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70 lines
2.5 KiB
70 lines
2.5 KiB
2 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class AngularMargin(nn.Layer):
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def __init__(self, margin=0.0, scale=1.0):
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super(AngularMargin, self).__init__()
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self.margin = margin
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self.scale = scale
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def forward(self, outputs, targets):
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outputs = outputs - self.margin * targets
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return self.scale * outputs
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class AdditiveAngularMargin(AngularMargin):
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def __init__(self, margin=0.0, scale=1.0, easy_margin=False):
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super(AdditiveAngularMargin, self).__init__(margin, scale)
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self.easy_margin = easy_margin
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self.cos_m = math.cos(self.margin)
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self.sin_m = math.sin(self.margin)
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self.th = math.cos(math.pi - self.margin)
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self.mm = math.sin(math.pi - self.margin) * self.margin
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def forward(self, outputs, targets):
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cosine = outputs.astype('float32')
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sine = paddle.sqrt(1.0 - paddle.pow(cosine, 2))
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phi = cosine * self.cos_m - sine * self.sin_m # cos(theta + m)
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if self.easy_margin:
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phi = paddle.where(cosine > 0, phi, cosine)
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else:
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phi = paddle.where(cosine > self.th, phi, cosine - self.mm)
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outputs = (targets * phi) + ((1.0 - targets) * cosine)
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return self.scale * outputs
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class LogSoftmaxWrapper(nn.Layer):
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def __init__(self, loss_fn):
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super(LogSoftmaxWrapper, self).__init__()
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self.loss_fn = loss_fn
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self.criterion = paddle.nn.KLDivLoss(reduction="sum")
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def forward(self, outputs, targets, length=None):
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targets = F.one_hot(targets, outputs.shape[1])
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try:
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predictions = self.loss_fn(outputs, targets)
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except TypeError:
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predictions = self.loss_fn(outputs)
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predictions = F.log_softmax(predictions, axis=1)
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loss = self.criterion(predictions, targets) / targets.sum()
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2 years ago
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return loss
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