forked from PulseFocusPlatform/PulseFocusPlatform
219 lines
7.9 KiB
Python
219 lines
7.9 KiB
Python
# Copyright (c) 2019 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 paddle import fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
from paddle.fluid.regularizer import L2Decay
|
|
|
|
from ppdet.experimental import mixed_precision_global_state
|
|
from ppdet.core.workspace import register
|
|
|
|
__all__ = ['MobileNet']
|
|
|
|
|
|
@register
|
|
class MobileNet(object):
|
|
"""
|
|
MobileNet v1, see https://arxiv.org/abs/1704.04861
|
|
|
|
Args:
|
|
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.
|
|
conv_group_scale (int): scaling factor for convolution groups
|
|
with_extra_blocks (bool): if extra blocks should be added
|
|
extra_block_filters (list): number of filter for each extra block
|
|
"""
|
|
__shared__ = ['norm_type', 'weight_prefix_name']
|
|
|
|
def __init__(self,
|
|
norm_type='bn',
|
|
norm_decay=0.,
|
|
conv_decay=0.,
|
|
conv_group_scale=1,
|
|
conv_learning_rate=1.0,
|
|
with_extra_blocks=False,
|
|
extra_block_filters=[[256, 512], [128, 256], [128, 256],
|
|
[64, 128]],
|
|
weight_prefix_name=''):
|
|
self.norm_type = norm_type
|
|
self.norm_decay = norm_decay
|
|
self.conv_decay = conv_decay
|
|
self.conv_group_scale = conv_group_scale
|
|
self.conv_learning_rate = conv_learning_rate
|
|
self.with_extra_blocks = with_extra_blocks
|
|
self.extra_block_filters = extra_block_filters
|
|
self.prefix_name = weight_prefix_name
|
|
|
|
def _conv_norm(self,
|
|
input,
|
|
filter_size,
|
|
num_filters,
|
|
stride,
|
|
padding,
|
|
num_groups=1,
|
|
act='relu',
|
|
use_cudnn=True,
|
|
name=None):
|
|
parameter_attr = ParamAttr(
|
|
learning_rate=self.conv_learning_rate,
|
|
initializer=fluid.initializer.MSRA(),
|
|
regularizer=L2Decay(self.conv_decay),
|
|
name=name + "_weights")
|
|
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=parameter_attr,
|
|
bias_attr=False)
|
|
|
|
bn_name = name + "_bn"
|
|
norm_decay = self.norm_decay
|
|
bn_param_attr = ParamAttr(
|
|
regularizer=L2Decay(norm_decay), name=bn_name + '_scale')
|
|
bn_bias_attr = ParamAttr(
|
|
regularizer=L2Decay(norm_decay), name=bn_name + '_offset')
|
|
return fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
param_attr=bn_param_attr,
|
|
bias_attr=bn_bias_attr,
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
def depthwise_separable(self,
|
|
input,
|
|
num_filters1,
|
|
num_filters2,
|
|
num_groups,
|
|
stride,
|
|
scale,
|
|
name=None):
|
|
mixed_precision_enabled = mixed_precision_global_state() is not None
|
|
depthwise_conv = self._conv_norm(
|
|
input=input,
|
|
filter_size=3,
|
|
num_filters=int(num_filters1 * scale),
|
|
stride=stride,
|
|
padding=1,
|
|
num_groups=int(num_groups * scale),
|
|
use_cudnn=mixed_precision_enabled,
|
|
name=name + "_dw")
|
|
|
|
pointwise_conv = self._conv_norm(
|
|
input=depthwise_conv,
|
|
filter_size=1,
|
|
num_filters=int(num_filters2 * scale),
|
|
stride=1,
|
|
padding=0,
|
|
name=name + "_sep")
|
|
return pointwise_conv
|
|
|
|
def _extra_block(self,
|
|
input,
|
|
num_filters1,
|
|
num_filters2,
|
|
num_groups,
|
|
stride,
|
|
name=None):
|
|
pointwise_conv = self._conv_norm(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=int(num_filters1),
|
|
stride=1,
|
|
num_groups=int(num_groups),
|
|
padding=0,
|
|
act='relu6',
|
|
name=name + "_extra1")
|
|
normal_conv = self._conv_norm(
|
|
input=pointwise_conv,
|
|
filter_size=3,
|
|
num_filters=int(num_filters2),
|
|
stride=2,
|
|
num_groups=int(num_groups),
|
|
padding=1,
|
|
act='relu6',
|
|
name=name + "_extra2")
|
|
return normal_conv
|
|
|
|
def __call__(self, input):
|
|
scale = self.conv_group_scale
|
|
|
|
blocks = []
|
|
# input 1/1
|
|
out = self._conv_norm(
|
|
input, 3, int(32 * scale), 2, 1, name=self.prefix_name + "conv1")
|
|
# 1/2
|
|
out = self.depthwise_separable(
|
|
out, 32, 64, 32, 1, scale, name=self.prefix_name + "conv2_1")
|
|
out = self.depthwise_separable(
|
|
out, 64, 128, 64, 2, scale, name=self.prefix_name + "conv2_2")
|
|
# 1/4
|
|
out = self.depthwise_separable(
|
|
out, 128, 128, 128, 1, scale, name=self.prefix_name + "conv3_1")
|
|
out = self.depthwise_separable(
|
|
out, 128, 256, 128, 2, scale, name=self.prefix_name + "conv3_2")
|
|
# 1/8
|
|
blocks.append(out)
|
|
out = self.depthwise_separable(
|
|
out, 256, 256, 256, 1, scale, name=self.prefix_name + "conv4_1")
|
|
out = self.depthwise_separable(
|
|
out, 256, 512, 256, 2, scale, name=self.prefix_name + "conv4_2")
|
|
# 1/16
|
|
blocks.append(out)
|
|
for i in range(5):
|
|
out = self.depthwise_separable(
|
|
out,
|
|
512,
|
|
512,
|
|
512,
|
|
1,
|
|
scale,
|
|
name=self.prefix_name + "conv5_" + str(i + 1))
|
|
module11 = out
|
|
|
|
out = self.depthwise_separable(
|
|
out, 512, 1024, 512, 2, scale, name=self.prefix_name + "conv5_6")
|
|
# 1/32
|
|
out = self.depthwise_separable(
|
|
out, 1024, 1024, 1024, 1, scale, name=self.prefix_name + "conv6")
|
|
module13 = out
|
|
blocks.append(out)
|
|
if not self.with_extra_blocks:
|
|
return blocks
|
|
|
|
num_filters = self.extra_block_filters
|
|
module14 = self._extra_block(module13, num_filters[0][0],
|
|
num_filters[0][1], 1, 2,
|
|
self.prefix_name + "conv7_1")
|
|
module15 = self._extra_block(module14, num_filters[1][0],
|
|
num_filters[1][1], 1, 2,
|
|
self.prefix_name + "conv7_2")
|
|
module16 = self._extra_block(module15, num_filters[2][0],
|
|
num_filters[2][1], 1, 2,
|
|
self.prefix_name + "conv7_3")
|
|
module17 = self._extra_block(module16, num_filters[3][0],
|
|
num_filters[3][1], 1, 2,
|
|
self.prefix_name + "conv7_4")
|
|
return module11, module13, module14, module15, module16, module17
|