset_optimizer=False, overwrite_global_states=True): + global global_step + global global_epoch + + print("Load checkpoint from: {}".format(path)) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + if not reset_optimizer: + optimizer_state = checkpoint["optimizer"] + if optimizer_state is not None: + print("Load optimizer state from {}".format(path)) + optimizer.load_state_dict(checkpoint["optimizer"]) + if overwrite_global_states: + global_step = checkpoint["global_step"] + global_epoch = checkpoint["global_epoch"] + + return model + +if __name__ == "__main__": + checkpoint_dir = args.checkpoint_dir + + # Dataset and Dataloader setup + train_dataset = Dataset('train') + test_dataset = Dataset('val') + + train_data_loader = data_utils.DataLoader( + train_dataset, batch_size=hparams.batch_size, shuffle=True, + num_workers=hparams.num_workers) + + test_data_loader = data_utils.DataLoader( + test_dataset, batch_size=hparams.batch_size, + num_workers=4) + + device = torch.device("cuda" if use_cuda else "cpu") + + # Model + model = Wav2Lip().to(device) + print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad))) + + optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad], + lr=hparams.initial_learning_rate) + + if args.checkpoint_path is not None: + load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False) + + load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True, overwrite_global_states=False) + + if not os.path.exists(checkpoint_dir): + os.mkdir(checkpoint_dir) + + # Train! + train(device, model, train_data_loader, test_data_loader, optimizer, + checkpoint_dir=checkpoint_dir, + checkpoint_interval=hparams.checkpoint_interval, + nepochs=hparams.nepochs) diff --git a/usecases/ai/digital-avatar/docker-compose.yml b/usecases/ai/digital-avatar/docker-compose.yml new file mode 100644 index 0000000..1c30f7c --- /dev/null +++ b/usecases/ai/digital-avatar/docker-compose.yml @@ -0,0 +1,168 @@ +services: + wav2lip: + build: + context: . + dockerfile: ./backend/wav2lip/Dockerfile + args: + - RENDER_GROUP_ID=110 # Please update this if you are not using Ubuntu 20.04 + image: digital-avatar.backend.wav2lip + container_name: digital-avatar.backend.wav2lip + privileged: true + restart: always + depends_on: + liveportrait: + condition: service_healthy + devices: + - /dev:/dev:rw + environment: + - SERVER_PORT=8011 + - DEVICE=CPU + - ALLOWED_CORS=["http://localhost"] # frontend url + volumes: + - wav2lip-checkpoints:/usr/src/app/wav2lip/checkpoints + - ./assets:/usr/src/app/assets + - ./weights/wav2lip_gan.pth:/usr/src/app/setup/wav2lip_gan.pth + ports: + - 8011:8011 + command: "python3 main.py" + + liveportrait: + build: + context: . + dockerfile: ./backend/liveportrait/Dockerfile + args: + - RENDER_GROUP_ID=110 # Please update this if you are not using Ubuntu 20.04 + image: digital-avatar.backend.liveportrait + container_name: digital-avatar.backend.liveportrait + privileged: true + restart: always + environment: + - SERVER_PORT=8012 + - ALLOWED_CORS=["http://localhost"] # frontend url + devices: + - /dev:/dev:rw + volumes: + - ./assets:/usr/src/app/assets + ports: + - 8012:8012 + command: "python3 main.py" + + frontend: + build: + context: . + dockerfile: frontend/Dockerfile + args: + - NEXT_PUBLIC_LLM_MODEL=${LLM_MODEL:-qwen2.5} + - NEXT_PUBLIC_TTS_URL=localhost:8013 + - NEXT_PUBLIC_LLM_URL=ollama:8015 + - NEXT_PUBLIC_STT_URL=localhost:8014 + - NEXT_PUBLIC_LIPSYNC_URL=localhost:8011 + image: digital-avatar.frontend + container_name: digital-avatar.frontend + restart: always + depends_on: + liveportrait: + condition: service_healthy + wav2lip: + condition: service_healthy + ollama: + condition: service_healthy + stt_service: + condition: service_healthy + tts_service: + condition: service_healthy + networks: + - app-network + volumes: + - ./assets:/app/public/assets + ports: + - "80:3000" + command: "npm start" + + tts_service: + build: + context: ../microservices/text-to-speech + dockerfile: Dockerfile + image: tts_service + hostname: tts_service + container_name: tts_service + privileged: true + restart: always + networks: + - app-network + environment: + - TTS_DEVICE=CPU + - ALLOWED_CORS=["http://localhost"] # frontend url + ports: + - 8013:5995 + devices: + - /dev:/dev:rw + - /lib/modules:/lib/modules:rw + command: "python3 -m uvicorn main:app --host tts_service --port 5995" + + stt_service: + build: + context: ../microservices/speech-to-text + dockerfile: Dockerfile + args: + - RENDER_GROUP_ID=110 # Please update this if you are not using Ubuntu 20.04 + image: stt_service + hostname: stt_service + container_name: stt_service + privileged: true + networks: + - app-network + environment: + - STT_MODEL_ID=base + - STT_ENCODED_DEVICE=CPU + - STT_DECODED_DEVICE=CPU + - ALLOWED_CORS=["http://localhost"] # frontend url + restart: always + ports: + - 8014:5996 + volumes: + - stt_volume:/usr/src/app/data:rw + devices: + - /dev:/dev:rw + - /lib/modules:/lib/modules:rw + command: "python3 -m uvicorn main:app --host stt_service --port 5996" + + ollama: + build: + context: ../microservices/ollama + dockerfile: Dockerfile + hostname: ollama + container_name: ollama + image: intel-ipex-ollama + restart: always + privileged: true + ports: + - 8015:8015 + environment: + - OLLAMA_HOST=0.0.0.0:8015 + - OLLAMA_NUM_GPU=999 + - ZES_ENABLE_SYSMAN=1 + - SYCL_CACHE_PERSISTENT=1 + - SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 + - ONEAPI_DEVICE_SELECTOR=level_zero:0 + - LLM_MODEL=${LLM_MODEL:-qwen2.5} + networks: + - app-network + devices: + - /dev:/dev:rw + - /lib/modules:/lib/modules:rw + volumes: + - ../microservices/ollama:/root/.ollama + command: "./run.sh" + +networks: + app-network: + + +volumes: + tts_volume: + stt_volume: + data_volume: + ollama: + name: "ollama-data" + wav2lip-checkpoints: diff --git a/usecases/ai/digital-avatar/docs/architecture.png b/usecases/ai/digital-avatar/docs/architecture.png new file mode 100644 index 0000000000000000000000000000000000000000..73919db688e5b4782ee763ec17f32aab1e8fad12 GIT binary patch literal 532394 zcmeEuc~q0v);GPaa@C@BKq@G-qRdDg02xyi98l&l%!+^{5XOKINGgg72vnJ57LYj* zrX)h=_m?k_eat2qE8Ndpmev?zi4QzxAz~wUkAC&N+MUv-j`(_SyR} z-pb;V)K1x*5)u+pS1y~_NJ#9elaP?