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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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Unflatten in PyTorch

#python #pytorch #unflatten #class

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*Memos:

Unflatten() can add zero or more dimensions to the 1D or more D tensor of zero or more elements, getting the 1D or more D tensor of zero or more elements as shown below:

*Memos:

  • The 1st argument for initialization is dim(Required-Type:int).
  • The 2nd argument for initialization is unflattened_size(Required-Type:tuple or list of int).
  • The 1st argument is input(Required-Type:tensor of int, float, complex or bool). *-1 infers and adjust the size.
  • The difference between Unflatten() and unflatten() is:
    • Unflatten() has unflattened_size argument which is identical to sizes argument of unflatten().
    • Basically, Unflatten() is used to define a model while unflatten() is not used to define a model. 
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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