mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-05-22 22:27:33 +08:00
vsr v1.0.0
This commit is contained in:
YaoFANGUK
307
backend/ppocr/modeling/backbones/rec_resnet_fpn.py
Normal file
307
backend/ppocr/modeling/backbones/rec_resnet_fpn.py
Normal file
@@ -0,0 +1,307 @@
|
||||
#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
|
||||
|
||||
from paddle import nn, ParamAttr
|
||||
from paddle.nn import functional as F
|
||||
import paddle.fluid as fluid
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
__all__ = ["ResNetFPN"]
|
||||
|
||||
|
||||
class ResNetFPN(nn.Layer):
|
||||
def __init__(self, in_channels=1, layers=50, **kwargs):
|
||||
super(ResNetFPN, self).__init__()
|
||||
supported_layers = {
|
||||
18: {
|
||||
'depth': [2, 2, 2, 2],
|
||||
'block_class': BasicBlock
|
||||
},
|
||||
34: {
|
||||
'depth': [3, 4, 6, 3],
|
||||
'block_class': BasicBlock
|
||||
},
|
||||
50: {
|
||||
'depth': [3, 4, 6, 3],
|
||||
'block_class': BottleneckBlock
|
||||
},
|
||||
101: {
|
||||
'depth': [3, 4, 23, 3],
|
||||
'block_class': BottleneckBlock
|
||||
},
|
||||
152: {
|
||||
'depth': [3, 8, 36, 3],
|
||||
'block_class': BottleneckBlock
|
||||
}
|
||||
}
|
||||
stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)]
|
||||
num_filters = [64, 128, 256, 512]
|
||||
self.depth = supported_layers[layers]['depth']
|
||||
self.F = []
|
||||
self.conv = ConvBNLayer(
|
||||
in_channels=in_channels,
|
||||
out_channels=64,
|
||||
kernel_size=7,
|
||||
stride=2,
|
||||
act="relu",
|
||||
name="conv1")
|
||||
self.block_list = []
|
||||
in_ch = 64
|
||||
if layers >= 50:
|
||||
for block in range(len(self.depth)):
|
||||
for i in range(self.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)
|
||||
block_list = self.add_sublayer(
|
||||
"bottleneckBlock_{}_{}".format(block, i),
|
||||
BottleneckBlock(
|
||||
in_channels=in_ch,
|
||||
out_channels=num_filters[block],
|
||||
stride=stride_list[block] if i == 0 else 1,
|
||||
name=conv_name))
|
||||
in_ch = num_filters[block] * 4
|
||||
self.block_list.append(block_list)
|
||||
self.F.append(block_list)
|
||||
else:
|
||||
for block in range(len(self.depth)):
|
||||
for i in range(self.depth[block]):
|
||||
conv_name = "res" + str(block + 2) + chr(97 + i)
|
||||
if i == 0 and block != 0:
|
||||
stride = (2, 1)
|
||||
else:
|
||||
stride = (1, 1)
|
||||
basic_block = self.add_sublayer(
|
||||
conv_name,
|
||||
BasicBlock(
|
||||
in_channels=in_ch,
|
||||
out_channels=num_filters[block],
|
||||
stride=stride_list[block] if i == 0 else 1,
|
||||
is_first=block == i == 0,
|
||||
name=conv_name))
|
||||
in_ch = basic_block.out_channels
|
||||
self.block_list.append(basic_block)
|
||||
out_ch_list = [in_ch // 4, in_ch // 2, in_ch]
|
||||
self.base_block = []
|
||||
self.conv_trans = []
|
||||
self.bn_block = []
|
||||
for i in [-2, -3]:
|
||||
in_channels = out_ch_list[i + 1] + out_ch_list[i]
|
||||
|
||||
self.base_block.append(
|
||||
self.add_sublayer(
|
||||
"F_{}_base_block_0".format(i),
|
||||
nn.Conv2D(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_ch_list[i],
|
||||
kernel_size=1,
|
||||
weight_attr=ParamAttr(trainable=True),
|
||||
bias_attr=ParamAttr(trainable=True))))
|
||||
self.base_block.append(
|
||||
self.add_sublayer(
|
||||
"F_{}_base_block_1".format(i),
|
||||
nn.Conv2D(
|
||||
in_channels=out_ch_list[i],
|
||||
out_channels=out_ch_list[i],
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
weight_attr=ParamAttr(trainable=True),
|
||||
bias_attr=ParamAttr(trainable=True))))
|
||||
self.base_block.append(
|
||||
self.add_sublayer(
|
||||
"F_{}_base_block_2".format(i),
|
||||
nn.BatchNorm(
|
||||
num_channels=out_ch_list[i],
|
||||
act="relu",
|
||||
param_attr=ParamAttr(trainable=True),
|
||||
bias_attr=ParamAttr(trainable=True))))
|
||||
self.base_block.append(
|
||||
self.add_sublayer(
|
||||
"F_{}_base_block_3".format(i),
|
||||
nn.Conv2D(
|
||||
in_channels=out_ch_list[i],
|
||||
out_channels=512,
|
||||
kernel_size=1,
|
||||
