355 lines
11 KiB
Python
355 lines
11 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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from paddle.fluid.param_attr import ParamAttr
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__all__ = ['InceptionV4']
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class InceptionV4():
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def __init__(self):
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pass
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def net(self, input, class_dim=1000):
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x = self.inception_stem(input)
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for i in range(4):
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x = self.inceptionA(x, name=str(i + 1))
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x = self.reductionA(x)
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for i in range(7):
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x = self.inceptionB(x, name=str(i + 1))
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x = self.reductionB(x)
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for i in range(3):
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x = self.inceptionC(x, name=str(i + 1))
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pool = fluid.layers.pool2d(
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input=x, pool_type='avg', global_pooling=True)
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drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
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stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=drop,
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size=class_dim,
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param_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name="final_fc_weights"),
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bias_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name="final_fc_offset"))
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return out
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def conv_bn_layer(self,
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data,
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num_filters,
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filter_size,
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stride=1,
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padding=0,
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groups=1,
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act='relu',
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name=None):
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conv = fluid.layers.conv2d(
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input=data,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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name=name)
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bn_name = name + "_bn"
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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name=bn_name,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def inception_stem(self, data, name=None):
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conv = self.conv_bn_layer(
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data, 32, 3, stride=2, act='relu', name="conv1_3x3_s2")
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conv = self.conv_bn_layer(conv, 32, 3, act='relu', name="conv2_3x3_s1")
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conv = self.conv_bn_layer(
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conv, 64, 3, padding=1, act='relu', name="conv3_3x3_s1")
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pool1 = fluid.layers.pool2d(
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input=conv, pool_size=3, pool_stride=2, pool_type='max')
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conv2 = self.conv_bn_layer(
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conv, 96, 3, stride=2, act='relu', name="inception_stem1_3x3_s2")
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concat = fluid.layers.concat([pool1, conv2], axis=1)
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conv1 = self.conv_bn_layer(
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concat, 64, 1, act='relu', name="inception_stem2_3x3_reduce")
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conv1 = self.conv_bn_layer(
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conv1, 96, 3, act='relu', name="inception_stem2_3x3")
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conv2 = self.conv_bn_layer(
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concat, 64, 1, act='relu', name="inception_stem2_1x7_reduce")
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conv2 = self.conv_bn_layer(
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conv2,
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64, (7, 1),
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padding=(3, 0),
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act='relu',
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name="inception_stem2_1x7")
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conv2 = self.conv_bn_layer(
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conv2,
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64, (1, 7),
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padding=(0, 3),
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act='relu',
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name="inception_stem2_7x1")
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conv2 = self.conv_bn_layer(
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conv2, 96, 3, act='relu', name="inception_stem2_3x3_2")
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concat = fluid.layers.concat([conv1, conv2], axis=1)
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conv1 = self.conv_bn_layer(
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concat,
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192,
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3,
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stride=2,
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act='relu',
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name="inception_stem3_3x3_s2")
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pool1 = fluid.layers.pool2d(
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input=concat, pool_size=3, pool_stride=2, pool_type='max')
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concat = fluid.layers.concat([conv1, pool1], axis=1)
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return concat
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def inceptionA(self, data, name=None):
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pool1 = fluid.layers.pool2d(
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input=data, pool_size=3, pool_padding=1, pool_type='avg')
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conv1 = self.conv_bn_layer(
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pool1, 96, 1, act='relu', name="inception_a" + name + "_1x1")
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conv2 = self.conv_bn_layer(
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data, 96, 1, act='relu', name="inception_a" + name + "_1x1_2")
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conv3 = self.conv_bn_layer(
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data, 64, 1, act='relu', name="inception_a" + name + "_3x3_reduce")
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conv3 = self.conv_bn_layer(
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conv3,
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96,
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3,
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padding=1,
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act='relu',
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name="inception_a" + name + "_3x3")
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conv4 = self.conv_bn_layer(
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data,
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64,
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1,
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act='relu',
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name="inception_a" + name + "_3x3_2_reduce")
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conv4 = self.conv_bn_layer(
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conv4,
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96,
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3,
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padding=1,
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act='relu',
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name="inception_a" + name + "_3x3_2")
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conv4 = self.conv_bn_layer(
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conv4,
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96,
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3,
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padding=1,
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act='relu',
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name="inception_a" + name + "_3x3_3")
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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def reductionA(self, data, name=None):
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pool1 = fluid.layers.pool2d(
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input=data, pool_size=3, pool_stride=2, pool_type='max')
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conv2 = self.conv_bn_layer(
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data, 384, 3, stride=2, act='relu', name="reduction_a_3x3")
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conv3 = self.conv_bn_layer(
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data, 192, 1, act='relu', name="reduction_a_3x3_2_reduce")
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conv3 = self.conv_bn_layer(
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conv3, 224, 3, padding=1, act='relu', name="reduction_a_3x3_2")
