Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Related tags

Deep Learning YTMT-Strategy
Overview

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021)

by Qiming Hu, Xiaojie Guo.

Dependencies

  • Python3
  • PyTorch>=1.0
  • OpenCV-Python, TensorboardX, Visdom
  • NVIDIA GPU+CUDA

Network Architecture

figure_arch

πŸš€ 1. Single Image Reflection Separation

Data Preparation

Training dataset

  • 7,643 images from the Pascal VOC dataset, center-cropped as 224 x 224 slices to synthesize training pairs.
  • 90 real-world training pairs provided by Zhang et al.

Tesing dataset

  • 45 real-world testing images from CEILNet dataset.
  • 20 real testing pairs provided by Zhang et al.
  • 454 real testing pairs from SIR^2 dataset, containing three subsets (i.e., Objects (200), Postcard (199), Wild (55)).

Usage

Training

  • For stage 1: python train_sirs.py --inet ytmt_ucs --model ytmt_model_sirs --name ytmt_ucs_sirs --hyper --if_align
  • For stage 2: python train_twostage_sirs.py --inet ytmt_ucs --model twostage_ytmt_model --name ytmt_uct_sirs --hyper --if_align --resume --resume_epoch xx --checkpoints_dir xxx

Testing

python test_sirs.py --inet ytmt_ucs --model twostage_ytmt_model --name ytmt_uct_sirs_test --hyper --if_align --resume --icnn_path ./checkpoints/ytmt_uct_sirs/twostage_unet_68_077_00595364.pt

Trained weights

Google Drive

Visual comparison on real20 and SIR^2

figure_eval

Visual comparison on real45

figure_test

πŸš€ 2. Single Image Denoising

Data Preparation

Training datasets

400 images from the Berkeley segmentation dataset, following DnCNN.

Tesing datasets

BSD68 dataset and Set12.

Usage

Training

python train_denoising.py --inet ytmt_pas --name ytmt_pas_denoising --preprocess True --num_of_layers 9 --mode B --preprocess True

Testing

python test_denoising.py --inet ytmt_pas --name ytmt_pas_denoising_blindtest_25 --test_noiseL 25 --num_of_layers 9 --test_data Set68 --icnn_path ./checkpoints/ytmt_pas_denoising_49_157500.pt

Trained weights

Google Drive

Visual comparison on a sample from BSD68

figure_eval_denoising

πŸš€ 3. Single Image Demoireing

Data Preparation

Training dataset

AIM 2019 Demoireing Challenge

Tesing dataset

100 moireing and clean pairs from AIM 2019 Demoireing Challenge.

Usage

Training

python train_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire --hyper --if_align

Testing

python test_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire_test --hyper --if_align --resume --icnn_path ./checkpoints/ytmt_ucs_demoire/ytmt_ucs_opt_086_00860000.pt

Trained weights

Google Drive

Visual comparison on the validation set of LCDMoire

figure_eval_demoire

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Comments
  • Datasets

    Datasets

    Hi,

    I have been trying to experiment with the model but I'm having trouble finding the correct datasets for testing. The Sirs2 dataset in the provided link doesn't have the images set up with the naming conventions used in the script. Could you please direct me to the correct data sets for testing and training? Is there a separate repository that you have used?

    Thanks so much,

    David

    opened by davidgaddie 3
  • About Training Details

    About Training Details

    Hello, thank you for sharing your wonderful work. I have some question about the triaining details. It says the training epoch is 120 in your paper but the epoch is set to 60 in YTMT-Strategy/options/net_options/train_options.py. Moreover, the best model in your paper is YTMT-UCT which need two stages training. Can you provide the training settings of the YTMT-UCT (epoch, batchsize...)? Look forward to your reply!

    opened by DUT-CSJ 2
  • CUDA vram allocation issue

    CUDA vram allocation issue

    Hi,

    I've been trying to run the reflection test code, but I get this error: RuntimeError: CUDA out of memory. Tried to allocate 15.66 GiB (GPU 0; 22.20 GiB total capacity; 16.09 GiB already allocated; 2.68 GiB free; 17.55 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

    I'm running on an A10G GPU on AWS. I suspect that maybe the dataset is incorrect as each image in the dataset I have is around 800MB. If that's the case can I please be directed to the correct repository for the read20_420 images?

    Thanks so much,

    David

    opened by davidgaddie 1
  • test demoire error

    test demoire error

    Thanks for your great work ,but some error when I run: python test_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire_test --hyper --if_align --resume --icnn_path checkpoints/ytmt_ucs_demoire/ytmt_ucs_demoire_opt_086_00860000.pt

    -------------- End ---------------- [i] initialization method [edsr] Traceback (most recent call last): File "test_demoire.py", line 28, in engine = Engine(opt) File "/nfs_data/code/YTMT-Strategy-main/engine.py", line 19, in init self.__setup() File "/nfs_data/code/YTMT-Strategy-main/engine.py", line 29, in __setup self.model.initialize(opt) File "/nfs_data/code/YTMT-Strategy-main/models/ytmt_model_demoire.py", line 242, in initialize self.load(self, opt.resume_epoch) File "/nfs_data/code/YTMT-Strategy-main/models/ytmt_model_demoire.py", line 413, in load model.net_i.load_state_dict(state_dict['icnn']) File "/opt/conda/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for YTMT_US: Missing key(s) in state_dict: "inc.ytmt_head.fusion_l.weight", "inc.ytmt_head.fusion_l.bias", "inc.ytmt_head.fusion_r.weight", "inc.ytmt_head.fusion_r.bias", "down1.model.ytmt_head.fusion_l.weight", "down1.model.ytmt_head.fusion_l.bias", "down1.model.ytmt_head.fusion_r.weight", "down1.model.ytmt_head.fusion_r.bias", "down2.model.ytmt_head.fusion_l.weight", "down2.model.ytmt_head.fusion_l.bias", "down2.model.ytmt_head.fusion_r.weight", "down2.model.ytmt_head.fusion_r.bias", "down3.model.ytmt_head.fusion_l.weight", "down3.model.ytmt_head.fusion_l.bias", "down3.model.ytmt_head.fusion_r.weight", "down3.model.ytmt_head.fusion_r.bias", "down4.model.ytmt_head.fusion_l.weight", "down4.model.ytmt_head.fusion_l.bias", "down4.model.ytmt_head.fusion_r.weight", "down4.model.ytmt_head.fusion_r.bias", "up1.model.ytmt_head.fusion_l.weight", "up1.model.ytmt_head.fusion_l.bias", "up1.model.ytmt_head.fusion_r.weight", "up1.model.ytmt_head.fusion_r.bias", "up2.model.ytmt_head.fusion_l.weight", "up2.model.ytmt_head.fusion_l.bias", "up2.model.ytmt_head.fusion_r.weight", "up2.model.ytmt_head.fusion_r.bias", "up3.model.ytmt_head.fusion_l.weight", "up3.model.ytmt_head.fusion_l.bias", "up3.model.ytmt_head.fusion_r.weight", "up3.model.ytmt_head.fusion_r.bias", "up4.model.ytmt_head.fusion_l.weight", "up4.model.ytmt_head.fusion_l.bias", "up4.model.ytmt_head.fusion_r.weight", "up4.model.ytmt_head.fusion_r.bias".

    opened by zdyshine 1
Owner
Qiming Hu
Qiming Hu
GitHub
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