forked from PulseFocusPlatform/PulseFocusPlatform
565 lines
20 KiB
Python
565 lines
20 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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
|
|
|
|
from collections import OrderedDict
|
|
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
from paddle.fluid.regularizer import L2Decay
|
|
|
|
import numpy as np
|
|
from ppdet.core.workspace import register
|
|
from numbers import Integral
|
|
|
|
__all__ = ['MobileNetV3', 'MobileNetV3RCNN']
|
|
|
|
|
|
@register
|
|
class MobileNetV3(object):
|
|
"""
|
|
MobileNet v3, see https://arxiv.org/abs/1905.02244
|
|
Args:
|
|
scale (float): scaling factor for convolution groups proportion of mobilenet_v3.
|
|
model_name (str): There are two modes, small and large.
|
|
norm_type (str): normalization type, 'bn' and 'sync_bn' are supported.
|
|
norm_decay (float): weight decay for normalization layer weights.
|
|
conv_decay (float): weight decay for convolution layer weights.
|
|
feature_maps (list): index of stages whose feature maps are returned.
|
|
extra_block_filters (list): number of filter for each extra block.
|
|
lr_mult_list (list): learning rate ratio of different blocks, lower learning rate ratio
|
|
is need for pretrained model got using distillation(default as
|
|
[1.0, 1.0, 1.0, 1.0, 1.0]).
|
|
freeze_norm (bool): freeze normalization layers.
|
|
multiplier (float): The multiplier by which to reduce the convolution expansion and
|
|
number of channels.
|
|
"""
|
|
__shared__ = ['norm_type']
|
|
|
|
def __init__(
|
|
self,
|
|
scale=1.0,
|
|
model_name='small',
|
|
feature_maps=[5, 6, 7, 8, 9, 10],
|
|
conv_decay=0.0,
|
|
norm_type='bn',
|
|
norm_decay=0.0,
|
|
extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]],
|
|
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
|
|
freeze_norm=False,
|
|
multiplier=1.0):
|
|
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.scale = scale
|
|
self.model_name = model_name
|
|
self.feature_maps = feature_maps
|
|
self.extra_block_filters = extra_block_filters
|
|
self.conv_decay = conv_decay
|
|
self.norm_decay = norm_decay
|
|
self.inplanes = 16
|
|
self.end_points = []
|
|
self.block_stride = 0
|
|
|
|
self.lr_mult_list = lr_mult_list
|
|
self.freeze_norm = freeze_norm
|
|
self.norm_type = norm_type
|
|
self.curr_stage = 0
|
|
|
|
if model_name == "large":
|
|
self.cfg = [
|
|
# kernel_size, expand, channel, se_block, act_mode, stride
|
|
[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 = [
|
|
# kernel_size, expand, channel, se_block, act_mode, stride
|
|
[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
|
|
|
|
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)
|
|
|
|
def _conv_bn_layer(self,
|
|
input,
|
|
filter_size,
|
|
num_filters,
|
|
stride,
|
|
padding,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act=None,
|
|
name=None,
|
|
use_cudnn=True):
|
|
lr_idx = self.curr_stage // 3
|
|
lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
|
|
lr_mult = self.lr_mult_list[lr_idx]
|
|
conv = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
groups=num_groups,
|
|
act=None,
|
|
use_cudnn=use_cudnn,
|
|
param_attr=ParamAttr(
|
|
name=name + '_weights',
|
|
learning_rate=lr_mult,
|
|
regularizer=L2Decay(self.conv_decay)),
|
|
bias_attr=False)
|
|
bn_name = name + '_bn'
|
|
bn = self._bn(conv, bn_name=bn_name)
|
|
|
|
if if_act:
|
|
if act == 'relu':
|
|
bn = fluid.layers.relu(bn)
|
|
elif act == 'hard_swish':
|
|
bn = self._hard_swish(bn)
|
|
elif act == 'relu6':
|
|
bn = fluid.layers.relu6(bn)
|
|
return bn
|
|
|
|
def _bn(self, input, act=None, bn_name=None):
|
|
lr_idx = self.curr_stage // 3
|
|
lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
|
|
lr_mult = self.lr_mult_list[lr_idx]
|
|
norm_lr = 0. if self.freeze_norm else lr_mult
|
|
norm_decay = self.norm_decay
|
|
pattr = ParamAttr(
|
|
name=bn_name + '_scale',
|
|
learning_rate=norm_lr,
|
|
regularizer=L2Decay(norm_decay))
|
|
battr = ParamAttr(
|
|
name=bn_name + '_offset',
|
|
learning_rate=norm_lr,
|
|
regularizer=L2Decay(norm_decay))
|
|
|
|
conv = input
|
|
|
|
if self.norm_type in ['bn', 'sync_bn']:
|
|
global_stats = True if self.freeze_norm else False
|
|
out = fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
name=bn_name + '.output.1',
|
|
param_attr=pattr,
|
|
bias_attr=battr,
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance',
|
|
use_global_stats=global_stats)
|
|
scale = fluid.framework._get_var(pattr.name)
|
|
bias = fluid.framework._get_var(battr.name)
|
|
elif self.norm_type == 'affine_channel':
|
|
scale = fluid.layers.create_parameter(
|
|
shape=[conv.shape[1]],
|
|
dtype=conv.dtype,
|
|
attr=pattr,
|
|
default_initializer=fluid.initializer.Constant(1.))
