42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# Copyright (c) 2022 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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import os
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from .resnet import ResNet50, ResNet101
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from ppdet.core.workspace import register
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__all__ = ['ResNetEmbedding']
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@register
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class ResNetEmbedding(nn.Layer):
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in_planes = 2048
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def __init__(self, model_name='ResNet50', last_stride=1):
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super(ResNetEmbedding, self).__init__()
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assert model_name in ['ResNet50', 'ResNet101'], "Unsupported ReID arch: {}".format(model_name)
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self.base = eval(model_name)(last_conv_stride=last_stride)
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self.gap = nn.AdaptiveAvgPool2D(output_size=1)
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self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
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self.bn = nn.BatchNorm1D(self.in_planes, bias_attr=False)
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def forward(self, x):
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base_out = self.base(x)
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global_feat = self.gap(base_out)
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global_feat = self.flatten(global_feat)
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global_feat = self.bn(global_feat)
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return global_feat
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