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- # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import paddle
- from .comfunc import rerange_index
- class MSMLoss(paddle.nn.Layer):
- """
- MSMLoss Loss, based on triplet loss. USE P * K samples.
- the batch size is fixed. Batch_size = P * K; but the K may vary between batches.
- same label gather together
- supported_metrics = [
- 'euclidean',
- 'sqeuclidean',
- 'cityblock',
- ]
- only consider samples_each_class = 2
- """
- def __init__(self, batch_size=120, samples_each_class=2, margin=0.1):
- super(MSMLoss, self).__init__()
- self.margin = margin
- self.samples_each_class = samples_each_class
- self.batch_size = batch_size
- self.rerange_index = rerange_index(batch_size, samples_each_class)
- def forward(self, input, target=None):
- #normalization
- features = input["features"]
- features = self._nomalize(features)
- samples_each_class = self.samples_each_class
- rerange_index = paddle.to_tensor(self.rerange_index)
- #calc sm
- diffs = paddle.unsqueeze(
- features, axis=1) - paddle.unsqueeze(
- features, axis=0)
- similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
- #rerange
- tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
- tmp = paddle.gather(tmp, index=rerange_index)
- similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])
- #split
- ignore, pos, neg = paddle.split(
- similary_matrix,
- num_or_sections=[1, samples_each_class - 1, -1],
- axis=1)
- ignore.stop_gradient = True
- hard_pos = paddle.max(pos)
- hard_neg = paddle.min(neg)
- loss = hard_pos + self.margin - hard_neg
- loss = paddle.nn.ReLU()(loss)
- return {"msmloss": loss}
- def _nomalize(self, input):
- input_norm = paddle.sqrt(
- paddle.sum(paddle.square(input), axis=1, keepdim=True))
- return paddle.divide(input, input_norm)
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