forked from PulseFocusPlatform/PulseFocusPlatform
410 lines
13 KiB
Python
410 lines
13 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 paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
from paddle import ParamAttr
|
|
from paddle.regularizer import L2Decay
|
|
from paddle.nn.initializer import KaimingNormal
|
|
from ppdet.core.workspace import register, serializable
|
|
from numbers import Integral
|
|
from ..shape_spec import ShapeSpec
|
|
|
|
__all__ = ['MobileNet']
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride,
|
|
padding,
|
|
num_groups=1,
|
|
act='relu',
|
|
conv_lr=1.,
|
|
conv_decay=0.,
|
|
norm_decay=0.,
|
|
norm_type='bn',
|
|
name=None):
|
|
super(ConvBNLayer, self).__init__()
|
|
self.act = act
|
|
self._conv = nn.Conv2D(
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
groups=num_groups,
|
|
weight_attr=ParamAttr(
|
|
learning_rate=conv_lr,
|
|
initializer=KaimingNormal(),
|
|
regularizer=L2Decay(conv_decay)),
|
|
bias_attr=False)
|
|
|
|
param_attr = ParamAttr(regularizer=L2Decay(norm_decay))
|
|
bias_attr = ParamAttr(regularizer=L2Decay(norm_decay))
|
|
if norm_type == 'sync_bn':
|
|
self._batch_norm = nn.SyncBatchNorm(
|
|
out_channels, weight_attr=param_attr, bias_attr=bias_attr)
|
|
else:
|
|
self._batch_norm = nn.BatchNorm(
|
|
out_channels,
|
|
act=None,
|
|
param_attr=param_attr,
|
|
bias_attr=bias_attr,
|
|
use_global_stats=False)
|
|
|
|
def forward(self, x):
|
|
x = self._conv(x)
|
|
x = self._batch_norm(x)
|
|
if self.act == "relu":
|
|
x = F.relu(x)
|
|
elif self.act == "relu6":
|
|
x = F.relu6(x)
|
|
return x
|
|
|
|
|
|
class DepthwiseSeparable(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels1,
|
|
out_channels2,
|
|
num_groups,
|
|
stride,
|
|
scale,
|
|
conv_lr=1.,
|
|
conv_decay=0.,
|
|
norm_decay=0.,
|
|
norm_type='bn',
|
|
name=None):
|
|
super(DepthwiseSeparable, self).__init__()
|
|
|
|
self._depthwise_conv = ConvBNLayer(
|
|
in_channels,
|
|
int(out_channels1 * scale),
|
|
kernel_size=3,
|
|
stride=stride,
|
|
padding=1,
|
|
num_groups=int(num_groups * scale),
|
|
conv_lr=conv_lr,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name=name + "_dw")
|
|
|
|
self._pointwise_conv = ConvBNLayer(
|
|
int(out_channels1 * scale),
|
|
int(out_channels2 * scale),
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
conv_lr=conv_lr,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name=name + "_sep")
|
|
|
|
def forward(self, x):
|
|
x = self._depthwise_conv(x)
|
|
x = self._pointwise_conv(x)
|
|
return x
|
|
|
|
|
|
class ExtraBlock(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels1,
|
|
out_channels2,
|
|
num_groups=1,
|
|
stride=2,
|
|
conv_lr=1.,
|
|
conv_decay=0.,
|
|
norm_decay=0.,
|
|
norm_type='bn',
|
|
name=None):
|
|
super(ExtraBlock, self).__init__()
|
|
|
|
self.pointwise_conv = ConvBNLayer(
|
|
in_channels,
|
|
int(out_channels1),
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
num_groups=int(num_groups),
|
|
act='relu6',
|
|
conv_lr=conv_lr,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name=name + "_extra1")
|
|
|
|
self.normal_conv = ConvBNLayer(
|
|
int(out_channels1),
|
|
int(out_channels2),
|
|
kernel_size=3,
|
|
stride=stride,
|
|
padding=1,
|
|
num_groups=int(num_groups),
|
|
act='relu6',
|
|
conv_lr=conv_lr,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name=name + "_extra2")
|
|
|
|
def forward(self, x):
|
|
x = self.pointwise_conv(x)
|
|
x = self.normal_conv(x)
|
|
return x
|
|
|
|
|
|
@register
|
|
@serializable
|
|
class MobileNet(nn.Layer):
|
|
__shared__ = ['norm_type']
|
|
|
|
def __init__(self,
|
|
norm_type='bn',
|
|
norm_decay=0.,
|
|
conv_decay=0.,
|
|
scale=1,
|
|
conv_learning_rate=1.0,
|
|
feature_maps=[4, 6, 13],
|
|
with_extra_blocks=False,
|
|
extra_block_filters=[[256, 512], [128, 256], [128, 256],
|
|
[64, 128]]):
|
|
super(MobileNet, self).__init__()
|
|
if isinstance(feature_maps, Integral):
|
|
feature_maps = [feature_maps]
|
|
self.feature_maps = feature_maps
|
|
self.with_extra_blocks = with_extra_blocks
|
|
self.extra_block_filters = extra_block_filters
|
|
|
|
self._out_channels = []
|
|
|
|
self.conv1 = ConvBNLayer(
|
|
in_channels=3,
|
|
out_channels=int(32 * scale),
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv1")
|
|
|
|
self.dwsl = []
|
|
dws21 = self.add_sublayer(
|
|
