PaddleClas/ppcls/modeling/architectures/squeezenet.py

134 lines
5.2 KiB
Python

#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
class SqueezeNet():
def __init__(self, version='1.0'):
self.version = version
def net(self, input, class_dim=1000):
version = self.version
assert version in ['1.0', '1.1'], \
"supported version are {} but input version is {}".format(['1.0', '1.1'], version)
if version == '1.0':
conv = fluid.layers.conv2d(
input,
num_filters=96,
filter_size=7,
stride=2,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
else:
conv = fluid.layers.conv2d(
input,
num_filters=64,
filter_size=3,
stride=2,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
conv = fluid.layers.dropout(conv, dropout_prob=0.5)
conv = fluid.layers.conv2d(
conv,
num_filters=class_dim,
filter_size=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name='conv10_offset'))
conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
out = fluid.layers.flatten(conv)
return out
def make_fire_conv(self,
input,
num_filters,
filter_size,
padding=0,
name=None):
conv = fluid.layers.conv2d(
input,
num_filters=num_filters,
filter_size=filter_size,
padding=padding,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + '_offset'))
return conv
def make_fire(self,
input,
squeeze_channels,
expand1x1_channels,
expand3x3_channels,
name=None):
conv = self.make_fire_conv(
input, squeeze_channels, 1, name=name + '_squeeze1x1')
conv_path1 = self.make_fire_conv(
conv, expand1x1_channels, 1, name=name + '_expand1x1')
conv_path2 = self.make_fire_conv(
conv, expand3x3_channels, 3, 1, name=name + '_expand3x3')
out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
return out
def SqueezeNet1_0():
model = SqueezeNet(version='1.0')
return model
def SqueezeNet1_1():
model = SqueezeNet(version='1.1')
return model