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@ -161,7 +161,7 @@ class NCELoss(nn.Layer):
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"""Post processing the score of post model(output of nn) of batchsize data
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"""
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scores = self.get_scores(idx, scores)
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scale = paddle.to_tensor([self.Z_offset], dtype='float32')
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scale = paddle.to_tensor([self.Z_offset], dtype='float64')
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scores = paddle.add(scores, -scale)
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prob = paddle.exp(scores)
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if sep_target:
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@ -225,3 +225,65 @@ class NCELoss(nn.Layer):
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loss = -(model_loss + noise_loss)
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return loss
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class FocalLoss(nn.Layer):
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"""This criterion is a implemenation of Focal Loss, which is proposed in
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Focal Loss for Dense Object Detection.
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Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
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The losses are averaged across observations for each minibatch.
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Args:
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alpha(1D Tensor, Variable) : the scalar factor for this criterion
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gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5),
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putting more focus on hard, misclassified examples
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size_average(bool): By default, the losses are averaged over observations for each minibatch.
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However, if the field size_average is set to False, the losses are
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instead summed for each minibatch.
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"""
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def __init__(self, alpha=1, gamma=0, size_average=True, ignore_index=-100):
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super(FocalLoss, self).__init__()
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self.alpha = alpha
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self.gamma = gamma
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self.size_average = size_average
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self.ce = nn.CrossEntropyLoss(
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ignore_index=ignore_index, reduction="none")
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def forward(self, outputs, targets):
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"""Forword inference.
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Args:
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outputs: input tensor
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target: target label tensor
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"""
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ce_loss = self.ce(outputs, targets)
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pt = paddle.exp(-ce_loss)
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focal_loss = self.alpha * (1 - pt)**self.gamma * ce_loss
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if self.size_average:
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return focal_loss.mean()
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else:
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return focal_loss.sum()
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if __name__ == "__main__":
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import numpy as np
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from paddlespeech.vector.utils.vector_utils import Q_from_tokens
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paddle.set_device("cpu")
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input_data = paddle.uniform([5, 100], dtype="float64")
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label_data = np.random.randint(0, 100, size=(5)).astype(np.int64)
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input = paddle.to_tensor(input_data)
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label = paddle.to_tensor(label_data)
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loss1 = FocalLoss()
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loss = loss1.forward(input, label)
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print("loss: %.5f" % (loss))
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Q = Q_from_tokens(100)
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loss2 = NCELoss(Q)
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loss = loss2.forward(input, label)
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print("loss: %.5f" % (loss))
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