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PaddleRS/paddlers/models/ppdet/modeling/backbones/mobilenet_v3.py

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# Copyright (c) 2022 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
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from paddlers.models.ppdet.core.workspace import register, serializable
from numbers import Integral
from ..shape_spec import ShapeSpec
__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 ConvBNLayer(nn.Layer):
def __init__(self,
in_c,
out_c,
filter_size,
stride,
padding,
num_groups=1,
act=None,
lr_mult=1.,
conv_decay=0.,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
name=""):
super(ConvBNLayer, self).__init__()
self.act = act
self.conv = nn.Conv2D(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(
learning_rate=lr_mult, regularizer=L2Decay(conv_decay)),
bias_attr=False)
norm_lr = 0. if freeze_norm else lr_mult
param_attr = ParamAttr(
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay),
trainable=False if freeze_norm else True)
bias_attr = ParamAttr(
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay),
trainable=False if freeze_norm else True)
global_stats = True if freeze_norm else None
if norm_type in ['sync_bn', 'bn']:
self.bn = nn.BatchNorm2D(
out_c,
weight_attr=param_attr,
bias_attr=bias_attr,
use_global_stats=global_stats)
norm_params = self.bn.parameters()
if freeze_norm:
for param in norm_params:
param.stop_gradient = True
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.act is not None:
if self.act == "relu":
x = F.relu(x)
elif self.act == "relu6":
x = F.relu6(x)
elif self.act == "hard_swish":
x = F.hardswish(x)
else:
raise NotImplementedError(
"The activation function is selected incorrectly.")
return x
class ResidualUnit(nn.Layer):
def __init__(self,
in_c,
mid_c,
out_c,
filter_size,
stride,
use_se,
lr_mult,
conv_decay=0.,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
act=None,
return_list=False,
name=''):
super(ResidualUnit, self).__init__()
self.if_shortcut = stride == 1 and in_c == out_c
self.use_se = use_se
self.return_list = return_list
self.expand_conv = ConvBNLayer(
in_c=in_c,
out_c=mid_c,
filter_size=1,
stride=1,
padding=0,
act=act,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
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,
act=act,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_depthwise")
if self.use_se:
self.mid_se = SEModule(
mid_c, lr_mult, conv_decay, name=name + "_se")
self.linear_conv = ConvBNLayer(
in_c=mid_c,
out_c=out_c,
filter_size=1,
stride=1,
padding=0,
act=None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_linear")
def forward(self, inputs):
y = self.expand_conv(inputs)
x = self.bottleneck_conv(y)
if self.use_se:
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.add(inputs, x)
if self.return_list:
return [y, x]
else:
return x
class SEModule(nn.Layer):
def __init__(self, channel, lr_mult, conv_decay, reduction=4, name=""):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
mid_channels = int(channel // reduction)
self.conv1 = nn.Conv2D(
in_channels=channel,
out_channels=mid_channels,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(
learning_rate=lr_mult, regularizer=L2Decay(conv_decay)),
bias_attr=ParamAttr(
learning_rate=lr_mult, regularizer=L2Decay(conv_decay)))
self.conv2 = nn.Conv2D(
in_channels=mid_channels,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(
learning_rate=lr_mult, regularizer=L2Decay(conv_decay)),
bias_attr=ParamAttr(
learning_rate=lr_mult, regularizer=L2Decay(conv_decay)))
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 paddle.multiply(x=inputs, y=outputs)
class ExtraBlockDW(nn.Layer):
def __init__(self,
in_c,
ch_1,
ch_2,
stride,
lr_mult,
conv_decay=0.,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
name=None):
super(ExtraBlockDW, self).__init__()
self.pointwise_conv = ConvBNLayer(
in_c=in_c,
out_c=ch_1,
filter_size=1,
stride=1,
padding='SAME',
act='relu6',
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_extra1")
self.depthwise_conv = ConvBNLayer(
in_c=ch_1,
out_c=ch_2,
filter_size=3,
stride=stride,
padding='SAME',
num_groups=int(ch_1),
act='relu6',
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_extra2_dw")
self.normal_conv = ConvBNLayer(
in_c=ch_2,
out_c=ch_2,
filter_size=1,
stride=1,
padding='SAME',
act='relu6',
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_extra2_sep")
def forward(self, inputs):
x = self.pointwise_conv(inputs)
x = self.depthwise_conv(x)
x = self.normal_conv(x)
return x
@register
@serializable
