282 lines
8.7 KiB
Python
282 lines
8.7 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 sys
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
|
|
__all__ = ['Xception', 'Xception41', 'Xception65', 'Xception71']
|
|
|
|
|
|
class Xception(object):
|
|
"""Xception"""
|
|
|
|
def __init__(self, entry_flow_block_num=3, middle_flow_block_num=8):
|
|
self.entry_flow_block_num = entry_flow_block_num
|
|
self.middle_flow_block_num = middle_flow_block_num
|
|
return
|
|
|
|
def net(self, input, class_dim=1000):
|
|
conv = self.entry_flow(input, self.entry_flow_block_num)
|
|
conv = self.middle_flow(conv, self.middle_flow_block_num)
|
|
conv = self.exit_flow(conv, class_dim)
|
|
|
|
return conv
|
|
|
|
def entry_flow(self, input, block_num=3):
|
|
'''xception entry_flow'''
|
|
name = "entry_flow"
|
|
conv = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=32,
|
|
filter_size=3,
|
|
stride=2,
|
|
act='relu',
|
|
name=name + "_conv1")
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
num_filters=64,
|
|
filter_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name=name + "_conv2")
|
|
|
|
if block_num == 3:
|
|
relu_first = [False, True, True]
|
|
num_filters = [128, 256, 728]
|
|
stride = [2, 2, 2]
|
|
elif block_num == 5:
|
|
relu_first = [False, True, True, True, True]
|
|
num_filters = [128, 256, 256, 728, 728]
|
|
stride = [2, 1, 2, 1, 2]
|
|
else:
|
|
sys.exit(-1)
|
|
|
|
for block in range(block_num):
|
|
curr_name = "{}_{}".format(name, block)
|
|
conv = self.entry_flow_bottleneck_block(
|
|
conv,
|
|
num_filters=num_filters[block],
|
|
name=curr_name,
|
|
stride=stride[block],
|
|
relu_first=relu_first[block])
|
|
|
|
return conv
|
|
|
|
def entry_flow_bottleneck_block(self,
|
|
input,
|
|
num_filters,
|
|
name,
|
|
stride=2,
|
|
relu_first=False):
|
|
'''entry_flow_bottleneck_block'''
|
|
short = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=1,
|
|
stride=stride,
|
|
padding=0,
|
|
act=None,
|
|
param_attr=ParamAttr(name + "_branch1_weights"),
|
|
bias_attr=False)
|
|
|
|
conv0 = input
|
|
if relu_first:
|
|
conv0 = fluid.layers.relu(conv0)
|
|
|
|
conv1 = self.separable_conv(
|
|
conv0, num_filters, stride=1, name=name + "_branch2a_weights")
|
|
|
|
conv2 = fluid.layers.relu(conv1)
|
|
conv2 = self.separable_conv(
|
|
conv2, num_filters, stride=1, name=name + "_branch2b_weights")
|
|
|
|
pool = fluid.layers.pool2d(
|
|
input=conv2,
|
|
pool_size=3,
|
|
pool_stride=stride,
|
|
pool_padding=1,
|
|
pool_type='max')
|
|
|
|
return fluid.layers.elementwise_add(x=short, y=pool)
|
|
|
|
def middle_flow(self, input, block_num=8):
|
|
'''xception middle_flow'''
|
|
num_filters = 728
|
|
conv = input
|
|
for block in range(block_num):
|
|
name = "middle_flow_{}".format(block)
|
|
conv = self.middle_flow_bottleneck_block(conv, num_filters, name)
|
|
|
|
return conv
|
|
|
|
def middle_flow_bottleneck_block(self, input, num_filters, name):
|
|
'''middle_flow_bottleneck_block'''
|
|
conv0 = fluid.layers.relu(input)
|
|
conv0 = self.separable_conv(
|
|
conv0,
|
|
num_filters=num_filters,
|
|
stride=1,
|
|
name=name + "_branch2a_weights")
|
|
|
|
conv1 = fluid.layers.relu(conv0)
|
|
conv1 = self.separable_conv(
|
|
conv1,
|
|
num_filters=num_filters,
|
|
stride=1,
|
|
name=name + "_branch2b_weights")
|
|
|
|
conv2 = fluid.layers.relu(conv1)
