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在 PyTorch 中展开

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发布： 2024-11-06 11:36:01

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在 pytorch 中展开

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*备忘录：

我的帖子解释了 unflatten()。
我的帖子解释了 flatten() 和 ravel()。
我的帖子解释了 flatten()。

unflatten() 可以向零个或多个元素的一维或多个 d 张量添加零个或多个维度，得到零个或多个元素的一维或多个 d 张量，如下所示：

*备忘录：

初始化的第一个参数是dim(required-type:int)。
初始化的第二个参数是 unflattened_size（必需类型：元组或 int 列表）。
第一个参数是输入（必需类型：int、float、complex 或 bool 的张量）。 *-1 推断并调整大小。
unflatten() 和 unflatten() 的区别是：
- unflatten() 具有 unflattened_size 参数，该参数与 unflatten() 的 size 参数相同。
- 基本上，unflatten() 用于定义模型，而 unflatten() 不用于定义模型。

import torch
from torch import nn

unflatten = nn.Unflatten()
unflatten
# Unflatten(dim=0, unflattened_size=(6,))

unflatten.dim
# 0

unflatten.unflattened_size
# (6,)

my_tensor = torch.tensor([7, 1, -8, 3, -6, 0])

unflatten = nn.Unflatten(dim=0, unflattened_size=(6,))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1,))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(6,))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1,))
unflatten(input=my_tensor)
# tensor([7, 1, -8, 3, -6, 0])

unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 6))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 6))
unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 6))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 6))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1))
unflatten(input=my_tensor)
# tensor([[7, 1, -8, 3, -6, 0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(2, 3))
unflatten = nn.Unflatten(dim=0, unflattened_size=(2, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(2, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(2, -1))
unflatten(input=my_tensor)
# tensor([[7, 1, -8], [3, -6, 0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(3, 2))
unflatten = nn.Unflatten(dim=0, unflattened_size=(3, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, 2))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, -1))
unflatten(input=my_tensor)
# tensor([[7, 1], [-8, 3], [-6, 0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(6, 1))
unflatten = nn.Unflatten(dim=0, unflattened_size=(6, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(6, 1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(6, -1))
unflatten(input=my_tensor)
# tensor([[7], [1], [-8], [3], [-6], [0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2, 3))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 2, 3))
unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1, 3))
unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 2, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 2, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 2, -1))
unflatten(input=my_tensor)
# tensor([[[7, 1, -8], [3, -6, 0]]])
etc

my_tensor = torch.tensor([[7, 1, -8], [3, -6, 0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(2,))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1,))
unflatten = nn.Unflatten(dim=1, unflattened_size=(3,))
unflatten = nn.Unflatten(dim=1, unflattened_size=(-1,))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(3,))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1,))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(2,))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1,))
unflatten(input=my_tensor)
# tensor([[7, 1, -8], [3, -6, 0]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 2))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 2))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1, 2))
unflatten(input=my_tensor)
# tensor([[[7, 1, -8], [3, -6, 0]]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(2, 1))
unflatten = nn.Unflatten(dim=0, unflattened_size=(2, -1))
unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 3))
unflatten = nn.Unflatten(dim=1, unflattened_size=(-1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 3))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(2, 1))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(2, -1))
unflatten(input=my_tensor)
# tensor([[[7, 1, -8]], [[3, -6, 0]]])

unflatten = nn.Unflatten(dim=1, unflattened_size=(3, 1))
unflatten = nn.Unflatten(dim=1, unflattened_size=(3, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, 1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, -1))
unflatten(input=my_tensor)
# tensor([[[7], [1], [-8]], [[3], [-6], [0]]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 1, 2))
unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 1, 2))
unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1, 2))
unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 1, -1))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 1, 2))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1, 1, 2))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, -1, 2))
unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 1, -1))
unflatten(input=my_tensor)
# tensor([[[[7, 1, -8], [3, -6, 0]]]])

unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 1, 3))
unflatten = nn.Unflatten(dim=1, unflattened_size=(-1, 1, 3))
unflatten = nn.Unflatten(dim=1, unflattened_size=(1, -1, 3))
unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 1, -1))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1, 3))
unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 1, -1))
unflatten(input=my_tensor)
# tensor([[[[7, 1, -8]]], [[[3, -6, 0]]]])

my_tensor = torch.tensor([[7., 1., -8.], [3., -6., 0.]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(2,))
unflatten(input=my_tensor)
# tensor([[7., 1., -8.], [3., -6., 0.]])

my_tensor = torch.tensor([[7.+0.j, 1.+0.j, -8.+0.j],
                          [3.+0.j, -6.+0.j, 0.+0.j]])
unflatten = nn.Unflatten(dim=0, unflattened_size=(2,))
unflatten(input=my_tensor)
# tensor([[7.+0.j, 1.+0.j, -8.+0.j],
#         [3.+0.j, -6.+0.j, 0.+0.j]])

my_tensor = torch.tensor([[True, False, True], [False, True, False]])

unflatten = nn.Unflatten(dim=0, unflattened_size=(2,))
unflatten(input=my_tensor)
# tensor([[True, False, True], [False, True, False]])

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