Difference between view, reshape and permute

autograd
2

reshape tries to return a view if possible, otherwise copies to data to a contiguous tensor and returns the view on it. From the docs:

Returns a tensor with the same data and number of elements as input , but with the specified shape. When possible, the returned tensor will be a view of input . Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior.
See torch.Tensor.view() on when it is possible to return a view.
A single dimension may be -1, in which case it’s inferred from the remaining dimensions and the number of elements in input .

Have a look at this example to demonstrate this behavior:

x = torch.arange(4*10*2).view(4, 10, 2)
y = x.permute(2, 0, 1)

# View works on contiguous tensors
print(x.is_contiguous())
print(x.view(-1))

# Reshape works on non-contugous tensors (contiguous() + view)
print(y.is_contiguous())
try: 
    print(y.view(-1))
except RuntimeError as e:
    print(e)
print(y.reshape(-1))
print(y.contiguous().view(-1))

permute is quite different to view and reshape:

# View vs. permute
x = torch.arange(2*4).view(2, 4)
print(x.view(4, 2))
> tensor([[0, 1],
          [2, 3],
          [4, 5],
          [6, 7]])
print(x.permute(1, 0))
> tensor([[0, 4],
          [1, 5],
          [2, 6],
          [3, 7]])
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