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216 lines
6.3 KiB
216 lines
6.3 KiB
# Copyright (c) 2019 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 __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 paddle.fluid as fluid
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from paddle.fluid.initializer import MSRA
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ['MobileNet']
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train_parameters = {
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"input_size": [3, 224, 224],
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"input_mean": [0.485, 0.456, 0.406],
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"input_std": [0.229, 0.224, 0.225],
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"learning_strategy": {
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"name": "piecewise_decay",
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"batch_size": 256,
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"epochs": [30, 60, 90],
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"steps": [0.1, 0.01, 0.001, 0.0001]
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}
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}
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class MobileNet():
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def __init__(self, name=""):
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self.params = train_parameters
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self.name = name
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def net(self, input, class_dim=1000, scale=1.0):
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# conv1: 112x112
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input = self.conv_bn_layer(
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input,
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filter_size=3,
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channels=3,
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num_filters=int(32 * scale),
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stride=2,
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padding=1,
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name=self.name + "_conv1")
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# 56x56
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input = self.depthwise_separable(
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input,
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num_filters1=32,
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num_filters2=64,
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num_groups=32,
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stride=1,
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scale=scale,
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name=self.name + "_conv2_1")
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input = self.depthwise_separable(
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input,
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num_filters1=64,
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num_filters2=128,
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num_groups=64,
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stride=2,
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scale=scale,
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name=self.name + "_conv2_2")
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# 28x28
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input = self.depthwise_separable(
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input,
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num_filters1=128,
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num_filters2=128,
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num_groups=128,
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stride=1,
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scale=scale,
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name=self.name + "_conv3_1")
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input = self.depthwise_separable(
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input,
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num_filters1=128,
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num_filters2=256,
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num_groups=128,
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stride=2,
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scale=scale,
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name=self.name + "_conv3_2")
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# 14x14
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input = self.depthwise_separable(
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input,
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num_filters1=256,
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num_filters2=256,
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num_groups=256,
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stride=1,
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scale=scale,
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name=self.name + "_conv4_1")
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input = self.depthwise_separable(
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input,
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num_filters1=256,
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num_filters2=512,
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num_groups=256,
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stride=2,
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scale=scale,
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name=self.name + "_conv4_2")
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# 14x14
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for i in range(5):
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input = self.depthwise_separable(
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input,
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num_filters1=512,
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num_filters2=512,
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num_groups=512,
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stride=1,
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scale=scale,
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name=self.name + "_conv5" + "_" + str(i + 1))
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# 7x7
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input = self.depthwise_separable(
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input,
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num_filters1=512,
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num_filters2=1024,
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num_groups=512,
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stride=2,
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scale=scale,
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name=self.name + "_conv5_6")
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input = self.depthwise_separable(
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input,
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num_filters1=1024,
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num_filters2=1024,
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num_groups=1024,
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stride=1,
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scale=scale,
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name=self.name + "_conv6")
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input = fluid.layers.pool2d(
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input=input,
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pool_size=0,
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pool_stride=1,
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pool_type='avg',
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global_pooling=True)
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output = fluid.layers.fc(
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input=input,
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size=class_dim,
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act='softmax',
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param_attr=ParamAttr(
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initializer=MSRA(), name=self.name + "_fc7_weights"),
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bias_attr=ParamAttr(name=self.name + "_fc7_offset"),
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name=self.name)
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return output
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def conv_bn_layer(self,
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input,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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num_groups=1,
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act='relu',
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use_cudnn=True,
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name=None):
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conv = fluid.layers.conv2d(
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input=input,
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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=num_groups,
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act=None,
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use_cudnn=use_cudnn,
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param_attr=ParamAttr(
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initializer=MSRA(), name=name + "_weights"),
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name=name,
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bias_attr=False)
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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=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 depthwise_separable(self,
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input,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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scale,
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name=None):
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depthwise_conv = self.conv_bn_layer(
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input=input,
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filter_size=3,
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num_filters=int(num_filters1 * scale),
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stride=stride,
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padding=1,
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num_groups=int(num_groups * scale),
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use_cudnn=False,
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name=name + "_dw")
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pointwise_conv = self.conv_bn_layer(
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input=depthwise_conv,
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0,
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name=name + "_sep")
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return pointwise_conv
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