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

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flatten and ravel in PyTorch

#python #pytorch #flatten #ravel

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*Memos:

flatten() can remove zero or more dimensions by selecting dimensions from the 0D 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:

  • flatten() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
  • The 2nd argument with torch or the 1st argument with a tensor is start_dim(Optional-Default:0-Type:int).
  • The 3rd argument with torch or the 2nd argument with a tensor is end_dim(Optional-Default:-1-Type:int).
  • flatten() can change a 0D tensor to a 1D tensor.
  • flatten() does nothing for a 1D tensor.
  • The difference between Flatten() and flatten() is:
    • The default value of start_dim for Flatten() is 1 while the default value of start_dim for flatten() is 0.
    • Basically, Flatten() is used to define a model while flatten() is not used to define a model. 
import torch

my_tensor = torch.tensor(7)

torch.flatten(input=my_tensor)
my_tensor.flatten()
torch.flatten(input=my_tensor, start_dim=0, end_dim=0)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=0)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=-1)
# tensor([7])

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

torch.flatten(input=my_tensor)
torch.flatten(input=my_tensor, start_dim=0, end_dim=0)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=0)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=-1)
# tensor([7, 1, -8, 3, -6, 0])

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

torch.flatten(input=my_tensor)
torch.flatten(input=my_tensor, start_dim=0, end_dim=1)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=-1)
# tensor([7, 1, -8, 3, -6, 0])

torch.flatten(input=my_tensor, start_dim=0, end_dim=0)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-2)
torch.flatten(input=my_tensor, start_dim=1, end_dim=1)
torch.flatten(input=my_tensor, start_dim=1, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=1)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=0)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=-2)
# tensor([[7, 1, -8], [3, -6, 0]])

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

torch.flatten(input=my_tensor)
torch.flatten(input=my_tensor, start_dim=0, end_dim=2)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=2)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=-1)
# tensor([7, 1, -8, 3, -6, 0])

torch.flatten(input=my_tensor, start_dim=0, end_dim=0)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-3)
torch.flatten(input=my_tensor, start_dim=1, end_dim=1)
torch.flatten(input=my_tensor, start_dim=1, end_dim=-2)
torch.flatten(input=my_tensor, start_dim=2, end_dim=2)
torch.flatten(input=my_tensor, start_dim=2, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=2)
torch.flatten(input=my_tensor, start_dim=-1, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=-2)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=0)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=-3)
# tensor([[[7], [1], [-8]], [[3], [-6], [0]]])

torch.flatten(input=my_tensor, start_dim=0, end_dim=1)
torch.flatten(input=my_tensor, start_dim=0, end_dim=-2)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=1)
torch.flatten(input=my_tensor, start_dim=-3, end_dim=-2)
# tensor([[7], [1], [-8], [3], [-6], [0]])

torch.flatten(input=my_tensor, start_dim=1, end_dim=2)
torch.flatten(input=my_tensor, start_dim=1, end_dim=-1)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=2)
torch.flatten(input=my_tensor, start_dim=-2, end_dim=-1)
# tensor([[7, 1, -8], [3, -6, 0]])

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

torch.flatten(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]]])
torch.flatten(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]]])
torch.flatten(input=my_tensor)
# tensor([True, False, True, False, True, False])
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ravel() can remove zero or more dimensions as much as possible from the 0D or more D tensor of zero or more elements, getting the 1D tensor of zero or more elements as shown below:

*Memos:

  • ravel() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
  • ravel() can change a 0D tensor to a 1D tensor.
  • ravel() does nothing for a 1D tensor. 
import torch

my_tensor = torch.tensor(7)

torch.ravel(input=my_tensor)
my_tensor.ravel()
# tensor([7])

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

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

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

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

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

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

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

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