RUNDIR: runs\seg_pascal\1980 Traceback (most recent call last): File "train.py", line 226, in <module> train(cfg, writer, logger) File "train.py", line 61, in train t_loader, batch_size=cfg["training"]["batch_size"], num_workers=cfg["training"]["n_workers"], shuffle=True File "C:\Users\yp\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 176, in __init__ sampler = RandomSampler(dataset) File "C:\Users\yp\Anaconda3\lib\site-packages\torch\utils\data\sampler.py", line 66, in __init__ "value, but got num_samples={}".format(self.num_samples)) ValueError: num_samples should be a positive integer value, but got num_samples=0 How can I solve this problem?Thank you!

I was trying to learn using my own data set, but when executing the code the following There was an error.

Traceback (most recent call last): File "main.py", line 4, in import data File "D:\EDSR-PyTorch-master\src\data_init_.py", line 2, in from dataloader import MSDataLoader File "D:\EDSR-PyTorch-master\src\dataloader.py", line 17, in from torch.utils.data.dataloader import _worker_manager_loop ImportError: cannot import name '_worker_manager_loop'

I was allowed to learn according to read me, but it will not work. What should I do now

I follow the steps from the readme file to run the demo file with Windows 10 x64 + python 3.6.6 + pytorch 0.4.1 cuda92. Firstly I got a division by zero exception. And I place DIV2K to the database folder. It cause another error. It prints error messages here:

PS D:\Document\Python\EDSR-PyTorch\code> sh demo.sh Making model... Preparing loss function: 1.000 * L1 [Epoch 1] Learning rate: 1.00e-4 rm: cannot remove '../experiment/RCAN_BIX2_G10R20P48/log.txt': Device or resource busy Making model... Preparing loss function: 1.000 * L1 [Epoch 1] Learning rate: 1.00e-4 Traceback (most recent call last): File "", line 1, in Traceback (most recent call last): File "main.py", line 19, in t.train() File "D:\Document\Python\EDSR-PyTorch\code\trainer.py", line 45, in train for batch, (lr, hr, _, idx_scale) in enumerate(self.loader_train): File "D:\Document\Python\EDSR-PyTorch\code\dataloader.py", line 144, in iter File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 105, in spawn_main return _MSDataLoaderIter(self) File "D:\Document\Python\EDSR-PyTorch\code\dataloader.py", line 117, in init exitcode = _main(fd) File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 114, in _main w.start() File "C:\ProgramData\Anaconda3\lib\multiprocessing\process.py", line 105, in start prepare(preparation_data) File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 225, in prepare self._popen = self._Popen(self) _fixup_main_from_path(data['init_main_from_path']) File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 223, in _Popen File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path return _default_context.get_context().Process._Popen(process_obj) run_name="mp_main") File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen

File "C:\ProgramData\Anaconda3\lib\runpy.py", line 263, in run_path return Popen(process_obj) File "C:\ProgramData\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in init pkg_name=pkg_name, script_name=fname) File "C:\ProgramData\Anaconda3\lib\runpy.py", line 96, in _run_module_code mod_name, mod_spec, pkg_name, script_name) File "C:\ProgramData\Anaconda3\lib\runpy.py", line 85, in _run_code reduction.dump(process_obj, to_child) exec(code, run_globals) File "C:\ProgramData\Anaconda3\lib\multiprocessing\reduction.py", line 60, in dump

File "D:\Document\Python\EDSR-PyTorch\code\main.py", line 19, in ForkingPickler(file, protocol).dump(obj) BrokenPipeErrort.train(): [Errno 32] Broken pipe File "D:\Document\Python\EDSR-PyTorch\code\trainer.py", line 45, in train

for batch, (lr, hr, _, idx_scale) in enumerate(self.loader_train):

File "D:\Document\Python\EDSR-PyTorch\code\dataloader.py", line 144, in iter return _MSDataLoaderIter(self) File "D:\Document\Python\EDSR-PyTorch\code\dataloader.py", line 117, in init w.start() File "C:\ProgramData\Anaconda3\lib\multiprocessing\process.py", line 105, in start self._popen = self._Popen(self) File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 223, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen return Popen(process_obj) File "C:\ProgramData\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 33, in init prep_data = spawn.get_preparation_data(process_obj._name) File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 143, in get_preparation_data _check_not_importing_main() File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main is not going to be frozen to produce an executable.''') RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

I do not know the code, so I can not modify by myself. I would be thankful if you could give me some advice. Thanks.

Hi, I'm trying to train EDSR_baseline_x4 with WGAN-GP, but I don't know how to do it. I want to ask the following questions:

1. In the discriminator, should batch normalization be removed? (I see that batch normalization has not been removed in your code )

2. How to set (beta1, beta2, learning rate) of Adam for optimizing discriminator and generator?

3. How to set the k value for adversarial loss? (I see that the default value of gan_k is 1 in your code )

4. How to set the weights of VGG54 and generator loss?

Can you give me some advice？

Thank you!

