GLANet - The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

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Deep Learning GLANet
Overview

GLANet

The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

Framework: image visualization results: image

Getting Started

Installation

This code was tested with Pytorch 1.7.0, CUDA 10.2, and Python 3.7

pip install visdom dominate
  • Clone this repo:
git clone https://github.com/ygjwd12345/GLANet.git
cd GLANet

Datasets

Please refer to the original CUT and CycleGAN to download datasets and learn how to create your own datasets.

    sh ./datasets/download_cyclegan_dataset.sh a2b

Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, facades, iphone2dslr_flower, ae_photos

    sh ./datasets/download_pix2pix_dataset.sh xx

Available datasets are night2day, edges2handbags, edges2shoes, facades, maps

The Cityscapes dataset can be downloaded from https://cityscapes-dataset.com. After that, use the script ./datasets/prepare_cityscapes_dataset.py to prepare the dataset.

Training

  • Train the single-modal I2I translation model. Please check run.sh. For instance:
python train.py  \
--dataroot ./datasets/summer2winter \
--name summer2winter \
--model sc \
--gpu_ids 0 \
--lambda_spatial 10 \
--lambda_gradient 0 \
--attn_layers 4,7,9 \
--loss_mode cos \
--gan_mode lsgan \
--display_port 8093 \
--direction BtoA \
--patch_size 64

Testing

  • Test the FID score for all training epochs, please also check run.sh. For instance:
python test_fid.py \
--dataroot ./datasets/horse2zebra \
--checkpoints_dir ./checkpoints \
--name horse2zebra \
--gpu_ids 0 \
--model sc \
--num_test 0
  • Test the KID, cityscape score, D&C, LPIPS, please check run_dc_lpips.sh in evaluations folder. For instance:
python PerceptualSimilarity/lpips_2dirs.py -d0 /data2/gyang/TAGAN/results/summer2winter-F64-mixer/test_350/images/real_B -d1 /data2/gyang/TAGAN/results/summer2winter-F64-mixer/test_350/images/fake_B -o ./example_dists.txt --use_gpu
python3 segment.py test -d ./datasets/cityscapes -c 19 --arch drn_d_22 \
    --pretrained ./drn_d_22_cityscapes.pth --phase val --batch-size 1

Acknowledge

Our code is developed based on FSeSim and unguided. We also thank pytorch-fid for FID computation, LPIPS for diversity score, and D&C for density and coverage evaluation.

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Comments
  • Dimensional problems of the test

    Dimensional problems of the test

    I want to output large size pictures

    train： python train.py
    --dataroot ./datasets/orange2tomato
    --name orange2tomato
    --model sc
    --gpu_ids 0
    --lambda_spatial 10
    --lambda_gradient 0
    --attn_layers 4,7,9
    --loss_mode cos
    --gan_mode lsgan
    --display_port 8093
    --patch_size 64

    test： python test_fid.py
    --dataroot ./datasets/orange2tomato
    --checkpoints_dir ./checkpoints
    --name orange2tomato
    --gpu_ids 0
    --model sc
    --num_test 1000
    --epoch 400
    --load_size 1024
    --crop_size 1024 \

    issues： Traceback (most recent call last): File "/home/jupyter-zhangziyi/zhangziyi/GLANet/test_fid.py", line 71, in model.data_dependent_initialize(data) File "/home/jupyter-zhangziyi/zhangziyi/GLANet/models/sc_model.py", line 123, in data_dependent_initialize self.forward() File "/home/jupyter-zhangziyi/zhangziyi/GLANet/models/sc_model.py", line 152, in forward self.fake,self.unguided_mean, self.unguided_sigma, self.posterior_mean, self.posterior_sigma, self.posterior_sample =self.netG(self.real,self.real_B,True) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/jupyter-zhangziyi/zhangziyi/GLANet/models/glanet.py", line 731, in forward source_style = self.style_encoder(source) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/jupyter-zhangziyi/zhangziyi/GLANet/models/glanet.py", line 673, in forward return self.fn(self.norm(x)) + x File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 301, in forward return self._conv_forward(input, self.weight, self.bias) File "/opt/tljh/user/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 297, in _conv_forward return F.conv1d(input, weight, bias, self.stride, RuntimeError: Given groups=1, weight of size [1024, 256, 1], expected input[1, 4096, 512] to have 256 channels, but got 4096 channels instead

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