Differences with torch.nn.MaxPool3d

torch.nn.MaxPool3d

torch.nn.MaxPool3d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)(input) -> Tensor

For more information, see torch.nn.MaxPool3d.

mindspore.nn.MaxPool3d

mindspore.nn.MaxPool3d(kernel_size=1, stride=1, pad_mode="valid", padding=0, dilation=1, return_indices=False, ceil_mode=False)(x) -> Tensor

For more information, see mindspore.nn.MaxPool3d.

Differences

PyTorch: Perform three-dimensional maximum pooling operations on the input multidimensional data.

MindSpore: This API implementation function of MindSpore is compatible with TensorFlow and PyTorch, When pad_mode is “valid” or “same”, the function is consistent with TensorFlow, and when pad_mode is “pad”, the function is consistent with PyTorch, MindSpore additionally supports 2D input, which is consistent with PyTorch 1.12.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	kernel_size	kernel_size	Consistent function, no default values for PyTorch
	Parameter 2	stride	stride	Consistent function, different default value
	Parameter 3	padding	padding	Consistent
	Parameter 4	dilation	dilation	Consistent
	Parameter 5	return_indices	return_indices	Consistent
	Parameter 6	ceil_mode	ceil_mode	Consistent
	Parameter 7	input	x	Consistent function, different parameter names
	Parameter 8	-	pad_mode	Control the padding mode, and PyTorch does not have this parameter

Code Example

Use pad mode to ensure functional consistency.

import mindspore as ms
from mindspore import Tensor
import mindspore.nn as nn
import torch
import numpy as np

np_x = np.random.randint(0, 10, [1, 2, 4, 4, 5])

x = Tensor(np_x, ms.float32)
max_pool = nn.MaxPool3d(kernel_size=2, stride=1, pad_mode='pad', padding=1, dilation=1, return_indices=False)
output = max_pool(x)
result = output.shape
print(result)
# (1, 2, 5, 5, 6)
x = torch.tensor(np_x, dtype=torch.float32)
max_pool = torch.nn.MaxPool3d(kernel_size=2, stride=1, padding=1, dilation=1, return_indices=False)
output = max_pool(x)
result = output.shape
print(result)
# torch.Size([1, 2, 5, 5, 6])