Hi,

I am interested in modifying the patch implementation here. I have a csv file that records all box location for my training images. And I only want these boxes to be the patch for training. I have found that I need to add an argument in option.py for my csv file and modify get_patch in common.pyto return the box coordinates. In addition, insrdata.py, I need to add another attribute for my csv file, also modifyget_patchto add one more parameter to callget_patchfromcommon.py`. What did I miss here? I wonder how you implemented the number of patches for each image, since the number of boxes for each image may be different.

Thanks for your help !!

I get out of memory error (in 12GB GPU RAM) when running final model of EDSR.

THCudaCheck FAIL file=/home/sibt/pytorch/aten/src/THC/generic/THCStorage.cu line=58 error=2 : out of memory
Traceback (most recent call last):
  File "main.py", line 14, in <module>
    while not t.terminate():
  File "/home/sibt/muneeb/_superResolution/code/trainer.py", line 164, in terminate
    self.test()
  File "/home/sibt/muneeb/_superResolution/code/trainer.py", line 98, in test
    output = _test_forward(input, scale)
  File "/home/sibt/muneeb/_superResolution/code/trainer.py", line 87, in _test_forward
    self.args.chop_shave, self.args.chop_size)
  File "/home/sibt/muneeb/_superResolution/code/utils.py", line 240, in chop_forward
    output_batch = model(input_batch)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/sibt/muneeb/_superResolution/code/model/EDSR.py", line 49, in forward
    x = self.tail(res)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/container.py", line 89, in forward
    input = module(input)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/container.py", line 89, in forward
    input = module(input)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/modules/pixelshuffle.py", line 40, in forward
    return F.pixel_shuffle(input, self.upscale_factor)
  File "/datadrive/anaconda2/lib/python2.7/site-packages/torch/nn/functional.py", line 1688, in pixel_shuffle
    shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
RuntimeError: cuda runtime error (2) : out of memory at /home/sibt/pytorch/aten/src/THC/generic/THCStorage.cu:58

Python command is attached below for your reference

python main.py --dir_data /datadrive --scale 4 --n_train 790 --n_val 10 --offset_val 790 --print_model --model EDSR --n_feats 256 --n_resblocks 32 --patch_size 96 --chop_forward --test_only

Referenced #128 #138

It seems that there are some problems with --chop argument, here are the instructions to reproduce the problem.

I set the DIV2K test data_range to 801-802 and used the orginal model EDSR_x2.pt.

python main.py --model edsr --scale 2 --save test --data_train DIV2K --dir_data . --save_results --epoch 2 --data_range 1-800/801-802 --data_test DIV2K --batch_size 16 --chop --patch_size 96 --test_only --pre_train D:\EDSR-PyTorch-master\models\downloaed_models\EDSR_x2.pt

Then an runtimerrror occurs:

It Expected 4-dimensional input for 4-dimensional weight 3 3 1, but got 3-dimensional input of size [1, 184, 270] instead.

Traceback (most recent call last): File "D:/EDSR-PyTorch-master/src/main.py", line 35, in <module> main() File "D:/EDSR-PyTorch-master/src/main.py", line 28, in main while not t.terminate(): File "D:\EDSR-PyTorch-master\src\trainer.py", line 160, in terminate self.test() File "D:\EDSR-PyTorch-master\src\trainer.py", line 109, in test sr = self.model(lr, idx_scale) File "D:\Anaconda3\envs\python3.6SRDenseNet\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "D:\EDSR-PyTorch-master\src\model\__init__.py", line 57, in forward return forward_function(x) File "D:\EDSR-PyTorch-master\src\model\__init__.py", line 135, in forward_chop y = self.forward_chop(*p, shave=shave, min_size=min_size) File "D:\EDSR-PyTorch-master\src\model\__init__.py", line 126, in forward_chop y = P.data_parallel(self.model, *x, range(n_GPUs)) File "D:\Anaconda3\envs\python3.6SRDenseNet\lib\site-packages\torch\nn\parallel\data_parallel.py", line 204, in data_parallel return module(*inputs[0], **module_kwargs[0]) File "D:\Anaconda3\envs\python3.6SRDenseNet\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "D:\EDSR-PyTorch-master\src\model\edsr.py", line 56, in forward x = self.sub_mean(x) File "D:\Anaconda3\envs\python3.6SRDenseNet\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "D:\Anaconda3\envs\python3.6SRDenseNet\lib\site-packages\torch\nn\modules\conv.py", line 338, in forward self.padding, self.dilation, self.groups) RuntimeError: Expected 4-dimensional input for 4-dimensional weight 3 3 1, but got 3-dimensional input of size [1, 184, 270] instead

Then I tried to determine the color space of DIV2K's 801-802 pictures using matlab, and find that it has 3 color channels (RGB) as excepted.