~`R8`vn;U7pPk|p_gxXv>FHtjacpmuWE8lbG z=OiTRQg&@z-v<1?Bj_?XR6^p^FQ5K=p>oriE+HW-wVXz#N?98OZ+_5bi(1Y)W2o~>VD+0m*pR+GH 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"plugins": [ + "import", + "tailwindcss" + ], + "rules": { + "tailwindcss/no-custom-classname": "off", + "tailwindcss/classnames-order": "off", + "import/order": [ + "error", + { + "groups": [ + "builtin", + "external", + "internal", + [ + "parent", + "sibling" + ], + "index", + "object", + "type" + ], + "newlines-between": "always", + "alphabetize": { + "order": "asc", + "caseInsensitive": true + } + } + ], + "@typescript-eslint/no-explicit-any": "off" // personal style + }, + "settings": { + "import/resolver": { + "typescript": { + "alwaysTryTypes": true + } + } + }, + "ignorePatterns": [ + "**/components/ui/**" + ] +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/.gitignore b/usecases/ai/digital-avatar/frontend/.gitignore new file mode 100644 index 0000000..fd3dbb5 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/.gitignore @@ -0,0 +1,36 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js +.yarn/install-state.gz + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env*.local + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/usecases/ai/digital-avatar/frontend/Dockerfile b/usecases/ai/digital-avatar/frontend/Dockerfile new file mode 100644 index 0000000..83f509c --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/Dockerfile @@ -0,0 +1,58 @@ +# INTEL CONFIDENTIAL +# Copyright (C) 2024, Intel Corporation + +FROM debian:12-slim AS base +ARG DEBIAN_FRONTEND=noninteractive +SHELL ["/bin/bash", "-o", "pipefail", "-c"] +ENV NEXT_TELEMETRY_DISABLED 1 +ENV PORT 3000 +RUN apt-get update \ + && apt-get upgrade -y \ + && apt-get install --no-install-recommends -y \ + sudo \ + curl \ + ca-certificates \ + && addgroup --system intel --gid 1000 \ + && adduser --system --ingroup intel --uid 1000 --home /home/intel intel \ + && echo "intel ALL=(ALL:ALL) NOPASSWD:ALL" > /etc/sudoers.d/intel \ + && mkdir -p /etc/apt/keyrings \ + && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ + && apt-get install --no-install-recommends -y \ + nodejs \ + && node -v \ + && npm -v \ + && rm -rf /var/lib/apt/lists/* \ + && mkdir -p /app \ + && chown -R intel:intel /app + +FROM base AS development +ENV NODE_ENV development +USER intel +WORKDIR /app +COPY --chown=intel:intel ./frontend/package*.json ./ +RUN npm install + +FROM base AS builder +USER intel +WORKDIR /app +COPY --chown=intel:intel ./frontend/package*.json ./ +RUN npm install +COPY --chown=intel:intel ./frontend ./ +# COPY --chown=intel ./assets/*.png ./assets/*.mp4 ./public/assets/ +ARG NEXT_PUBLIC_LLM_MODEL +ARG NEXT_PUBLIC_TTS_URL +ARG NEXT_PUBLIC_LLM_URL +ARG NEXT_PUBLIC_STT_URL +ARG NEXT_PUBLIC_LIPSYNC_URL +RUN npm run build + +FROM base AS runner +ENV NODE_ENV production +USER intel +WORKDIR /app +COPY --from=builder --chown=intel:intel /app/package*.json ./ +COPY --from=builder --chown=intel:intel /app/.next ./.next +RUN npm install --omit=dev + +HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \ + CMD npm --version || exit 1 \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/README.md b/usecases/ai/digital-avatar/frontend/README.md new file mode 100644 index 0000000..e215bc4 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/usecases/ai/digital-avatar/frontend/app/api/chat/route.ts b/usecases/ai/digital-avatar/frontend/app/api/chat/route.ts new file mode 100644 index 0000000..0c062e4 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/app/api/chat/route.ts @@ -0,0 +1,44 @@ +import { CoreMessage, StreamData, streamText } from 'ai'; +import { createOllama } from 'ollama-ai-provider'; + +export async function POST(req: Request) { + const { messages }: { messages: CoreMessage[] } = await req.json(); + const ollama = createOllama({ + baseURL: `http://${process.env.NEXT_PUBLIC_LLM_URL}/api`, + }) + + let message = ''; + let wordCount = 0; + let index = 0; + const minimumWordCount = 10; + const punctuations = ',.!?;:*'; + const data = new StreamData(); + + const result = await streamText({ + model: ollama(`${process.env.NEXT_PUBLIC_LLM_MODEL ?? 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z)zD^VUx(6IRTea3*X0U4gZEYJSVX2J*E81y`XiniRE5tH2I2zccwu{;zq@aA4USu2 zjLhxT+_?Hz=;=N=o>#30?Wx1!oO5ejFsI9=9_bd_eFMYFft6%O4iqg>!ZfQ0)K-Lv z^JM!jVDgQTp9X#rl76h@ikCvVl0ElVqI*1X9l9S&COz@R5c)(@7=>B2T;?uyaX)nL zhWec$K!2K4N}uBl8r#DSJ8GvvP&g)RKcm7Kl@c&!IZ)E&N@Xc=MbC2uvT)ICaQQ$K z3Df}zxi<3&zM-6BPON72w`L8$YWD<;3nZFu`;kS$W6&jf1)KUzkz=L G)cz05(PHWV literal 0 HcmV?d00001 diff --git a/usecases/ai/digital-avatar/frontend/app/globals.css b/usecases/ai/digital-avatar/frontend/app/globals.css new file mode 100644 index 0000000..1dcb0fc --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/app/globals.css @@ -0,0 +1,78 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +body { + font-family: Arial, Helvetica, sans-serif; +} + +@layer utilities { + .text-balance { + text-wrap: balance; + } +} + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 0 0% 3.9%; + --card: 0 0% 100%; + --card-foreground: 0 0% 3.9%; + --popover: 0 0% 100%; + --popover-foreground: 0 0% 3.9%; + --primary: 0 0% 9%; + --primary-foreground: 0 0% 98%; + --secondary: 0 0% 96.1%; + --secondary-foreground: 0 0% 9%; + --muted: 0 0% 96.1%; + --muted-foreground: 0 0% 45.1%; + --accent: 0 0% 96.1%; + --accent-foreground: 0 0% 9%; + --destructive: 0 84.2% 60.2%; + --destructive-foreground: 0 0% 98%; + --border: 0 0% 89.8%; + --input: 0 0% 89.8%; + --ring: 0 0% 3.9%; + --chart-1: 12 76% 61%; + --chart-2: 173 58% 39%; + --chart-3: 197 37% 24%; + --chart-4: 43 74% 66%; + --chart-5: 27 87% 67%; + --radius: 0.5rem; + } + .dark { + --background: 0 0% 3.9%; + --foreground: 0 0% 98%; + --card: 0 0% 3.9%; + --card-foreground: 0 0% 98%; + --popover: 0 0% 3.9%; + --popover-foreground: 0 0% 98%; + --primary: 0 0% 98%; + --primary-foreground: 0 0% 9%; + --secondary: 0 0% 14.9%; + --secondary-foreground: 0 0% 98%; + --muted: 0 0% 14.9%; + --muted-foreground: 0 0% 63.9%; + --accent: 0 0% 14.9%; + --accent-foreground: 0 0% 98%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 0 0% 98%; + --border: 0 0% 14.9%; + --input: 0 0% 14.9%; + --ring: 0 0% 83.1%; + --chart-1: 220 70% 50%; + --chart-2: 160 60% 45%; + --chart-3: 30 80% 55%; + --chart-4: 280 65% 60%; + --chart-5: 340 75% 55%; + } +} + +@layer base { + * { + @apply border-border; + } + body { + @apply bg-background text-foreground; + } +} diff --git a/usecases/ai/digital-avatar/frontend/app/layout.tsx b/usecases/ai/digital-avatar/frontend/app/layout.tsx new file mode 100644 index 0000000..a1847ff --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/app/layout.tsx @@ -0,0 +1,40 @@ +import localFont from "next/font/local"; + +import "./globals.css"; +import Providers from "@/providers"; + +import type { Metadata } from "next"; + +const geistSans = localFont({ + src: "./fonts/GeistVF.woff", + variable: "--font-geist-sans", + weight: "100 900", +}); +const geistMono = localFont({ + src: "./fonts/GeistMonoVF.woff", + variable: "--font-geist-mono", + weight: "100 900", +}); + +export const metadata: Metadata = { + title: "Create Next App", + description: "Generated by create next app", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + + {children} + + + + ); +} diff --git a/usecases/ai/digital-avatar/frontend/app/page.tsx b/usecases/ai/digital-avatar/frontend/app/page.tsx new file mode 100644 index 0000000..3bf98f8 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/app/page.tsx @@ -0,0 +1,20 @@ +'use client' + +import Avatar from "@/components/avatar" +import Chat from "@/components/chat" + +export default function Component() { + + return ( +
