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ConvMAE: Masked Convolution Meets Masked Autoencoders

This folder contains the implementation of the ConvMAE transfer learning for semantic segmentation on ADE-20K. It is based on MMSegmentation.

Pipeline

tenser

Models Pretrain Pretrain Epochs Finetune Iters #Params(M) FLOPs(T) mIoU logs/weights
ConvMAE-B IN1K 1600 16K 153 0.6 51.7 log/weight

Usage

Install

  • Clone this repo:
git clone https://github.com/Alpha-VL/ConvMAE
cd ConvMAE/SEG
  • Create a conda environment and activate it:
conda create -n upernet python=3.7
conda activate upernet
  • Install Pytorch==1.8.0 and torchvision==0.9.0 with CUDA==11.1
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install mmcv-full==1.3.0 mmsegmentation==0.11.0
pip install scipy timm==0.3.2
  • Install apex for mixed-precision training
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Data preparation

Follow the guide in mmseg to prepare the ADE20k dataset.

Training

Download the pretrained model here.

./tools/dist_train.sh <CONFIG_PATH> <NUM_GPUS>  --work-dir <SAVE_PATH> --options model.pretrained=<PRETRAINED_MODEL_PATH>

For example:

./tools/dist_train.sh \
    configs/convmae/upernet_convmae_base_512_slide_160k_ade20k.py 8 \
    --work-dir /path/to/save \
    --options model.pretrained=/path/to/pretrained/weights

Evaluation

Download the fine-tuned checkpoint here.

./tools/dist_test.sh  <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU

Run

./tools/dist_test.sh configs/convmae/upernet_convmae_base_512_slide_160k_ade20k.py /path/to/finetuned/weights 8 --eval mIoU

This should give

+--------+-------+-------+-------+
| Scope  | mIoU  | mAcc  | aAcc  |
+--------+-------+-------+-------+
| global | 51.66 | 63.88 | 84.45 |
+--------+-------+-------+-------+