Function Differences with torch.nn.MaxPool2d

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torch.nn.MaxPool2d

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

For more information, see torch.nn.MaxPool2d.

mindspore.nn.MaxPool2d

class mindspore.nn.MaxPool2d(
    kernel_size=1,
    stride=1,
    pad_mode="valid",
    data_format="NCHW"
)

For more information, see mindspore.nn.MaxPool2d.

Differences

PyTorch：The output shape can be adjusted through the padding parameter. If the shape of input is \( (N, C, H_{in}, W_{in}) \)，the shape of output is \( (N, C, H_{out}, W_{out}) \), where

\[ H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor \]
\[ W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor \]

MindSpore：There is no padding parameter, the pad mode is controlled by the pad_mode parameter only. If the shape of input is \( (N, C, H_{in}, W_{in}) \)，the shape of output is \( (N, C, H_{out}, W_{out}) \), where

  1. pad_mode is “valid”：

    \[ H_{out} = \left\lfloor\frac{H_{in} - ({kernel\_size[0]} - 1)}{\text{stride[0]}}\right\rfloor \]
    \[ W_{out} = \left\lfloor\frac{W_{in} - ({kernel\_size[1]} - 1)}{\text{stride[1]}}\right\rfloor \]

  2. pad_mode is “same”：

    \[ H_{out} = \left\lfloor\frac{H_{in}}{\text{stride[0]}}\right\rfloor \]
    \[ W_{out} = \left\lfloor\frac{W_{in}}{\text{stride[1]}}\right\rfloor \]

Code Example

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

# In MindSpore, pad_mode="valid"
pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="valid")
input_x = ms.Tensor(np.random.randn(20, 16, 50, 32).astype(np.float32))
output = pool(input_x)
print(output.shape)
# Out：
# (20, 16, 24, 15)

# In MindSpore, pad_mode="same"
pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
input_x = ms.Tensor(np.random.randn(20, 16, 50, 32).astype(np.float32))
output = pool(input_x)
print(output.shape)
# Out：
# (20, 16, 25, 16)


# In torch, padding=1
m = torch.nn.MaxPool2d(3, stride=2, padding=1)
input_x = torch.randn(20, 16, 50, 32)
output = m(input_x)
print(output.shape)
# Out：
# torch.Size([20, 16, 25, 16])