    +
    +
    + +
    +
    + +
    +
    +
    + ) +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/components.json b/usecases/ai/digital-avatar/frontend/components.json new file mode 100644 index 0000000..bcec1f9 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components.json @@ -0,0 +1,20 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": true, + "tsx": true, + "tailwind": { + "config": "tailwind.config.ts", + "css": "app/globals.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + } +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/components/avatar/index.tsx b/usecases/ai/digital-avatar/frontend/components/avatar/index.tsx new file mode 100644 index 0000000..5d8708d --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/avatar/index.tsx @@ -0,0 +1,171 @@ +import { useEffect, useRef, useState } from 'react'; + +import useVideoQueue from '@/hooks/useVideoQueue'; + +export default function Avatar() { + const { currentVideo, popVideo } = useVideoQueue(); + const videoRef = useRef(null); + const canvasRef = useRef(null); + const [frames, setFrames] = useState([]); + const [videoDuration, setVideoDuration] = useState(0) + const [timeoutId, setTimeoutId] = useState(null); + const [isPlaying, setIsPlaying] = useState("0"); + const [isLoading, setIsLoading] = useState(true) + const [isReversed, setIsReversed] = useState(false) + + const FPS = 30 + + // Read all the frames from video and store it in frames state + const handleVideoLoaded = () => { + const video = videoRef.current; + const canvas = canvasRef.current; + if (!canvas || !video) + return; + const aspectRatio = video.videoWidth / video.videoHeight; + canvas.width = video.videoWidth; + canvas.height = canvas.width / aspectRatio; + + const tempCanvas = document.createElement("canvas") + tempCanvas.width = video.videoWidth; + tempCanvas.height = tempCanvas.width / aspectRatio; + const context = tempCanvas.getContext('2d', { willReadFrequently: true }); + if (!context) + return; + + const frameArray: ImageData[] = []; + const captureFrame = () => { + context.drawImage(video, 0, 0, canvas.width, canvas.height); + const imageData = context.getImageData(0, 0, canvas.width, canvas.height); + if (imageData.data.some(value => value !== 0)) { + frameArray.push(imageData); + } + }; + video.addEventListener("play", () => { + setVideoDuration(video.duration) + const interval = setInterval(() => { + if (video.paused || video.ended) { + clearInterval(interval); + setIsLoading(false) + setFrames(frameArray) + } else { + captureFrame(); + } + }, 1000 / FPS); + }) + + video.play() + } + + useEffect(() => { + const videoElement = videoRef.current; + if (videoElement) + videoElement.src = "/assets/video.mp4"; + }, []) + + useEffect(() => { + if (isPlaying === "0" && currentVideo?.url && !isReversed) { + if (timeoutId) + clearTimeout(timeoutId) + setIsPlaying(currentVideo.url) + } + else if (frames.length > 0 && isPlaying === "0") { + if (timeoutId) + clearTimeout(timeoutId) + setIsPlaying("1") + } + }, [frames, isPlaying, currentVideo, timeoutId, isReversed]) + + useEffect(() => { + if (isPlaying === "1") { + const canvas = canvasRef.current; + if (!canvas || frames.length === 0) return; + const context = canvas.getContext('2d'); + if (!context) return; + let currentFrameIndex = isReversed ? frames.length - 1 : 0; + const direction = isReversed ? -1 : 1; + const frameDelay = 1000 / FPS; + + const playFrames = () => { + context.putImageData(frames[currentFrameIndex], 0, 0); + currentFrameIndex += direction; + if (currentFrameIndex === frames.length - 1 || currentFrameIndex === 0) { + setIsReversed(prev => !prev) + setIsPlaying("0") + return + } + const timeoutId = setTimeout(playFrames, frameDelay); + setTimeoutId(timeoutId) + }; + + playFrames(); + } else if (isPlaying && isPlaying !== "0" && isPlaying !== "1" && !isReversed) { + console.log('play') + const video = document.createElement('video'); + video.src = isPlaying; + + const canvas = canvasRef.current; + if (!canvas) return; + const context = canvas.getContext('2d'); + if (!context) return; + + video.addEventListener('play', () => { + const drawFrame = () => { + if (video.paused || video.ended) { + popVideo(); + const duration = video.duration + console.log(duration, videoDuration, Math.round(duration / videoDuration) % 2) + if (duration <= videoDuration || Math.round(duration / videoDuration) % 2 === 1) { + setIsReversed(true) + } else { + setIsReversed(false) + } + setIsPlaying("0") + return + }; + context.drawImage(video, 0, 0, canvas.width, canvas.height); + requestAnimationFrame(drawFrame); + }; + drawFrame(); + }); + + video.play() + } + }, [frames, isPlaying, popVideo, isReversed, videoDuration]) + + + return ( + <> +
    + { + isLoading ? +
    + + + + + +
    + : null + } + +
    + + ); +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/components/chat/Markdown.tsx b/usecases/ai/digital-avatar/frontend/components/chat/Markdown.tsx new file mode 100644 index 0000000..f801455 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/chat/Markdown.tsx @@ -0,0 +1,19 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 + +// Framework +import React from "react"; +import ReactMarkdown from "react-markdown"; +import remarkGfm from "remark-gfm"; + +interface MarkdownProps { + content: string; +} + +export default function Markdown({ content }: MarkdownProps): React.JSX.Element { + return ( +
    + {content} +