I removed the --chop argument and rerun, it seems OK this time, however CUDA out of memory.

In conclusion, it seems there are some problems with --chop argument when testing with DIV2K 801-900 images, guessing out of size?

Hope somebody else can reproduce this issue and figure it out.

Hi, thanks for you sharing.

when I test the DIV2K validation dataset, the result is very good. But when I test the B100 dataset, the result is very bad and the value of psnr is 25.571 in scale 4. Both pre-trained model of the two tests are EDSR_baseline_x4.pt.

The images in B100 dataset are jpg format and I get low resolution jpg images by bicubic downsampling.Then I used these jpg images to participate in the test. Howeve, the reconstruct result is bad and psnr is very low.

Could you tell me how do you test on benchmark dataset such as B100? Thank you very much!

https://github.com/thstkdgus35/EDSR-PyTorch/issues/51#issuecomment-409424414

Follow the above method, create custom.py, and create dataset/CUSTOM/ folders.

However, in the process of importing data, the following errors were encountered:

ValueError: some of the strides of a given numpy array are negative. This is currently not supported, but will be added in future releases.

So, after checking the print, we found that there was a part that was zeroed, such as HR size = (192, 0, 3) during dataset loading. Do you know how to solve it?

Hi,i want to test a picture which pixel >255,if i use demo.sh directly,i got a white result,how should i change the code?

Did you use unit8?how should i do if i want t use unit16?I dong't find method.

i will appreciate for your help!

Dear Author(s)

Thanks for sharing your codebase. Here I have got a question about pre processing: I see many binary ".pt" files are generated, what is the purpose of this operation?

Hello. I want to do EDSR transfer learning with my data. Can you guide me on how to do it?

#transferlearning #transfer_learning #fine_tune #fine-tune #finetune

please help me, i am a beginner and i have no idea about this error!!! Thank you for you to give me some information

Traceback (most recent call last): File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/main.py", line 33, in main() File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/main.py", line 27, in main t.train() File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/trainer.py", line 42, in train for batch, (lr, hr, _,) in enumerate(self.loader_train): File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 435, in next data = self._next_data() File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1085, in _next_data return self._process_data(data) File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data data.reraise() File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/_utils.py", line 428, in reraise raise self.exc_type(msg) TypeError: Caught TypeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop data = fetcher.fetch(index) File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/Husiyu2022/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataset.py", line 218, in getitem return self.datasets[dataset_idx][sample_idx] File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/data/srdata.py", line 100, in getitem pair = self.get_patch(lr, hr) File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/data/srdata.py", line 138, in get_patch lr, hr = common.get_patch( File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/data/common.py", line 29, in get_patch *[a[ty:ty + tp, tx:tx + tp, :] for a in args[1:]] File "/home/Husiyu2022/PycharmProjects/EDSR-PyTorch/src/data/common.py", line 29, in *[a[ty:ty + tp, tx:tx + tp, :] for a in args[1:]] TypeError: list indices must be integers or slices, not tuple

The dataset I'm working that contains 1-channel gray images. Is this mean shift function necessary to apply on gray images? if so, how to change the below code for 1-channel gray images.

https://github.com/sanghyun-son/EDSR-PyTorch/blob/9a9d7d74a5a7e010e34bc9784f0e83d6055d85e7/src/model/common.py#L12

I use batchsize=1 to train the model, but only dozens of K images can produce results when reasoning. For example, 2M images will show insufficient memory. Why is this?

Hello. I want to give some PNG images as input to EDSR model, at first I was faced with the error below: RuntimeError: Given groups=1, weight of size [3, 3, 1, 1], expected input[1, 4, 268, 300] to have 3 channels, but got 4 channels instead. and it was solved with the help of guide lr = lr[:, :3, :, :] after that I faced another error as below: RuntimeError: The size of tensor a (1070) must match the size of tensor b (698) at non-singleton dimension 3. and applied guide [issuecomment](Bug in testing Set5 X3 with args.chop=True · Issue #223 · sanghyun-son/EDSR-PyTorch · GitHub).

I used this advice and replaced the code that put in this page with def forward_chop in the EDSR-PyTorch-master/src/model/__init__.py file. But I encountered a new error as below and i don't know how to fix it: IndentationError: unindent does not match any outer indentation level I would be very grateful if you could please tell me how I can solve this problem or guide me if I did something wrong. @HolmesShuan

full error : Traceback (most recent call last): File "main.py", line 5, in <module> import model File "/content/EDSR-PyTorch/src/model/__init__.py", line 174 def forward_x8(self, *args, forward_function=None): ^ IndentationError: unindent does not match any outer indentation level