mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-03 19:29:18 +00:00
285 lines
9.3 KiB
Python
285 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 paddle
|
|
from paddle import ParamAttr
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
__all__ = ["ResNet_SAST"]
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride=1,
|
|
groups=1,
|
|
is_vd_mode=False,
|
|
act=None,
|
|
name=None, ):
|
|
super(ConvBNLayer, self).__init__()
|
|
|
|
self.is_vd_mode = is_vd_mode
|
|
self._pool2d_avg = nn.AvgPool2D(
|
|
kernel_size=2, stride=2, padding=0, ceil_mode=True)
|
|
self._conv = nn.Conv2D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=(kernel_size - 1) // 2,
|
|
groups=groups,
|
|
weight_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
if name == "conv1":
|
|
bn_name = "bn_" + name
|
|
else:
|
|
bn_name = "bn" + name[3:]
|
|
self._batch_norm = nn.BatchNorm(
|
|
out_channels,
|
|
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 forward(self, inputs):
|
|
if self.is_vd_mode:
|
|
inputs = self._pool2d_avg(inputs)
|
|
y = self._conv(inputs)
|
|
y = self._batch_norm(y)
|
|
return y
|
|
|
|
|
|
class BottleneckBlock(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride,
|
|
shortcut=True,
|
|
if_first=False,
|
|
name=None):
|
|
super(BottleneckBlock, self).__init__()
|
|
|
|
self.conv0 = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
act='relu',
|
|
name=name + "_branch2a")
|
|
self.conv1 = ConvBNLayer(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
stride=stride,
|
|
act='relu',
|
|
name=name + "_branch2b")
|
|
self.conv2 = ConvBNLayer(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels * 4,
|
|
kernel_size=1,
|
|
act=None,
|
|
name=name + "_branch2c")
|
|
|
|
if not shortcut:
|
|
self.short = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels * 4,
|
|
kernel_size=1,
|
|
stride=1,
|
|
is_vd_mode=False if if_first else True,
|
|
name=name + "_branch1")
|
|
|
|
self.shortcut = shortcut
|
|
|
|
def forward(self, inputs):
|
|
y = self.conv0(inputs)
|
|
conv1 = self.conv1(y)
|
|
conv2 = self.conv2(conv1)
|
|
|
|
if self.shortcut:
|
|
short = inputs
|
|
else:
|
|
short = self.short(inputs)
|
|
y = paddle.add(x=short, y=conv2)
|
|
y = F.relu(y)
|
|
return y
|
|
|
|
|
|
class BasicBlock(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride,
|
|
shortcut=True,
|
|
if_first=False,
|
|
name=None):
|
|
super(BasicBlock, self).__init__()
|
|
self.stride = stride
|
|
self.conv0 = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
stride=stride,
|
|
act='relu',
|
|
name=name + "_branch2a")
|
|
self.conv1 = ConvBNLayer(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
act=None,
|
|
name=name + "_branch2b")
|
|
|
|
if not shortcut:
|
|
self.short = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
is_vd_mode=False if if_first else True,
|
|
name=name + "_branch1")
|
|
|
|
self.shortcut = shortcut
|
|
|
|
def forward(self, inputs):
|
|
y = self.conv0(inputs)
|
|
conv1 = self.conv1(y)
|
|
|
|
if self.shortcut:
|
|
short = inputs
|
|
else:
|
|
short = self.short(inputs)
|
|
y = paddle.add(x=short, y=conv1)
|
|
y = F.relu(y)
|
|
return y
|
|
|
|
|
|
class ResNet_SAST(nn.Layer):
|
|
def __init__(self, in_channels=3, layers=50, **kwargs):
|
|
super(ResNet_SAST, self).__init__()
|
|
|
|
self.layers = layers
|
|
supported_layers = [18, 34, 50, 101, 152, 200]
|
|
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]
|
|
depth = [3, 4, 6, 3, 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]
|
|
# num_channels = [64, 256, 512,
|
|
# 1024] if layers >= 50 else [64, 64, 128, 256]
|
|
# num_filters = [64, 128, 256, 512]
|
|
num_channels = [64, 256, 512,
|
|
1024, 2048] if layers >= 50 else [64, 64, 128, 256]
|
|
num_filters = [64, 128, 256, 512, 512]
|
|
|
|
self.conv1_1 = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=32,
|
|
kernel_size=3,
|
|
stride=2,
|
|
act='relu',
|
|
name="conv1_1")
|
|
self.conv1_2 = ConvBNLayer(
|
|
in_channels=32,
|
|
out_channels=32,
|
|
kernel_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name="conv1_2")
|
|
self.conv1_3 = ConvBNLayer(
|
|
in_channels=32,
|
|
out_channels=64,
|
|
kernel_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name="conv1_3")
|
|
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
|
|
|
|
self.stages = []
|
|
self.out_channels = [3, 64]
|
|
if layers >= 50:
|
|
for block in range(len(depth)):
|
|
block_list = []
|
|
shortcut = False
|
|
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)
|
|
bottleneck_block = self.add_sublayer(
|
|
'bb_%d_%d' % (block, i),
|
|
BottleneckBlock(
|
|
in_channels=num_channels[block]
|
|
if i == 0 else num_filters[block] * 4,
|
|
out_channels=num_filters[block],
|
|
stride=2 if i == 0 and block != 0 else 1,
|
|
shortcut=shortcut,
|
|
if_first=block == i == 0,
|
|
name=conv_name))
|
|
shortcut = True
|
|
block_list.append(bottleneck_block)
|
|
self.out_channels.append(num_filters[block] * 4)
|
|
self.stages.append(nn.Sequential(*block_list))
|
|
else:
|
|
for block in range(len(depth)):
|
|
block_list = []
|
|
shortcut = False
|
|
for i in range(depth[block]):
|
|
conv_name = "res" + str(block + 2) + chr(97 + i)
|
|
basic_block = self.add_sublayer(
|
|
'bb_%d_%d' % (block, i),
|
|
BasicBlock(
|
|
in_channels=num_channels[block]
|
|
if i == 0 else num_filters[block],
|
|
out_channels=num_filters[block],
|
|
stride=2 if i == 0 and block != 0 else 1,
|
|
shortcut=shortcut,
|
|
if_first=block == i == 0,
|
|
name=conv_name))
|
|
shortcut = True
|
|
block_list.append(basic_block)
|
|
self.out_channels.append(num_filters[block])
|
|
self.stages.append(nn.Sequential(*block_list))
|
|
|
|
def forward(self, inputs):
|
|
out = [inputs]
|
|
y = self.conv1_1(inputs)
|
|
y = self.conv1_2(y)
|
|
y = self.conv1_3(y)
|
|
out.append(y)
|
|
y = self.pool2d_max(y)
|
|
for block in self.stages:
|
|
y = block(y)
|
|
out.append(y)
|
|
return out |