    + ); +}; \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/components/chat/index.tsx b/usecases/ai/digital-avatar/frontend/components/chat/index.tsx new file mode 100644 index 0000000..e0ed56b --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/chat/index.tsx @@ -0,0 +1,166 @@ +import { useChat } from 'ai/react' +import { Send, Mic, StopCircle } from 'lucide-react' +import { useEffect, useRef, useState } from 'react' + +import { Button } from "@/components/ui/button" +import { Input } from "@/components/ui/input" +import { ScrollArea } from "@/components/ui/scroll-area" +import useAudioRecorder from '@/hooks/useAudioRecorder' +import { useGetLipsync } from '@/hooks/useLipsync' +import { useGetTTSAudio } from '@/hooks/useTTS' +import useVideoQueue from '@/hooks/useVideoQueue' + +import Markdown from './Markdown' + + +interface StreamData { + index: number, + message: string, + processed: boolean +} + +export default function Chat() { + const { messages, input, setInput, append, handleSubmit, data, isLoading } = useChat(); + const { startRecording, stopRecording, recording, durationSeconds, visualizerData, sttMutation } = useAudioRecorder(); + const { addVideo, updateVideo } = useVideoQueue() + const chatBottomRef = useRef(null) + const getTTSAudio = useGetTTSAudio() + const getLipsync = useGetLipsync() + const [taskQueue, setTaskQueue] = useState[]>([]) + const [isProcessing, setIsProcessing] = useState(false) + + // Ensure scroll to bottom when new message is added + useEffect(() => { + if (chatBottomRef.current) { + chatBottomRef.current.scrollIntoView() + } + }, [messages]) + + // Add each trun sentence to task queue + useEffect(() => { + if (data) { + const streamData: (StreamData | null)[] = data as (StreamData | null)[] + streamData.forEach((d) => { + if (d && !d.processed) { + d.processed = true + setTaskQueue(prev => { return [...prev, { id: d.index, text: d.message }] }) + } + }) + } + }, [data]) + + // Process task queue items one by one + useEffect(() => { + const processText = async (index: number, text: string) => { + addVideo({ id: index, url: undefined }) + + console.time(`process for message ${index}`) + // TTS + const speech = await getTTSAudio.mutateAsync({ text }) + + const file = new File([speech], "speech.wav", { type: "audio/wav" }) + + // Lipsync + const form = new FormData() + form.append("file", file) + const response = await getLipsync.mutateAsync({ data: form }) + const url = URL.createObjectURL(response) + updateVideo(index, url) + console.timeEnd(`process for message ${index}`) + + setIsProcessing(false) + setTaskQueue(prev => { return prev.slice(1) }) + } + + if (taskQueue.length > 0 && !isProcessing) { + const task = taskQueue[0] + setIsProcessing(true) + processText(task.id as number, task.text as string) + } + + + }, [taskQueue, isProcessing, addVideo, getTTSAudio, getLipsync, updateVideo]) + + const formatSeconds = (seconds: number) => { + const minutes = Math.floor(seconds / 60); + const remainingSeconds = seconds % 60; + const formattedSeconds = remainingSeconds < 10 ? `0${remainingSeconds}` : remainingSeconds; + return `${minutes}:${formattedSeconds}`; + }; + + useEffect(() => { + if (sttMutation.status === "success" && sttMutation.data) { + append({ content: sttMutation.data.text, role: "user" }) + sttMutation.reset() + } + // eslint-disable-next-line react-hooks/exhaustive-deps -- sttMutation.reset added to dependency will cause infinite loop + }, [append, sttMutation.status, sttMutation.data]) + + return ( + <> +
    +

    Chat

    +
    + + {messages.map((message, index) => ( +
    +
    + +
    +
    + ))} +
    + +
    + {recording ? ( +
    + {formatSeconds(durationSeconds)} +
    + {visualizerData.slice().reverse().map((rms, index) => ( +
    +
    +
    + ))} +
    +
    + ) : ( + <> + setInput(e.target.value)} + onKeyDown={async (ev) => { + if (ev.key === "Enter") { + append({ content: input, role: "user" }) + setInput("") + } + }} + disabled={isLoading} + className="grow" + /> + + + )} + + {recording ? ( + + ) : ( + + )} +
    + + ) +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/components/ui/button.tsx b/usecases/ai/digital-avatar/frontend/components/ui/button.tsx new file mode 100644 index 0000000..65d4fcd --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/ui/button.tsx @@ -0,0 +1,57 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: + "bg-primary text-primary-foreground shadow hover:bg-primary/90", + destructive: + "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90", + outline: + "border border-input bg-background shadow-sm hover:bg-accent hover:text-accent-foreground", + secondary: + "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80", + ghost: "hover:bg-accent hover:text-accent-foreground", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: "h-9 px-4 py-2", + sm: "h-8 rounded-md px-3 text-xs", + lg: "h-10 rounded-md px-8", + icon: "h-9 w-9", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +export interface ButtonProps + extends React.ButtonHTMLAttributes, + VariantProps { + asChild?: boolean +} + +const Button = React.forwardRef( + ({ className, variant, size, asChild = false, ...props }, ref) => { + const Comp = asChild ? Slot : "button" + return ( + + ) + } +) +Button.displayName = "Button" + +export { Button, buttonVariants } diff --git a/usecases/ai/digital-avatar/frontend/components/ui/input.tsx b/usecases/ai/digital-avatar/frontend/components/ui/input.tsx new file mode 100644 index 0000000..5af26b2 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/ui/input.tsx @@ -0,0 +1,25 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +export interface InputProps + extends React.InputHTMLAttributes {} + +const Input = React.forwardRef( + ({ className, type, ...props }, ref) => { + return ( + + ) + } +) +Input.displayName = "Input" + +export { Input } diff --git a/usecases/ai/digital-avatar/frontend/components/ui/scroll-area.tsx b/usecases/ai/digital-avatar/frontend/components/ui/scroll-area.tsx new file mode 100644 index 0000000..0b4a48d --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/components/ui/scroll-area.tsx @@ -0,0 +1,48 @@ +"use client" + +import * as React from "react" +import * as ScrollAreaPrimitive from "@radix-ui/react-scroll-area" + +import { cn } from "@/lib/utils" + +const ScrollArea = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + + + {children} + + + + +)) +ScrollArea.displayName = ScrollAreaPrimitive.Root.displayName + +const ScrollBar = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, orientation = "vertical", ...props }, ref) => ( + + + +)) +ScrollBar.displayName = ScrollAreaPrimitive.ScrollAreaScrollbar.displayName + +export { ScrollArea, ScrollBar } diff --git a/usecases/ai/digital-avatar/frontend/context/VideoQueueContext.tsx b/usecases/ai/digital-avatar/frontend/context/VideoQueueContext.tsx new file mode 100644 index 0000000..f008a14 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/context/VideoQueueContext.tsx @@ -0,0 +1,49 @@ +import React, { createContext, useState, ReactNode, useMemo, useCallback } from 'react'; + +interface Video { + id: number; + url?: string | undefined; +} + +export interface VideoQueueContextProps { + queue: Video[]; + addVideo: (video: Video) => void; + popVideo: () => void; + currentVideo: Video | undefined; + updateVideo: (id: number, url: string) => void; +} + +export const VideoQueueContext = createContext({} as VideoQueueContextProps); + +export const VideoQueueProvider = ({ children }: { children: ReactNode }) => { + const [queue, setQueue] = useState([]); + + const addVideo = (video: Video) => { + setQueue((prevQueue) => [...prevQueue, video]); + }; + + const updateVideo = (id: number, url: string) => { + setQueue((prevQueue) => { + return prevQueue.map((item) => { + if (item.id === id) { + return { ...item, url } + } + return { ...item } + }) + }); + }; + + const popVideo = useCallback(() => { + setQueue((prevQueue) => { return prevQueue.slice(1) }); + }, []); + + + const currentVideo = useMemo(() => { + return queue[0]; + }, [queue]) + return ( + + {children} + + ); +}; \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/hooks/useAudioRecorder.tsx b/usecases/ai/digital-avatar/frontend/hooks/useAudioRecorder.tsx new file mode 100644 index 0000000..af7eb18 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/hooks/useAudioRecorder.tsx @@ -0,0 +1,220 @@ +"use client" + +import { useCallback, useEffect, useRef, useState } from 'react'; + +import { useGetSTT } from './useSTT'; + +export default function useAudioRecorder() { + const MIN_DECIBELS = -45; + const VISUALIZER_BUFFER_LENGTH = 300; + + const [initialLoad, setInitialLoad] = useState(false); + const [mediaRecorder, setMediaRecorder] = useState(null); + const [recording, setRecording] = useState(false); + const chunks = useRef([]); + const [analyser, setAnalyser] = useState(null) + const [durationCounter, setDurationCounter] = useState(null) + const [durationSeconds, setDurationSeconds] = useState(0); + const [visualizerData, setVisualizerData] = useState(Array(VISUALIZER_BUFFER_LENGTH).fill(0)) + + const sttMutation = useGetSTT() + + // Function to calculate the RMS level from time domain data + const calculateRMS = (data: Uint8Array) => { + let sumSquares = 0; + for (let i = 0; i < data.length; i++) { + const normalizedValue = (data[i] - 128) / 128; // Normalize the data + sumSquares += normalizedValue * normalizedValue; + } + return Math.sqrt(sumSquares / data.length); + }; + + const normalizeRMS = (rms: number) => { + rms = rms * 10; + const exp = 1.5; // Adjust exponent value; values greater than 1 expand larger numbers more and compress smaller numbers more + const scaledRMS = Math.pow(rms, exp); + + // Scale between 0.01 (1%) and 1.0 (100%) + return Math.min(1.0, Math.max(0.01, scaledRMS)); + }; + + const analyseAudio = useCallback(() => { + if (!analyser) return; + const bufferLength = analyser.frequencyBinCount; + + const domainData = new Uint8Array(bufferLength); + const timeDomainData = new Uint8Array(analyser.fftSize); + + let lastSoundTime = Date.now(); + let hasStartedSpeaking = false + + const detectSound = () => { + const processFrame = () => { + if (recording) { + analyser.getByteTimeDomainData(timeDomainData); + analyser.getByteFrequencyData(domainData); + + // Calculate RMS level from time domain data + const rmsLevel = calculateRMS(timeDomainData); + // Push the calculated decibel level to visualizerData + setVisualizerData((prev) => { + if (prev.length >= VISUALIZER_BUFFER_LENGTH) { + prev.shift(); + } + return [...prev, normalizeRMS(rmsLevel)]; + }) + + // Check if initial speech/noise has started + const hasSound = domainData.some((value) => value > 0); + if (hasSound) { + if (!hasStartedSpeaking) { + hasStartedSpeaking = true + } + + lastSoundTime = Date.now(); + } + + // Start silence detection only after initial speech/noise has been detected + if (hasStartedSpeaking) { + if (Date.now() - lastSoundTime > 2000) { + if (mediaRecorder) { + console.log('stop') + mediaRecorder.stop(); + return; + } + } + } + + window.requestAnimationFrame(processFrame); + } + }; + + window.requestAnimationFrame(processFrame); + }; + + detectSound(); + }, [analyser, mediaRecorder, recording]); + + const initiateMediaRecoder = useCallback( + (stream: MediaStream) => { + const blobToFile = (blob: Blob, fileName: string) => { + // Create a new File object from the Blob + const file = new File([blob], fileName, { type: blob.type }); + return file; + }; + + const transcribe = async (file: File) => { + const form = new FormData() + form.append("file", file) + form.append("language", "english") + sttMutation.mutate({ data: form }) + } + + const wavRecorder = new MediaRecorder(stream); + + // Event handler when recording starts + wavRecorder.onstart = () => { + chunks.current = []; // Resetting chunks array + }; + + // Event handler when data becomes available during recording + wavRecorder.ondataavailable = (ev: BlobEvent) => { + chunks.current.push(ev.data); // Storing data chunks + }; + + // Event handler when recording stops + wavRecorder.onstop = () => { + const mimeType = wavRecorder.mimeType; + const audioBlob = new Blob(chunks.current, { type: mimeType }); + const file = blobToFile(audioBlob, 'recording.wav'); + transcribe(file) + setRecording(false); + }; + + setMediaRecorder(wavRecorder); + + // Analyzer to activate only on certain noise level + const audioCtx = new AudioContext(); + const sourceAnalyser = audioCtx.createAnalyser(); + const source = audioCtx.createMediaStreamSource(stream); + source.connect(sourceAnalyser); + sourceAnalyser.minDecibels = MIN_DECIBELS + setAnalyser(sourceAnalyser) + }, + [MIN_DECIBELS, sttMutation] + ); + + const startRecording = useCallback(() => { + const startDurationCounter = () => { + setDurationCounter(setInterval(() => { + setDurationSeconds(prev => prev + 1); + }, 1000)); + }; + + if (mediaRecorder) { + startDurationCounter(); + setRecording(true); + mediaRecorder.start(); + } + }, [mediaRecorder]) + + const stopRecording = useCallback(() => { + const stopDurationCounter = () => { + if (durationCounter !