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#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
#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.
# This code is refer from: https://github.com/KaiyangZhou/pytorch-center-loss
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class CenterLoss(nn.Layer):
"""
Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
"""
def __init__(self, num_classes=6625, feat_dim=96, center_file_path=None):
super().__init__()
self.num_classes = num_classes
self.feat_dim = feat_dim
self.centers = paddle.randn(
shape=[self.num_classes, self.feat_dim]).astype("float64")
if center_file_path is not None:
assert os.path.exists(
center_file_path
), f"center path({center_file_path}) must exist when it is not None."
with open(center_file_path, 'rb') as f:
char_dict = pickle.load(f)
for key in char_dict.keys():
self.centers[key] = paddle.to_tensor(char_dict[key])
def __call__(self, predicts, batch):
assert isinstance(predicts, (list, tuple))
features, predicts = predicts
feats_reshape = paddle.reshape(
features, [-1, features.shape[-1]]).astype("float64")
label = paddle.argmax(predicts, axis=2)
label = paddle.reshape(label, [label.shape[0] * label.shape[1]])
batch_size = feats_reshape.shape[0]
#calc l2 distance between feats and centers
square_feat = paddle.sum(paddle.square(feats_reshape),
axis=1,
keepdim=True)
square_feat = paddle.expand(square_feat, [batch_size, self.num_classes])
square_center = paddle.sum(paddle.square(self.centers),
axis=1,
keepdim=True)
square_center = paddle.expand(
square_center, [self.num_classes, batch_size]).astype("float64")
square_center = paddle.transpose(square_center, [1, 0])
distmat = paddle.add(square_feat, square_center)
feat_dot_center = paddle.matmul(feats_reshape,
paddle.transpose(self.centers, [1, 0]))
distmat = distmat - 2.0 * feat_dot_center
#generate the mask
classes = paddle.arange(self.num_classes).astype("int64")
label = paddle.expand(
paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
mask = paddle.equal(
paddle.expand(classes, [batch_size, self.num_classes]),
label).astype("float64")
dist = paddle.multiply(distmat, mask)
loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
return {'loss_center': loss}
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