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
)

For more information, see torch.nn.MaxPool1d.

mindspore.nn.MaxPool1d

class mindspore.nn.MaxPool1d(
    kernel_size=1,
    stride=1,
    pad_mode="valid"
)

For more information, see mindspore.nn.MaxPool1d.

Differences

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

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

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

1. pad_mode is “valid”：

   \[ L_{out} = \left\lfloor \frac{L_{in} - (\text{kernel_size} - 1)}{\text{stride}}\right\rfloor \]

2. pad_mode is “same”：

   \[ L_{out} = \left\lfloor \frac{L_{in}}{\text{stride}}\right\rfloor \]

Code Example

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

# In MindSpore, pad_mode="valid"
pool = nn.MaxPool1d(kernel_size=3, stride=2, pad_mode="valid")
input_x = ms.Tensor(np.random.randn(20, 16, 50).astype(np.float32))
output = pool(input_x)
print(output.shape)
# Out：
# (20, 16, 24)

# In MindSpore, pad_mode="same"
pool = nn.MaxPool1d(kernel_size=3, stride=2, pad_mode="same")
input_x = ms.Tensor(np.random.randn(20, 16, 50).astype(np.float32))
output = pool(input_x)
print(output.shape)
# Out：
# (20, 16, 25)


# In torch, padding=1
m = torch.nn.MaxPool1d(3, stride=2, padding=1)
input_x = torch.randn(20, 16, 50)
output = m(input_x)
print(output.shape)
# Out：
# torch.Size([20, 16, 25])