Function Differences with torch.nn.AvgPool2d

torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)(input) -> Tensor

mindspore.nn.AvgPool2d(kernel_size=1, stride=1, pad_mode='valid', padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None, data_format='NCHW')(x) -> Tensor

# PyTorch
import torch
import torch.nn as nn

m = nn.AvgPool2d(kernel_size=1, stride=1)
input_x = torch.tensor([[[[1, 0, 1], [0, 1, 1]]]],dtype=torch.float32)
output = m(input_x)
print(output.numpy())
# [[[[1. 0. 1.]
#    [0. 1. 1.]]]]

# MindSpore
import mindspore
import mindspore.nn as nn
from mindspore import Tensor

pool = nn.AvgPool2d(kernel_size=1, stride=1)
x = Tensor([[[[1, 0, 1], [0, 1, 1]]]], dtype=mindspore.float32)
output = pool(x)
print(output)
# [[[[1. 0. 1.]
#    [0. 1. 1.]]]]

import torch
import mindspore.nn as nn
import mindspore.ops as ops

pool = nn.AvgPool2d(4, stride=1, ceil_mode=True, pad_mode='pad', padding=2)
x1 = ops.randn(6, 6, 8, 8)
output = pool(x1)
print(output.shape)
# (6, 6, 9, 9)

pool = torch.nn.AvgPool2d(4, stride=1, ceil_mode=True, padding=2)
x1 = torch.randn(6, 6, 8, 8)
output = pool(x1)
print(output.shape)
# torch.Size([6, 6, 9, 9])