Pytorch：cat、stack、squeeze、unsqueeze的用法
torch.cat
在指定原有维度上链接传入的张量，所有传入的张量都必须是相同形状
x = torch.tensor([[0, 1, 2]], dtype= torch.float)
y = torch.tensor([[3, 4, 5]], dtype= torch.int)
print(x.shape, y.shape)
print("-"*50)
z = torch.cat((x, y), dim= 0)
print(z)
print(z.shape)
print("-"*50)
z = torch.cat((x, y), dim= 1)
print(z)
print(z.shape)
torch.stack
在一个新的维度上链接张量，输入张量都必须是相同形状的
x = torch.tensor([[0, 1, 2]])
y = torch.tensor([[3, 4, 5]])
print(x.shape, y.shape)
print("-"*50)
z = torch.stack((x, y), dim= 0)
print(z)
print(z.shape)
print("-"*50)
z = torch.stack((x, y), dim= 1)
print(z)
print(z.shape)
print("-"*50)
z = torch.stack((x, y), dim= 2)
print(z)
print(z.shape)
torch.squeeze
压缩张量，去掉输入张量中大小为1的维度，例如：(Ax1xBxCx1)->(AxBxC)
x = torch.tensor([[0, 1, 2]])
y = torch.rand(size= (1, 2, 1, 2, 1))
print(x.shape, y.shape)
print("-"*50)
z = torch.squeeze(x)
print(z)
print(z.shape)
print("-"*50)
z = torch.squeeze(y)
print(z)
print(z.shape)
torch.unsqueeze
在输入张量中指定位置插入一个大小为1的维度
x = torch.randn(size= (2,3))
print(x.shape)
print("-"*50)
z = torch.unsqueeze(x, 0)
print(z)
print(z.shape)
print("-"*50)
z = torch.unsqueeze(x, 1)
print(z)
print(z.shape)
