360 lines
11 KiB
Python
360 lines
11 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 numpy as np
|
|
import paddle
|
|
from paddle import ParamAttr
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
from paddle.nn.functional.activation import hard_sigmoid, hard_swish
|
|
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
|
|
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
|
|
from paddle.regularizer import L2Decay
|
|
|
|
import math
|
|
|
|
__all__ = [
|
|
"MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
|
|
"MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0",
|
|
"MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35",
|
|
"MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75",
|
|
"MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25"
|
|
]
|
|
|
|
|
|
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,
|
|
scale=1.0,
|
|
model_name="small",
|
|
dropout_prob=0.2,
|
|
class_dim=1000):
|
|
super(MobileNetV3, self).__init__()
|
|
|
|
inplanes = 16
|
|
if model_name == "large":
|
|
self.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, "hard_swish", 2],
|
|
[3, 200, 80, False, "hard_swish", 1],
|
|
[3, 184, 80, False, "hard_swish", 1],
|
|
[3, 184, 80, False, "hard_swish", 1],
|
|
[3, 480, 112, True, "hard_swish", 1],
|
|
[3, 672, 112, True, "hard_swish", 1],
|
|
[5, 672, 160, True, "hard_swish", 2],
|
|
[5, 960, 160, True, "hard_swish", 1],
|
|
[5, 960, 160, True, "hard_swish", 1],
|
|
]
|
|
self.cls_ch_squeeze = 960
|
|
self.cls_ch_expand = 1280
|
|
elif model_name == "small":
|
|
self.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, "hard_swish", 2],
|
|
[5, 240, 40, True, "hard_swish", 1],
|
|
[5, 240, 40, True, "hard_swish", 1],
|
|
[5, 120, 48, True, "hard_swish", 1],
|
|
[5, 144, 48, True, "hard_swish", 1],
|
|
[5, 288, 96, True, "hard_swish", 2],
|
|
[5, 576, 96, True, "hard_swish", 1],
|
|
[5, 576, 96, True, "hard_swish", 1],
|
|
]
|
|
self.cls_ch_squeeze = 576
|
|
self.cls_ch_expand = 1280
|
|
else:
|
|
raise NotImplementedError(
|
|
"mode[{}_model] is not implemented!".format(model_name))
|
|
|
|
self.conv1 = ConvBNLayer(
|
|
in_c=3,
|
|
out_c=make_divisible(inplanes * scale),
|
|
filter_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act="hard_swish",
|
|
name="conv1")
|
|
|
|
self.block_list = []
|
|
i = 0
|
|
inplanes = make_divisible(inplanes * scale)
|
|
for (k, exp, c, se, nl, s) in self.cfg:
|
|
block = self.add_sublayer(
|
|
"conv" + str(i + 2),
|
|
ResidualUnit(
|
|
in_c=inplanes,
|
|
mid_c=make_divisible(scale * exp),
|
|
out_c=make_divisible(scale * c),
|
|
filter_size=k,
|
|
stride=s,
|
|
use_se=se,
|
|
act=nl,
|
|
name="conv" + str(i + 2)))
|
|
self.block_list.append(block)
|
|
inplanes = make_divisible(scale * c)
|
|
i += 1
|
|
|
|
self.last_second_conv = ConvBNLayer(
|
|
in_c=inplanes,
|
|
out_c=make_divisible(scale * self.cls_ch_squeeze),
|
|
filter_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act="hard_swish",
|
|
name="conv_last")
|
|
|
|
self.pool = AdaptiveAvgPool2D(1)
|
|
|
|
self.last_conv = Conv2D(
|
|
in_channels=make_divisible(scale * self.cls_ch_squeeze),
|
|
out_channels=self.cls_ch_expand,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(name="last_1x1_conv_weights"),
|
|
bias_attr=False)
|
|
|
|
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
|
|
|
|
self.out = Linear(
|
|
self.cls_ch_expand,
|
|
class_dim,
|
|
weight_attr=ParamAttr("fc_weights"),
|
|
bias_attr=ParamAttr(name="fc_offset"))
|
|
|
|
def forward(self, inputs, label=None):
|
|
x = self.conv1(inputs)
|
|
|
|
for block in self.block_list:
|
|
x = block(x)
|
|
|
|
x = self.last_second_conv(x)
|
|
x = self.pool(x)
|
|
|
|
x = self.last_conv(x)
|
|
x = hard_swish(x)
|
|
x = self.dropout(x)
|
|
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
|
|
x = self.out(x)
|
|
|
|
return x
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(self,
|
|
in_c,
|
|
out_c,
|
|
filter_size,
|
|
stride,
|
|
padding,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act=None,
|
|
use_cudnn=True,
|
|
name=""):
|
|
super(ConvBNLayer, self).__init__()
|
|
self.if_act = if_act
|
|
self.act = act
|
|
self.conv = Conv2D(
|
|
in_channels=in_c,
|
|
out_channels=out_c,
|
|
kernel_size=filter_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
groups=num_groups,
|
|
weight_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
self.bn = BatchNorm(
|
|
num_channels=out_c,
|
|
act=None,
|
|
param_attr=ParamAttr(
|
|
name=name + "_bn_scale", regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(
|
|
name=name + "_bn_offset", regularizer=L2Decay(0.0)),
|
|
moving_mean_name=name + "_bn_mean",
|
|
moving_variance_name=name + "_bn_variance")
|
|
|
|
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 == "hard_swish":
|
|
x = hard_swish(x)
|
|
else:
|
|
print("The activation function is selected incorrectly.")