== null) { + clearInterval(durationCounter); + } + setDurationSeconds(0); + }; + + + if (mediaRecorder) { + stopDurationCounter() + mediaRecorder.stop(); + } + }, [durationCounter, mediaRecorder]); + + const pauseRecording = useCallback(() => { + if (mediaRecorder) { + mediaRecorder.pause(); + } + }, [mediaRecorder]) + + const resumeRecording = useCallback(() => { + if (mediaRecorder) { + mediaRecorder.resume(); + } + }, [mediaRecorder]) + + + useEffect(() => { + async function loadRecorder(): Promise { + + const stream = await navigator.mediaDevices.getUserMedia({ audio: true }); + initiateMediaRecoder(stream); + } + + if (typeof window !== 'undefined' && !mediaRecorder && !initialLoad) { + setInitialLoad(true); + void loadRecorder() + } + }, [mediaRecorder, initialLoad, initiateMediaRecoder]); + + useEffect(() => { + if (recording) { + analyseAudio() + } + }, [analyseAudio, recording]) + + return { + startRecording, + stopRecording, + visualizerData, + recording, + durationSeconds, + sttMutation, + pauseRecording, + resumeRecording + }; +}; + +export const preferredRegion = 'home' \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/hooks/useLipsync.tsx b/usecases/ai/digital-avatar/frontend/hooks/useLipsync.tsx new file mode 100644 index 0000000..4cb3829 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/hooks/useLipsync.tsx @@ -0,0 +1,19 @@ +"use client" + +import { useMutation } from "@tanstack/react-query"; + +import { FetchAPI } from "@/lib/api"; + +const LipsyncAPI = new FetchAPI(`${process.env.NEXT_PUBLIC_LIPSYNC_URL}`); + +export function useGetLipsync() { + return useMutation({ + mutationFn: async ({ + data + }: { data: FormData }) => { + // const response = await fetch("/api/lipsync", { body: data, method: "POST" }) + const response = await LipsyncAPI.file('inference', data, { headers: {} }) + return await response.blob() + }, + }); +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/hooks/useSTT.tsx b/usecases/ai/digital-avatar/frontend/hooks/useSTT.tsx new file mode 100644 index 0000000..e11e08a --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/hooks/useSTT.tsx @@ -0,0 +1,22 @@ +"use client" + +import { useMutation, UseMutationResult } from "@tanstack/react-query"; + +import { FetchAPI } from "@/lib/api"; + +const STTAPI = new FetchAPI(`${process.env.NEXT_PUBLIC_STT_URL}`); + +export function useGetSTT(): UseMutationResult, Error, { data: FormData }> { + return useMutation({ + mutationFn: async ({ + data + }: { data: FormData }) => { + try { + const response = await STTAPI.file('audio/transcriptions', data, { headers: {} }) + return await response.json() + } catch (error) { + return { status: false, message: "Failed to process audio", data: error } + } + }, + }); +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/hooks/useTTS.tsx b/usecases/ai/digital-avatar/frontend/hooks/useTTS.tsx new file mode 100644 index 0000000..ff4c7ce --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/hooks/useTTS.tsx @@ -0,0 +1,19 @@ +"use client" + +import { useMutation } from "@tanstack/react-query"; + +import { FetchAPI } from "@/lib/api"; + +const TTSAPI = new FetchAPI(`${process.env.NEXT_PUBLIC_TTS_URL}`); + +export function useGetTTSAudio() { + return useMutation({ + mutationFn: async ({ + text + }: { text: string }) => { + // const response = await fetch("/api/tts", { method: "POST", body: JSON.stringify({ input: text, voice: "EN-US", model: " - " }) }) + const response = await TTSAPI.file('audio/speech', { input: text, voice: "EN-US", model: "-", speed: 1.2 }) + return await response.blob() + }, + }); +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/hooks/useVideoQueue.tsx b/usecases/ai/digital-avatar/frontend/hooks/useVideoQueue.tsx new file mode 100644 index 0000000..3c03c29 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/hooks/useVideoQueue.tsx @@ -0,0 +1,8 @@ +import { useContext } from 'react'; + +import { VideoQueueContext, VideoQueueContextProps } from '@/context/VideoQueueContext'; + + +const useVideoQueue: () => VideoQueueContextProps = () => useContext(VideoQueueContext); + +export default useVideoQueue; \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/lib/api.ts b/usecases/ai/digital-avatar/frontend/lib/api.ts new file mode 100644 index 0000000..486cc83 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/lib/api.ts @@ -0,0 +1,97 @@ +// INTEL CONFIDENTIAL +// Copyright (C) 2024, Intel Corporation +import { type APIResponse } from '@/types/api'; + +interface RequestConfig { + headers?: Record; + data?: any; + tags?: string[]; + revalidate?: number; + raw_response?: boolean; +} + +type HttpMethod = 'GET' | 'POST' | 'PUT' | 'PATCH' | 'DELETE'; +const apiVersion = 'v1'; + +export class FetchAPI { + private baseURL: string; + + constructor(baseURL: string) { + if (!baseURL.match(/^https?:\/\//)) { + baseURL = 'http://' + baseURL; + } + this.baseURL = new URL(`${apiVersion}/path`, baseURL).toString(); + } + + private async request(method: HttpMethod, url: string, config: RequestConfig = {}): Promise { + try { + const { data, tags, revalidate, headers, raw_response } = config; + const fullURL = new URL(url, this.baseURL).toString(); + const options: RequestInit = { + method, + headers: headers ?? { 'Content-Type': 'application/json' }, + next: { + ...(tags && { tags }), + ...((revalidate || revalidate === 0) && { revalidate }), + }, + }; + + const request = new Request(fullURL, options); + if (data && request.headers.get('Content-Type') === 'application/json') { + options.body = JSON.stringify(data); + } else { + options.body = data; + } + const response = await fetch(fullURL, options); + return raw_response ? response : this.handleResponse(response); + } catch (err) { + console.log(err); + return { + status: false, + message: 'Error communicating with backend', + } as APIResponse; + } + } + + private async handleResponse(response: Response): Promise { + const responseURL = response.url; + const data = await response.json(); + if (!response.ok) { + return { + status: false, + message: 'Error communicating with backend', + url: response.url, + } as APIResponse; + } + if ("status" in data) { + return data as APIResponse + } + return { status: true, data, url: responseURL } as APIResponse; + } + + public async get(url: string, config?: RequestConfig): Promise { + return await this.request('GET', url, config) as APIResponse; + } + + public async post(url: string, data?: Record, config?: RequestConfig): Promise { + return await this.request('POST', url, { ...config, data }) as APIResponse; + } + + public async put(url: string, data?: Record, config?: