246 lines
8.1 KiB
Python
Executable File
246 lines
8.1 KiB
Python
Executable File
#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
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
|
|
__all__ = [
|
|
"ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"
|
|
]
|
|
|
|
Trainable = True
|
|
w_nolr = fluid.ParamAttr(trainable=Trainable)
|
|
train_parameters = {
|
|
"input_size": [3, 224, 224],
|
|
"input_mean": [0.485, 0.456, 0.406],
|
|
"input_std": [0.229, 0.224, 0.225],
|
|
"learning_strategy": {
|
|
"name": "piecewise_decay",
|
|
"batch_size": 256,
|
|
"epochs": [30, 60, 90],
|
|
"steps": [0.1, 0.01, 0.001, 0.0001]
|
|
}
|
|
}
|
|
|
|
|
|
class ResNet():
|
|
def __init__(self, params):
|
|
self.layers = params['layers']
|
|
self.params = train_parameters
|
|
|
|
def __call__(self, input):
|
|
layers = self.layers
|
|
supported_layers = [18, 34, 50, 101, 152]
|
|
assert layers in supported_layers, \
|
|
"supported layers are {} but input layer is {}".format(supported_layers, layers)
|
|
|
|
if layers == 18:
|
|
depth = [2, 2, 2, 2]
|
|
elif layers == 34 or layers == 50:
|
|
depth = [3, 4, 6, 3]
|
|
elif layers == 101:
|
|
depth = [3, 4, 23, 3]
|
|
elif layers == 152:
|
|
depth = [3, 8, 36, 3]
|
|
stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)]
|
|
num_filters = [64, 128, 256, 512]
|
|
|
|
conv = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=64,
|
|
filter_size=7,
|
|
stride=2,
|
|
act='relu',
|
|
name="conv1")
|
|
F = []
|
|
if layers >= 50:
|
|
for block in range(len(depth)):
|
|
for i in range(depth[block]):
|
|
if layers in [101, 152] 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_filters=num_filters[block],
|
|
stride=stride_list[block] if i == 0 else 1,
|
|
name=conv_name)
|
|
F.append(conv)
|
|
else:
|
|
for block in range(len(depth)):
|
|
for i in range(depth[block]):
|
|
conv_name = "res" + str(block + 2) + chr(97 + i)
|
|
|
|
if i == 0 and block != 0:
|
|
stride = (2, 1)
|
|
else:
|
|
stride = (1, 1)
|
|
|
|
conv = self.basic_block(
|
|
input=conv,
|
|
num_filters=num_filters[block],
|
|
stride=stride_list[block] if i == 0 else 1,
|
|
is_first=block == i == 0,
|
|
name=conv_name)
|
|
F.append(conv)
|
|
base = F[-1]
|
|
for i in [-2, -3]:
|
|
b, c, w, h = F[i].shape
|
|
if (w, h) == base.shape[2:]:
|
|
base = base
|
|
else:
|
|
base = fluid.layers.conv2d_transpose(
|
|
input=base,
|
|
num_filters=c,
|
|
filter_size=4,
|
|
stride=2,
|
|
padding=1,
|
|
act=None,
|
|
param_attr=w_nolr,
|
|
bias_attr=w_nolr)
|
|
base = fluid.layers.batch_norm(
|
|
base, act="relu", param_attr=w_nolr, bias_attr=w_nolr)
|
|
base = fluid.layers.concat([base, F[i]], axis=1)
|
|
base = fluid.layers.conv2d(
|
|
base,
|
|
num_filters=c,
|
|
filter_size=1,
|
|
param_attr=w_nolr,
|
|
bias_attr=w_nolr)
|
|
base = fluid.layers.conv2d(
|
|
base,
|
|
num_filters=c,
|
|
filter_size=3,
|
|
padding=1,
|
|
param_attr=w_nolr,
|
|
bias_attr=w_nolr)
|
|
base = fluid.layers.batch_norm(
|
|
base, act="relu", param_attr=w_nolr, bias_attr=w_nolr)
|
|
|
|
base = fluid.layers.conv2d(
|
|
base,
|
|
num_filters=512,
|
|
filter_size=1,
|
|
bias_attr=w_nolr,
|
|
param_attr=w_nolr)
|
|
|
|
return base
|
|
|
|
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=2 if stride == (1, 1) else filter_size,
|
|
dilation=2 if stride == (1, 1) else 1,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
act=None,
|
|
param_attr=ParamAttr(
|
|
name=name + "_weights", trainable=Trainable),
|
|
bias_attr=False,
|
|
name=name + '.conv2d.output.1')
|
|
|
|
if name == "conv1":
|
|
bn_name = "bn_" + name
|
|
else:
|
|
bn_name = "bn" + name[3:]
|
|
return fluid.layers.batch_norm(
|
|
input=conv,
|
|
act=act,
|
|
name=bn_name + '.output.1',
|
|
param_attr=ParamAttr(
|
|
name=bn_name + '_scale', trainable=Trainable),
|
|
bias_attr=ParamAttr(
|
|
bn_name + '_offset', trainable=Trainable),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance', )
|
|
|
|
def shortcut(self, input, ch_out, stride, is_first, name):
|
|
ch_in = input.shape[1]
|
|
if ch_in != ch_out or stride != 1 or is_first == True:
|
|
if stride == (1, 1):
|
|
return self.conv_bn_layer(input, ch_out, 1, 1, name=name)
|
|
else: #stride == (2,2)
|
|
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
|
|
|
|
else:
|
|
return input
|
|
|
|
def bottleneck_block(self, input, num_filters, stride, name):
|
|
conv0 = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=1,
|
|
act='relu',
|
|
name=name + "_branch2a")
|
|
conv1 = self.conv_bn_layer(
|
|
input=conv0,
|
|
num_filters=num_filters,
|
|
filter_size=3,
|
|
stride=stride,
|
|
act='relu',
|
|
name=name + "_branch2b")
|
|
conv2 = self.conv_bn_layer(
|
|
input=conv1,
|
|
num_filters=num_filters * 4,
|
|
filter_size=1,
|
|
act=None,
|
|
name=name + "_branch2c")
|
|
|
|
short = self.shortcut(
|
|
input,
|
|
num_filters * 4,
|
|
stride,
|
|
is_first=False,
|
|
name=name + "_branch1")
|
|
|
|
return fluid.layers.elementwise_add(
|
|
x=short, y=conv2, act='relu', name=name + ".add.output.5")
|
|
|
|
def basic_block(self, input, num_filters, stride, is_first, name):
|
|
conv0 = self.conv_bn_layer(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=3,
|
|
act='relu',
|
|
stride=stride,
|
|
name=name + "_branch2a")
|
|
conv1 = self.conv_bn_layer(
|
|
input=conv0,
|
|
num_filters=num_filters,
|
|
filter_size=3,
|
|
act=None,
|
|
name=name + "_branch2b")
|
|
short = self.shortcut(
|
|
input, num_filters, stride, is_first, name=name + "_branch1")
|
|
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
|