"conv2_1",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(32 * scale),
|
|
out_channels1=32,
|
|
out_channels2=64,
|
|
num_groups=32,
|
|
stride=1,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv2_1"))
|
|
self.dwsl.append(dws21)
|
|
self._update_out_channels(64, len(self.dwsl), feature_maps)
|
|
dws22 = self.add_sublayer(
|
|
"conv2_2",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(64 * scale),
|
|
out_channels1=64,
|
|
out_channels2=128,
|
|
num_groups=64,
|
|
stride=2,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv2_2"))
|
|
self.dwsl.append(dws22)
|
|
self._update_out_channels(128, len(self.dwsl), feature_maps)
|
|
# 1/4
|
|
dws31 = self.add_sublayer(
|
|
"conv3_1",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(128 * scale),
|
|
out_channels1=128,
|
|
out_channels2=128,
|
|
num_groups=128,
|
|
stride=1,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv3_1"))
|
|
self.dwsl.append(dws31)
|
|
self._update_out_channels(128, len(self.dwsl), feature_maps)
|
|
dws32 = self.add_sublayer(
|
|
"conv3_2",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(128 * scale),
|
|
out_channels1=128,
|
|
out_channels2=256,
|
|
num_groups=128,
|
|
stride=2,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv3_2"))
|
|
self.dwsl.append(dws32)
|
|
self._update_out_channels(256, len(self.dwsl), feature_maps)
|
|
# 1/8
|
|
dws41 = self.add_sublayer(
|
|
"conv4_1",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(256 * scale),
|
|
out_channels1=256,
|
|
out_channels2=256,
|
|
num_groups=256,
|
|
stride=1,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv4_1"))
|
|
self.dwsl.append(dws41)
|
|
self._update_out_channels(256, len(self.dwsl), feature_maps)
|
|
dws42 = self.add_sublayer(
|
|
"conv4_2",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(256 * scale),
|
|
out_channels1=256,
|
|
out_channels2=512,
|
|
num_groups=256,
|
|
stride=2,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv4_2"))
|
|
self.dwsl.append(dws42)
|
|
self._update_out_channels(512, len(self.dwsl), feature_maps)
|
|
# 1/16
|
|
for i in range(5):
|
|
tmp = self.add_sublayer(
|
|
"conv5_" + str(i + 1),
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=512,
|
|
out_channels1=512,
|
|
out_channels2=512,
|
|
num_groups=512,
|
|
stride=1,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv5_" + str(i + 1)))
|
|
self.dwsl.append(tmp)
|
|
self._update_out_channels(512, len(self.dwsl), feature_maps)
|
|
dws56 = self.add_sublayer(
|
|
"conv5_6",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(512 * scale),
|
|
out_channels1=512,
|
|
out_channels2=1024,
|
|
num_groups=512,
|
|
stride=2,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv5_6"))
|
|
self.dwsl.append(dws56)
|
|
self._update_out_channels(1024, len(self.dwsl), feature_maps)
|
|
# 1/32
|
|
dws6 = self.add_sublayer(
|
|
"conv6",
|
|
sublayer=DepthwiseSeparable(
|
|
in_channels=int(1024 * scale),
|
|
out_channels1=1024,
|
|
out_channels2=1024,
|
|
num_groups=1024,
|
|
stride=1,
|
|
scale=scale,
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv6"))
|
|
self.dwsl.append(dws6)
|
|
self._update_out_channels(1024, len(self.dwsl), feature_maps)
|
|
|
|
if self.with_extra_blocks:
|
|
self.extra_blocks = []
|
|
for i, block_filter in enumerate(self.extra_block_filters):
|
|
in_c = 1024 if i == 0 else self.extra_block_filters[i - 1][1]
|
|
conv_extra = self.add_sublayer(
|
|
"conv7_" + str(i + 1),
|
|
sublayer=ExtraBlock(
|
|
in_c,
|
|
block_filter[0],
|
|
block_filter[1],
|
|
conv_lr=conv_learning_rate,
|
|
conv_decay=conv_decay,
|
|
norm_decay=norm_decay,
|
|
norm_type=norm_type,
|
|
name="conv7_" + str(i + 1)))
|
|
self.extra_blocks.append(conv_extra)
|
|
self._update_out_channels(
|
|
block_filter[1],
|
|
len(self.dwsl) + len(self.extra_blocks), 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):
|
|
outs = []
|
|
y = self.conv1(inputs['image'])
|
|
for i, block in enumerate(self.dwsl):
|
|
y = block(y)
|
|
if i + 1 in self.feature_maps:
|
|
outs.append(y)
|
|
|
|
if not self.with_extra_blocks:
|
|
return outs
|
|
|
|
y = outs[-1]
|
|
for i, block in enumerate(self.extra_blocks):
|
|
idx = i + len(self.dwsl)
|
|
y = block(y)
|
|
if idx + 1 in self.feature_maps:
|
|
outs.append(y)
|
|
return outs
|
|
|
|
@property
|
|
def out_shape(self):
|
|
return [ShapeSpec(channels=c) for c in self._out_channels]
|