未验证 提交 df63725b 编写于 作者: F Feng Ni 提交者: GitHub

[Dygraph] add GhostNet backbone (#2377)

* add dy_ghostnet_ssdlite

* precommit format
上级 83714347
architecture: SSD
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/GhostNet_x1_3_ssld_pretrained.pdparams
SSD:
backbone: GhostNet
ssd_head: SSDHead
post_process: BBoxPostProcess
GhostNet:
scale: 1.3
conv_decay: 0.00004
with_extra_blocks: true
extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
feature_maps: [13, 18, 19, 20, 21, 22]
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
SSDHead:
use_sepconv: True
conv_decay: 0.00004
anchor_generator:
steps: [16, 32, 64, 107, 160, 320]
aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
min_ratio: 20
max_ratio: 95
base_size: 320
min_sizes: []
max_sizes: []
offset: 0.5
flip: true
clip: true
min_max_aspect_ratios_order: false
BBoxPostProcess:
decode:
name: SSDBox
nms:
name: MultiClassNMS
keep_top_k: 200
score_threshold: 0.01
nms_threshold: 0.45
nms_top_k: 400
nms_eta: 1.0
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'_base_/optimizer_1700e.yml',
'_base_/ssdlite_ghostnet_320.yml',
'_base_/ssdlite320_reader.yml',
]
weights: output/ssdlite_ghostnet_320_coco/model_final
epoch: 1700
LearningRate:
base_lr: 0.2
schedulers:
- !CosineDecay
max_epochs: 1700
- !LinearWarmup
start_factor: 0.33333
steps: 2000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
...@@ -19,6 +19,7 @@ from . import mobilenet_v1 ...@@ -19,6 +19,7 @@ from . import mobilenet_v1
from . import mobilenet_v3 from . import mobilenet_v3
from . import hrnet from . import hrnet
from . import blazenet from . import blazenet
from . import ghostnet
from .vgg import * from .vgg import *
from .resnet import * from .resnet import *
...@@ -27,3 +28,4 @@ from .mobilenet_v1 import * ...@@ -27,3 +28,4 @@ from .mobilenet_v1 import *
from .mobilenet_v3 import * from .mobilenet_v3 import *
from .hrnet import * from .hrnet import *
from .blazenet import * from .blazenet import *
from .ghostnet import *
# copyright (c) 2021 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.
import math
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Uniform, KaimingNormal
from ppdet.core.workspace import register, serializable
from numbers import Integral
from ..shape_spec import ShapeSpec
from .mobilenet_v3 import make_divisible, ConvBNLayer
__all__ = ['GhostNet']
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=0,
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=1, #
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=0,
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
class SEBlock(nn.Layer):
def __init__(self, num_channels, lr_mult, reduction_ratio=4, name=None):
super(SEBlock, self).__init__()
self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
stdv = 1.0 / math.sqrt(num_channels * 1.0)
med_ch = num_channels // reduction_ratio
self.squeeze = Linear(
num_channels,
med_ch,
weight_attr=ParamAttr(
learning_rate=lr_mult,
initializer=Uniform(-stdv, stdv),
name=name + "_1_weights"),
bias_attr=ParamAttr(
learning_rate=lr_mult, name=name + "_1_offset"))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_channels,
weight_attr=ParamAttr(
learning_rate=lr_mult,
initializer=Uniform(-stdv, stdv),
name=name + "_2_weights"),
bias_attr=ParamAttr(
learning_rate=lr_mult, name=name + "_2_offset"))
def forward(self, inputs):
pool = self.pool2d_gap(inputs)
pool = paddle.squeeze(pool, axis=[2, 3])
squeeze = self.squeeze(pool)
squeeze = F.relu(squeeze)
excitation = self.excitation(squeeze)
excitation = paddle.clip(x=excitation, min=0, max=1)
excitation = paddle.unsqueeze(excitation, axis=[2, 3])
out = paddle.multiply(inputs, excitation)
return out
class GhostModule(nn.Layer):
def __init__(self,
in_channels,
output_channels,
kernel_size=1,
ratio=2,
dw_size=3,
stride=1,
relu=True,
lr_mult=1.,
conv_decay=0.,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
name=None):
super(GhostModule, self).__init__()
init_channels = int(math.ceil(output_channels / ratio))
new_channels = int(init_channels * (ratio - 1))
self.primary_conv = ConvBNLayer(
in_c=in_channels,
out_c=init_channels,
filter_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2),
num_groups=1,
act="relu" if relu else None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_primary_conv")
self.cheap_operation = ConvBNLayer(
in_c=init_channels,
out_c=new_channels,
filter_size=dw_size,
stride=1,
padding=int((dw_size - 1) // 2),
num_groups=init_channels,
act="relu" if relu else None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_cheap_operation")
def forward(self, inputs):
x = self.primary_conv(inputs)
y = self.cheap_operation(x)
out = paddle.concat([x, y], axis=1)
return out
class GhostBottleneck(nn.Layer):
def __init__(self,
in_channels,
hidden_dim,
output_channels,
kernel_size,
stride,
use_se,
lr_mult,
conv_decay=0.,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
return_list=False,
name=None):
super(GhostBottleneck, self).__init__()