|
|
bias = fluid.layers.create_parameter(
|
|
shape=[conv.shape[1]],
|
|
dtype=conv.dtype,
|
|
attr=battr,
|
|
default_initializer=fluid.initializer.Constant(0.))
|
|
out = fluid.layers.affine_channel(
|
|
x=conv, scale=scale, bias=bias, act=act)
|
|
|
|
if self.freeze_norm:
|
|
scale.stop_gradient = True
|
|
bias.stop_gradient = True
|
|
|
|
return out
|
|
|
|
def _hard_swish(self, x):
|
|
return fluid.layers.elementwise_mul(x, fluid.layers.relu6(x + 3) / 6.)
|
|
|
|
def _se_block(self, input, num_out_filter, ratio=4, name=None):
|
|
lr_idx = self.curr_stage // 3
|
|
lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
|
|
lr_mult = self.lr_mult_list[lr_idx]
|
|
|
|
num_mid_filter = int(num_out_filter // ratio)
|
|
pool = fluid.layers.pool2d(
|
|
input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
|
|
conv1 = fluid.layers.conv2d(
|
|
input=pool,
|
|
filter_size=1,
|
|
num_filters=num_mid_filter,
|
|
act='relu',
|
|
param_attr=ParamAttr(
|
|
name=name + '_1_weights',
|
|
learning_rate=lr_mult,
|
|
regularizer=L2Decay(self.conv_decay)),
|
|
bias_attr=ParamAttr(
|
|
name=name + '_1_offset',
|
|
learning_rate=lr_mult,
|
|
regularizer=L2Decay(self.conv_decay)))
|
|
conv2 = fluid.layers.conv2d(
|
|
input=conv1,
|
|
filter_size=1,
|
|
num_filters=num_out_filter,
|
|
act='hard_sigmoid',
|
|
param_attr=ParamAttr(
|
|
name=name + '_2_weights',
|
|
learning_rate=lr_mult,
|
|
regularizer=L2Decay(self.conv_decay)),
|
|
bias_attr=ParamAttr(
|
|
name=name + '_2_offset',
|
|
learning_rate=lr_mult,
|
|
regularizer=L2Decay(self.conv_decay)))
|
|
|
|
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
|
|
return scale
|
|
|
|
def _residual_unit(self,
|
|
input,
|
|
num_in_filter,
|
|
num_mid_filter,
|
|
num_out_filter,
|
|
stride,
|
|
filter_size,
|
|
act=None,
|
|
use_se=False,
|
|
name=None):
|
|
input_data = input
|
|
conv0 = self._conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_mid_filter,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=True,
|
|
act=act,
|
|
name=name + '_expand')
|
|
|
|
if self.block_stride == 4 and stride == 2:
|
|
self.block_stride += 1
|
|
if self.block_stride in self.feature_maps:
|
|
self.end_points.append(conv0)
|
|
|
|
with fluid.name_scope('res_conv1'):
|
|
conv1 = self._conv_bn_layer(
|
|
input=conv0,
|
|
filter_size=filter_size,
|
|
num_filters=num_mid_filter,
|
|
stride=stride,
|
|
padding=int((filter_size - 1) // 2),
|
|
if_act=True,
|
|
act=act,
|
|
num_groups=num_mid_filter,
|
|
use_cudnn=False,
|
|
name=name + '_depthwise')
|
|
|
|
if use_se:
|
|
with fluid.name_scope('se_block'):
|
|
conv1 = self._se_block(
|
|
input=conv1,
|
|
num_out_filter=num_mid_filter,
|
|
name=name + '_se')
|
|
|
|
conv2 = self._conv_bn_layer(
|
|
input=conv1,
|
|
filter_size=1,
|
|
num_filters=num_out_filter,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=False,
|
|
name=name + '_linear')
|
|
if num_in_filter != num_out_filter or stride != 1:
|
|
return conv2
|
|
else:
|
|
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
|
|
|
