class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): super(FocalLoss, self).__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = 'none' # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) # p_t = torch.exp(-loss) # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py pred_prob = torch.sigmoid(pred) # prob from logits p_t = true * pred_prob + (1 - true) * (1 - pred_prob) alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = (1.0 - p_t) ** self.gamma loss *= alpha_factor * modulating_factor if self.reduction == 'mean': return loss.mean() elif self.reduction == 'sum': return loss.sum() else: # 'none' return loss
时间: 2023-06-20 16:05:18 浏览: 89
这个代码实现了一个 Focal Loss 损失函数,它是对二分类问题中的交叉熵损失函数的一种改进。它主要通过增加一个可调的超参数 $\gamma$,来调整难易样本的权重,从而解决类别不平衡问题。在代码中,它被实现为一个 PyTorch 模块,它的 forward 方法接受两个张量,即预测值和真实值,并返回计算出来的损失值。在实现中,它首先对传入的 loss_fcn 进行了封装,将其 reduction 属性设为 'none',然后在 forward 方法中使用封装后的 loss_fcn 计算出二分类交叉熵损失,接着使用预测值和真实值计算出概率值,根据概率值和超参数 $\gamma$ 计算出一个调制因子,最后将原始损失值乘以调制因子和一个可调的超参数 $\alpha$,从而得到最终的 Focal Loss 损失。
相关问题
def wraper_func(test_func): @wraps(test_func) # @wraps:避免被装饰函数自身的信息丢失 def inner_func(self): failures = str([fail[0] for fail in self._outcome.result.failures]) if test_func.__name__ in failures: if self._id() == self._testMethodName: test = False else: test = True else: test = True return test(self) if inner_func(self) == True: return True else: return False 这个代码有错吗,怎么更改
这段代码有一些问题。首先,在 `inner_func` 函数中,`test` 变量被赋值为一个布尔值,但在返回时却被当作了一个函数来调用。其次,在 `wraper_func` 函数中,`inner_func` 函数应该返回一个函数对象,而不是一个布尔值。
以下是更改后的代码:
```
from functools import wraps
def wraper_func(test_func):
@wraps(test_func)
def inner_func(self):
failures = str([fail[0] for fail in self._outcome.result.failures])
if test_func.__name__ in failures:
if self._id() == self._testMethodName:
test = lambda: False
else:
test = lambda: True
else:
test = lambda: True
return test
return inner_func(self)
# 测试
class Test:
def test_func(self):
return True
test = Test()
result = wraper_func(test.test_func)
print(result()) # 输出 True
```
在更改后的代码中,`inner_func` 函数返回一个匿名函数,该函数根据条件返回 `True` 或 `False`。在 `wraper_func` 函数中,我们调用 `inner_func` 并传入 `self` 参数,该参数在测试类的实例化对象中提供。然后,我们将返回的函数对象存储在 `result` 变量中,并在需要时调用它来执行测试。
java.lang.reflect.UndeclaredThrowableException: null
This exception typically occurs when a method in Java's reflection API is invoked on a proxy object, but the method could not be implemented by the proxy object. The proxy object is an object that acts as an interface to another object, allowing the user to interact with the underlying object without directly accessing it.
The UndeclaredThrowableException is a checked exception that wraps around the actual exception that occurred, such as an IllegalAccessException, InvocationTargetException or NoSuchMethodException. It is thrown when a method call on the proxy object results in an exception that is not declared in the method's throws clause.
To resolve this issue, the actual exception that caused the UndeclaredThrowableException needs to be investigated and addressed. This may involve modifying the proxy object's implementation or modifying the code that is calling the method on the proxy object.
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