|
|
exit()
|
|
return x
|
|
|
|
|
|
class ResidualUnit(nn.Layer):
|
|
def __init__(self,
|
|
in_c,
|
|
mid_c,
|
|
out_c,
|
|
filter_size,
|
|
stride,
|
|
use_se,
|
|
act=None,
|
|
name=''):
|
|
super(ResidualUnit, self).__init__()
|
|
self.if_shortcut = stride == 1 and in_c == out_c
|
|
self.if_se = use_se
|
|
|
|
self.expand_conv = ConvBNLayer(
|
|
in_c=in_c,
|
|
out_c=mid_c,
|
|
filter_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=True,
|
|
act=act,
|
|
name=name + "_expand")
|
|
self.bottleneck_conv = ConvBNLayer(
|
|
in_c=mid_c,
|
|
out_c=mid_c,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=int((filter_size - 1) // 2),
|
|
num_groups=mid_c,
|
|
if_act=True,
|
|
act=act,
|
|
name=name + "_depthwise")
|
|
if self.if_se:
|
|
self.mid_se = SEModule(mid_c, name=name + "_se")
|
|
self.linear_conv = ConvBNLayer(
|
|
in_c=mid_c,
|
|
out_c=out_c,
|
|
filter_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=False,
|
|
act=None,
|
|
name=name + "_linear")
|
|
|
|
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, channel, reduction=4, name=""):
|
|
super(SEModule, self).__init__()
|
|
self.avg_pool = AdaptiveAvgPool2D(1)
|
|
self.conv1 = Conv2D(
|
|
in_channels=channel,
|
|
out_channels=channel // reduction,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(name=name + "_1_weights"),
|
|
bias_attr=ParamAttr(name=name + "_1_offset"))
|
|
self.conv2 = Conv2D(
|
|
in_channels=channel // reduction,
|
|
out_channels=channel,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(name + "_2_weights"),
|
|
bias_attr=ParamAttr(name=name + "_2_offset"))
|
|
|
|
def forward(self, inputs):
|
|
outputs = self.avg_pool(inputs)
|
|
outputs = self.conv1(outputs)
|
|
outputs = F.relu(outputs)
|
|
outputs = self.conv2(outputs)
|
|
outputs = hard_sigmoid(outputs)
|
|
return paddle.multiply(x=inputs, y=outputs)
|
|
|
|
|
|
def MobileNetV3_small_x0_35(**args):
|
|
model = MobileNetV3(model_name="small", scale=0.35, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_small_x0_5(**args):
|
|
model = MobileNetV3(model_name="small", scale=0.5, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_small_x0_75(**args):
|
|
model = MobileNetV3(model_name="small", scale=0.75, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_small_x1_0(**args):
|
|
model = MobileNetV3(model_name="small", scale=1.0, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_small_x1_25(**args):
|
|
model = MobileNetV3(model_name="small", scale=1.25, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_large_x0_35(**args):
|
|
model = MobileNetV3(model_name="large", scale=0.35, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_large_x0_5(**args):
|
|
model = MobileNetV3(model_name="large", scale=0.5, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_large_x0_75(**args):
|
|
model = MobileNetV3(model_name="large", scale=0.75, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_large_x1_0(**args):
|
|
model = MobileNetV3(model_name="large", scale=1.0, **args)
|
|
return model
|
|
|
|
|
|
def MobileNetV3_large_x1_25(**args):
|
|
model = MobileNetV3(model_name="large", scale=1.25, **args)
|
|
return model
|