|
def _extra_block_dw(self,
|
|
input,
|
|
num_filters1,
|
|
num_filters2,
|
|
stride,
|
|
name=None):
|
|
pointwise_conv = self._conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=int(num_filters1),
|
|
stride=1,
|
|
padding="SAME",
|
|
act='relu6',
|
|
name=name + "_extra1")
|
|
depthwise_conv = self._conv_bn_layer(
|
|
input=pointwise_conv,
|
|
filter_size=3,
|
|
num_filters=int(num_filters2),
|
|
stride=stride,
|
|
padding="SAME",
|
|
num_groups=int(num_filters1),
|
|
act='relu6',
|
|
use_cudnn=False,
|
|
name=name + "_extra2_dw")
|
|
normal_conv = self._conv_bn_layer(
|
|
input=depthwise_conv,
|
|
filter_size=1,
|
|
num_filters=int(num_filters2),
|
|
stride=1,
|
|
padding="SAME",
|
|
act='relu6',
|
|
name=name + "_extra2_sep")
|
|
return normal_conv
|
|
|
|
def _make_divisible(self, 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
|
|
|
|
def __call__(self, input):
|
|
scale = self.scale
|
|
inplanes = self.inplanes
|
|
cfg = self.cfg
|
|
blocks = []
|
|
|
|
#conv1
|
|
conv = self._conv_bn_layer(
|
|
input,
|
|
filter_size=3,
|
|
num_filters=self._make_divisible(inplanes * scale),
|
|
stride=2,
|
|
padding=1,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv1')
|
|
i = 0
|
|
inplanes = self._make_divisible(inplanes * scale)
|
|
for layer_cfg in cfg:
|
|
if layer_cfg[5] == 2:
|
|
self.block_stride += 1
|
|
if self.block_stride in self.feature_maps:
|
|
self.end_points.append(conv)
|
|
|
|
conv = self._residual_unit(
|
|
input=conv,
|
|
num_in_filter=inplanes,
|
|
num_mid_filter=self._make_divisible(scale * layer_cfg[1]),
|
|
num_out_filter=self._make_divisible(scale * layer_cfg[2]),
|
|
act=layer_cfg[4],
|
|
stride=layer_cfg[5],
|
|
filter_size=layer_cfg[0],
|
|
use_se=layer_cfg[3],
|
|
name='conv' + str(i + 2))
|
|
inplanes = self._make_divisible(scale * layer_cfg[2])
|
|
i += 1
|
|
self.curr_stage += 1
|
|
self.block_stride += 1
|
|
if self.block_stride in self.feature_maps:
|
|
self.end_points.append(conv)
|
|
|
|
# extra block
|
|
# check whether conv_extra is needed
|
|
if self.block_stride < max(self.feature_maps):
|
|
conv_extra = self._conv_bn_layer(
|
|
conv,
|
|
filter_size=1,
|
|
num_filters=self._make_divisible(scale * cfg[-1][1]),
|
|
stride=1,
|
|
padding="SAME",
|
|
num_groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv' + str(i + 2))
|
|
self.block_stride += 1
|
|
if self.block_stride in self.feature_maps:
|
|
self.end_points.append(conv_extra)
|
|
i += 1
|
|
for block_filter in self.extra_block_filters:
|
|
conv_extra = self._extra_block_dw(conv_extra, block_filter[0],
|
|
block_filter[1], 2,
|
|
'conv' + str(i + 2))
|
|
self.block_stride += 1
|
|
if self.block_stride in self.feature_maps:
|
|
self.end_points.append(conv_extra)
|
|
i += 1
|
|
|
|
return OrderedDict([('mbv3_{}'.format(idx), feat)
|
|
for idx, feat in enumerate(self.end_points)])
|
|
|
|
|
|
@register
|
|
class MobileNetV3RCNN(MobileNetV3):
|
|
def __init__(self,
|
|
scale=1.0,
|
|
model_name='large',
|
|
conv_decay=0.0,
|
|
norm_type='bn',
|
|
norm_decay=0.0,
