269 lines
8.7 KiB
Python
Executable File
269 lines
8.7 KiB
Python
Executable File
# 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 paddle
|
|
from paddle import nn
|
|
import paddle.nn.functional as F
|
|
from paddle import ParamAttr
|
|
|
|
__all__ = ['MobileNetV3']
|
|
|
|
|
|
def make_divisible(v, divisor=8, min_value=None):
|
|
if min_value is None:
|
|
min_value = divisor
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
|
if new_v < 0.9 * v:
|
|
new_v += divisor
|
|
return new_v
|
|
|
|
|
|
class MobileNetV3(nn.Layer):
|
|
def __init__(self,
|
|
in_channels=3,
|
|
model_name='large',
|
|
scale=0.5,
|
|
disable_se=False,
|
|
**kwargs):
|
|
"""
|
|
the MobilenetV3 backbone network for detection module.
|
|
Args:
|
|
params(dict): the super parameters for build network
|
|
"""
|
|
super(MobileNetV3, self).__init__()
|
|
|
|
self.disable_se = disable_se
|
|
|
|
if model_name == "large":
|
|
cfg = [
|
|
# k, exp, c, se, nl, s,
|
|
[3, 16, 16, False, 'relu', 1],
|
|
[3, 64, 24, False, 'relu', 2],
|
|
[3, 72, 24, False, 'relu', 1],
|
|
[5, 72, 40, True, 'relu', 2],
|
|
[5, 120, 40, True, 'relu', 1],
|
|
[5, 120, 40, True, 'relu', 1],
|
|
[3, 240, 80, False, 'hardswish', 2],
|
|
[3, 200, 80, False, 'hardswish', 1],
|
|
[3, 184, 80, False, 'hardswish', 1],
|
|
[3, 184, 80, False, 'hardswish', 1],
|
|
[3, 480, 112, True, 'hardswish', 1],
|
|
[3, 672, 112, True, 'hardswish', 1],
|
|
[5, 672, 160, True, 'hardswish', 2],
|
|
[5, 960, 160, True, 'hardswish', 1],
|
|
[5, 960, 160, True, 'hardswish', 1],
|
|
]
|
|
cls_ch_squeeze = 960
|
|
elif model_name == "small":
|
|
cfg = [
|
|
# k, exp, c, se, nl, s,
|
|
[3, 16, 16, True, 'relu', 2],
|
|
[3, 72, 24, False, 'relu', 2],
|
|
[3, 88, 24, False, 'relu', 1],
|
|
[5, 96, 40, True, 'hardswish', 2],
|
|
[5, 240, 40, True, 'hardswish', 1],
|
|
[5, 240, 40, True, 'hardswish', 1],
|
|
[5, 120, 48, True, 'hardswish', 1],
|
|
[5, 144, 48, True, 'hardswish', 1],
|
|
[5, 288, 96, True, 'hardswish', 2],
|
|
[5, 576, 96, True, 'hardswish', 1],
|
|
[5, 576, 96, True, 'hardswish', 1],
|
|
]
|
|
cls_ch_squeeze = 576
|
|
else:
|
|
raise NotImplementedError("mode[" + model_name +
|
|
"_model] is not implemented!")