RequestConfig): Promise { + return await this.request('PUT', url, { ...config, data }) as APIResponse; + } + + public async patch(url: string, data?: Record, config?: RequestConfig): Promise { + return await this.request('PATCH', url, { ...config, data }) as APIResponse; + } + + public async delete(url: string, config?: RequestConfig): Promise { + return await this.request('DELETE', url, config) as APIResponse; + } + + public async file(url: string, data?: Record, config?: RequestConfig): Promise { + return await this.request('POST', url, { ...config, raw_response: true, data }) as Response; + } +} + +export const API = new FetchAPI(`http://${process.env.NEXT_PUBLIC_API_URL ?? "localhost"}:5999`); diff --git a/usecases/ai/digital-avatar/frontend/lib/utils.ts b/usecases/ai/digital-avatar/frontend/lib/utils.ts new file mode 100644 index 0000000..bd0c391 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/lib/utils.ts @@ -0,0 +1,6 @@ +import { clsx, type ClassValue } from "clsx" +import { twMerge } from "tailwind-merge" + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)) +} diff --git a/usecases/ai/digital-avatar/frontend/next.config.mjs b/usecases/ai/digital-avatar/frontend/next.config.mjs new file mode 100644 index 0000000..c58c974 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/next.config.mjs @@ -0,0 +1,6 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: false, +}; + +export default nextConfig; diff --git a/usecases/ai/digital-avatar/frontend/package-lock.json b/usecases/ai/digital-avatar/frontend/package-lock.json new file mode 100644 index 0000000..1810ad9 --- /dev/null +++ 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"^0.16.0", + "react": "^18", + "react-dom": "^18", + "react-markdown": "^9.0.1", + "remark-gfm": "^4.0.0", + "tailwind-merge": "^2.5.4", + "tailwindcss-animate": "^1.0.7" + }, + "devDependencies": { + "@tailwindcss/typography": "^0.5.15", + "@types/d3-scale": "^4.0.8", + "@types/node": "^20", + "@types/pdf-parse": "^1.1.4", + "@types/react": "^18", + "@types/react-dom": "^18", + "drizzle-kit": "^0.25.0", + "eslint": "^8.57.0", + "eslint-config-next": "^14.2.16", + "eslint-config-prettier": "^9.1.0", + "eslint-import-resolver-typescript": "^3.6.3", + "eslint-plugin-import": "^2.31.0", + "eslint-plugin-tailwindcss": "^3.17.5", + "postcss": "^8", + "prettier": "^3.3.3", + "tailwindcss": "^3.4.1", + "tsx": "^4.19.1", + "typescript": "^5" + } +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/postcss.config.mjs b/usecases/ai/digital-avatar/frontend/postcss.config.mjs new file mode 100644 index 0000000..1a69fd2 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/postcss.config.mjs @@ -0,0 +1,8 @@ +/** @type {import('postcss-load-config').Config} */ +const config = { + plugins: { + tailwindcss: {}, + }, +}; + +export default config; diff --git a/usecases/ai/digital-avatar/frontend/providers/index.tsx b/usecases/ai/digital-avatar/frontend/providers/index.tsx new file mode 100644 index 0000000..9970b4a --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/providers/index.tsx @@ -0,0 +1,19 @@ +"use client" + +import { VideoQueueProvider } from "@/context/VideoQueueContext"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; + +export default function Providers({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + const queryClient = new QueryClient() + return ( + + + {children} + + + ) +} \ No newline at end of file diff --git a/usecases/ai/digital-avatar/frontend/tailwind.config.ts b/usecases/ai/digital-avatar/frontend/tailwind.config.ts new file mode 100644 index 0000000..ebc9f38 --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/tailwind.config.ts @@ -0,0 +1,63 @@ +import type { Config } from "tailwindcss"; + +const config: Config = { + darkMode: ["class"], + content: [ + "./pages/**/*.{js,ts,jsx,tsx,mdx}", + "./components/**/*.{js,ts,jsx,tsx,mdx}", + "./app/**/*.{js,ts,jsx,tsx,mdx}", + ], + theme: { + extend: { + colors: { + background: 'hsl(var(--background))', + foreground: 'hsl(var(--foreground))', + card: { + DEFAULT: 'hsl(var(--card))', + foreground: 'hsl(var(--card-foreground))' + }, + popover: { + DEFAULT: 'hsl(var(--popover))', + foreground: 'hsl(var(--popover-foreground))' + }, + primary: { + DEFAULT: 'hsl(var(--primary))', + foreground: 'hsl(var(--primary-foreground))' + }, + secondary: { + DEFAULT: 'hsl(var(--secondary))', + foreground: 'hsl(var(--secondary-foreground))' + }, + muted: { + DEFAULT: 'hsl(var(--muted))', + foreground: 'hsl(var(--muted-foreground))' + }, + accent: { + DEFAULT: 'hsl(var(--accent))', + foreground: 'hsl(var(--accent-foreground))' + }, + destructive: { + DEFAULT: 'hsl(var(--destructive))', + foreground: 'hsl(var(--destructive-foreground))' + }, + border: 'hsl(var(--border))', + input: 'hsl(var(--input))', + ring: 'hsl(var(--ring))', + chart: { + '1': 'hsl(var(--chart-1))', + '2': 'hsl(var(--chart-2))', + '3': 'hsl(var(--chart-3))', + '4': 'hsl(var(--chart-4))', + '5': 'hsl(var(--chart-5))' + } + }, + borderRadius: { + lg: 'var(--radius)', + md: 'calc(var(--radius) - 2px)', + sm: 'calc(var(--radius) - 4px)' + } + } + }, + plugins: [require("tailwindcss-animate")], +}; +export default config; diff --git a/usecases/ai/digital-avatar/frontend/tsconfig.json b/usecases/ai/digital-avatar/frontend/tsconfig.json new file mode 100644 index 0000000..e7ff90f --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/tsconfig.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules"] +} diff --git a/usecases/ai/digital-avatar/frontend/types/api.ts b/usecases/ai/digital-avatar/frontend/types/api.ts new file mode 100644 index 0000000..f6e931b --- /dev/null +++ b/usecases/ai/digital-avatar/frontend/types/api.ts @@ -0,0 +1,10 @@ +// INTEL CONFIDENTIAL +// Copyright (C) 2024, Intel Corporation + +export interface APIResponse { + status: boolean; + url: string; + data?: any; + message?: string; + detail?: any; +} diff --git a/usecases/ai/microservices/ollama/Dockerfile b/usecases/ai/microservices/ollama/Dockerfile index 86a8837..6a63100 100644 --- a/usecases/ai/microservices/ollama/Dockerfile +++ b/usecases/ai/microservices/ollama/Dockerfile @@ -5,7 +5,7 @@ FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04 WORKDIR /opt/intel/llm-app RUN apt update \ - && apt install -y python3.11-venv \ + && apt install -y python3.11-venv jq \ wget \ && wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg \ && echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list \ @@ -20,4 +20,8 @@ RUN python3 -m pip install --upgrade pip \ && python3 -m pip install --upgrade accelerate==0.33.0 \ && init-ollama -CMD ["/opt/intel/llm-app/ollama", "serve"] +COPY ./run.sh . + +COPY ./healthcheck.sh . +HEALTHCHECK --interval=60s --timeout=20m --start-period=5s --retries=20 \ + CMD ./healthcheck.sh \ No newline at end of file diff --git a/usecases/ai/microservices/ollama/healthcheck.sh b/usecases/ai/microservices/ollama/healthcheck.sh new file mode 100755 index 0000000..a454883 --- /dev/null +++ b/usecases/ai/microservices/ollama/healthcheck.