90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
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#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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 os
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import pickle
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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 CenterLoss(nn.Layer):
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"""
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Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
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"""
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def __init__(self,
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num_classes=6625,
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feat_dim=96,
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init_center=False,
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center_file_path=None):
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super().__init__()
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self.num_classes = num_classes
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self.feat_dim = feat_dim
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self.centers = paddle.randn(
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shape=[self.num_classes, self.feat_dim]).astype(
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"float64") #random center
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if init_center:
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assert os.path.exists(
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center_file_path
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), f"center path({center_file_path}) must exist when init_center is set as True."
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with open(center_file_path, 'rb') as f:
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char_dict = pickle.load(f)
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for key in char_dict.keys():
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self.centers[key] = paddle.to_tensor(char_dict[key])
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def __call__(self, predicts, batch):
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assert isinstance(predicts, (list, tuple))
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features, predicts = predicts
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feats_reshape = paddle.reshape(
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features, [-1, features.shape[-1]]).astype("float64")
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label = paddle.argmax(predicts, axis=2)
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label = paddle.reshape(label, [label.shape[0] * label.shape[1]])
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batch_size = feats_reshape.shape[0]
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#calc feat * feat
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dist1 = paddle.sum(paddle.square(feats_reshape), axis=1, keepdim=True)
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dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
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#dist2 of centers
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dist2 = paddle.sum(paddle.square(self.centers), axis=1,
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keepdim=True) #num_classes
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dist2 = paddle.expand(dist2,
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[self.num_classes, batch_size]).astype("float64")
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dist2 = paddle.transpose(dist2, [1, 0])
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#first x * x + y * y
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distmat = paddle.add(dist1, dist2)
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tmp = paddle.matmul(feats_reshape,
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paddle.transpose(self.centers, [1, 0]))
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distmat = distmat - 2.0 * tmp
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#generate the mask
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classes = paddle.arange(self.num_classes).astype("int64")
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label = paddle.expand(
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paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
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mask = paddle.equal(
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paddle.expand(classes, [batch_size, self.num_classes]),
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label).astype("float64") #get mask
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dist = paddle.multiply(distmat, mask)
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loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
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return {'loss_center': loss}
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