|
|
|
|
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
|
|
assert scale in supported_scale, \
|
|
"supported scale are {} but input scale is {}".format(supported_scale, scale)
|
|
inplanes = 16
|
|
# conv1
|
|
self.conv = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=make_divisible(inplanes * scale),
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
groups=1,
|
|
if_act=True,
|
|
act='hardswish')
|
|
|
|
self.stages = []
|
|
self.out_channels = []
|
|
block_list = []
|
|
i = 0
|
|
inplanes = make_divisible(inplanes * scale)
|
|
for (k, exp, c, se, nl, s) in cfg:
|
|
se = se and not self.disable_se
|
|
start_idx = 2 if model_name == 'large' else 0
|
|
if s == 2 and i > start_idx:
|
|
self.out_channels.append(inplanes)
|
|
self.stages.append(nn.Sequential(*block_list))
|
|
block_list = []
|
|
block_list.append(
|
|
ResidualUnit(
|
|
in_channels=inplanes,
|
|
mid_channels=make_divisible(scale * exp),
|
|
out_channels=make_divisible(scale * c),
|
|
kernel_size=k,
|
|
stride=s,
|
|
use_se=se,
|
|
act=nl))
|
|
inplanes = make_divisible(scale * c)
|
|
i += 1
|
|
block_list.append(
|
|
ConvBNLayer(
|
|
in_channels=inplanes,
|
|
out_channels=make_divisible(scale * cls_ch_squeeze),
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
groups=1,
|
|
if_act=True,
|
|
act='hardswish'))
|
|
self.stages.append(nn.Sequential(*block_list))
|
|
self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
|
|
for i, stage in enumerate(self.stages):
|
|
self.add_sublayer(sublayer=stage, name="stage{}".format(i))
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
out_list = []
|
|
for stage in self.stages:
|
|
x = stage(x)
|
|
out_list.append(x)
|
|
return out_list
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride,
|
|
padding,
|
|
groups=1,
|
|
if_act=True,
|
|
act=None):
|
|
super(ConvBNLayer, self).__init__()
|
|
self.if_act = if_act
|
|
self.act = act
|
|
self.conv = nn.Conv2D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
groups=groups,
|
|
bias_attr=False)
|
|
|
|
self.bn = nn.BatchNorm(num_channels=out_channels, act=None)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.bn(x)
|
|
if self.if_act:
|
|
if self.act == "relu":
|
|
x = F.relu(x)
|
|
elif self.act == "hardswish":
|
|
x = F.hardswish(x)
|
|
else:
|
|
print("The activation function({}) is selected incorrectly.".
|
|
format(self.act))
|
|
exit()
|
|
return x
|
|
|
|
|
|
class ResidualUnit(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
mid_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride,
|
|
use_se,
|
|
act=None):
|
|
super(ResidualUnit, self).__init__()
|
|
self.if_shortcut = stride == 1 and in_channels == out_channels
|
|
self.if_se = use_se
|
|
|
|
self.expand_conv = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=mid_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=True,
|
|
act=act)
|
|
self.bottleneck_conv = ConvBNLayer(
|
|
in_channels=mid_channels,
|
|
out_channels=mid_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=int((kernel_size - 1) // 2),
|
|
groups=mid_channels,
|
|
if_act=True,
|
|
act=act)
|
|
if self.if_se:
|
|
self.mid_se = SEModule(mid_channels)
|
|
self.linear_conv = ConvBNLayer(
|
|
in_channels=mid_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=False,
|
|
act=None)
|
|
|
|
def forward(self, inputs):
|
|
x = self.expand_conv(inputs)
|
|
x = self.bottleneck_conv(x)
|
|
if self.if_se:
|
|
x = self.mid_se(x)
|
|
x = self.linear_conv(x)
|
|
if self.if_shortcut:
|
|
x = paddle.add(inputs, x)
|
|
return x
|
|
|
|
|
|
class SEModule(nn.Layer):
|
|
def __init__(self, in_channels, reduction=4):
|
|
super(SEModule, self).__init__()
|
|
self.avg_pool = nn.AdaptiveAvgPool2D(1)
|
|
self.conv1 = nn.Conv2D(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels // reduction,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.conv2 = nn.Conv2D(
|
|
in_channels=in_channels // reduction,
|
|
out_channels=in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
|
|
def forward(self, inputs):
|
|
outputs = self.avg_pool(inputs)
|
|
outputs = self.conv1(outputs)
|
|
outputs = F.relu(outputs)
|
|
outputs = self.conv2(outputs)
|
|
outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
|
|
return inputs * outputs
|