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conv3 = self.conv_bn_layer(
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conv3, 256, 3, stride=2, act='relu', name="reduction_a_3x3_3")
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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return concat
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def inceptionB(self, data, name=None):
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pool1 = fluid.layers.pool2d(
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input=data, pool_size=3, pool_padding=1, pool_type='avg')
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conv1 = self.conv_bn_layer(
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pool1, 128, 1, act='relu', name="inception_b" + name + "_1x1")
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conv2 = self.conv_bn_layer(
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data, 384, 1, act='relu', name="inception_b" + name + "_1x1_2")
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conv3 = self.conv_bn_layer(
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data,
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192,
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1,
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act='relu',
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name="inception_b" + name + "_1x7_reduce")
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conv3 = self.conv_bn_layer(
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conv3,
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224, (1, 7),
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padding=(0, 3),
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act='relu',
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name="inception_b" + name + "_1x7")
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conv3 = self.conv_bn_layer(
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conv3,
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256, (7, 1),
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padding=(3, 0),
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act='relu',
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name="inception_b" + name + "_7x1")
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conv4 = self.conv_bn_layer(
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data,
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192,
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1,
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act='relu',
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name="inception_b" + name + "_7x1_2_reduce")
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conv4 = self.conv_bn_layer(
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conv4,
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192, (1, 7),
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padding=(0, 3),
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act='relu',
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name="inception_b" + name + "_1x7_2")
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conv4 = self.conv_bn_layer(
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conv4,
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224, (7, 1),
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padding=(3, 0),
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act='relu',
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name="inception_b" + name + "_7x1_2")
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conv4 = self.conv_bn_layer(
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conv4,
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224, (1, 7),
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padding=(0, 3),
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act='relu',
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name="inception_b" + name + "_1x7_3")
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conv4 = self.conv_bn_layer(
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conv4,
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256, (7, 1),
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padding=(3, 0),
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act='relu',
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name="inception_b" + name + "_7x1_3")
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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def reductionB(self, data, name=None):
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pool1 = fluid.layers.pool2d(
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input=data, pool_size=3, pool_stride=2, pool_type='max')
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conv2 = self.conv_bn_layer(
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data, 192, 1, act='relu', name="reduction_b_3x3_reduce")
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conv2 = self.conv_bn_layer(
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conv2, 192, 3, stride=2, act='relu', name="reduction_b_3x3")
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conv3 = self.conv_bn_layer(
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data, 256, 1, act='relu', name="reduction_b_1x7_reduce")
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conv3 = self.conv_bn_layer(
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conv3,
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256, (1, 7),
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padding=(0, 3),
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act='relu',
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name="reduction_b_1x7")
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conv3 = self.conv_bn_layer(
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conv3,
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320, (7, 1),
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padding=(3, 0),
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act='relu',
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name="reduction_b_7x1")
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conv3 = self.conv_bn_layer(
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conv3, 320, 3, stride=2, act='relu', name="reduction_b_3x3_2")
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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return concat
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def inceptionC(self, data, name=None):
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pool1 = fluid.layers.pool2d(
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input=data, pool_size=3, pool_padding=1, pool_type='avg')
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conv1 = self.conv_bn_layer(
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pool1, 256, 1, act='relu', name="inception_c" + name + "_1x1")
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conv2 = self.conv_bn_layer(
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data, 256, 1, act='relu', name="inception_c" + name + "_1x1_2")
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conv3 = self.conv_bn_layer(
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data, 384, 1, act='relu', name="inception_c" + name + "_1x1_3")
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conv3_1 = self.conv_bn_layer(
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conv3,
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256, (1, 3),
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padding=(0, 1),
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act='relu',
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name="inception_c" + name + "_1x3")
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conv3_2 = self.conv_bn_layer(
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conv3,
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256, (3, 1),
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padding=(1, 0),
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act='relu',
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name="inception_c" + name + "_3x1")
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conv4 = self.conv_bn_layer(
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data, 384, 1, act='relu', name="inception_c" + name + "_1x1_4")
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conv4 = self.conv_bn_layer(
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conv4,
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448, (1, 3),
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padding=(0, 1),
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act='relu',
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name="inception_c" + name + "_1x3_2")
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conv4 = self.conv_bn_layer(
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conv4,
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512, (3, 1),
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padding=(1, 0),
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act='relu',
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name="inception_c" + name + "_3x1_2")
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conv4_1 = self.conv_bn_layer(
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conv4,
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256, (1, 3),
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padding=(0, 1),
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act='relu',
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name="inception_c" + name + "_1x3_3")
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conv4_2 = self.conv_bn_layer(
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conv4,
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256, (3, 1),
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padding=(1, 0),
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act='relu',
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name="inception_c" + name + "_3x1_3")
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concat = fluid.layers.concat(
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[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
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return concat
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