|
|
conv2 = self.separable_conv(
|
|
conv2,
|
|
num_filters=num_filters,
|
|
stride=1,
|
|
name=name + "_branch2c_weights")
|
|
|
|
return fluid.layers.elementwise_add(x=input, y=conv2)
|
|
|
|
def exit_flow(self, input, class_dim):
|
|
'''xception exit flow'''
|
|
name = "exit_flow"
|
|
num_filters1 = 728
|
|
num_filters2 = 1024
|
|
conv0 = self.exit_flow_bottleneck_block(
|
|
input, num_filters1, num_filters2, name=name + "_1")
|
|
|
|
conv1 = self.separable_conv(
|
|
conv0, num_filters=1536, stride=1, name=name + "_2")
|
|
conv1 = fluid.layers.relu(conv1)
|
|
|
|
conv2 = self.separable_conv(
|
|
conv1, num_filters=2048, stride=1, name=name + "_3")
|
|
conv2 = fluid.layers.relu(conv2)
|
|
|
|
pool = fluid.layers.pool2d(
|
|
input=conv2, pool_type='avg', global_pooling=True)
|
|
|
|
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
|
|
out = fluid.layers.fc(
|
|
input=pool,
|
|
size=class_dim,
|
|
param_attr=fluid.param_attr.ParamAttr(
|
|
name='fc_weights',
|
|
initializer=fluid.initializer.Uniform(-stdv, stdv)),
|
|
bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
|
|
|
|
return out
|
|
|
|
def exit_flow_bottleneck_block(self, input, num_filters1, num_filters2,
|
|
name):
|
|
'''entry_flow_bottleneck_block'''
|
|
short = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters2,
|
|
filter_size=1,
|
|
stride=2,
|
|
padding=0,
|
|
act=None,
|
|
param_attr=ParamAttr(name + "_branch1_weights"),
|
|
bias_attr=False)
|
|
|
|
conv0 = fluid.layers.relu(input)
|
|
conv1 = self.separable_conv(
|
|
conv0, num_filters1, stride=1, name=name + "_branch2a_weights")
|
|
|
|
conv2 = fluid.layers.relu(conv1)
|
|
conv2 = self.separable_conv(
|
|
conv2, num_filters2, stride=1, name=name + "_branch2b_weights")
|
|
|
|
pool = fluid.layers.pool2d(
|
|
input=conv2,
|
|
pool_size=3,
|
|
pool_stride=2,
|
|
pool_padding=1,
|
|
pool_type='max')
|
|
|
|
return fluid.layers.elementwise_add(x=short, y=pool)
|
|
|
|
def separable_conv(self, input, num_filters, stride=1, name=None):
|
|
"""separable_conv"""
|
|
pointwise_conv = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_filters,
|
|
stride=1,
|
|
name=name + "_sep")
|
|
|
|
depthwise_conv = self.conv_bn_layer(
|
|
input=pointwise_conv,
|
|
filter_size=3,
|
|
num_filters=num_filters,
|
|
stride=stride,
|
|
groups=num_filters,
|
|
use_cudnn=False,
|
|
name=name + "_dw")
|
|
|
|
return depthwise_conv
|
|
|
|
def conv_bn_layer(self,
|
|
input,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
act=None,
|
|
use_cudnn=True,
|
|
name=None):
|
|
"""conv_bn_layer"""
|
|
conv = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
act=None,
|
|
param_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False,
|
|
use_cudnn=use_cudnn)
|
|
|
|
bn_name = "bn_" + name
|
|
|
|
return fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
param_attr=ParamAttr(name=bn_name + '_scale'),
|
|
bias_attr=ParamAttr(bn_name + '_offset'),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
|
|
def Xception41():
|
|
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8)
|
|
return model
|
|
|
|
|
|
def Xception65():
|
|
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16)
|
|
return model
|
|
|
|
|
|
def Xception71():
|
|
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16)
|
|
return model
|