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79 lines
2.7 KiB
79 lines
2.7 KiB
3 years ago
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# 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 MSMLoss(paddle.nn.Layer):
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"""
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MSMLoss 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(MSMLoss, 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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#normalization
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features = input["features"]
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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)
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hard_neg = paddle.min(neg)
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loss = hard_pos + self.margin - hard_neg
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loss = paddle.nn.ReLU()(loss)
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return {"msmloss": 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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