xiaofeiwang2018/dr

dr's Issues

about the number of epochs

您好，请问在第一阶段训练中，epoch数真的是30000吗？

RandomCrop如何导入

您好，可以麻烦问一下这个怎么导入嘛？我搜不到这个库呀？
from transform.transforms_group import RandomCrop
我运行代码，一直在这个地方报错是怎么回事呢？
if self.transform is not None: # train
info = self.hr_transform(info)
报错如下
File "E:\MyUse\CVcode\DR-main\data_process.py", line 91, in getitem
info = self.hr_transform(info)
File "D:\Soft\Anaconda\envs\pytorchgpu_38\lib\site-packages\torchvision\transforms\transforms.py", line 60, in call
img = t(img)
File "D:\Soft\Anaconda\envs\pytorchgpu_38\lib\site-packages\torch\nn\modules\module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "D:\Soft\Anaconda\envs\pytorchgpu_38\lib\site-packages\torchvision\transforms\transforms.py", line 586, in forward
width, height = F._get_image_size(img)
File "D:\Soft\Anaconda\envs\pytorchgpu_38\lib\site-packages\torchvision\transforms\functional.py", line 67, in _get_image_size
return F_pil._get_image_size(img)
File "D:\Soft\Anaconda\envs\pytorchgpu_38\lib\site-packages\torchvision\transforms\functional_pil.py", line 26, in _get_image_size
raise TypeError("Unexpected type {}".format(type(img)))
TypeError: Unexpected type <class 'list'>

This is not the right code for an indicated article. Can you share the updated code?

GMSV algorithm?

Can you share the code of the GMSV algorithm in the article, not in the conference paper?

there is no U_Net_Cut model in network file?

Can you update the files?

MultiScale_Intergrate should not remove the grad_fn?

Should we use F.interpolate instead of resizing it to prevent grad_fn disapeering?

Code Issue?

            extractor = ModelOutputs(cls_net)
            features, fc_output = extractor(cls_input)
            cam_all_sr = {'size256': [], 'size128': [], 'size64': [], 'size32': []}
            for batch in range(fc_output.shape[0]):
                device = fc_output[batch].device.index
                index = np.argmax(fc_output[batch].cpu().data.numpy())
                if index == 0:
                    one_hot = np.zeros((1, fc_output[batch].size()[-1]), dtype=np.float32)
                    one_hot[0][0] = 1
                if index > 0:
                    one_hot = np.ones((1, fc_output[batch].size()[-1]), dtype=np.float32)
                    one_hot[0][0] = 0
                if batch == 0:
                    one_hot_all = Variable(torch.from_numpy(one_hot).cuda(device), requires_grad=True)
                else:
                    one_hot_all = torch.cat((Variable(torch.from_numpy(one_hot).cuda(device), requires_grad=True), one_hot_all), 0)

if the index of batches is the same, there is no problem. But if they are different, it calculates the opposite loss between one_hot_all and fc_output.

Example:

fc_output one_hot_all
[[0.9, 0.1, 0.1, 0.1, 0.1] [[1, 0, 0, 0, 0]
[0.8, 0.2, 0.2, 0.3, 0.4]] [1, 0, 0, 0, 0]] There is no problem.

fc_output one_hot_all
[[0.2, 0.1, 0.1, 0.1, 0.8] [[0, 1, 1, 1, 1]
[0.3, 0.2, 0.7, 0.3, 0.4]] [0, 1, 1, 1, 1]] There is no problem.

fc_output one_hot_all
[[0.9, 0.1, 0.1, 0.1, 0.7] [[0, 1, 1, 1, 1]
[0.3, 0.2, 0.4, 0.9, 0.4]] [1, 0, 0, 0, 0]] There is problem here because of this line torch.cat((Variable(torch.from_numpy(one_hot).cuda(device), requires_grad=True), one_hot_all), 0).

should be one_hot_all = torch.cat((one_hot_all, Variable(torch.from_numpy(one_hot).cuda(device), requires_grad=True)), 0) ???

Thanks