# 比较与torch.nn.AvgPool3d的差异

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## torch.nn.AvgPool3d

```text
torch.nn.AvgPool3d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)(input) -> Tensor
```

更多内容详见[torch.nn.AvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool3d.html)。

## mindspore.nn.AvgPool3d

```text
mindspore.nn.AvgPool3d(kernel_size=1, stride=1, pad_mode='valid', padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)(x) -> Tensor
```

更多内容详见[mindspore.nn.AvgPool3d](https://www.mindspore.cn/docs/zh-CN/r2.3.1/api_python/nn/mindspore.nn.AvgPool3d.html)。

## 差异对比

PyTorch：对由多个输入平面组成的输入信号应用二维平均池化。

MindSpore：MindSpore此API实现功能同时兼容TensorFlow和PyTorch，`pad_mode` 为 "valid" 或者 "same" 时，功能与TensorFlow一致，`pad_mode` 为 "pad" 时，功能与PyTorch一致，MindSpore相比PyTorch1.8.1额外支持了维度为4的输入，与PyTorch1.12一致。

| 分类 | 子类   | PyTorch               | MindSpore   | 差异                                                         |
| ---- | ------ | --------------------- | ----------- | ------------------------------------------------------------ |
| 参数 | 参数1  | kernel_size           | kernel_size | 功能一致，PyTorch无默认值                                    |
|      | 参数2  | stride                | stride      | 功能一致，参数默认值不同                                     |
|      | 参数3  | padding           | padding  | 功能一致 |
|      | 参数4  | ceil_mode         | ceil_mode   | 功能一致 |
|      | 参数5  | count_include_pad | count_include_pad   | 功能一致 |
|      | 参数6  | divisor_override | divisor_override    | 功能一致 |
|      | 参数7  | -                     | pad_mode    | MindSpore指定池化的填充方式，可选值为"same"，"valid" 或者 "pad"，PyTorch无此参数 |
| 输入 | 单输入 | input                 | x           | 功能一致，参数名不同                               |

### 代码示例

> 两API实现功能一致，用法相同。

```python
# PyTorch
import torch
import torch.nn as nn

m = nn.AvgPool3d(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.AvgPool3d(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.]]]]
```

### 代码示例2

> 使用pad模式保证功能一致。

```python
import torch
import mindspore.nn as nn
import mindspore.ops as ops

pool = nn.AvgPool3d(4, stride=1, ceil_mode=True, pad_mode='pad', padding=2)
x1 = ops.randn(6, 6, 8, 8, 8)
output = pool(x1)
print(output.shape)
# (6, 6, 9, 9, 9)

pool = torch.nn.AvgPool3d(4, stride=1, ceil_mode=True, padding=2)
x1 = torch.randn(6, 6, 8, 8, 8)
output = pool(x1)
print(output.shape)
# torch.Size([6, 6, 9, 9, 9])
```