Differences with torch.nn.functional.avg_pool1d
The following mapping relationships can be found in this file.
PyTorch APIs |
MindSpore APIs |
---|---|
torch.nn.functional.avg_pool1d |
mindspore.ops.avg_pool1d |
torch.nn.functional.avg_pool2d |
mindspore.ops.avg_pool2d |
torch.nn.functional.avg_pool3d |
mindspore.ops.avg_pool3d |
torch.nn.functional.avg_pool1d
torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)
For more information, see torch.nn.functional.avg_pool1d.
mindspore.ops.avg_pool1d
mindspore.ops.avg_pool1d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, count_include_pad=True)
For more information, see mindspore.ops.avg_pool1d.
Differences
PyTorch: Perform average pooling operations on time series data.
MindSpore: MindSpore API function is basically the same as pytorch, with different default values for some inputs.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
input |
input_x |
Different parameter names |
Parameter 2 |
kernel_size |
kernel_size |
The pytorch parameter has no default value and the MindSpore parameter has a default value of 1. |
|
Parameter 3 |
stride |
stride |
The default value of pytorch parameter is None, which is consistent with kernel_size by default, and the default value of MindSpore Parameter is 1. |
|
Parameter 4 |
padding |
padding |
||
Parameter 5 |
ceil_mode |
ceil_mode |
||
Parameter 6 |
count_include_pad |
count_include_pad |
Code Example 1
# PyTorch
import torch
import numpy as np
input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
output = torch.nn.functional.avg_pool1d(input, kernel_size=3, stride=2)
print(output)
# tensor([[[ 2., 4., 6.]]])
# MindSpore
import mindspore
from mindspore import Tensor, ops
input_x = Tensor([[[1, 2, 3, 4, 5, 6, 7]]], mindspore.float32)
output = ops.avg_pool1d(input_x, kernel_size=3, stride=2)
print(output)
# [[[ 2. 4. 6.]]]