Diffusion-Based Adaptation for Classification of Unknown Degraded Images

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This repository contains the source code associated with the paper "Diffusion-Based Adaptation for Classification of Unknown Degraded Images", presented at CVPR 2024 NTIRE workshop.

Abstract

Classification of unknown degraded images is essential in practical applications since image-degraded models are usually unknown. Diffusion-based models provide enhanced performance for image enhancement and image restoration from degraded images. In this study, we use the diffusion-based model for the adaptation instead of restoration. Restoration from the degraded image aims to restore the degrade-free clean image, while adaptation from the degraded image transforms the degraded image towards a clean image domain. However, the diffusion models struggle to perform image adaptation in case of specific degradations attributable to the unknown degradation models. To address the issue of imperfect adapted clean images from diffusion models for the classification of degraded images, we propose a novel Diffusion-based Adaptation for Unknown Degraded images (DiffAUD) method based on robust classifiers trained on a few known degradations. Our proposed method complements the diffusion models and consistently generalizes well on different types of degradations with varying severities. DiffAUD improves the performance from the baseline diffusion model and clean classifier on the Imagenet-C dataset by 5.5%, 5%, and 5% with ResNet-50, Swin Transformer (Tiny), and ConvNeXt-Tiny backbones, respectively. Moreover, we exhibit that training classifiers using known degradations provides significant performance gains for classifying degraded images.

Brief Introduction

Our proposed method is split into three steps as follows:

1. Apply DDPM on the degraded images $X_{deg}$ to yield adapted images $X_{adapt}$.
2. Feed adapted images $X_{adapt}$ to a distilled classifier trained on adapted images from known degradations, i.e., $DC_{adapt}$ and in parallel, we input degraded images $X_{deg}$ directly to a distilled classifier trained on known degradation images, i.e., $DC_{deg}$.
3. Apply ensemble on the outputs of two distilled classifiers to output $Y_{deg}$.

Figure 1. Architecture diagram of the proposed method, where the top figure shows the overall inferencing process and the bottom block represents the training process of $DC$ using knowledge distillation from pre-trained teacher networks. Symbol © represents concatenation of inputs $X_{JPEG}$, $X_{GBlur}$, and $X_{AWGN}$. Grey/blue and orange/blue blocks represent pre-trained teacher and student networks.

Installation

To install all the pre-requisite libraries install the docker container from docker/Dockerfile. Additionally, you could manually install the missing dependencies described in docker/Dockerfile and docker/requirements.txt.

To properly reproduce the results from the paper, please use the provided docker Dockerfile and docker/requirements.txt. Sample docker build and run commands are as follows:

• docker build -f docker/Dockerfile -t pytorch_1.12 .
• docker run -v /:/data --shm-size 50G -p 8008:8008 -it --gpus '"device=0,1"' pytorch_1.12 /bin/bash

Repository Setup

• Structure of Repository:

  • Primarily, our repository is based on two repositories, i.e., FusionDistill for Distilled Classifier and DDA for diffusion model/process.
  • DDA directory includes code to apply DDPM adaptation process using image_adapt subdirectory as well as to perform inference of recognition models using model_adapt subdirectory.
  • DistilledClassfier directory includes code to train distilled classifiers based on known degradations.
  • Specific steps are discussed in the next section.

• Evaluation Dataset - Unknown Degradations: We utilize CIFAR-10-C and Imagenet-C dataset for single degradation images. Also, sequence of degradation dataset could be prepared by applying apply_sequence_degs function available in DistilledClassifier/utils/data/degtransforms.py on the given images on both CIFAR-10 and Imagenet datasets.

• Diffusion Models and Backbones: Since, we use pre-trained model trained on clean images, please download relevant models described here.

Training / Evaluation

Detailed steps to reproduce the results of our proposed method are as follows:

1. Prepare adapted images of known and unknown degradations:

• Known degradations: In DDA directory, utilize image_adapt.sh to prepare adapted images of known degraded imagenet dataset. First initialize the parameter values and next run the image_sample.py for sampling.

