How do I display a single image in PyTorch?

As you can see matplotlib works fine even without conversion to numpy array. But PyTorch Tensors ("Image tensors") are channel first, so to use them with matplotlib you need to reshape it:

Code:

from scipy.misc import face
import matplotlib.pyplot as plt
import torch

np_image = face()
print(type(np_image), np_image.shape)
tensor_image = torch.from_numpy(np_image)
print(type(tensor_image), tensor_image.shape)
# reshape to channel first:
tensor_image = tensor_image.view(tensor_image.shape[2], tensor_image.shape[0], tensor_image.shape[1])
print(type(tensor_image), tensor_image.shape)

# If you try to plot image with shape (C, H, W)
# You will get TypeError:
# plt.imshow(tensor_image)

# So we need to reshape it to (H, W, C):
tensor_image = tensor_image.view(tensor_image.shape[1], tensor_image.shape[2], tensor_image.shape[0])
print(type(tensor_image), tensor_image.shape)

plt.imshow(tensor_image)
plt.show()

Output:

<class 'numpy.ndarray'> (768, 1024, 3)
<class 'torch.Tensor'> torch.Size([768, 1024, 3])
<class 'torch.Tensor'> torch.Size([3, 768, 1024])
<class 'torch.Tensor'> torch.Size([768, 1024, 3])

Given the image is loaded as described and stored in the variable image:

plt.imshow(transforms.ToPILImage()(image), interpolation="bicubic")
#transforms.ToPILImage()(image).show() # Alternatively

---

Or as Soumith suggested:

def show(img):
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)), interpolation='nearest')

Given a Tensor representing the image, use .permute() to put the channels as the last dimension:

plt.imshow(  tensor_image.permute(1, 2, 0)  )

Note: permute does not copy or allocate memory, and from_numpy() doesn't either.