# 缺失API处理策略
[](https://gitee.com/mindspore/docs/blob/r2.4.10/docs/mindspore/source_zh_cn/migration_guide/missing_api_processing_policy.md)
有以下方法来处理缺失API的情况。
## 1. 等价替换
在有些场景下API的功能是可以等价替换的，比如：
- Squeeze，Flatten，ExpandDims等没有实际的计算，只是改变Tensor shape的API均可以用Reshape代替；
- AdaptiveAvgPool，AdaptiveMaxPool在输出的shape是1时，与ReduceMean，ReduceMax在设置keep_dims=True时是等价的；
- MaxPool和MaxPoolWithArgmax在不使用indices的情况是等价的；
- Sort和在全排序场景下的TopK是等价的。
## 2. 使用已有API包装等价功能逻辑
对于一些缺失的API，可以基于MindSpore已有的API实现等价功能。下面举一个`sigmoid focal loss`的例子：
先来分析一下这个API的算法基础。
Focal Loss[1]是一种用来处理单阶段目标检测器训练过程中出现的正负样本、难易样本不平衡问题的方法。
常用的sigmoid focal loss的API接口是MMDetection的实现，PyTorch实现代码参考下方左侧。
参考API映射表，可以看到PyTorch代码中使用的API在MindSpore上都有对应实现，没有缺失。
根据PyTorch的实现，MindSpore的版本参考下方右侧。
<table class="colwidths-auto docutils align-default">
<tr>
<td style="text-align:center"> PyTorch </td> <td style="text-align:center"> MindSpore </td>
</tr>
<tr>
<td style="vertical-align:top"><pre>
```python
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
# none: 0, elementwise_mean:1, sum: 2
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
if weight is not None:
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
else:
# if reduction is mean, then average the loss by avg_factor
if reduction == 'mean':
loss = loss.sum() / avg_factor
# if reduction is 'none', then do nothing, otherwise raise an error
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
def py_sigmoid_focal_loss(pred, target, weight=None, gamma=2.0, alpha=0.25,
reduction='mean', avg_factor=None):
"""PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the
number of classes
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
pt = (1 - pred_sigmoid) * target +
pred_sigmoid * (1 - target)
focal_weight = (alpha * target +
(1 - alpha) *
(1 - target)) * pt.pow(gamma)
loss = F.binary_cross_entropy_with_logits(
pred, target, reduction='none') * focal_weight
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
```
</pre>
</td>
<td style="vertical-align:top"><pre>
```python
import mindspore as ms
from mindspore import nn, ops
class SigmoidFoaclLoss(nn.Cell):
def __init__(self, weight=None, gamma=2.0, alpha=0.25, reduction='mean', avg_factor=None):
super(SigmoidFoaclLoss, self).__init__()
self.sigmoid = ops.Sigmoid()
self.alpha = alpha
self.gamma = gamma
self.weight = ms.Tensor(weight) if weight is not None else weight
self.reduction = reduction
self.avg_factor = avg_factor
self.binary_cross_entropy_with_logits = nn.BCEWithLogitsLoss(reduction="none")
self.is_weight = (weight is not None)
def reduce_loss(self, loss):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
Return:
Tensor: Reduced loss tensor.
"""
if self.reduction == "mean":
return loss.mean()
elif self.reduction == "sum":
return loss.sum()
return loss
def weight_reduce_loss(self, loss):
# if avg_factor is not specified, just reduce the loss
if self.avg_factor is None:
loss = self.reduce_loss(loss)
else:
# if reduction is mean, then average the loss by avg_factor
if self.reduction == 'mean':
loss = loss.sum() / self.avg_factor
# if reduction is 'none', then do nothing, otherwise raise an error
elif self.reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
def construct(self, pred, target):
pred_sigmoid = self.sigmoid(pred)
target = ops.cast(target, pred.dtype)
pt = (1 - pred_sigmoid) * target +
pred_sigmoid * (1 - target)
focal_weight = (self.alpha * target +
(1 - self.alpha) *
(1 - target)) * ops.pow(pt, self.gamma)
loss = self.binary_cross_entropy_with_logits(pred, target) * focal_weight
if self.is_weight:
weight = self.weight
if self.weight.shape != loss.shape:
if self.weight.shape[0] == loss.shape[0]:
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = self.weight.view(-1, 1)
elif self.weight.size == loss.size:
# Sometimes, weight per anchor per class is also needed.
# e.g. in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
weight = self.weight.view(loss.shape[0], -1)
elif self.weight.ndim != loss.ndim:
raise ValueError(f"weight shape {self.weight.shape} is not match to loss shape {loss.shape}")
loss = loss * weight
loss = self.weight_reduce_loss(loss)
return loss
```
</pre>
</td>
</tr>
</table>
然后我们做个测试：
```python
import torch
import numpy as np
np.random.seed(1)
def test_compare(pred, target, weight, gamma=2.0, alpha=0.25, reduction='mean', avg_factor=None):
ms_s_focal_loss = SigmoidFoaclLoss(weight=weight, gamma=gamma, alpha=alpha,
reduction=reduction, avg_factor=avg_factor)
loss_ms = ms_s_focal_loss(ms.Tensor(pred), ms.Tensor(target))
loss_pt = py_sigmoid_focal_loss(torch.from_numpy(pred), torch.from_numpy(target), weight=torch.from_numpy(weight),
gamma=gamma, alpha=alpha, reduction=reduction, avg_factor=avg_factor)
print(np.max(np.abs(loss_ms.asnumpy() - loss_pt.numpy())))
pred = np.random.uniform(-1, 1, (3, 4)).astype(np.float32)
target = np.random.uniform(-1, 1, (3, 4)).astype(np.float32)
weight = np.random.uniform(0, 1, (3,)).astype(np.float32)
test_compare(pred, target, weight, gamma=2.0, alpha=0.25, reduction='mean', avg_factor=None)
test_compare(pred, target, weight, gamma=1.0, alpha=0.5, reduction='sum', avg_factor=None)
test_compare(pred, target, weight, gamma=2.0, alpha=0.25, reduction='mean', avg_factor=0.3)
test_compare(pred, target, weight, gamma=2.0, alpha=0.25, reduction='none', avg_factor=None)
```
可以看到最后的误差在1e-5以下，是合理的精度误差：
```text
6.891787e-08
1.4305115e-06
2.8014183e-06
3.799796e-07
```
## 3. 自定义算子
当有些情况无法使用已有的API进行包装，或者用Cell封装的方式性能非常差，这个时候就需要使用自定义算子，详情请参考Custom算子的[使用指南](https://www.mindspore.cn/docs/zh-CN/r2.4.10/model_train/custom_program/op_custom.html)。
除了可以自己迁移实现API，也可以利用`Custom`算子的`aot`开发方式调用PyTorch Aten的算子进行快速验证，请参考[基于自定义算子接口调用第三方算子库](https://www.mindspore.cn/docs/zh-CN/r2.4.10/migration_guide/use_third_party_op.html)。
**注意，PyTorch实现的算子迁移到GPU和CPU上比较方便，这里展示的也大多是GPU和CPU的，Ascend的算子由于需要使用TBE进行算子开发，门槛较高，推荐使用官方实现的算子进行包装。**
## 4. 社区求助
在MindSpore的[Gitee](https://gitee.com/mindspore/mindspore/issues)上提交issue建议开发缺失API。
[1] Lin, T. Y. , et al. "Focal Loss for Dense Object Detection." IEEE Transactions on Pattern Analysis & Machine Intelligence PP.99(2017):2999-3007.