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在 PyTorch 中展开
ID:20444 / 打印
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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]])
