def img_convert(tensor):
image = tensor.clone().detach().numpy()
image = image.transpose(1, 2, 0)
print(image.shape)
image = image*np.array(0.5,) + np.array(0.5,)
image = image.clip(0, 1)
return image
dataiter = iter(training_loader)
images, labels = dataiter.next()
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(20):
ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])
plt.imshow(img_convert(images[idx]))
ax.set_title([labels[idx].item()])
Can someone help me with this error can’t figure out how to resolve this, first I had gray scale problem then after fixing it there is a new issue.
Invalid dimension for image data
Could you print the shape of images[idx] and the returned numpy array from img_convert?
PS: You can add code snippets using three backticks ``` 
On printing the shape of images[idx] I find the shape is reversed
Output:
torch.Size([1, 28, 28])
(28, 28, 1)
However if I put a transpose command
images[idx].transpose(1, 2, 0)
so which 2 positional arguments should I give
If you are dealing with grayscale images, you should remove the channel dimension for matplotlib:
plt.imshow(np.random.randn(24, 24, 3)) # works
plt.imshow(np.random.randn(24, 24)) # works
plt.imshow(np.random.randn(24, 24, 1)) # fails
So what do you suggest I should do in my above mentioned code
Where should I remove the channel dimensions in the img_conver method or in the below for loop
I would add something like:
if image.shape[2] == 1:
image = image[:, :, 0]
return image
into your img_convert method.
Thanks, it worked!!!
@ptrblck I was having a Tiff file dataset and the problem am having is ,couldn’t plot the tiff file and these are the following information about the tiff file.
here is the following link to colab Notebook
plss tell me , how to resolve this problem
I guess you are slicing the image in diaplay_images or are iterating each row later. Try to load the image once manually and display it directly without any additional logic to plot multiple images etc.
