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143 lines
4.0 KiB
143 lines
4.0 KiB
# Copyright (c) 2017 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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from paddle.trainer_config_helpers import *
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settings(batch_size=16)
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channels = get_config_arg("channels", int, 2)
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def two_conv(input, group_name):
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out1 = img_conv_layer(input=input,
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name=group_name+'_conv1_',
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filter_size=1,
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num_filters=channels,
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padding=0,
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shared_biases=True,
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act=ReluActivation())
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out2 = img_conv_layer(input=input,
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name=group_name+'_conv2_',
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filter_size=3,
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num_filters=channels,
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padding=1,
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shared_biases=True,
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act=ReluActivation())
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return out1, out2
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def two_conv_bn(input, group_name):
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out1, out2 = two_conv(input, group_name)
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out1 = batch_norm_layer(input=out1,
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name=group_name+'_bn1_',
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use_global_stats=False,
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act=ReluActivation())
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out2 = batch_norm_layer(input=out2,
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name=group_name+'_bn2_',
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use_global_stats=False,
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act=ReluActivation())
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return out1, out2
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def two_conv_pool(input, group_name):
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out1, out2 = two_conv(input, group_name)
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out1 = img_pool_layer(input=out1,
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name=group_name+'_pool1_',
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pool_size=3,
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stride=2,
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padding=0,
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pool_type=MaxPooling())
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out2 = img_pool_layer(input=out2,
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name=group_name+'_pool2_',
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pool_size=5,
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stride=2,
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padding=1,
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pool_type=MaxPooling())
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return out1, out2
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def two_fc(input, group_name):
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out1 = fc_layer(input=input,
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name=group_name+'_fc1_',
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size=channels,
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bias_attr=False,
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act=LinearActivation())
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out2 = fc_layer(input=input,
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name=group_name+'_fc2_',
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size=channels,
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bias_attr=False,
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act=LinearActivation())
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return out1, out2
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data = data_layer(name ="input", size=channels*16*16)
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tmp = img_conv_layer(input=data,
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num_channels=channels,
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filter_size=3,
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num_filters=channels,
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padding=1,
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shared_biases=True,
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act=ReluActivation())
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a1, a2 = two_conv(tmp, 'conv_branch')
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tmp = addto_layer(input=[a1, a2],
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act=ReluActivation(),
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bias_attr=False)
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tmp = img_pool_layer(input=tmp,
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pool_size=3,
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stride=2,
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padding=1,
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pool_type=AvgPooling())
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b1, b2 = two_conv_pool(tmp, 'pool_branch')
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tmp = concat_layer(input=[b1, b2])
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tmp = img_pool_layer(input=tmp,
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num_channels=channels*2,
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pool_size=3,
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stride=2,
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padding=1,
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pool_type=MaxPooling())
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tmp = img_conv_layer(input=tmp,
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filter_size=3,
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num_filters=channels,
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padding=1,
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stride=2,
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shared_biases=True,
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act=LinearActivation(),
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bias_attr=False)
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tmp = batch_norm_layer(input=tmp,
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use_global_stats=False,
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act=ReluActivation())
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c1, c2 = two_conv_bn(tmp, 'bn_branch')
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tmp = addto_layer(input=[c1, c2],
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act=ReluActivation(),
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bias_attr=False)
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tmp = fc_layer(input=tmp, size=channels,
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bias_attr=True,
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act=ReluActivation())
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d1, d2 = two_fc(tmp, 'fc_branch')
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tmp = addto_layer(input=[d1, d2])
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out = fc_layer(input=tmp, size=10,
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bias_attr=True,
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act=SoftmaxActivation())
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outputs(out)
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