# 比较与torch.nn.BatchNorm3d的差异

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## torch.nn.BatchNorm3d

```text
class torch.nn.BatchNorm3d(
    num_features,
    eps=1e-05,
    momentum=0.1,
    affine=True,
    track_running_stats=True
)(input) -> Tensor
```

更多内容详见[torch.nn.BatchNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm3d.html)。

## mindspore.nn.BatchNorm3d

```text
class mindspore.nn.BatchNorm3d(
    num_features,
    eps=1e-5,
    momentum=0.9,
    affine=True,
    gamma_init='ones',
    beta_init='zeros',
    moving_mean_init='zeros',
    moving_var_init='ones',
    use_batch_statistics=None
)(x) -> Tensor
```

更多内容详见[mindspore.nn.BatchNorm3d](https://www.mindspore.cn/docs/zh-CN/r2.1/api_python/nn/mindspore.nn.BatchNorm3d.html)。

## 差异对比

PyTorch：在五维输入(具有额外mini-batch和channel通道的三维输入)上应用批归一化处理，以避免内部协变量偏移。

MindSpore：此API实现功能与PyTorch基本一致，典型区别有两点。MindSpore中momentum参数默认值为0.9，与PyTorch的momentum转换关系为1-momentum，默认值行为与PyTorch相同；训练以及推理时的参数更新策略和PyTorch有所不同，详细区别请参考[与PyTorch典型区别-BatchNorm](https://www.mindspore.cn/docs/zh-CN/r2.1/migration_guide/typical_api_comparision.html#nn.BatchNorm2d)。

| 分类 | 子类 |PyTorch | MindSpore | 差异 |
| --- | --- | --- | --- |---|
| 参数 | 参数1 | num_features | num_features | - |
| | 参数2 | eps | eps | - |
| | 参数3 | momentum | momentum | 功能一致，但PyTorch中的默认值是0.1，MindSpore中是0.9，与PyTorch的momentum转换关系为1-momentum，默认值行为与PyTorch相同 |
| | 参数4 | affine | affine |- |
| | 参数5 | track_running_stats | use_batch_statistics | 功能一致，不同值对应的默认方式不同，详细区别请参考[与PyTorch典型区别-nn.BatchNorm](https://www.mindspore.cn/docs/zh-CN/r2.1/migration_guide/typical_api_comparision.html#nn.BatchNorm2d)  |
| | 参数6 | - | gamma_init |γ 参数的初始化方法，默认值："ones" |
| | 参数7 | - | beta_init |β 参数的初始化方法，默认值："zeros" |
| | 参数8 | - | moving_mean_init |动态平均值的初始化方法，默认值："zeros" |
| | 参数9 | - | moving_var_init |动态方差的初始化方法，默认值："ones" |
| 输入 | 单输入 | input | x | 接口输入，功能一致，仅参数名不同 |

### 代码示例

> PyTorch中，1-momentum后的值等于MindSpore的momentum，都使用mini-batch数据和学习参数进行训练。

```python
# PyTorch
from torch import nn, tensor
import numpy as np

m = nn.BatchNorm3d(num_features=2, momentum=0.1)
input_x = tensor(np.array([[[[[0.1, 0.2], [0.3, 0.4]]],
                             [[[0.9, 1], [1.1, 1.2]]]]]).astype(np.float32))
output = m(input_x)
print(output.detach().numpy())
# [[[[[-1.3411044  -0.44703478]
#     [ 0.4470349   1.3411044 ]]]
#
#
#   [[[-1.3411034  -0.44703388]
#     [ 0.44703573  1.3411053 ]]]]]

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

m = nn.BatchNorm3d(num_features=2, momentum=0.9)
m.set_train()
# BatchNorm3d<
#      (bn2d): BatchNorm2d<num_features=2, eps=1e-05, momentum=0.9, gamma=Parameter (name=bn2d.gamma, shape=(2,), dtype=Float32, requires_grad=True), beta=Parameter (name=bn2d.beta, shape=(2,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=bn2d.moving_mean, shape=(2,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=bn2d.moving_variance, shape=(2,), dtype=Float32, requires_grad=False)>
#      >
input_x = Tensor(np.array([[[[[0.1, 0.2], [0.3, 0.4]]],
                             [[[0.9, 1], [1.1, 1.2]]]]]).astype(np.float32))
output = m(input_x)
print(output)
# [[[[[-1.3411044  -0.44703478]
#     [ 0.4470349   1.3411044 ]]]
#
#
#   [[[-1.3411039  -0.44703427]
#     [ 0.44703534  1.341105  ]]]]]
```