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

Posted on • Edited on

tile in PyTorch

#python #pytorch #tile #repeat

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*Memos:

tile() can get the 1D or more D tensor of zero or more repeated elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • tile() 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 or more arguments with a tensor are dims(Required-Type:tuple of int, list of int or size()): *Memos:
    • If at least one dimension is 0, an empty tensor is returned.
    • dims= mustn't be used for the one or more dimensions without a tuple or list. 
import torch my_tensor = torch.tensor([7, 4, 5]) torch.tile(input=my_tensor, dims=(0,)) my_tensor.tile(dims=(0,)) my_tensor.tile(0,) torch.tile(input=my_tensor, dims=torch.tensor([]).size()) # tensor([], dtype=torch.int64)  torch.tile(input=my_tensor, dims=()) torch.tile(input=my_tensor, dims=(1,)) torch.tile(input=my_tensor, dims=torch.tensor(8).size()) torch.tile(input=my_tensor, dims=torch.tensor([8]).size()) # tensor([7, 4, 5])  torch.tile(input=my_tensor, dims=(2,)) torch.tile(input=my_tensor, dims=torch.tensor([8, 3]).size()) # tensor([7, 4, 5, 7, 4, 5])  torch.tile(input=my_tensor, dims=(3,)) torch.tile(input=my_tensor, dims=torch.tensor([8, 3, 6]).size()) # tensor([7, 4, 5, 7, 4, 5, 7, 4, 5]) etc. torch.tile(input=my_tensor, dims=(1, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8]]).size()) # tensor([[7, 4, 5]])  torch.tile(input=my_tensor, dims=(1, 2)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 3]]).size()) # tensor([[7, 4, 5, 7, 4, 5]])  torch.tile(input=my_tensor, dims=(1, 3)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2, 4]]).size()) # tensor([[3, 5, 1, 3, 5, 1, 3, 5, 1]]) etc. torch.tile(input=my_tensor, dims=(2, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8], [2]]).size()) # tensor([[3, 5, 1], # [3, 5, 1]])  torch.tile(input=my_tensor, dims=(2, 2)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2], [4, 0]]).size()) # tensor([[3, 5, 1, 3, 5, 1], # [3, 5, 1, 3, 5, 1]])  torch.tile(input=my_tensor, dims=(2, 3)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2, 4], [0, 7, 9]]).size()) # tensor([[3, 5, 1, 3, 5, 1, 3, 5, 1], # [3, 5, 1, 3, 5, 1, 3, 5, 1]]) etc. torch.tile(input=my_tensor, dims=(3, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8], [2], [4]]).size()) # tensor([[3, 5, 1], # [3, 5, 1], # [3, 5, 1]]) etc. torch.tile(input=my_tensor, dims=(1, 1, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[[8]]]).size()) # tensor([[[3, 5, 1]]]) etc. torch.tile(input=my_tensor, dims=(3, 2, 1)) # tensor([[[3, 5, 1], [3, 5, 1]], # [[3, 5, 1], [3, 5, 1]], # [[3, 5, 1], [3, 5, 1]]])  torch.tile(input=my_tensor, dims=(1, 0, 1)) # tensor([], size=(1, 0, 3), dtype=torch.int64)  my_tensor = torch.tensor([3., 5., 1.]) torch.tile(input=my_tensor, dims=(2,)) # tensor([3., 5., 1., 3., 5., 1.])  my_tensor = torch.tensor([3.+0.j, 5.+0.j, 1.+0.j]) torch.tile(input=my_tensor, dims=(2,)) # tensor([3.+0.j, 5.+0.j, 1.+0.j, 3.+0.j, 5.+0.j, 1.+0.j])  my_tensor = torch.tensor([True, False, True]) torch.tile(input=my_tensor, dims=(2,)) # tensor([True, False, True, True, False, True])  my_tensor = torch.tensor([[3, 5, 1], [6, 0, 5]]) torch.tile(input=my_tensor, dims=()) torch.tile(input=my_tensor, dims=(1,)) torch.tile(input=my_tensor, dims=torch.tensor(8).size()) torch.tile(input=my_tensor, dims=torch.tensor([8]).size()) # tensor([[3, 5, 1], # [6, 0, 5]])  torch.tile(input=my_tensor, dims=(2,)) torch.tile(input=my_tensor, dims=torch.tensor([8, 2]).size()) # tensor([[3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5]])  torch.tile(input=my_tensor, dims=(3,)) torch.tile(input=my_tensor, dims=torch.tensor([8, 2, 4]).size()) # tensor([[3, 5, 1, 3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5, 6, 0, 5]]) etc. torch.tile(input=my_tensor, dims=(1, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8]]).size()) # tensor([[3, 5, 1], # [6, 0, 5]])  torch.tile(input=my_tensor, dims=(1, 2)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2]]).size()) # tensor([[3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5]])  torch.tile(input=my_tensor, dims=(1, 3)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2, 4]]).size()) # tensor([[3, 5, 1, 3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5, 6, 0, 5]]) etc. torch.tile(input=my_tensor, dims=(2, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8], [2]]).size()) # tensor([[3, 5, 1], # [6, 0, 5], # [3, 5, 1], # [6, 0, 5]])  torch.tile(input=my_tensor, dims=(2, 2)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2], [4, 0]]).size()) # tensor([[3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5], # [3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5]])  torch.tile(input=my_tensor, dims=(2, 3)) torch.tile(input=my_tensor, dims=torch.tensor([[8, 2, 4], [0, 7, 9]]).size()) # tensor([[3, 5, 1, 3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5, 6, 0, 5], # [3, 5, 1, 3, 5, 1, 3, 5, 1], # [6, 0, 5, 6, 0, 5, 6, 0, 5]]) etc. torch.tile(input=my_tensor, dims=(3, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[8], [2], [4]]).size()) # tensor([[3, 5, 1], # [6, 0, 5], # [3, 5, 1], # [6, 0, 5], # [3, 5, 1], # [6, 0, 5]]) etc. torch.tile(input=my_tensor, dims=(1, 1, 1)) torch.tile(input=my_tensor, dims=torch.tensor([[[8]]]).size()) # tensor([[[3, 5, 1], # [6, 0, 5]]]) etc.
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