Hi, thank you for your share. When I train DnCNN-S, I get this error after I get the train.h5 and the val.h5.
Could you please help me solve it?
......
file: data/Set12/09.png
file: data/Set12/10.png
file: data/Set12/11.png
file: data/Set12/12.png
training set, # samples 238400

val set, # samples 12

Loading dataset ...

of training samples: 238400

learning rate 0.001000
Traceback (most recent call last):
File "train.py", line 126, in
main()
File "train.py", line 88, in main
(epoch+1, i+1, len(loader_train), loss.item(), psnr_train))
File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 67, in getattr
return object.getattribute(self, name)
AttributeError: 'Variable' object has no attribute 'item'

Dear author,
I `ve found a difference between your train code and CSZN DNCNN pytorch train code.

1. In his train code, the loss is calculated like this:

loss = criterion(model(batch_y), batch_x)_
Since the output of his model was clean image. He define the loss by output and clean-groundtruth.

2. But in ur train code, the loss is calculated like this:
   loss = criterion(out_train, noise) / (imgn_train.size()[0]*2)
   Since the output of ur model was the residual noise map. You define the loss by output and the noise-groundtruth

So the question is:

• Would this difference influence the training result?
• For denoising, is it maybe a better way to take clean image as ground-truth instead of noise residual?

Why is the bias term in all the convolution layers is false?

Thanks!

What changes required for color images?

Hello and thanks for your reproduction!
When I went through model.py, I noticed that in the forward method, you returned out instead of x - out, which is corresponding to residual learning in the author's article, I wonder if it's a mistake or I missed something. Thanks!

how to solve

Hi SaoYan,

I was wondering if by any chance you are working or already have PyTorch version of FFDNet. It is an advanced version of DnCNN -- but only MatConvNet version is available from author's GitHub.

https://github.com/cszn/FFDNet

Thanks,
Touqeer

您好！
在您写的代码中，有一段没有看懂，望指点：
在train.py中，
out_train = model(imgn_train)
loss = criterion(out_train, noise) / (imgn_train.size()[0]*2)
...

results

model.eval()
out_train = torch.clamp(imgn_train-model(imgn_train), 0., 1.)
psnr_train = batch_PSNR(out_train, img_train, 1.)

1.为什么已经定义了out_train，后面还要重新定义out_train呢
2.为什么要除imgn_train.size()[0]*2。0.,1. 是什么意思啊
3.残差是指的代码中的哪个呢，文章中是 l = 1/2N ||R(y) - (y -x) ||2，文章中的残差是R(y)吧

新手，谢谢指点

i have some images with fuzzy&small target,i wanna to get a higher signal-to-miscellaneous ratio and higher quality image,how can i make noise images and .h5 train &test file??

Hi SaoYan,

Thank you for sharing your codes at GitHub!
I was trying to test the model on my Mac machine -- but I am having issue with it, can you please advise as how to proceed with that. The models have been trained using GPU and I am trying on CPU -- so it is causing problem when I try to load the model. I am a newbie to PyTorch, any help in this regards will be great.

Thanks,
Touqeer

File "h5py_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py\h5f.pyx", line 106, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
下面这段代码出错（wrong)
class Dataset(udata.Dataset):
def init(self, train=True):
super(Dataset, self).init()
self.train = train
if self.train:
h5f = h5py.File('train.h5', 'r')
# h5f = h5py.File('C:/pytorch/DnCNN-PyTorch-master/train.py', 'r')
else:
h5f = h5py.File('val.h5', 'r')
self.keys = list(h5f.keys())
random.shuffle(self.keys)
h5f.close()
def len(self):
return len(self.keys)
def getitem(self, index):
if self.train:
h5f = h5py.File('train.h5', 'r')
else:
h5f = h5py.File('val.h5', 'r')
key = self.keys[index]
data = np.array(h5f[key])
h5f.close()
return torch.Tensor(data)