|
|
freeze_norm=True,
|
|
feature_maps=[2, 3, 4, 5],
|
|
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
|
|
super(MobileNetV3RCNN, self).__init__(
|
|
scale=scale,
|
|
model_name=model_name,
|
|
conv_decay=conv_decay,
|
|
norm_type=norm_type,
|
|
norm_decay=norm_decay,
|
|
lr_mult_list=lr_mult_list,
|
|
feature_maps=feature_maps,
|
|
freeze_norm=freeze_norm)
|
|
self.curr_stage = 0
|
|
self.block_stride = 1
|
|
|
|
def _residual_unit(self,
|
|
input,
|
|
num_in_filter,
|
|
num_mid_filter,
|
|
num_out_filter,
|
|
stride,
|
|
filter_size,
|
|
act=None,
|
|
use_se=False,
|
|
name=None):
|
|
input_data = input
|
|
conv0 = self._conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_mid_filter,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=True,
|
|
act=act,
|
|
name=name + '_expand')
|
|
|
|
feature_level = int(np.log2(self.block_stride))
|
|
if feature_level in self.feature_maps and stride == 2:
|
|
self.end_points.append(conv0)
|
|
|
|
conv1 = self._conv_bn_layer(
|
|
input=conv0,
|
|
filter_size=filter_size,
|
|
num_filters=num_mid_filter,
|
|
stride=stride,
|
|
padding=int((filter_size - 1) // 2),
|
|
if_act=True,
|
|
act=act,
|
|
num_groups=num_mid_filter,
|
|
use_cudnn=False,
|
|
name=name + '_depthwise')
|
|
|
|
if use_se:
|
|
conv1 = self._se_block(
|
|
input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
|
|
|
|
conv2 = self._conv_bn_layer(
|
|
input=conv1,
|
|
filter_size=1,
|
|
num_filters=num_out_filter,
|
|
stride=1,
|
|
padding=0,
|
|
if_act=False,
|
|
name=name + '_linear')
|
|
if num_in_filter != num_out_filter or stride != 1:
|
|
return conv2
|
|
else:
|
|
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
|
|
|
|
def __call__(self, input):
|
|
scale = self.scale
|
|
inplanes = self.inplanes
|
|
cfg = self.cfg
|
|
#conv1
|
|
conv = self._conv_bn_layer(
|
|
input,
|
|
filter_size=3,
|
|
num_filters=self._make_divisible(inplanes * scale),
|
|
stride=2,
|
|
padding=1,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv1')
|
|
i = 0
|
|
inplanes = self._make_divisible(inplanes * scale)
|
|
for layer_cfg in cfg:
|
|
self.block_stride *= layer_cfg[5]
|
|
conv = self._residual_unit(
|
|
input=conv,
|
|
num_in_filter=inplanes,
|
|
num_mid_filter=self._make_divisible(scale * layer_cfg[1]),
|
|
num_out_filter=self._make_divisible(scale * layer_cfg[2]),
|
|
act=layer_cfg[4],
|
|
stride=layer_cfg[5],
|
|
filter_size=layer_cfg[0],
|
|
use_se=layer_cfg[3],
|
|
name='conv' + str(i + 2))
|
|
inplanes = self._make_divisible(scale * layer_cfg[2])
|
|
i += 1
|
|
self.curr_stage += 1
|
|
|
|
if np.max(self.feature_maps) >= 5:
|
|
conv = self._conv_bn_layer(
|
|
input=conv,
|
|
filter_size=1,
|
|
num_filters=self._make_divisible(scale * cfg[-1][1]),
|
|
stride=1,
|
|
padding=0,
|
|
num_groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv_last')
|
|
self.end_points.append(conv)
|
|
i += 1
|
|
|
|
res = OrderedDict([('mv3_{}'.format(idx), self.end_points[idx])
|
|
for idx, feat_idx in enumerate(self.feature_maps)])
|
|
return res
|