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53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
# copyright (c) 2020 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 math
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import paddle
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from paddle import nn, ParamAttr
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import paddle.nn.functional as F
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class ClsHead(nn.Layer):
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"""
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Class orientation
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Args:
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params(dict): super parameters for build Class network
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"""
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def __init__(self, in_channels, class_dim, **kwargs):
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super(ClsHead, self).__init__()
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self.pool = nn.AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(in_channels * 1.0)
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self.fc = nn.Linear(
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in_channels,
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class_dim,
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weight_attr=ParamAttr(
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name="fc_0.w_0",
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initializer=nn.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_0.b_0"), )
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def forward(self, x, targets=None):
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x = self.pool(x)
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x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
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x = self.fc(x)
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if not self.training:
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x = F.softmax(x, axis=1)
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return x
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