134 lines
5.2 KiB
Python
134 lines
5.2 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__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
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class SqueezeNet():
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def __init__(self, version='1.0'):
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self.version = version
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def net(self, input, class_dim=1000):
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version = self.version
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assert version in ['1.0', '1.1'], \
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"supported version are {} but input version is {}".format(['1.0', '1.1'], version)
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if version == '1.0':
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conv = fluid.layers.conv2d(
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input,
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num_filters=96,
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filter_size=7,
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stride=2,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
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bias_attr=ParamAttr(name='conv1_offset'))
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 16, 64, 64, name='fire2')
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conv = self.make_fire(conv, 16, 64, 64, name='fire3')
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conv = self.make_fire(conv, 32, 128, 128, name='fire4')
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 32, 128, 128, name='fire5')
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conv = self.make_fire(conv, 48, 192, 192, name='fire6')
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conv = self.make_fire(conv, 48, 192, 192, name='fire7')
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conv = self.make_fire(conv, 64, 256, 256, name='fire8')
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 64, 256, 256, name='fire9')
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else:
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conv = fluid.layers.conv2d(
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input,
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num_filters=64,
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filter_size=3,
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stride=2,
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padding=1,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
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bias_attr=ParamAttr(name='conv1_offset'))
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 16, 64, 64, name='fire2')
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conv = self.make_fire(conv, 16, 64, 64, name='fire3')
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 32, 128, 128, name='fire4')
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conv = self.make_fire(conv, 32, 128, 128, name='fire5')
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conv = fluid.layers.pool2d(
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conv, pool_size=3, pool_stride=2, pool_type='max')
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conv = self.make_fire(conv, 48, 192, 192, name='fire6')
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conv = self.make_fire(conv, 48, 192, 192, name='fire7')
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conv = self.make_fire(conv, 64, 256, 256, name='fire8')
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conv = self.make_fire(conv, 64, 256, 256, name='fire9')
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conv = fluid.layers.dropout(conv, dropout_prob=0.5)
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conv = fluid.layers.conv2d(
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conv,
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num_filters=class_dim,
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filter_size=1,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
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bias_attr=ParamAttr(name='conv10_offset'))
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conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
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out = fluid.layers.flatten(conv)
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return out
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def make_fire_conv(self,
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input,
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num_filters,
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filter_size,
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padding=0,
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name=None):
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conv = fluid.layers.conv2d(
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input,
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num_filters=num_filters,
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filter_size=filter_size,
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padding=padding,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
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bias_attr=ParamAttr(name=name + '_offset'))
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return conv
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def make_fire(self,
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input,
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squeeze_channels,
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expand1x1_channels,
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expand3x3_channels,
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name=None):
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conv = self.make_fire_conv(
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input, squeeze_channels, 1, name=name + '_squeeze1x1')
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conv_path1 = self.make_fire_conv(
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conv, expand1x1_channels, 1, name=name + '_expand1x1')
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conv_path2 = self.make_fire_conv(
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conv, expand3x3_channels, 3, 1, name=name + '_expand3x3')
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out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
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return out
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def SqueezeNet1_0():
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model = SqueezeNet(version='1.0')
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return model
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def SqueezeNet1_1():
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model = SqueezeNet(version='1.1')
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return model
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