*Memos:
- My post explains transpose() and t().
- My post explains adjoint(), mH and mT.
- My post explains permute().
- My post explains movedim().
reshape() or view() can get the 0D or more D reshaped tensor of zero or more elements without losing data from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
reshape()can be used with torch or a tensor whileview()can be used with a tensor but not withtorch. - For
reshape(), the 1st argument(input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - For
reshape(), the 2nd argument withtorch(Type:tupleofint,listofintor size()) or the 1st or more arguments with a tensor(Type:int,tupleofint,listofintor size()) areshape(Required). - For
view(), using a tensor(Required-Type:tensorofint,float,complexorbool). - For
view(), the 1st or more arguments with a tensor are*shape(Required-Type:int,tupleofint,listofintor size()). *Don't use any keyword like*shape=orshape=. - For
view(), the 1st argument with a tensor isdtype(Optional-Default:None-Type:dtype): *Memos:- If it's
None, it's inferred from a tensor. -
My post explains
dtypeargument.
- If it's
- For
reshape()andview(), setting-1as the 1st number can adjust the size automatically so you don't need to set12,2,3,4or6as the 1st number. *-1is available only as the 1st number. -
reshape()may create the copy of a tensor, taking more memory whileview()doesn't create the copy of a tensor soview()can be ligher and faster thanreshape().
import torch
my_tensor = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]])
torch.reshape(input=my_tensor, shape=(12,))
torch.reshape(input=my_tensor, shape=(-1,))
my_tensor.reshape(shape=(12,))
my_tensor.reshape(shape=(-1,))
my_tensor.reshape(12)
my_tensor.reshape(-1)
my_tensor.view((12,))
my_tensor.view((-1,))
my_tensor.view(12)
my_tensor.view(-1)
# tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
torch.reshape(input=my_tensor, shape=(1, 12))
torch.reshape(input=my_tensor, shape=(-1, 12))
my_tensor.reshape(shape=(1, 12))
my_tensor.reshape(shape=(-1, 12))
my_tensor.reshape(1, 12)
my_tensor.reshape(-1, 12)
my_tensor.view((1, 12))
my_tensor.view((-1, 12))
my_tensor.view(1, 12)
my_tensor.view(-1, 12)
# tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
torch.reshape(input=my_tensor, shape=(2, 6))
torch.reshape(input=my_tensor, shape=(-1, 6))
my_tensor.reshape(shape=(2, 6))
my_tensor.reshape(shape=(-1, 6))
my_tensor.reshape(2, 6)
my_tensor.reshape(-1, 6)
my_tensor.view((2, 6))
my_tensor.view((-1, 6))
my_tensor.view(2, 6)
my_tensor.view(-1, 6)
# tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
torch.reshape(input=my_tensor, shape=(3, 4))
torch.reshape(input=my_tensor, shape=(-1, 4))
my_tensor.reshape(shape=(3, 4))
my_tensor.reshape(shape=(-1, 4))
my_tensor.reshape(3, 4)
my_tensor.reshape(-1, 4)
my_tensor.view((3, 4))
my_tensor.view((-1, 4))
my_tensor.view(3, 4)
my_tensor.view(-1, 4)
# tensor([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])
torch.reshape(input=my_tensor, shape=(4, 3))
torch.reshape(input=my_tensor, shape=(-1, 3))
torch.reshape(input=my_tensor, shape=my_tensor.size())
my_tensor.reshape(shape=(4, 3))
my_tensor.reshape(shape=(-1, 3))
my_tensor.reshape(4, 3)
my_tensor.reshape(-1, 3)
my_tensor.reshape(shape=my_tensor.size())
my_tensor.view((4, 3))
my_tensor.view((-1, 3))
my_tensor.view(4, 3)
my_tensor.view(-1, 3)
my_tensor.view(my_tensor.size())
# tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]])
torch.reshape(input=my_tensor, shape=(6, 2))
torch.reshape(input=my_tensor, shape=(-1, 2))
my_tensor.reshape(shape=(6, 2))
my_tensor.reshape(shape=(-1, 2))
my_tensor.reshape(6, 2)
my_tensor.reshape(-1, 2)
my_tensor.view((6, 2))
my_tensor.view((-1, 2))
my_tensor.view(6, 2)
my_tensor.view(-1, 2)
# tensor([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
torch.reshape(input=my_tensor, shape=(12, 1))
torch.reshape(input=my_tensor, shape=(-1, 1))
my_tensor.reshape(shape=(12, 1))
my_tensor.reshape(shape=(-1, 1))
my_tensor.reshape(12, 1)
my_tensor.reshape(-1, 1)
my_tensor.view((12, 1))
my_tensor.view((-1, 1))
my_tensor.view(12, 1)
my_tensor.view(-1, 1)
# tensor([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]])
torch.reshape(input=my_tensor, shape=(2, 2, 3))
torch.reshape(input=my_tensor, shape=(-1, 2, 3))
my_tensor.reshape(shape=(2, 2, 3))
my_tensor.reshape(shape=(-1, 2, 3))
my_tensor.reshape(2, 2, 3)
my_tensor.reshape(-1, 2, 3)
my_tensor.view((2, 2, 3))
my_tensor.view((-1, 2, 3))
my_tensor.view(2, 2, 3)
my_tensor.view(-1, 2, 3)
# tensor([[[0, 1, 2], [3, 4, 5]],
# [[6, 7, 8], [9, 10, 11]]])
etc.
torch.reshape(input=my_tensor, shape=(1, 2, 2, 3))
torch.reshape(input=my_tensor, shape=(-1, 2, 2, 3))
my_tensor.reshape(shape=(1, 2, 2, 3))
my_tensor.reshape(shape=(-1, 2, 2, 3))
my_tensor.reshape(1, 2, 2, 3)
my_tensor.reshape(-1, 2, 2, 3)
my_tensor.view((1, 2, 2, 3))
my_tensor.view((-1, 2, 2, 3))
my_tensor.view(1, 2, 2, 3)
my_tensor.view(-1, 2, 2, 3)
# tensor([[[[0, 1, 2], [3, 4, 5]],
# [[6, 7, 8], [9, 10, 11]]]])
etc.
my_tensor = torch.tensor([[0., 1., 2.], [3., 4., 5.],
[6., 7., 8.], [9., 10., 11.]])
torch.reshape(input=my_tensor, shape=(12,))
torch.reshape(input=my_tensor, shape=(-1,))
my_tensor.view((12,))
my_tensor.view((-1,))
# tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
my_tensor = torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j],
[3.+0.j, 4.+0.j, 5.+0.j],
[6.+0.j, 7.+0.j, 8.+0.j],
[9.+0.j, 10.+0.j, 11.+0.j]])
torch.reshape(input=my_tensor, shape=(12,))
torch.reshape(input=my_tensor, shape=(-1,))
my_tensor.view((12,))
my_tensor.view((-1,))
# tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 5.+0.j,
# 6.+0.j, 7.+0.j, 8.+0.j, 9.+0.j, 10.+0.j, 11.+0.j])
my_tensor = torch.tensor([[True, False, True], [True, False, True],
[False, True, False], [False, True, False]])
torch.reshape(input=my_tensor, shape=(12,))
torch.reshape(input=my_tensor, shape=(-1,))
my_tensor.view((12,))
my_tensor.view((-1,))
# tensor([True, False, True, True, False, True,
# False, True, False, False, True, False])
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