Pytorch 维度变换

view reshape

import torch
a = torch.rand(4, 1, 28, 28)
print("a.shape:t", a.shape)
# prod(a.size) = prod(a'.size)
b = a.view(4, 28*28)
print("b:t", b)

c = a.view(4, 28*28).shape
print("c:t", c)

d = a.view(4, 784).shape
print("d:t", d)

e = a.view(4*28, 28).shape
print("e:t", e)

f = a.view(4*1, 28, 28).shape
print("f:t", f)

g = a.view(4, 784)
print("g:t", g)

a.shape:	 torch.Size([4, 1, 28, 28])
b:	 tensor([[0.7603, 0.8994, 0.2818,  ..., 0.1005, 0.2483, 0.6218],
        [0.3760, 0.4059, 0.1720,  ..., 0.8974, 0.9278, 0.8993],
        [0.9729, 0.7864, 0.0886,  ..., 0.1318, 0.5178, 0.1670],
        [0.7797, 0.0013, 0.5324,  ..., 0.2778, 0.4463, 0.6496]])
c:	 torch.Size([4, 784])
d:	 torch.Size([4, 784])
e:	 torch.Size([112, 28])
f:	 torch.Size([4, 28, 28])
g:	 tensor([[0.7603, 0.8994, 0.2818,  ..., 0.1005, 0.2483, 0.6218],
        [0.3760, 0.4059, 0.1720,  ..., 0.8974, 0.9278, 0.8993],
        [0.9729, 0.7864, 0.0886,  ..., 0.1318, 0.5178, 0.1670],
        [0.7797, 0.0013, 0.5324,  ..., 0.2778, 0.4463, 0.6496]])

unsqueeze 新插入一个维度

import torch
a = torch.rand(4, 1, 28, 28)
b = torch.Size([4, 1, 28, 28])
# 在最前面增加了一个维度
c = a.unsqueeze(0).shape
print("c:t", c)

d = a.unsqueeze(-1).shape
print("d:t", d)

e = a.unsqueeze(4).shape
print("e:t", e)

f = a.unsqueeze(-4).shape
print("f:t", f)

g = a.unsqueeze(-5).shape
print("g:t", g)

h = torch.tensor([1.2, 2.3])
I = h.unsqueeze(-1)
print("I:t", I)

J = h.unsqueeze(0)
print("J:t", J)

c:	 torch.Size([1, 4, 1, 28, 28])
d:	 torch.Size([4, 1, 28, 28, 1])
e:	 torch.Size([4, 1, 28, 28, 1])
f:	 torch.Size([4, 1, 1, 28, 28])
g:	 torch.Size([1, 4, 1, 28, 28])
I:	 tensor([[1.2000], [2.3000]])
J:	 tensor([[1.2000, 2.3000]])

import torch
a = torch.rand(32)
print("a:t", a)

b = torch.rand(4, 32, 14, 14)
c = a.unsqueeze(1).unsqueeze(2).unsqueeze(0)

print("c.shape:t", c.shape)

a:	 tensor([0.3070, 0.2438, 0.3220, 0.2777, 0.6202, 0.7803, 0.3970, 0.5021, 0.1788,
        0.2335, 0.3152, 0.0943, 0.8410, 0.9082, 0.3120, 0.9525, 0.4011, 0.1827,
        0.4022, 0.5850, 0.7507, 0.6316, 0.1653, 0.7424, 0.0729, 0.1857, 0.4692,
        0.1159, 0.9131, 0.3927, 0.0413, 0.8741])
c.shape:	 torch.Size([1, 32, 1, 1])

Squeeze：

import torch
a = torch.rand(32)
b = a.unsqueeze(1).unsqueeze(2).unsqueeze(0)
print("b.shape:t", b.shape)
# squeeze
c = b.squeeze().shape
print("c:t", c)

d = b.squeeze(0).shape
print("d:t", d)

e = b.squeeze(-1).shape
print("e:t", e)
# shape不为1， 因此32不会变
f = b.squeeze(1).shape
print("f:t", f)

g = b.squeeze(-4).shape
print("g:t", g)

b.shape:	 torch.Size([1, 32, 1, 1])
c:	 torch.Size([32])
d:	 torch.Size([32, 1, 1])
e:	 torch.Size([1, 32, 1])
f:	 torch.Size([1, 32, 1, 1])
g:	 torch.Size([32, 1, 1])

Expand （扩张） ：

import torch
a = torch.rand(32)
b = a.unsqueeze(1).unsqueeze(2).unsqueeze(0)
print("b.shape:t", b.shape)

c = b.expand(4, 32, 14, 14).shape
print("c:t", c)
# -1表示不变 
d = b.expand(-1, 32, -1, -1).shape
print("d:t", d)

e = b.expand(-1, 32, -1, -4).shape
print("e:t", e)

b.shape:	 torch.Size([1, 32, 1, 1])
c:	 torch.Size([4, 32, 14, 14])
d:	 torch.Size([1, 32, 1, 1])
e:	 torch.Size([1, 32, 1, -4])

repeat（扩张/重复）：

import torch
a = torch.rand(32)
b = a.unsqueeze(1).unsqueeze(2).unsqueeze(0)
print("b.shape:t", b.shape)
# 每一个维度要重复的次数
c = b.repeat(4, 32, 1, 1).shape
print("c:t", c)

d = b.repeat(4, 1, 1, 1).shape
print("d:t", d)

e = b.repeat(4, 1, 32, 32).shape
print("e:t", e)

b.shape:	 torch.Size([1, 32, 1, 1])
c:	 torch.Size([4, 1024, 1, 1])
d:	 torch.Size([4, 32, 1, 1]) 
e:	 torch.Size([4, 32, 32, 32])

Transpose：

permute：

import torch
a = torch.rand(4, 3, 28, 28)
b = a.transpose(1, 3).shape
print("b:t", b)

c = torch.rand(4, 3, 28, 32)
d = c.transpose(1, 3).shape
print("d:t", d)

e = c.transpose(1, 3).transpose(1, 2).shape
print("e:t", e)

f = c.permute(0, 2, 3, 1).shape
print("f:t", f)

b:	 torch.Size([4, 28, 28, 3])
d:	 torch.Size([4, 32, 28, 3])
e:	 torch.Size([4, 28, 32, 3])
f:	 torch.Size([4, 28, 32, 3])

最后

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