47 lines
1.6 KiB
Python
47 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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import paddle.fluid as fluid
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class ClsHead(object):
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def __init__(self, params):
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super(ClsHead, self).__init__()
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self.class_dim = params['class_dim']
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def __call__(self, inputs, labels=None, mode=None):
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pool = fluid.layers.pool2d(
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input=inputs, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=pool,
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size=self.class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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name="fc_0.w_0",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=fluid.param_attr.ParamAttr(name="fc_0.b_0"))
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softmax_out = fluid.layers.softmax(out, use_cudnn=False)
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out_label = fluid.layers.argmax(out, axis=1)
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predicts = {'predict': softmax_out, 'decoded_out': out_label}
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return predicts
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