forked from PulseFocusPlatform/PulseFocusPlatform
430 lines
15 KiB
Python
430 lines
15 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 collections import OrderedDict
|
|
|
|
from paddle import fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
from paddle.fluid.framework import Variable
|
|
from paddle.fluid.regularizer import L2Decay
|
|
|
|
from ppdet.core.workspace import register, serializable
|
|
from numbers import Integral
|
|
from paddle.fluid.initializer import MSRA
|
|
import math
|
|
|
|
__all__ = ['HRNet']
|
|
|
|
|
|
@register
|
|
@serializable
|
|
class HRNet(object):
|
|
"""
|
|
HRNet, see https://arxiv.org/abs/1908.07919
|
|
Args:
|
|
width (int): network width, should be 18, 30, 32, 40, 44, 48, 60 or 64
|
|
has_se (bool): whether contain squeeze_excitation(SE) block or not
|
|
freeze_at (int): freeze the backbone at which stage
|
|
norm_type (str): normalization type, 'bn'/'sync_bn'
|
|
freeze_norm (bool): freeze normalization layers
|
|
norm_decay (float): weight decay for normalization layer weights
|
|
feature_maps (list): index of stages whose feature maps are returned
|
|
"""
|
|
|
|
def __init__(self,
|
|
width=40,
|
|
has_se=False,
|
|
freeze_at=2,
|
|
norm_type='bn',
|
|
freeze_norm=True,
|
|
norm_decay=0.,
|
|
feature_maps=[2, 3, 4, 5]):
|
|
super(HRNet, self).__init__()
|
|
|
|
if isinstance(feature_maps, Integral):
|
|
feature_maps = [feature_maps]
|
|
|
|
assert 0 <= freeze_at <= 4, "freeze_at should be 0, 1, 2, 3 or 4"
|
|
assert len(feature_maps) > 0, "need one or more feature maps"
|
|
assert norm_type in ['bn', 'sync_bn']
|
|
|
|
self.width = width
|
|
self.has_se = has_se
|
|
self.channels = {
|
|
18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]],
|
|
30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
|
|
32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]],
|
|
40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
|
|
44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]],
|
|
48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]],
|
|
60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]],
|
|
64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]],
|
|
}
|
|
|
|
self.freeze_at = freeze_at
|
|
self.norm_type = norm_type
|
|
self.norm_decay = norm_decay
|
|
self.freeze_norm = freeze_norm
|
|
self._model_type = 'HRNet'
|
|
self.feature_maps = feature_maps
|
|
self.end_points = []
|
|
return
|
|
|
|
def net(self, input, class_dim=1000):
|
|
width = self.width
|
|
channels_2, channels_3, channels_4 = self.channels[width]
|
|
num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
|
|
|
|
x = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=3,
|
|
num_filters=64,
|
|
stride=2,
|
|
if_act=True,
|
|
name='layer1_1')
|
|
x = self.conv_bn_layer(
|
|
input=x,
|
|
filter_size=3,
|
|
num_filters=64,
|
|
stride=2,
|
|
if_act=True,
|
|
name='layer1_2')
|
|
|
|
la1 = self.layer1(x, name='layer2')
|
|
tr1 = self.transition_layer([la1], [256], channels_2, name='tr1')
|
|
st2 = self.stage(tr1, num_modules_2, channels_2, name='st2')
|
|
tr2 = self.transition_layer(st2, channels_2, channels_3, name='tr2')
|
|
st3 = self.stage(tr2, num_modules_3, channels_3, name='st3')
|
|
tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3')
|
|
st4 = self.stage(tr3, num_modules_4, channels_4, name='st4')
|
|
|
|
self.end_points = st4
|
|
return st4[-1]
|
|
|
|
def layer1(self, input, name=None):
|
|
conv = input
|
|
for i in range(4):
|
|
conv = self.bottleneck_block(
|
|
conv,
|
|
num_filters=64,
|
|
downsample=True if i == 0 else False,
|
|
name=name + '_' + str(i + 1))
|
|
return conv
|
|
|
|
def transition_layer(self, x, in_channels, out_channels, name=None):
