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82 lines
2.8 KiB
82 lines
2.8 KiB
# Copyright (c) 2018 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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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import paddle |
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from .comfunc import rerange_index |
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class TriHardLoss(paddle.nn.Layer): |
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""" |
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TriHard Loss, based on triplet loss. USE P * K samples. |
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the batch size is fixed. Batch_size = P * K; but the K may vary between batches. |
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same label gather together |
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supported_metrics = [ |
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'euclidean', |
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'sqeuclidean', |
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'cityblock', |
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] |
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only consider samples_each_class = 2 |
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""" |
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def __init__(self, batch_size=120, samples_each_class=2, margin=0.1): |
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super(TriHardLoss, self).__init__() |
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self.margin = margin |
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self.samples_each_class = samples_each_class |
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self.batch_size = batch_size |
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self.rerange_index = rerange_index(batch_size, samples_each_class) |
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def forward(self, input, target=None): |
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features = input["features"] |
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assert (self.batch_size == features.shape[0]) |
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#normalization |
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features = self._nomalize(features) |
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samples_each_class = self.samples_each_class |
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rerange_index = paddle.to_tensor(self.rerange_index) |
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#calc sm |
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diffs = paddle.unsqueeze( |
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features, axis=1) - paddle.unsqueeze( |
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features, axis=0) |
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similary_matrix = paddle.sum(paddle.square(diffs), axis=-1) |
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#rerange |
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tmp = paddle.reshape(similary_matrix, shape=[-1, 1]) |
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tmp = paddle.gather(tmp, index=rerange_index) |
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similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size]) |
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#split |
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ignore, pos, neg = paddle.split( |
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similary_matrix, |
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num_or_sections=[1, samples_each_class - 1, -1], |
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axis=1) |
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ignore.stop_gradient = True |
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hard_pos = paddle.max(pos, axis=1) |
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hard_neg = paddle.min(neg, axis=1) |
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loss = hard_pos + self.margin - hard_neg |
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loss = paddle.nn.ReLU()(loss) |
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loss = paddle.mean(loss) |
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return {"trihardloss": loss} |
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def _nomalize(self, input): |
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input_norm = paddle.sqrt( |
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paddle.sum(paddle.square(input), axis=1, keepdim=True)) |
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return paddle.divide(input, input_norm)
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