bias_attr=ParamAttr(trainable=True),
|
||||
weight_attr=ParamAttr(trainable=True))))
|
||||
self.out_channels = 512
|
||||
|
||||
def __call__(self, x):
|
||||
x = self.conv(x)
|
||||
fpn_list = []
|
||||
F = []
|
||||
for i in range(len(self.depth)):
|
||||
fpn_list.append(np.sum(self.depth[:i + 1]))
|
||||
|
||||
for i, block in enumerate(self.block_list):
|
||||
x = block(x)
|
||||
for number in fpn_list:
|
||||
if i + 1 == number:
|
||||
F.append(x)
|
||||
base = F[-1]
|
||||
|
||||
j = 0
|
||||
for i, block in enumerate(self.base_block):
|
||||
if i % 3 == 0 and i < 6:
|
||||
j = j + 1
|
||||
b, c, w, h = F[-j - 1].shape
|
||||
if [w, h] == list(base.shape[2:]):
|
||||
base = base
|
||||
else:
|
||||
base = self.conv_trans[j - 1](base)
|
||||
base = self.bn_block[j - 1](base)
|
||||
base = paddle.concat([base, F[-j - 1]], axis=1)
|
||||
base = block(base)
|
||||
return base
|
||||
|
||||
|
||||
class ConvBNLayer(nn.Layer):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
groups=1,
|
||||
act=None,
|
||||
name=None):
|
||||
super(ConvBNLayer, self).__init__()
|
||||
self.conv = nn.Conv2D(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=2 if stride == (1, 1) else kernel_size,
|
||||
dilation=2 if stride == (1, 1) else 1,
|
||||
stride=stride,
|
||||
padding=(kernel_size - 1) // 2,
|
||||
groups=groups,
|
||||
weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'),
|
||||
bias_attr=False, )
|
||||
|
||||
if name == "conv1":
|
||||
bn_name = "bn_" + name
|
||||
else:
|
||||
bn_name = "bn" + name[3:]
|
||||
self.bn = nn.BatchNorm(
|
||||
num_channels=out_channels,
|
||||
act=act,
|
||||
param_attr=ParamAttr(name=name + '.output.1.w_0'),
|
||||
bias_attr=ParamAttr(name=name + '.output.1.b_0'),
|
||||
moving_mean_name=bn_name + "_mean",
|
||||
moving_variance_name=bn_name + "_variance")
|
||||
|
||||
def __call__(self, x):
|
||||
x = self.conv(x)
|
||||
x = self.bn(x)
|
||||
return x
|
||||
|
||||
|
||||
class ShortCut(nn.Layer):
|
||||
def __init__(self, in_channels, out_channels, stride, name, is_first=False):
|
||||
super(ShortCut, self).__init__()
|
||||
self.use_conv = True
|
||||
|
||||
if in_channels != out_channels or stride != 1 or is_first == True:
|
||||
if stride == (1, 1):
|
||||
self.conv = ConvBNLayer(
|
||||
in_channels, out_channels, 1, 1, name=name)
|
||||
else: # stride==(2,2)
|
||||
self.conv = ConvBNLayer(
|
||||
in_channels, out_channels, 1, stride, name=name)
|
||||
else:
|
||||
self.use_conv = False
|
||||
|
||||
def forward(self, x):
|
||||
if self.use_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class BottleneckBlock(nn.Layer):
|
||||
def __init__(self, in_channels, out_channels, stride, name):
|
||||
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")
|
||||
|
||||
self.short = ShortCut(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels * 4,
|
||||
stride=stride,
|
||||
is_first=False,
|
||||
name=name + "_branch1")
|
||||
self.out_channels = out_channels * 4
|
||||
|
||||
def forward(self, x):
|
||||
y = self.conv0(x)
|
||||
y = self.conv1(y)
|
||||
y = self.conv2(y)
|
||||
y = y + self.short(x)
|
||||
y = F.relu(y)
|
||||
return y
|
||||
|
||||
|
||||
class BasicBlock(nn.Layer):
|
||||
def __init__(self, in_channels, out_channels, stride, name, is_first):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv0 = ConvBNLayer(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=3,
|
||||
act='relu',
|
||||
stride=stride,
|
||||
name=name + "_branch2a")
|
||||
self.conv1 = ConvBNLayer(
|
||||
in_channels=out_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=3,
|
||||
act=None,
|
||||
name=name + "_branch2b")
|
||||
self.short = ShortCut(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
stride=stride,
|
||||
is_first=is_first,
|
||||
name=name + "_branch1")
|
||||
self.out_channels = out_channels
|
||||
|
||||
def forward(self, x):
|
||||
y = self.conv0(x)
|
||||
y = self.conv1(y)
|
||||
y = y + self.short(x)
|
||||
return F.relu(y)
|
||||
Reference in New Issue
Block a user