clamp() can get the 0D or more D tensor of zero or more elements from the 0D or more D tensor of zero or more elements, bounded between min and max as shown below:
*Memos:
-
clamp()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,floatorbool). - The 2nd argument with
torchor the 1st argument ismin(Optional-Type:scalarofintorfloatortensorofint,floatorbool). - The 3rd argument with
torchor the 1st argument ismax(Optional-Type:scalarofintorfloatortensorofint,floatorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
- The combination of
minandmaxcannot be a scalar and tensor and vice versa and bothNone. - The combination of
minandmaxcannot be both tensors(bool) but a tensor(bool) andNoneand vice versa is possible. - If a
minis greater than amaxvalue, themaxvalue is set regardless of the value of an input tensor.
import torch my_tensor = torch.tensor([0., 1., 2., 3., 4., 5., 6., 7.]) torch.clamp(input=my_tensor, min=2., max=5.) my_tensor.clamp(min=2., max=5.) torch.clamp(input=my_tensor, min=torch.tensor(2.), max=torch.tensor(5.)) torch.clamp(input=my_tensor, min=torch.tensor([2., 2., 2., 2., 2., 2., 2., 2.]), max=torch.tensor([5., 5., 5., 5., 5., 5., 5., 5.])) torch.clamp(input=my_tensor, min=torch.tensor(2.), max=torch.tensor([5., 5., 5., 5., 5., 5., 5., 5.])) torch.clamp(input=my_tensor, min=torch.tensor([2., 2., 2., 2., 2., 2., 2., 2.]), max=torch.tensor(5.)) # tensor([2., 2., 2., 3., 4., 5., 5., 5.]) torch.clamp(input=my_tensor, min=2.) torch.clamp(input=my_tensor, min=torch.tensor(2.)) torch.clamp(input=my_tensor, min=torch.tensor([2., 2., 2., 2., 2., 2., 2., 2.])) # tensor([2., 2., 2., 3., 4., 5., 6., 7.]) torch.clamp(input=my_tensor, max=5.) torch.clamp(input=my_tensor, max=torch.tensor(5.)) torch.clamp(input=my_tensor, max=torch.tensor([5., 5., 5., 5., 5., 5., 5., 5.])) # tensor([0., 1., 2., 3., 4., 5., 5., 5.]) torch.clamp(input=my_tensor, min=5., max=2.) torch.clamp(input=my_tensor, min=torch.tensor(5.), max=torch.tensor(2.)) torch.clamp(input=my_tensor, min=torch.tensor([5., 5., 5., 5., 5., 5., 5., 5.]), max=torch.tensor([2., 2., 2., 2., 2., 2., 2., 2.])) # tensor([2., 2., 2., 2., 2., 2., 2., 2.]) torch.clamp(input=my_tensor, min=torch.tensor([2., 0., 2., 0., 2., 0., 2., 0.]), max=torch.tensor([0., 5., 0., 5., 0., 5., 0., 5.])) # tensor([0., 1., 0., 3., 0., 5., 0., 5.]) torch.clamp(input=my_tensor, min=torch.tensor([2., 0., 2., 0., 2., 0., 2., 0.])) # tensor([2., 1., 2., 3., 4., 5., 6., 7.]) torch.clamp(input=my_tensor, max=torch.tensor([0., 5., 0., 5., 0., 5., 0., 5.])) # tensor([0., 1., 0., 3., 0., 5., 0., 5.]) my_tensor = torch.tensor([[0., 1., 2., 3.], [4., 5., 6., 7.]]) torch.clamp(input=my_tensor, min=2., max=5.) torch.clamp(input=my_tensor, min=torch.tensor(2.), max=torch.tensor(5.)) torch.clamp(input=my_tensor, min=torch.tensor([2., 2., 2., 2.]), max=torch.tensor([5., 5., 5., 5.])) torch.clamp(input=my_tensor, min=torch.tensor(2.), max=torch.tensor([5., 5., 5., 5.])) torch.clamp(input=my_tensor, min=torch.tensor([2., 2., 2., 2.]), max=torch.tensor(5.)) # tensor([[2., 2., 2., 3.], # [4., 5., 5., 5.]]) my_tensor = torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]) torch.clamp(input=my_tensor, min=2, max=5) torch.clamp(input=my_tensor, min=torch.tensor([2, 2, 2, 2]), max=torch.tensor([5, 5, 5, 5])) # tensor([[2., 2., 2., 3.], # [4., 5., 5., 5.]]) my_tensor = torch.tensor([[True, False, True, False], [False, True, False, True]]) torch.clamp(input=my_tensor, min=torch.tensor([False, True, False, True])) # tensor([[True, True, True, True], # [False, True, False, True]]) torch.clamp(input=my_tensor, max=torch.tensor([False, True, False, True])) # tensor([[False, False, False, False], # [False, True, False, True]]) 
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