Thanks for your patience. Iam using pytorch 0.4. Can you provide me the code to unsqueenze the tensor twice.
I have tried it like this,
iter=0
for epoch in range(num_epochs):
for img, labels in train_loader:
img=img.unsqueeze(0)
img=img.unsqueeze(0)
print(img.size())
img=Variable(img)
labels=Variable(labels)
It again shows the same error
This is the size of the tensor
torch.Size([1, 1, 100, 3, 64, 64])
Probably I was wrong.
The shape before unsqueeze looks like you are using a batch size of 100 and 3 channels for each image.
Are you loading the images using PIL?
What is the shape of gray if you use my short code snippet?
Iam using custom dataset, so I am using Data loader to iterate images. Shape of the gray image is still (64,64,3) when I read it using cv2.imread(path). When I load a image using PIL, how should I check the shape?..
You could use torchvision.transforms.functional.to_tensor(pil_image), which will return a normalized tensor of shape [1, h, w].
If you donāt want it to be normalized, you could use torch.from_numpy(np.array(pil_image)).
Thanks. I used torchvision.transforms.functional.to_tensor(gray_image).size()
Output:
torch.Size([1, 64, 64])
Perfect! Now you would have to add the batch dimension before passing it to the model using tensor = tensor.unsqueeze(0).
I tried this,
iter=0
for epoch in range(num_epochs):
for img, labels in train_loader:
img=torchvision.transforms.functional.to_tensor(img)
print(img.size())
img=Variable(img)
labels=Variable(labels)
It shows ,
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-258-5fa2f20aa942> in <module>
2 for epoch in range(num_epochs):
3 for img, labels in train_loader:
----> 4 img=torchvision.transforms.functional.to_tensor(img)
5 print(img.size())
6 img=Variable(img)
~\Anaconda3\lib\site-packages\torchvision\transforms\functional.py in to_tensor(pic)
42 """
43 if not(_is_pil_image(pic) or _is_numpy_image(pic)):
---> 44 raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
45
46 if isinstance(pic, np.ndarray):
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
This is the only transformation I made ,
transform=transforms.Compose([ToTensor()])
train_data = ImageFolder(root=Root,transform=transform)
I tried your code on single image and it workded,
I=Image.open('train_set/1/2.png')
torchvision.transforms.functional.to_tensor(I).unsqueeze(0).size()
How should I use it in training code section?
I also tried ToPILImage() transformation separately but it also shows error
Can you please give a snippet of the code
torch.from_numpy(np.asarray(yourimage))
Would do the job
It works on single image. I opened a single image using PIL and used both torch.from_numpy(np.asarray(yourimage)) and torchvision.transforms.functional.to_tensor(I) and both are giving same shape (1,64,64) when I unsqueeze it. But when use you code in the training part like this,
iter=0
for epoch in range(num_epochs):
for img, labels in train_loader:
img=torchvision.transforms.functional.to_tensor(I).unsqueeze(0)
print(img.size())
img=Variable(img)
It shows ,
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
you already have image given by the train loader just use it. Do
image.shape
To see its shape, it should be a tensor already, unsqueeze its dimension 0 to get [1,1,h,w] and you are good to bass it to your network.
iter=0
for epoch in range(num_epochs):
for img, labels in train_loader:
print(img.shape)
img=img.unsqueeze(0)
print(img.shape)
img=Variable(img)
labels=Variable(labels)
Output:
torch.Size([100, 3, 64, 64])
torch.Size([1, 100, 3, 64, 64])
You still donāt open your images correctly in your train loader, and 100 is your batch size, if you already have a batch you need to have a shape of [100,1,h,w] because your gray image should comes out as [h,w]
Please give me a code snippet of how to do it.
https://pytorch.org/tutorials/beginner/data_loading_tutorial.html
Use the tutorial and adapt it to open images using the correct gray scale, and reshape them correctly in get_item.
A gray scale image open by PIL is of the shape [h,w], if you unsqueeze it to be [1,h,w], and let your dataloader to create batches you will have : [B,1,h,w] with B the batch size, h your height and w you width.
Thanks. I tried this,
iter=0
for epoch in range(num_epochs):
for labels, img in enumerate(train_loader):
img=np.asarray(img[0])
print(img.shape)
print(type(img))
img=torch.from_numpy(np.asarray(img))
labels=torch.from_numpy(np.asarray(labels))
print(img.shape)
img=Variable(img)
labels=Variable(labels)
The shape is still (64,64,3)ā¦I dont know what to do. I open a image and try your code and then the shape is (1,64,64)
I told you, your trainloader is opening your images using 3 channels. You have to change thatā¦ You need to understand how Tensors work and how the train loader is loading your data.
Thanks for your patience. I solved the problem by
iter=0
for epoch in range(num_epochs):
for i,(img, labels) in enumerate(train_loader):
img,labels=(img, labels)
img=img.narrow(1,0,1)
img=Variable(img)
Now the shape became (100,1,64,64)ā¦Training is going on now. @ptrblck thanks for your patience
you could just do :
for img, labels in train_loader:
img=img.narrow(1,0,1)
img=Variable(img)
Thanks. Will use it next time. Actually all the 3 channels were gray scale and I wanted only one channel for networkās input.
You could have done something simpler using a Dataset class and dataloader, to get directly a 1 channel input, but anyway if it works for you, no need to complicate things.