When I run the python main.py --cuda --batchSize 4, I got this error.

Namespace(batchSize=4, clip=0.1, cuda=True, lr=0.0001, momentum=0.9, nEpochs=500, pretrained='', resume='', start_epoch=1, step=500, threads=1, weight_decay=0)
('Random Seed: ', 4270)
===> Loading datasets
===> Building model
===> Setting GPU
===> Setting Optimizer
===> Training
epoch = 1 lr = 0.0001
Traceback (most recent call last):
  File "main.py", line 130, in <module>
    main()
  File "main.py", line 82, in main
    train(training_data_loader, optimizer, model, criterion, epoch)
  File "main.py", line 108, in train
    loss = criterion(model(input), target)
  File "/home/shuai/.local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/shuai/SRResNet/srresnet.py", line 65, in forward
    out = self.relu(self.conv_input(x))
  File "/home/shuai/.local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/shuai/.local/lib/python2.7/site-packages/torch/nn/modules/conv.py", line 237, in forward
    self.padding, self.dilation, self.groups)
  File "/home/shuai/.local/lib/python2.7/site-packages/torch/nn/functional.py", line 40, in conv2d
    return f(input, weight, bias)
RuntimeError: CUDNN_STATUS_BAD_PARAM

I generated the trianing data with generate_train.m from VDSR.

Hi, When i'm displaying my 3 channeled output image, it is not at all good and looks something like this:-

When I'm displaying my single channeled output ,it still shows proper image features ,like this:-

Though I've started to train my model and it isn't even 50 epochs till now, but I really want to know why is this happening.

Thank you .

I have converted my images to .mat files, later I realised that my files are not same as the testsets provided in the repository. Did anybody faced similar situation as mine?

Dear all, I encounter this problem at the start of my training. Could anyone help? The error message is as follows:

user@userVM:~/\u4e0b\u8f7d/SRResNet$ python main_srresnet.py --cuda --vgg_loss --gpus 0 /home/user/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type. from ._conv import register_converters as _register_converters Namespace(batchSize=16, cuda=True, gpus='0', lr=0.0001, nEpochs=500, pretrained='', resume='', start_epoch=1, step=200, threads=0, vgg_loss=True) => use gpu id: '0' Random Seed: 3780 ===> Loading datasets ===> Loading VGG model ===> Building model /home/user/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead. warnings.warn(warning.format(ret)) ===> Setting GPU ===> Setting Optimizer ===> Training Epoch=1, lr=0.0001 Traceback (most recent call last): File "main_srresnet.py", line 166, in main() File "main_srresnet.py", line 104, in main train(training_data_loader, optimizer, model, criterion, epoch) File "main_srresnet.py", line 122, in train for iteration, batch in enumerate(training_data_loader, 1): File "/home/user/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 313, in next indices = next(self.sample_iter) # may raise StopIteration File "/home/user/anaconda3/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 138, in iter for idx in self.sampler: File "/home/user/anaconda3/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 51, in iter return iter(torch.randperm(len(self.data_source)).tolist()) File "/home/user/\u4e0b\u8f7d/SRResNet/dataset.py", line 16, in len return self.data.shape[0] AttributeError: 'NoneType' object has no attribute 'shape'

The generate_train_srresnet.m script is extremely slow. To speed up, I fix downsizes to 0.5 and generate a srresnet_x4.h5 (2.3GB). I checked the subim_input and subim_input, which seem valid. But I just do not understand why this happens. Could anyone help me with this? Thanks.

UnicodeDecodeError Traceback (most recent call last) in () ----> 1 weights = torch.load('Saved_model//model_srresnet.pth') 2 model.load_state_dict(weights['model'].state_dict())

~\Anaconda3\lib\site-packages\torch\serialization.py in load(f, map_location, pickle_module) 301 f = open(f, 'rb') 302 try: --> 303 return _load(f, map_location, pickle_module) 304 finally: 305 if new_fd:

~\Anaconda3\lib\site-packages\torch\serialization.py in _load(f, map_location, pickle_module) 467 unpickler = pickle_module.Unpickler(f) 468 unpickler.persistent_load = persistent_load --> 469 result = unpickler.load() 470 471 deserialized_storage_keys = pickle_module.load(f)

UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 918: ordinal not in range(128)

I've also tried your previous answers of changing encoding type in serialization.py as 'latin1' , it didn't work.

@twtygqyy ，Hi，I run python eval.py with pytorch0.4 and python3.5 and get erro like this： Traceback (most recent call last): File "/home/hpl/code/2018/Super_Resolution/SRResNet/pytorch-SRResNet-master_imaginput_twosatge/eval.py", line 42, in model = torch.load(opt.model)["model"] File "/home/hpl/anaconda2/envs/pytorch0.3_p3.5/lib/python3.5/site-packages/torch/serialization.py", line 229, in load return _load(f, map_location, pickle_module) File "/home/hpl/anaconda2/envs/pytorch0.3_p3.5/lib/python3.5/site-packages/torch/serialization.py", line 377, in _load result = unpickler.load() AttributeError: Can't get attribute '_rebuild_tensor_v2' on <module 'torch._utils' from '/home/hpl/anaconda2/envs/pytorch0.3_p3.5/lib/python3.5/site-packages/torch/_utils.py'>

Regarding your SRResNet implementation (the generator), I was wondering why you used a LeakyReLU instead of a PReLU with a trainable "a". Reading the paper and seing your implementation raised this question, because the two are related, but a bit different. It seems to me that the author meant a trainable PReLU in the original implementation.

Also, why did you use InstanceNorm2d instead of BatchNorm2d? Were you feeding the network with single images instead of a 16 sized batch as the paper specifies?

My last question would be what dataset you used to train your implementation? I've seen you used data augmentation, but I didn't find info on the used dataset.

I'm studying the paper and planning on running it on Imagenet, as the author did, expecting to get similar results. I also will definetly take a look on your evaluation code implementation, the Matlab tip, which increases PSNR values, seems valuable.

My implementation is a bit different than yours and it's getting 29,75dB PSNR on Set5. To get these results I trained it with a sampled 5500 images from Imagenet and I'm hoping to get an improvement when I train it with the 350k images they mention in the paper.

Hi , I'm training srresnet on DIV2K from scratch(no pretrained) at lr: e-4 , and it is decayed by 0.1 for step=200, but I'm having MSE loss(at 402 epoch) as:-

Epoch=402, lr=1.0000000000000002e-06 Epoch [402/500], Step [1/50], Loss: 74.55887,Total_loss_old:74.55887 Epoch [402/500], Step [2/50], Loss : 77.55219,Total_loss_old:152.11106 Epoch [402/500], Step [3/50], Loss: 58.70598,Total_loss_old:210.81704 Epoch [402/500], Step [4/50],Loss: 78.69186,Total_loss_old:289.50890 Epoch [402/500], Step [5/50], Loss: 87.79494,Total_loss_old:377.30384 Epoch [402/500], Step [6/50], Loss: 24.87956,Total_loss_old:402.18340 ..... Overall Total_loss: 72.45043201446533

I dont know if actually training or not. Please check code once:- code.zip This is my result:- result_images.docx

@twtygqyy ，Hi, I trained normally but the test showed memory issue error like this. I don't know why it needs so much memory. RuntimeError: cuda runtime error (2) : out of memory at /opt/conda/conda bld/pytorch_1501969512886/work/pytorch-0.1.12/torch/lib/THC/generic/THCStorage.cu:66