更换文档检测模型
This commit is contained in:
478
paddle_detection/ppdet/modeling/backbones/mobilenet_v3.py
Normal file
478
paddle_detection/ppdet/modeling/backbones/mobilenet_v3.py
Normal file
@@ -0,0 +1,478 @@
|
||||
# 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
|
||||
import paddle.nn as nn
|
||||
import paddle.nn.functional as F
|
||||
from paddle import ParamAttr
|
||||
from paddle.regularizer import L2Decay
|
||||
from 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]
|
||||
Reference in New Issue
Block a user