比较与torch.nn.MaxPool2d的差异

torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)(input) -> Tensor

mindspore.nn.MaxPool2d(kernel_size=1, stride=1, pad_mode="valid", padding=0, dilation=1, return_indices=False, ceil_mode=False, data_format="NCHW")(x) -> Tensor

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

pool = torch.nn.MaxPool2d(kernel_size=3, stride=1)
x = tensor(np.random.randint(0, 10, [1, 2, 4, 4]), dtype=torch.float32)
output = pool(x)
result = output.shape
print(tuple(result))
# (1, 2, 2, 2)

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

pool = mindspore.nn.MaxPool2d(kernel_size=3, stride=1)
x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
output = pool(x)
result = output.shape
print(result)
# (1, 2, 2, 2)

# PyTorch
import torch
import numpy as np

np_x = np.random.randint(0, 10, [1, 2, 4, 4])
x = torch.tensor(np_x, dtype=torch.float32)
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=1, padding=1, dilation=1, return_indices=False)
output = max_pool(x)
result = output.shape
print(tuple(result))
# (1, 2, 5, 5)

# MindSpore
import mindspore as ms
from mindspore import Tensor
import mindspore.nn as nn
import numpy as np

np_x = np.random.randint(0, 10, [1, 2, 4, 4])
x = Tensor(np_x, ms.float32)
max_pool = nn.MaxPool2d(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)