Function Differences with torch.nn.MaxPool1d

torch.nn.MaxPool1d

torch.nn.MaxPool1d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)(input) -> Tensor

For more information, see torch.nn.MaxPool1d.

mindspore.nn.MaxPool1d

mindspore.nn.MaxPool1d(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.MaxPool1d.

Differences

PyTorch: Perform maximum pooling operations on temporal 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 filling mode, and PyTorch does not have this parameter

Code Example 1

Construct a pooling layer with a convolution kernel size of 1x3 and a step size of 1. The padding defaults to 0 and no element filling is performed. The default value of dilation is 1, and the elements in the window are contiguous. The default value of pooling padding mode is valid, which returns the output from a valid calculation without padding, and any extra pixels that do not satisfy the calculation are discarded. With the same parameter settings, the two APIs achieve the same function and perform the maximum pooling operation on the data.

# PyTorch
import torch
from torch import tensor
import numpy as np

max_pool = torch.nn.MaxPool1d(kernel_size=3, stride=1)
x = tensor(np.random.randint(0, 10, [1, 2, 4]), dtype=torch.float32)
output = max_pool(x)
result = output.shape
print(tuple(result))
# (1, 2, 2)

# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np

max_pool = mindspore.nn.MaxPool1d(kernel_size=3, stride=1)
x = Tensor(np.random.randint(0, 10, [1, 2, 4]), mindspore.float32)
output = max_pool(x)
result = output.shape
print(result)
# (1, 2, 2)

Code Example 2

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])

x = Tensor(np_x, ms.float32)
max_pool = nn.MaxPool1d(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)
x = torch.tensor(np_x, dtype=torch.float32)
max_pool = torch.nn.MaxPool1d(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])