self._stride = stride
self._use_se = use_se
self._num_channels = in_channels
self._output_channels = output_channels
self.return_list = return_list
self.ghost_module_1 = GhostModule(
in_channels=in_channels,
output_channels=hidden_dim,
kernel_size=1,
stride=1,
relu=True,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_ghost_module_1")
if stride == 2:
self.depthwise_conv = ConvBNLayer(
in_c=hidden_dim,
out_c=hidden_dim,
filter_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2),
num_groups=hidden_dim,
act=None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name +
"_depthwise_depthwise" # looks strange due to an old typo, will be fixed later.
)
if use_se:
self.se_block = SEBlock(hidden_dim, lr_mult, name=name + "_se")
self.ghost_module_2 = GhostModule(
in_channels=hidden_dim,
output_channels=output_channels,
kernel_size=1,
relu=False,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_ghost_module_2")
if stride != 1 or in_channels != output_channels:
self.shortcut_depthwise = ConvBNLayer(
in_c=in_channels,
out_c=in_channels,
filter_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2),
num_groups=in_channels,
act=None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name +
"_shortcut_depthwise_depthwise" # looks strange due to an old typo, will be fixed later.
)
self.shortcut_conv = ConvBNLayer(
in_c=in_channels,
out_c=output_channels,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
act=None,
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name=name + "_shortcut_conv")
def forward(self, inputs):
y = self.ghost_module_1(inputs)
x = y
if self._stride == 2:
x = self.depthwise_conv(x)
if self._use_se:
x = self.se_block(x)
x = self.ghost_module_2(x)
if self._stride == 1 and self._num_channels == self._output_channels:
shortcut = inputs
else:
shortcut = self.shortcut_depthwise(inputs)
shortcut = self.shortcut_conv(shortcut)
x = paddle.add(x=x, y=shortcut)
if self.return_list:
return [y, x]
else:
return x
@register
@serializable
class GhostNet(nn.Layer):
__shared__ = ['norm_type']
def __init__(
self,
scale=1.3,
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.,
norm_type='bn',
norm_decay=0.0,
freeze_norm=False):
super(GhostNet, 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
self.cfgs = [
# k, t, c, SE, s
[3, 16, 16, 0, 1],
[3, 48, 24, 0, 2],
[3, 72, 24, 0, 1],
[5, 72, 40, 1, 2],
[5, 120, 40, 1, 1],
[3, 240, 80, 0, 2],
[3, 200, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 480, 112, 1, 1],
[3, 672, 112, 1, 1],
[5, 672, 160, 1, 2], # SSDLite output
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1],
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1]
]
self.scale = scale
conv1_out_ch = int(make_divisible(inplanes * self.scale, 4))
self.conv1 = ConvBNLayer(
in_c=3,
out_c=conv1_out_ch,
filter_size=3,
stride=2,
padding=1,
num_groups=1,
act="relu",
lr_mult=1.,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name="conv1")
# build inverted residual blocks
self._out_channels = []
self.ghost_bottleneck_list = []
idx = 0
inplanes = conv1_out_ch
for k, exp_size, c, use_se, s in self.cfgs:
lr_idx = min(idx // 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 idx + 2 in self.feature_maps
ghost_bottleneck = self.add_sublayer(
"_ghostbottleneck_" + str(idx),
sublayer=GhostBottleneck(
in_channels=inplanes,
hidden_dim=int(make_divisible(exp_size * self.scale, 4)),
output_channels=int(make_divisible(c * self.scale, 4)),
kernel_size=k,
stride=s,
use_se=use_se,
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="_ghostbottleneck_" + str(idx)))
self.ghost_bottleneck_list.append(ghost_bottleneck)
inplanes = int(make_divisible(c * self.scale, 4))
idx += 1
self._update_out_channels(
int(make_divisible(exp_size * self.scale, 4))
if return_list else inplanes, idx + 1, feature_maps)
if self.with_extra_blocks:
self.extra_block_list = []
extra_out_c = int(make_divisible(self.scale * self.cfgs[-1][1], 4))
lr_idx = min(idx // 3, len(lr_mult_list) - 1)
lr_mult = lr_mult_list[lr_idx]
conv_extra = self.add_sublayer(
"conv" + str(idx + 2),
sublayer=ConvBNLayer(
in_c=inplanes,
out_c=extra_out_c,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
act="relu6",
lr_mult=lr_mult,
conv_decay=conv_decay,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
name="conv" + str(idx + 2)))
self.extra_block_list.append(conv_extra)
idx += 1
self._update_out_channels(extra_out_c, idx + 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(idx + 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(idx + 2)))
self.extra_block_list.append(conv_extra)
idx += 1
self._update_out_channels(block_filter[1], idx + 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, ghost_bottleneck in enumerate(self.ghost_bottleneck_list):
x = ghost_bottleneck(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.ghost_bottleneck_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]
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