|
|
num_in = len(in_channels)
|
|
num_out = len(out_channels)
|
|
out = []
|
|
for i in range(num_out):
|
|
if i < num_in:
|
|
if in_channels[i] != out_channels[i]:
|
|
residual = self.conv_bn_layer(
|
|
x[i],
|
|
filter_size=3,
|
|
num_filters=out_channels[i],
|
|
name=name + '_layer_' + str(i + 1))
|
|
out.append(residual)
|
|
else:
|
|
out.append(x[i])
|
|
else:
|
|
residual = self.conv_bn_layer(
|
|
x[-1],
|
|
filter_size=3,
|
|
num_filters=out_channels[i],
|
|
stride=2,
|
|
name=name + '_layer_' + str(i + 1))
|
|
out.append(residual)
|
|
return out
|
|
|
|
def branches(self, x, block_num, channels, name=None):
|
|
out = []
|
|
for i in range(len(channels)):
|
|
residual = x[i]
|
|
for j in range(block_num):
|
|
residual = self.basic_block(
|
|
residual,
|
|
channels[i],
|
|
name=name + '_branch_layer_' + str(i + 1) + '_' +
|
|
str(j + 1))
|
|
out.append(residual)
|
|
return out
|
|
|
|
def fuse_layers(self, x, channels, multi_scale_output=True, name=None):
|
|
out = []
|
|
for i in range(len(channels) if multi_scale_output else 1):
|
|
residual = x[i]
|
|
for j in range(len(channels)):
|
|
if j > i:
|
|
y = self.conv_bn_layer(
|
|
x[j],
|
|
filter_size=1,
|
|
num_filters=channels[i],
|
|
if_act=False,
|
|
name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
|
|
y = fluid.layers.resize_nearest(input=y, scale=2**(j - i))
|
|
residual = fluid.layers.elementwise_add(
|
|
x=residual, y=y, act=None)
|
|
elif j < i:
|
|
y = x[j]
|
|
for k in range(i - j):
|
|
if k == i - j - 1:
|
|
y = self.conv_bn_layer(
|
|
y,
|
|
filter_size=3,
|
|
num_filters=channels[i],
|
|
stride=2,
|
|
if_act=False,
|
|
name=name + '_layer_' + str(i + 1) + '_' +
|
|
str(j + 1) + '_' + str(k + 1))
|
|
else:
|
|
y = self.conv_bn_layer(
|
|
y,
|
|
filter_size=3,
|
|
num_filters=channels[j],
|
|
stride=2,
|
|
name=name + '_layer_' + str(i + 1) + '_' +
|
|
str(j + 1) + '_' + str(k + 1))
|
|
residual = fluid.layers.elementwise_add(
|
|
x=residual, y=y, act=None)
|
|
|
|
residual = fluid.layers.relu(residual)
|
|
out.append(residual)
|
|
return out
|
|
|
|
def high_resolution_module(self,
|
|
x,
|
|
channels,
|
|
multi_scale_output=True,
|
|
name=None):
|
|
residual = self.branches(x, 4, channels, name=name)
|
|
out = self.fuse_layers(
|
|
residual,
|
|
channels,
|
|
multi_scale_output=multi_scale_output,
|
|
name=name)
|
|
return out
|
|
|
|
def stage(self,
|
|
x,
|
|
num_modules,
|
|
channels,
|
|
multi_scale_output=True,
|
|
name=None):
|
|
out = x
|
|
for i in range(num_modules):
|
|
if i == num_modules - 1 and multi_scale_output == False:
|
|
out = self.high_resolution_module(
|
|
out,
|
|
channels,
|
|
multi_scale_output=False,
|
|
name=name + '_' + str(i + 1))
|
|
else:
|
|
out = self.high_resolution_module(
|
|
out, channels, name=name + '_' + str(i + 1))
|
|
|
|
return out
|
|
|
|
def last_cls_out(self, x, name=None):
|
|
out = []
|
|
num_filters_list = [128, 256, 512, 1024]
|
|
for i in range(len(x)):
|
|
out.append(
|
|
self.conv_bn_layer(
|
|
input=x[i],
|
|
filter_size=1,
|
|
num_filters=num_filters_list[i],
|
|
name=name + 'conv_' + str(i + 1)))
|
|
return out
|
|
|
|
def basic_block(self,
|
|
input,
|
|
num_filters,
|
|
stride=1,
|
|
downsample=False,
|
|
name=None):
|
|
residual = input
|
|
conv = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=3,
|
|
num_filters=num_filters,
|
|
stride=stride,
|
|
name=name + '_conv1')
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
filter_size=3,
|
|
num_filters=num_filters,
|
|
if_act=False,
|
|
name=name + '_conv2')
|
|
if downsample:
|
|
residual = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_filters,
|