  MODEL_PATHS="--model_path ckpt/256x256_diffusion_uncond.pt" # Pre-trained downloaded model
  
  MODEL_FLAGS="--image_size 256 --attention_resolutions 32,16,8 --class_cond False --learn_sigma True  --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
  
  DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear"
  SAMPLE_FLAGS="--batch_size 4 --num_samples 10000 --timestep_respacing 100" 
  
  DATA_FLAGS="--base_samples dataset/imagenetc_5k --dataset imagenetc --save_dir dataset/generated/imagenetc_5k/" 
  

  CUDA_VISIBLE_DEVICES=0,1 mpiexec -n 2 python image_adapt/scripts/image_sample.py \
                                        $MODEL_FLAGS $DIFFUSION_FLAGS $SAMPLE_FLAGS $DATA_FLAGS $MODEL_PATHS \
                                        --D 50 --N 4 --scale 6 \
                                        --corruption "<CORRUPTION>" --train True
  

• Unknown degradations: Prepare adapted images of unknown degradations: Similar to the above script change the DATA_FLAGS and turn train parameter as False while keeping the other parameters same.

  DATA_FLAGS="--base_samples dataset/imagenet_50k_deg --dataset imagenet --save_dir dataset/generated/imagenet_50k_deg" 
  

  CUDA_VISIBLE_DEVICES=0,1 mpiexec -n 2 python image_adapt/scripts/image_sample.py \
                                        $MODEL_FLAGS $DIFFUSION_FLAGS $SAMPLE_FLAGS $DATA_FLAGS $MODEL_PATHS \
                                        --D 50 --N 4 --scale 6 \
                                        --corruption "<CORRUPTION>" --train False
  

2. Train individual recognition models on known degradation images: Prepare classifier for all three known degradations separately, i.e., jpeg, blur, and noise with parameter DEG_TYPE. To train classifier on degraded images use deg.yaml config and adapt.yaml config for adapted images.

     python train.py -c configs/ind/<DATASET>/<BACKBONE>/deg.yaml --dt <DEG_TYPE> 
   

     python train.py -c configs/ind/<DATASET>/<BACKBONE>/adapt.yaml --dt <DEG_TYPE> 
   

3. Train distilled classifier models using the three individual classifiers trained on known degradation images. To train classifier on degraded images use degDistill.yaml config and adaptDistill.yaml config for adapted images.

   python train_all_deg.py -c configs/deg_all/<DATASET>/<BACKBONE>/deg_all.yaml 
   python train_all_deg.py -c configs/deg_all/$DATASET/<BACKBONE>/degDistill.yaml
   

   python train_all_deg.py -c configs/deg_all/<DATASET>/<BACKBONE>/adaptDistill.yaml 
   

4. Evaluation - Apply ensemble of degraded and adapted distilled classifiers. Replace DEG_DISTILL_CL_PATH and ADAPT_DISTILL_CL_PATH with distilled classifier trained on degraded and adapted images respectively. Replace MODEL_ROOT_DIR with the path to DistilledClassifier directory.

   DATA_FLAGS="--data_prefix1 dataset/imagenetc_5k --data_prefix2 dataset/generated/imagenetc_5k"
   

     python model_adapt/test_ensemble.py model_adapt/configs/ensemble/<BACKBONE>_ensemble_b64_<DATASET>.py \
               <DEG_DISTILL_CL_PATH> --second_model_prefix <ADAPT_DISTILL_CL_PATH> --metrics accuracy --ensemble sum --corruption "<CORRUPTION>" --severity "<SEVERITY>" $DATA_FLAGS \
               --out "<MODEL_ROOT_DIR>/saved/jpeg_blur_noise/Diffusion/DDPM+AdaptDistillCL+DegDistillCL_${BACKBONE}_<DATASET>/eval" 
   

Notes for running experiments:

• Replace <DATASET> with either "CIFAR10", or "Imagenet" to run the experiments for specific dataset for training or evaluation scripts.
• Replace <DEG_TYPE> with degradations such as jpeg, blur, and noise when training classifier for individual degradation.
• Replace <BACKBONE> with recognition models such as ResNet50, convnextT, and swinT.
• If you train the model from scratch you need to change pretrained_path paths in config yaml files.
• Replace <CORRUPTION> and <SEVERITY> with specific corruption of single degradation or sequence of degradations and severity levels respectively.

Citation

If you find our work or this repository helpful, please consider citing our work:

@InProceedings{Daultani_2024_CVPR,
    author    = {Daultani, Dinesh and Tanaka, Masayuki and Okutomi, Masatoshi and Endo, Kazuki},
    title     = {Diffusion-Based Adaptation for Classification of Unknown Degraded Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2024},
    pages     = {5982-5991}
}