295 lines
9.3 KiB
Python
295 lines
9.3 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 math
|
|
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
|
|
__all__ = [
|
|
"Res2Net_vd", "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s",
|
|
"Res2Net50_vd_14w_8s", "Res2Net50_vd_26w_6s", "Res2Net50_vd_26w_8s",
|
|
"Res2Net101_vd_26w_4s", "Res2Net152_vd_26w_4s", "Res2Net200_vd_26w_4s"
|
|
]
|
|
|
|
|
|
class Res2Net_vd():
|
|
def __init__(self, layers=50, scales=4, width=26):
|
|
self.layers = layers
|
|
self.scales = scales
|
|
self.width = width
|
|
|
|
def net(self, input, class_dim=1000):
|
|
layers = self.layers
|
|
supported_layers = [50, 101, 152, 200]
|
|
assert layers in supported_layers, \
|
|
"supported layers are {} but input layer is {}".format(
|
|
supported_layers, layers)
|
|
basic_width = self.width * self.scales
|
|
num_filters1 = [basic_width * t for t in [1, 2, 4, 8]]
|
|
num_filters2 = [256 * t for t in [1, 2, 4, 8]]
|
|
if layers == 50:
|
|
depth = [3, 4, 6, 3]
|
|
elif layers == 101:
|
|
depth = [3, 4, 23, 3]
|
|
elif layers == 152:
|
|
depth = [3, 8, 36, 3]
|
|
elif layers == 200:
|
|
depth = [3, 12, 48, 3]
|
|
conv = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=32,
|
|
filter_size=3,
|
|
stride=2,
|
|
act='relu',
|
|
name='conv1_1')
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
num_filters=32,
|
|
filter_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name='conv1_2')
|
|
conv = self.conv_bn_layer(
|
|
input=conv,
|
|
num_filters=64,
|
|
filter_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name='conv1_3')
|
|
|
|
conv = fluid.layers.pool2d(
|
|
input=conv,
|
|
pool_size=3,
|
|
pool_stride=2,
|
|
pool_padding=1,
|
|
pool_type='max')
|
|
for block in range(len(depth)):
|
|
for i in range(depth[block]):
|
|
if layers in [101, 152, 200] and block == 2:
|
|
if i == 0:
|
|
conv_name = "res" + str(block + 2) + "a"
|
|
else:
|
|
conv_name = "res" + str(block + 2) + "b" + str(i)
|
|
else:
|
|
conv_name = "res" + str(block + 2) + chr(97 + i)
|
|
conv = self.bottleneck_block(
|
|
input=conv,
|
|
num_filters1=num_filters1[block],
|
|
num_filters2=num_filters2[block],
|
|
stride=2 if i == 0 and block != 0 else 1,
|
|
if_first=block == i == 0,
|
|
name=conv_name)
|
|
pool = fluid.layers.pool2d(
|
|
input=conv,
|
|
pool_size=7,
|
|
pool_stride=1,
|
|
pool_type='avg',
|
|
global_pooling=True)
|
|
|
|
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
|
|
out = fluid.layers.fc(
|
|
input=pool,
|
|
size=class_dim,
|
|
param_attr=fluid.param_attr.ParamAttr(
|
|
initializer=fluid.initializer.Uniform(-stdv, stdv),
|
|
name='fc_weights'),
|
|
bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
|
|
return out
|
|
|
|
def conv_bn_layer(self,
|
|
input,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
act=None,
|
|
name=None):
|
|
conv = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
act=None,
|
|
param_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
if name == "conv1":
|
|
bn_name = "bn_" + name
|
|
else:
|
|
bn_name = "bn" + name[3:]
|
|
return fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
param_attr=ParamAttr(name=bn_name + '_scale'),
|
|
bias_attr=ParamAttr(bn_name + '_offset'),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
def conv_bn_layer_new(self,
|
|
input,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
act=None,
