# Function Differences with torch.nn.functional.pad

torch.nn.functional.pad

torch.nn.functional.pad(
    input,
    pad,
    mode='constant',
    value=0
)

For more information, see torch.nn.functional.pad.

mindspore.ops.pad

mindspore.ops.pad(
    input_x,
    padding,
    mode='constant',
    value=None
)

For more information, see mindspore.ops.pad.

Differences

PyTorch：The pad parameter is a tuple with m values, m/2 is less than or equal to the dimension of the input data, and m is even. Negative dimensions are supported. Assuming pad=(k1, k2, …, kl, km), the shape of the input x is (d1, d2…, dg), then the two sides of the dg dimension are filled with the values of lengths k1 and k2 respectively. Similarly, the two sides of the d1 dimension are filled with the values of length kl and km respectively.

MindSpore：The function and usage of the padding parameter of MindSpore is completely consistent with that of the pad parameter of PyTorch. In addition, MindSpore supports the input form of Tensor type in addition to PyTorch.

Classification	Subclass	PyTorch	MindSpore	difference
parameter	parameter1	input	input_x	Same functions, different parameter names
	parameter2	pad	padding	Same functions, different parameter names
	parameter3	mode	mode	The functions are consistent. MindSpore is temporarily missing the circular mode
	parameter4	value	value	The functions are consistent. In constant mode, the default value is 0 when MindSpore enters the parameter None

Code Example

# In MindSpore.
import numpy as np
import torch
import mindspore.ops as ops
import mindspore as ms

x = ms.Tensor(np.ones([1, 2, 2, 3]).astype(np.float32))
padding = (1, 1, 2, 2)
output = ops.pad(x, padding)
print(output.shape)
# Out:
# (1, 2, 6, 5)

# In Pytorch.
x = torch.empty(1, 2, 2, 3)
pad = (1, 1, 2, 2)
output = torch.nn.functional.pad(x, pad)
print(output.size())
# Out:
# torch.Size([1, 2, 6, 5])