Code repo for "Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution" (CVPRW NTIRE2020).
Home Page: https://replicate.com/raoumer/srrescgan
License: MIT License
Thanks for sharing! the trainning code just for color image ,are there any instructions to use your code for training gray image?
Thank you for sharing your great work.
I have been trying to train a model using my own data, but when running the main_sr_color.py, it results in the following error:
Traceback (most recent call last):
File "main_sr_color.py", line 14, in
from utils import util
ImportError: cannot import name 'util' from 'utils' (C:\Users\moaaz\anaconda3\envs\zssr\lib\site-packages\utils_init_.py)
At this point, I was unsure whether "utils" refer to the python package that is called "utils" or it is referring to the utils folder in the project main directory, so I disabled the "utils" package to narrow down the issue. But that resulted in this error:
Traceback (most recent call last):
File "main_sr_color.py", line 15, in
from utils import utils_common
File "E:\P10\SRResCGAN\training_codes\utils\utils_common.py", line 9, in
from utils import utils_image
ImportError: cannot import name 'utils_image' from 'utils' (unknown location)
Going back to the utils (the python package), i tried to find the utils_image & utils_model to see whether I have something wrong with my dependencies. But there I found out that python "utils" does not have those two (utils_image & utils_model )
So I am wondering whether I am missing those two files in the (training_codes\utils) directory, or it is a problem with my dependencies.
Any help would be appreciated.
Hi, I have encountered an error when training.
I am trying to train the model using DIV2K dataset, DIV2K_train_HR , DIV2K_train_LR_bicubic/X4.
After python create_dataset.py, I successfully generated the data. According to the LRHR_dataset.py, I put data in the right place. When I start to train, it downloaded the pretrained model, and I got an error like this:
LogHandlers setup!
21-06-15 20:41:57.700 : ===================== Selected training parameters =====================
21-06-15 20:41:57.701 : Namespace(D_init_iters=0, D_update_ratio=1, alpha=1.2, amsgrad=False, beta1_D=0.9, beta1_G=0.9, beta2_D=0.999, beta2_G=0.999, cuda=True, eps_D=1e-08, eps_G=1e-08, feature_criterion='l1', feature_weight=1.0, gan_type='ragan', gan_weight=1.0, imdbTestPath='./datasets/', imdbTrainPath='./datasets/', in_nc=3, is_mixup=True, is_train=True, lr_D=0.0001, lr_G=0.0001, lr_gamma=0.5, lr_milestones=[5000, 10000, 20000, 30000], lr_restart=None, lr_restart_weights=None, nf=64, niter=51000, numWorkers=4, patch_size=40, pixel_criterion='l1', pixel_weight=10.0, pretrain=True, pretrainedModelPath='pretrained_nets/SRResDNet/G_perceptual.pth', resdnet_depth=5, resume=True, resume_start_epoch=0, rgb_range=255, saveBest=True, saveImgsPath='results', saveLogsPath='logs', saveTrainedModelsPath='trained_nets', save_checkpoint_freq=20, save_path_best_lpips='/best_lpips/', save_path_best_psnr='/best_psnr/', save_path_netD='/netD/', save_path_netG='/netG/', save_path_training_states='/training_states/', seed=123, testBatchSize=1, test_stdn=[0.0], trainBatchSize=16, train_stdn=[0.0], tv_criterion='l1', tv_weight=1.0, upscale_factor=4, use_bn=False, use_chop=False, use_filters=True, warmup_iter=-1, weightdecay_D=0, weightdecay_G=0).
21-06-15 20:41:57.701 : ===================== Loading dataset =====================
21-06-15 20:41:57.706 : training dataset: 2400
21-06-15 20:41:57.706 : training loaders: 150
21-06-15 20:41:57.707 : testing dataset: 100
21-06-15 20:41:57.707 : testing loaders: 100
21-06-15 20:41:57.707 : ===================== Building model =====================
21-06-15 20:41:57.803 : Initialized model with pretrained net from pretrained_nets/SRResDNet/G_perceptual.pth.
Setting up Perceptual loss...
Loading model from: /home/xuwh/RJPcode/SRResCGAN-master/training_codes/modules/weights/v0.1/alex.pth
...[net-lin [alex]] initialized
...Done
21-06-15 20:42:01.452 : Network G structure: SRResDNet, with parameters: 380,356
21-06-15 20:42:01.452 : SRResDNet(
(model): ResDNet(
(conv1): Conv2d(3, 64, kernel_size=(5, 5), stride=(1, 1))
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
)
(2): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
)
(3): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
)
(4): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(relu1): PReLU(num_parameters=64)
(relu2): PReLU(num_parameters=64)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
)
)
(conv_out): ConvTranspose2d(64, 3, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(l2proj): L2Proj()
)
(noise_estimator): Wmad_estimator()
(bbproj): Hardtanh(min_val=0.0, max_val=255.0)
)
21-06-15 20:42:01.453 : Network D structure: Discriminator_VGG_128, with parameters: 14,499,401
21-06-15 20:42:01.453 : Discriminator_VGG_128(
(conv0_0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv0_1): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(bn0_1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1_0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1_0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1_1): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(bn1_1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2_0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2_0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2_1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(bn2_1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3_0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn3_0): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3_1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(bn3_1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv4_0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn4_0): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv4_1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(bn4_1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(linear1): Linear(in_features=8192, out_features=100, bias=True)
(linear2): Linear(in_features=100, out_features=1, bias=True)
(lrelu): LeakyReLU(negative_slope=0.2, inplace=True)
)
21-06-15 20:42:01.453 : Network F structure: VGGFeatureExtractor, with parameters: 20,024,384
21-06-15 20:42:01.453 : VGGFeatureExtractor(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
(16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU(inplace=True)
(18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace=True)
(23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): ReLU(inplace=True)
(25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(26): ReLU(inplace=True)
(27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace=True)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): ReLU(inplace=True)
(32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(33): ReLU(inplace=True)
(34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
21-06-15 20:42:01.454 : ===================== start training =====================
21-06-15 20:42:01.454 : ===================== resume training =====================
21-06-15 20:42:01.454 : ===> No saved training states to resume.
21-06-15 20:42:01.454 : ===> start training from epoch: 0, iter: 0.
21-06-15 20:42:01.454 : Total # of epochs for training: 340.
21-06-15 20:42:01.454 : ===> train:: Epoch[1]
21-06-15 20:42:03.040 : ===> train:: Epoch[1] Iter-step[1]
Traceback (most recent call last):
File "main_sr_color.py", line 1057, in <module>
main()
File "main_sr_color.py", line 964, in main
current_step)
File "main_sr_color.py", line 418, in train
pred_g_fake = netD(filter_high(fake_H))
File "/home/xuwh/anaconda3/envs/srrescgan/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/xuwh/RJPcode/SRResCGAN-master/training_codes/models/discriminator_vgg_arch.py", line 57, in forward
fea = self.lrelu(self.linear1(fea))
File "/home/xuwh/anaconda3/envs/srrescgan/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/xuwh/anaconda3/envs/srrescgan/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "/home/xuwh/anaconda3/envs/srrescgan/lib/python3.6/site-packages/torch/nn/functional.py", line 1370, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [16 x 12800], m2: [8192 x 100] at /opt/conda/conda-bld/pytorch_1579027003190/work/aten/src/THC/generic/THCTensorMathBlas.cu:290
I understand that m1 [a * b], m2: [c * d] where b=c. But how does this error occur even if I leave the source code same?
Is that the problems of hpyer-parameter?
I am new in deep learning. So really confused why does it happen and wonder how can I fix it?
Thanks in advance.
hi,
Will you release codes?
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.