219 lines
6.1 KiB
Python
219 lines
6.1 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 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__ = [
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'MobileNetV1', 'MobileNetV1_x0_25', 'MobileNetV1_x0_5', 'MobileNetV1_x1_0',
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'MobileNetV1_x0_75'
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]
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class MobileNetV1():
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def __init__(self, scale=1.0):
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self.scale = scale
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def net(self, input, class_dim=1000):
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scale = self.scale
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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="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="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="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="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="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="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="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="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="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="conv6")
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input = fluid.layers.pool2d(
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input=input, pool_type='avg', global_pooling=True)
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output = fluid.layers.fc(input=input,
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size=class_dim,
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param_attr=ParamAttr(
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initializer=MSRA(), name="fc7_weights"),
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bias_attr=ParamAttr(name="fc7_offset"))
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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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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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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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def MobileNetV1_x0_25():
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model = MobileNetV1(scale=0.25)
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return model
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def MobileNetV1_x0_5():
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model = MobileNetV1(scale=0.5)
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return model
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def MobileNetV1_x1_0():
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model = MobileNetV1(scale=1.0)
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return model
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def MobileNetV1_x0_75():
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model = MobileNetV1(scale=0.75)
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return model
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