class MobileNetV3(nn.Layer):
__shared__ = ['norm_type']
def __init__(self,
scale=1.0,
model_name="large",
feature_maps=[6, 12, 15],
with_extra_blocks=False,
extra_block_filters=[[256, 512], [128, 256], [128, 256],
[64, 128]],
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
conv_decay=0.0,
multiplier=1.0,
norm_type='bn',
norm_decay=0.0,
freeze_norm=False):
super(MobileNetV3, self).__init__()
if isinstance(feature_maps, Integral):
feature_maps = [feature_maps]
if norm_type == 'sync_bn' and freeze_norm:
raise ValueError(
"The norm_type should not be sync_bn when freeze_norm is True")
self.feature_maps = feature_maps
self.with_extra_blocks = with_extra_blocks
self.extra_block_filters = extra_block_filters
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], # RCNN output
[5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1], # YOLOv3 output
[3, 240, 80, False, "hard_swish", 2], # RCNN output
[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], # YOLOv3 output
[5, 672, 160, True, "hard_swish",
2], # SSD/SSDLite/RCNN output
[5, 960, 160, True, "hard_swish", 1],
[5, 960, 160, True, "hard_swish", 1], # YOLOv3 output
]
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2], # RCNN output
[3, 88, 24, False, "relu", 1], # YOLOv3 output
[5, 96, 40, True, "hard_swish", 2], # RCNN output
[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], # YOLOv3 output
[5, 288, 96, True, "hard_swish", 2], # SSD/SSDLite/RCNN output
[5, 576, 96, True, "hard_swish", 1],
[5, 576, 96, True, "hard_swish", 1], # YOLOv3 output
]
else:
raise NotImplementedError(
"mode[{}_model] is not implemented!".format(model_name))
if multiplier != 1.0:
self.cfg[-3][2] = int(self.cfg[-3][2] * multiplier)
self.cfg[-2][1] = int(self.cfg[-2][1] * multiplier)
self.cfg[-2][2] = int(self.cfg[-2][2] * multiplier)
self.cfg[-1][1] = int(self.cfg[-1][1] * multiplier)
self.cfg[-1][2] = int(self.cfg[-1][2] * multiplier)
self.conv1 = ConvBNLayer(
in_c=3,
out_c=make_divisible(inplanes * scale),
filter_size=3,
stride=2,
padding=1,
num_groups=1,
act="hard_swish",
lr_mult=lr_mult_list[0],
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name="conv1")
self._out_channels = []
self.block_list = []
i = 0
inplanes = make_divisible(inplanes * scale)
for (k, exp, c, se, nl, s) in self.cfg:
lr_idx = min(i // 3, len(lr_mult_list) - 1)
lr_mult = lr_mult_list[lr_idx]
# for SSD/SSDLite, first head input is after ResidualUnit expand_conv
return_list = self.with_extra_blocks and i + 2 in self.feature_maps
block = self.add_sublayer(
"conv" + str(i + 2),
sublayer=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,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
return_list=return_list,
name="conv" + str(i + 2)))
self.block_list.append(block)
inplanes = make_divisible(scale * c)
i += 1
self._update_out_channels(
make_divisible(scale * exp)
if return_list else inplanes, i + 1, feature_maps)
if self.with_extra_blocks:
self.extra_block_list = []
extra_out_c = make_divisible(scale * self.cfg[-1][1])
lr_idx = min(i // 3, len(lr_mult_list) - 1)
lr_mult = lr_mult_list[lr_idx]
conv_extra = self.add_sublayer(
"conv" + str(i + 2),
sublayer=ConvBNLayer(
in_c=inplanes,
out_c=extra_out_c,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
act="hard_swish",
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name="conv" + str(i + 2)))
self.extra_block_list.append(conv_extra)
i += 1
self._update_out_channels(extra_out_c, i + 1, feature_maps)
for j, block_filter in enumerate(self.extra_block_filters):
in_c = extra_out_c if j == 0 else self.extra_block_filters[
j - 1][1]
conv_extra = self.add_sublayer(
"conv" + str(i + 2),
sublayer=ExtraBlockDW(
in_c,
block_filter[0],
block_filter[1],
stride=2,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name='conv' + str(i + 2)))
self.extra_block_list.append(conv_extra)
i += 1
self._update_out_channels(block_filter[1], i + 1, feature_maps)
def _update_out_channels(self, channel, feature_idx, feature_maps):
if feature_idx in feature_maps:
self._out_channels.append(channel)
def forward(self, inputs):
x = self.conv1(inputs['image'])
outs = []
for idx, block in enumerate(self.block_list):
x = block(x)
if idx + 2 in self.feature_maps:
if isinstance(x, list):
outs.append(x[0])
x = x[1]
else:
outs.append(x)
if not self.with_extra_blocks:
return outs
for i, block in enumerate(self.extra_block_list):
idx = i + len(self.block_list)
x = block(x)
if idx + 2 in self.feature_maps:
outs.append(x)
return outs
@property
def out_shape(self):
return [ShapeSpec(channels=c) for c in self._out_channels]