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +# Ensure OLLAMA_HOST is set +if [ -z "$OLLAMA_HOST" ]; then + echo "Error: OLLAMA_HOST environment variable is not set." + exit 1 +fi + +# Ensure LLM_MODEL is set +if [ -z "$LLM_MODEL" ]; then + echo "Error: LLM_MODEL environment variable is not set." + exit 1 +fi + +PORT=$(echo "$OLLAMA_HOST" | cut -d':' -f2) + +# Capture the HTTP response code and response body separately +http_code=$(curl -s -o response.txt -w "%{http_code}" -X POST "http://localhost:$PORT/api/show" -d "{\"model\": \"$LLM_MODEL\"}") +response=$(cat response.txt) + +echo "HTTP Response Code: $http_code" + +if [ "$http_code" -ne 200 ]; then + echo "Error: HTTP request failed with status code $http_code" + rm response.txt + exit 1 +fi + +if echo "$response" | grep -q "\"error\""; then + echo "Error in response: $(echo "$response" | sed -n 's/.*\"error\":\"\([^\"]*\)\".*/\1/p')" + rm response.txt + exit 1 +else + echo "Ollama is up and running." + rm response.txt + exit 0 +fi \ No newline at end of file diff --git a/usecases/ai/microservices/ollama/run.sh b/usecases/ai/microservices/ollama/run.sh new file mode 100755 index 0000000..76db2f5 --- /dev/null +++ b/usecases/ai/microservices/ollama/run.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +# Ensure OLLAMA_HOST is set +if [ -z "$OLLAMA_HOST" ]; then + echo "Error: OLLAMA_HOST environment variable is not set." + exit 1 +fi + +# Ensure LLM_MODEL is set +if [ -z "$LLM_MODEL" ]; then + echo "Error: LLM_MODEL environment variable is not set." + exit 1 +fi + +echo "Starting Ollama..." +./ollama serve & + +# Give Ollama a moment to start up +sleep 5 + +echo "Checking if Ollama is up..." +PORT=$(echo "$OLLAMA_HOST" | cut -d':' -f2) + +echo "Downloading Model: $LLM_MODEL" +curl -# "http://localhost:$PORT/api/pull" -d "{\"name\": \"$LLM_MODEL\"}" + +# Warm Up Model +curl -# http://localhost:"$PORT"/api/generate -d "{\"model\": \"$LLM_MODEL\", \"prompt\": \"Why is the sky blue?\"}" + +# To keep the container running, use an indefinite wait +echo "Ollama is running. Keeping container alive..." +tail -f /dev/null \ No newline at end of file diff --git a/usecases/ai/microservices/speech-to-text/Dockerfile b/usecases/ai/microservices/speech-to-text/Dockerfile index b2d3330..0fac679 100644 --- a/usecases/ai/microservices/speech-to-text/Dockerfile +++ b/usecases/ai/microservices/speech-to-text/Dockerfile @@ -32,12 +32,12 @@ RUN wget https://github.com/oneapi-src/level-zero/releases/download/v1.17.6/leve RUN pip install "transformers>=4.35" "torch>=2.1" onnx "git+https://github.com/huggingface/optimum-intel.git" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu RUN rm -r *.deb neo npu-driver -RUN groupadd -g 110 render +ARG RENDER_GROUP_ID +RUN groupadd -g ${RENDER_GROUP_ID} render COPY requirements.txt . RUN python3 -m pip install -r requirements.txt COPY . . - -HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \ - CMD wget --no-verbose -O /dev/null --tries=1 http://stt_service:5996/healthcheck || exit 1 \ No newline at end of file +HEALTHCHECK --interval=60s --timeout=180s --start-period=5s --retries=3 \ + CMD wget --no-verbose --no-proxy -O /dev/null --tries=1 http://stt_service:5996/healthcheck || exit 1 \ No newline at end of file diff --git a/usecases/ai/microservices/speech-to-text/main.py b/usecases/ai/microservices/speech-to-text/main.py index 873151d..93a04e2 100644 --- a/usecases/ai/microservices/speech-to-text/main.py +++ b/usecases/ai/microservices/speech-to-text/main.py @@ -9,6 +9,7 @@ import logging from pathlib import Path import soundfile as sf +import json from typing import Optional from fastapi import FastAPI, UploadFile, Form, File, HTTPException from contextlib import asynccontextmanager @@ -18,6 +19,7 @@ inference_translate ) from pydub import AudioSegment +from fastapi.middleware.cors import CORSMiddleware logger = logging.getLogger('uvicorn.error') ASR_MODEL = None @@ -41,8 +43,15 @@ async def lifespan(app: FastAPI): clean_up() +allowed_cors = json.loads(os.getenv("ALLOWED_CORS", '["http://localhost"]')) app = FastAPI(lifespan=lifespan) - +app.add_middleware( + CORSMiddleware, + allow_origins=allowed_cors, + allow_credentials=True, + allow_methods=['*'], + allow_headers=['*'], +) @app.post("/v1/audio/transcriptions") async def stt_transcription(file: UploadFile = File(...), language: Optional[str] = Form(None)): diff --git a/usecases/ai/microservices/text-to-speech/Dockerfile b/usecases/ai/microservices/text-to-speech/Dockerfile index 5c5a3bd..2784ae1 100644 --- a/usecases/ai/microservices/text-to-speech/Dockerfile +++ b/usecases/ai/microservices/text-to-speech/Dockerfile @@ -29,4 +29,4 @@ RUN python3 nltk_download.py COPY . . HEALTHCHECK --interval=30s --timeout=180s --start-period=50s --retries=5 \ - CMD wget --no-verbose -O /dev/null --tries=1 http://tts_service:5995/healthcheck || exit 1 \ No newline at end of file + CMD wget --no-verbose --no-proxy -O /dev/null --tries=1 http://tts_service:5995/healthcheck || exit 1 \ No newline at end of file diff --git a/usecases/ai/microservices/text-to-speech/main.py b/usecases/ai/microservices/text-to-speech/main.py index 3714665..3c3d7d0 100644 --- a/usecases/ai/microservices/text-to-speech/main.py +++ b/usecases/ai/microservices/text-to-speech/main.py @@ -18,8 +18,9 @@ from fastapi.responses import FileResponse from fastapi import FastAPI, HTTPException from starlette.background import BackgroundTasks - +from fastapi.middleware.cors import CORSMiddleware from melo.api import TTS +import json TTS_PROCESSOR = None TTS_SPEAKER_IDS = None @@ -62,9 +63,15 @@ async def lifespan(app: FastAPI): yield clean_up() +allowed_cors = json.loads(os.getenv("ALLOWED_CORS", '["http://localhost"]')) app = FastAPI(lifespan=lifespan) - - +app.add_middleware( + CORSMiddleware, + allow_origins=allowed_cors, + allow_credentials=True, + allow_methods=['*'], + allow_headers=['*'], +) class TTSData(BaseModel): input: str model: str = '-'

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