pytorch如何增加维度_PyTorch ---- torch.nn.function.pad 函数用法（补充维度上的数值）...
1.二维数组：对最内部元素左侧增加元素(例如 1 的左侧)
a = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
a1 = torch.nn.functional.pad(a, pad=(1, 0, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[1, 2, 3, 4],
[1, 2, 3, 4]])
a1 = tensor([[1, 1, 2, 3, 4],
[1, 1, 2, 3, 4]])
2.二维数组：对最内部元素右侧增加元素(例如 4 右侧)
a = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
a1 = torch.nn.functional.pad(a, pad=(0, 1, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[1, 2, 3, 4],
[1, 2, 3, 4]])
a1 = tensor([[1, 2, 3, 4, 1],
[1, 2, 3, 4, 1]])
3.二维数组：对最内部一维数组左侧增加元素(例如 [1, 2, 3, 4] 左侧)
a = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 1, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[1, 2, 3, 4],
[1, 2, 3, 4]])
a1 = tensor([[1, 1, 1, 1],
[1, 2, 3, 4],
[1, 2, 3, 4]])
4.二维数组：对最内部一维数组右侧增加元素(例如 [1, 2, 3, 4] 右侧)
a = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 1), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[1, 2, 3, 4],
[1, 2, 3, 4]])
a1 = tensor([[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 1, 1, 1]])
5.三维数组：对最内部元素左侧增加元素(例如 1 左侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(1, 0, 0, 0, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 1, 2, 3, 4],
[1, 5, 6, 7, 8]],
[[1, 1, 2, 3, 4],
[1, 5, 6, 7, 8]]])
6.三维数组：对最内部元素右侧增加元素(例如 4 右侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(0, 1, 0, 0, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 2, 3, 4, 1],
[5, 6, 7, 8, 1]],
[[1, 2, 3, 4, 1],
[5, 6, 7, 8, 1]]])
7.三维数组：对最内部一维数组左侧增加元素(例如 [1, 2, 3, 4] 左侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 1, 0, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 1, 1, 1],
[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 1, 1, 1],
[1, 2, 3, 4],
[5, 6, 7, 8]]])
8.三维数组：对最内部一维数组右侧增加元素(例如 [5, 6, 7, 8] 右侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 1, 0, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 1, 1, 1]],
[[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 1, 1, 1]]])
9.三维数组：对最内部二维数组左侧增加元素(例如 [[1, 2, 3, 4], [5, 6, 7, 8]] 左侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 0, 1, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
10.三维数组：对最内部二维数组左侧增加元素 x2(例如 [[1, 2, 3, 4], [5, 6, 7, 8]] 左侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 0, 2, 0), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
a1 = tensor([[[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[1, 2, 3, 4],
[5, 6, 7, 8]]])
11.三维数组：对最内部二维数组右侧增加元素 (例如 [[11, 22, 33, 44], [55, 66, 77, 88]] 右侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[11, 22, 33, 44], [55, 66, 77, 88]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 0, 0, 1), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[ 1, 2, 3, 4],
[ 5, 6, 7, 8]],
[[11, 22, 33, 44],
[55, 66, 77, 88]]])
a1 = tensor([[[ 1, 2, 3, 4],
[ 5, 6, 7, 8]],
[[11, 22, 33, 44],
[55, 66, 77, 88]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1]]])
12.三维数组：对最内部二维数组右侧增加元素 x2 (例如 [[11, 22, 33, 44], [55, 66, 77, 88]] 右侧)
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[11, 22, 33, 44], [55, 66, 77, 88]]])
a1 = torch.nn.functional.pad(a, pad=(0, 0, 0, 0, 0, 2), mode='constant', value=1)
print("a = ", a)
print("a1 = ", a1)
运行结果：
a = tensor([[[ 1, 2, 3, 4],
[ 5, 6, 7, 8]],
[[11, 22, 33, 44],
[55, 66, 77, 88]]])
a1 = tensor([[[ 1, 2, 3, 4],
[ 5, 6, 7, 8]],
[[11, 22, 33, 44],
[55, 66, 77, 88]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1]]])