lavi135246 / pytorch-learning-to-see-in-the-dark Goto Github PK

when you test,you can use torch.nograd().

Hi, thanks for your work!
I am trying test.py on gpu, but got error "cuda out of memory".(rtx 2080ti)
Any solution could solve this?
thanks!

Hello,
You mentioned that you slice the original input images into small pieces(1024*1024) in readme, however, I cannot find the corresponding operations in the test code. Would you please tell me where is the slice operation? thank you~

I noticed that the initializing operation was just defined as a function, and was not used in the init() part. Does it still work in this way?

Hey, how much time it approximately takes to train the SONY model using the specified configuration?

You have added a _initialize_weights() function, does it is necessary to pytorch?

def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.normal_(0.0, 0.02) if isinstance(m, nn.ConvTranspose2d): m.weight.data.normal_(0.0, 0.02)

Hi ninetf135246, I am not able to understand why you are permuting the input tensor in line #142 of train_Sony.py. Please help me to understand your code.
https://github.com/ninetf135246/pytorch-Learning-to-See-in-the-Dark/blob/8af3e309d6947b1addf52765869190ec1e6e2ded/train_Sony.py#L142

In the train_sony code,how to modify the batch_size?

Here in the second if, I think the axis should be 2. axis=0 means flip along the batch dimension

    if np.random.randint(2,size=1)[0] == 1:  # random flip 
        input_patch = np.flip(input_patch, axis=1)
        gt_patch = np.flip(gt_patch, axis=1)
    if np.random.randint(2,size=1)[0] == 1: 
        input_patch = np.flip(input_patch, **axis=0**)
        gt_patch = np.flip(gt_patch, **axis=0**)
    if np.random.randint(2,size=1)[0] == 1:  # random transpose 
        input_patch = np.transpose(input_patch, (0,2,1,3))
        gt_patch = np.transpose(gt_patch, (0,2,1,3))

gt_full = np.expand_dims(np.float32(im/65535.0),axis = 0)，
I think it should be 65532.

Hi

CPU works, but cuda shows error. I cant figure out how to fix it.
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Thanks

虽然时间过去很久了，但是可以的话还是想问下，颜色信息这个模型是学不出来吗？我刚练到40多个epoch

Thanks for releasing the code. I want to know if you have got the results of the paper when using the pytorch codes.

How to understand im = raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)

I want to use pytorch to implement a new loss function. And almost all the online information say that I should make a new class that inherit the nn.Module. But I found you just create a function ‘reduce_mean()’. Does it means that it is just so easy in pytorch to make an own funtion.

I'm pretty sure you can just pass the whole image into the network for testing, so you wouldn't need to slice it up.

How to understand origin_full = scale_full？Why are the results very different?
How to understand scale_full = scale_full*np.mean(gt_full)/np.mean(scale_full)？
What is the role of scale_full?