|
if_act=False,
|
|
name=name + '_downsample')
|
|
if self.has_se:
|
|
conv = self.squeeze_excitation(
|
|
input=conv,
|
|
num_channels=num_filters,
|
|
reduction_ratio=16,
|
|
name='fc' + name)
|
|
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
|
|
|
|
def bottleneck_block(self,
|
|
input,
|
|
num_filters,
|
|
stride=1,
|
|
downsample=False,
|
|
name=None):
|
|
residual = input
|
|
conv = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_filters,
|
|
name=name + '_conv1')
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
filter_size=3,
|
|
num_filters=num_filters,
|
|
stride=stride,
|
|
name=name + '_conv2')
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
filter_size=1,
|
|
num_filters=num_filters * 4,
|
|
if_act=False,
|
|
name=name + '_conv3')
|
|
if downsample:
|
|
residual = self.conv_bn_layer(
|
|
input=input,
|
|
filter_size=1,
|
|
num_filters=num_filters * 4,
|
|
if_act=False,
|
|
name=name + '_downsample')
|
|
if self.has_se:
|
|
conv = self.squeeze_excitation(
|
|
input=conv,
|
|
num_channels=num_filters * 4,
|
|
reduction_ratio=16,
|
|
name='fc' + name)
|
|
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
|
|
|
|
def squeeze_excitation(self,
|
|
input,
|
|
num_channels,
|
|
reduction_ratio,
|
|
name=None):
|
|
pool = fluid.layers.pool2d(
|
|
input=input, pool_size=0, pool_type='avg', global_pooling=True)
|
|
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
|
|
squeeze = fluid.layers.fc(
|
|
input=pool,
|
|
size=num_channels / reduction_ratio,
|
|
act='relu',
|
|
param_attr=fluid.param_attr.ParamAttr(
|
|
initializer=fluid.initializer.Uniform(-stdv, stdv),
|
|
name=name + '_sqz_weights'),
|
|
bias_attr=ParamAttr(name=name + '_sqz_offset'))
|
|
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
|
|
excitation = fluid.layers.fc(
|
|
input=squeeze,
|
|
size=num_channels,
|
|
act='sigmoid',
|
|
param_attr=fluid.param_attr.ParamAttr(
|
|
initializer=fluid.initializer.Uniform(-stdv, stdv),
|
|
name=name + '_exc_weights'),
|
|
bias_attr=ParamAttr(name=name + '_exc_offset'))
|
|
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
|
|
return scale
|
|
|
|
def conv_bn_layer(self,
|
|
input,
|
|
filter_size,
|
|
num_filters,
|
|
stride=1,
|
|
padding=1,
|
|
num_groups=1,
|
|
if_act=True,
|
|
name=None):
|
|
conv = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=num_groups,
|
|
act=None,
|
|
param_attr=ParamAttr(
|
|
initializer=MSRA(), name=name + '_weights'),
|
|
bias_attr=False)
|
|
bn_name = name + '_bn'
|
|
bn = self._bn(input=conv, bn_name=bn_name)
|
|
if if_act:
|
|
bn = fluid.layers.relu(bn)
|
|
return bn
|
|
|
|
def _bn(self, input, act=None, bn_name=None):
|
|
norm_lr = 0. if self.freeze_norm else 1.
|
|
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))
|
|
|
|
global_stats = True if self.freeze_norm else False
|
|
out = fluid.layers.batch_norm(
|
|
input=input,
|
|
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)
|
|
if self.freeze_norm:
|
|
scale.stop_gradient = True
|
|
bias.stop_gradient = True
|
|
return out
|
|
|
|
def __call__(self, input):
|
|
assert isinstance(input, Variable)
|
|
assert not (set(self.feature_maps) - set([2, 3, 4, 5])), \
|
|
"feature maps {} not in [2, 3, 4, 5]".format(self.feature_maps)
|
|
|
|
res_endpoints = []
|
|
|
|
res = input
|
|
feature_maps = self.feature_maps
|
|
self.net(input)
|
|
|
|
for i in feature_maps:
|
|
res = self.end_points[i - 2]
|
|
if i in self.feature_maps:
|
|
res_endpoints.append(res)
|
|
if self.freeze_at >= i:
|
|
res.stop_gradient = True
|
|
|
|
return OrderedDict([('res{}_sum'.format(self.feature_maps[idx]), feat)
|
|
for idx, feat in enumerate(res_endpoints)])
|