|
|
name=None):
|
|
pool = fluid.layers.pool2d(
|
|
input=input,
|
|
pool_size=2,
|
|
pool_stride=2,
|
|
pool_padding=0,
|
|
pool_type='avg',
|
|
ceil_mode=True)
|
|
|
|
conv = fluid.layers.conv2d(
|
|
input=pool,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=1,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
act=None,
|
|
param_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
if name == "conv1":
|
|
bn_name = "bn_" + name
|
|
else:
|
|
bn_name = "bn" + name[3:]
|
|
return fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
param_attr=ParamAttr(name=bn_name + '_scale'),
|
|
bias_attr=ParamAttr(bn_name + '_offset'),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
def shortcut(self, input, ch_out, stride, name, if_first=False):
|
|
ch_in = input.shape[1]
|
|
if ch_in != ch_out or stride != 1:
|
|
if if_first:
|
|
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
|
|
else:
|
|
return self.conv_bn_layer_new(
|
|
input, ch_out, 1, stride, name=name)
|
|
elif if_first:
|
|
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
|
|
else:
|
|
return input
|
|
|
|
def bottleneck_block(self, input, num_filters1, num_filters2, stride, name,
|
|
if_first):
|
|
conv0 = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=num_filters1,
|
|
filter_size=1,
|
|
stride=1,
|
|
act='relu',
|
|
name=name + '_branch2a')
|
|
|
|
xs = fluid.layers.split(conv0, self.scales, 1)
|
|
ys = []
|
|
for s in range(self.scales - 1):
|
|
if s == 0 or stride == 2:
|
|
ys.append(
|
|
self.conv_bn_layer(
|
|
input=xs[s],
|
|
num_filters=num_filters1 // self.scales,
|
|
stride=stride,
|
|
filter_size=3,
|
|
act='relu',
|
|
name=name + '_branch2b_' + str(s + 1)))
|
|
else:
|
|
ys.append(
|
|
self.conv_bn_layer(
|
|
input=xs[s] + ys[-1],
|
|
num_filters=num_filters1 // self.scales,
|
|
stride=stride,
|
|
filter_size=3,
|
|
act='relu',
|
|
name=name + '_branch2b_' + str(s + 1)))
|
|
|
|
if stride == 1:
|
|
ys.append(xs[-1])
|
|
else:
|
|
ys.append(
|
|
fluid.layers.pool2d(
|
|
input=xs[-1],
|
|
pool_size=3,
|
|
pool_stride=stride,
|
|
pool_padding=1,
|
|
pool_type='avg'))
|
|
|
|
conv1 = fluid.layers.concat(ys, axis=1)
|
|
conv2 = self.conv_bn_layer(
|
|
input=conv1,
|
|
num_filters=num_filters2,
|
|
filter_size=1,
|
|
act=None,
|
|
name=name + "_branch2c")
|
|
|
|
short = self.shortcut(
|
|
input,
|
|
num_filters2,
|
|
stride,
|
|
if_first=if_first,
|
|
name=name + "_branch1")
|
|
|
|
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
|
|
|
|
|
|
def Res2Net50_vd_48w_2s():
|
|
model = Res2Net_vd(layers=50, scales=2, width=48)
|
|
return model
|
|
|
|
|
|
def Res2Net50_vd_26w_4s():
|
|
model = Res2Net_vd(layers=50, scales=4, width=26)
|
|
return model
|
|
|
|
|
|
def Res2Net50_vd_14w_8s():
|
|
model = Res2Net_vd(layers=50, scales=8, width=14)
|
|
return model
|
|
|
|
|
|
def Res2Net50_vd_26w_6s():
|
|
model = Res2Net_vd(layers=50, scales=6, width=26)
|
|
return model
|
|
|
|
|
|
def Res2Net50_vd_26w_8s():
|
|
model = Res2Net_vd(layers=50, scales=8, width=26)
|
|
return model
|
|
|
|
|
|
def Res2Net101_vd_26w_4s():
|
|
model = Res2Net_vd(layers=101, scales=4, width=26)
|
|
return model
|
|
|
|
|
|
def Res2Net152_vd_26w_4s():
|
|
model = Res2Net_vd(layers=152, scales=4, width=26)
|
|
return model
|
|
|
|
|
|
def Res2Net200_vd_26w_4s():
|
|
model = Res2Net_vd(layers=200, scales=4, width=26)
|
|
return model
|