In this article, we will discuss How to Pad the Input Tensor Boundaries With a Constant Value in PyTorch. We can pad the input tensor boundaries with a constant value by using torch.nn.ConstantPad2D() method.
torch.nn.ConstantPad2D() methodÂ
We can pad the boundaries of 3D and 4D tensors and the shape of the input tensor is [C, H, W] and [N, C, H, W] respectively, where N represents the mini-batch size, C represents the number of channels, and  H, W represents the height and width respectively. This method accepts the size of padding and a constant value as input. The boundaries may be the same or different from all sides (left, right, top, bottom). we can increase the height and width of a padded tensor by using top+bottom and left+right respectively. The below syntax is used to pad the input tensor boundaries with a Constant Value.
Syntax: torch.nn.ConstantPad2d(pad, value)
Parameter:
- pad (int, tuple): This is size of padding. The size of padding is an integer or a tuple.
- value: This is constant value.Â
Return: This method returns a new tensor with boundaries.
Example 1:
In this example, we will see how to add the same padding sizes to all sides.
Python3
# Import required libraryimport torchimport torch.nn as nnÂ
# define a tensortens = torch.tensor([[[21, 22], [23, 24]]])print("\n Input Tensor: \n", tens)Â
# give padding size same for all sidespad = nn.ConstantPad2d(2, 9)output = pad(tens)Â
# display resultprint("\n After Pad Input Tensor: \n", output) |
Output:
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Example 2:
In this example, we will see how to add unique padding sizes to all sides (left, right, top, bottom).
Python3
# Import required libraryimport torchimport torch.nn as nnÂ
# define a tensortens = torch.tensor([[[11, 12], [13, 14]]])print("\n Input Tensor: \n", tens)Â
# add unique padding sizes to all sides# (left, right, top, bottom)pad = nn.ConstantPad2d((1, 2, 3, 4), 8)output = pad(tens)Â
# display resultprint("\n After Pad Input Tensor:\n", output) |
Output:
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Example 3:
In this example, we will see how to pad the boundaries of a batch of tensors.
Python3
# Import required libraryimport torchimport torch.nn as nnÂ
# define a batch of tensortens = torch.tensor([[[11, 12], [13, 14]],                     [[21, 22], [23, 24]]])Â
print("\n Input Tensor: \n", tens)Â
# add unique padding sizes to all sides# (left, right, top, bottom)pad = nn.ConstantPad2d(1, 8)output = pad(tens)Â
# display resultprint("\n After Pad Input Tensor:\n", output) |
Output:
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