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ghostnet.py
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ghostnet.py
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#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.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = ["GhostNet", "GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3"]
class GhostNet():
def __init__(self, scale):
cfgs = [
# k, t, c, SE, s
[3, 16, 16, 0, 1],
[3, 48, 24, 0, 2],
[3, 72, 24, 0, 1],
[5, 72, 40, 1, 2],
[5, 120, 40, 1, 1],
[3, 240, 80, 0, 2],
[3, 200, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 480, 112, 1, 1],
[3, 672, 112, 1, 1],
[5, 672, 160, 1, 2],
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1],
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1]
]
self.cfgs = cfgs
self.scale = scale
def net(self, input, class_dim=1000):
# build first layer:
output_channel = int(self._make_divisible(16 * self.scale, 4))
x = self.conv_bn_layer(input=input,
num_filters=output_channel,
filter_size=3,
stride=2,
groups=1,
act="relu",
name="conv1")
# build inverted residual blocks
idx = 0
for k, exp_size, c, use_se, s in self.cfgs:
output_channel = int(self._make_divisible(c * self.scale, 4))
hidden_channel = int(self._make_divisible(exp_size * self.scale, 4))
x = self.ghost_bottleneck(input=x,
hidden_dim=hidden_channel,
output=output_channel,
kernel_size=k,
stride=s,
use_se=use_se,
name="_ghostbottleneck_" + str(idx))
idx += 1
# build last several layers
output_channel = int(self._make_divisible(exp_size * self.scale, 4))
x = self.conv_bn_layer(input=x,
num_filters=output_channel,
filter_size=1,
stride=1,
groups=1,
act="relu",
name="conv_last")
x = fluid.layers.pool2d(input=x, pool_type='avg', global_pooling=True)
output_channel = 1280
stdv = 1.0 / math.sqrt(x.shape[1] * 1.0)
out = self.conv_bn_layer(input=x,
num_filters=output_channel,
filter_size=1,
stride=1,
act="relu",
name="fc_0")
out = fluid.layers.dropout(x=out, dropout_prob=0.2)
stdv = 1.0 / math.sqrt(out.shape[1] * 1.0)
out = fluid.layers.fc(input=out,
size=class_dim,
param_attr=ParamAttr(name="fc_1_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_1_offset"))
return out
def _make_divisible(self, v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
x = 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(
initializer=fluid.initializer.MSRA(), name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
x = fluid.layers.batch_norm(input=x,
act=act,
param_attr=ParamAttr(
name=bn_name + "_scale",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
bias_attr=ParamAttr(
name=bn_name + "_offset",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
moving_mean_name=bn_name + "_mean",
moving_variance_name=name + "_variance")
return x
def se_block(self, input, num_channels, reduction_ratio=4, name=None):
pool = fluid.layers.pool2d(input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
squeeze = fluid.layers.fc(input=pool,
size=num_channels // reduction_ratio,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_1_weights'),
bias_attr=ParamAttr(name=name + '_1_offset'))
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(input=squeeze,
size=num_channels,
act=None,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_2_weights'),
bias_attr=ParamAttr(name=name + '_2_offset'))
excitation = fluid.layers.clip(x=excitation, min=0, max=1)
se_scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return se_scale
def depthwise_conv(self,
input,
output,
kernel_size,
stride=1,
relu=False,
name=None):
return self.conv_bn_layer(input=input,
num_filters=output,
filter_size=kernel_size,
stride=stride,
groups=input.shape[1],
act="relu" if relu else None,
name=name + "_depthwise")
def ghost_module(self,
input,
output,
kernel_size=1,
ratio=2,
dw_size=3,
stride=1,
relu=True,
name=None):
self.output = output
init_channels = int(math.ceil(output / ratio))
new_channels = int(init_channels * (ratio - 1))
primary_conv = self.conv_bn_layer(input=input,
num_filters=init_channels,
filter_size=kernel_size,
stride=stride,
groups=1,
act="relu" if relu else None,
name=name + "_primary_conv")
cheap_operation = self.conv_bn_layer(input=primary_conv,
num_filters=new_channels,
filter_size=dw_size,
stride=1,
groups=init_channels,
act="relu" if relu else None,
name=name + "_cheap_operation")
out = fluid.layers.concat([primary_conv, cheap_operation], axis=1)
return out
def ghost_bottleneck(self,
input,
hidden_dim,
output,
kernel_size,
stride,
use_se,
name=None):
inp_channels = input.shape[1]
x = self.ghost_module(input=input,
output=hidden_dim,
kernel_size=1,
stride=1,
relu=True,
name=name + "_ghost_module_1")
if stride == 2:
x = self.depthwise_conv(input=x,
output=hidden_dim,
kernel_size=kernel_size,
stride=stride,
relu=False,
name=name + "_depthwise")
if use_se:
x = self.se_block(input=x, num_channels=hidden_dim, name=name + "_se")
x = self.ghost_module(input=x,
output=output,
kernel_size=1,
relu=False,
name=name + "_ghost_module_2")
if stride == 1 and inp_channels == output:
shortcut = input
else:
shortcut = self.depthwise_conv(input=input,
output=inp_channels,
kernel_size=kernel_size,
stride=stride,
relu=False,
name=name + "_shortcut_depthwise")
shortcut = self.conv_bn_layer(input=shortcut,
num_filters=output,
filter_size=1,
stride=1,
groups=1,
act=None,
name=name + "_shortcut_conv")
return fluid.layers.elementwise_add(x=x,
y=shortcut,
axis=-1)
def GhostNet_x0_5():
model = GhostNet(scale=0.5)
return model
def GhostNet_x1_0():
model = GhostNet(scale=1.0)
return model
def GhostNet_x1_3():
model = GhostNet(scale=1.3)
return model