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未验证 提交 140a50df 编写于 作者: D Double_V 提交者： GitHub
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Merge pull request #6717 from wangjingyeye/dyg_db 

add db++
上级 0b4ccc38 00e67e0b
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Showing with 741 addition and 25 deletion
configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml
浏览文件 @ 140a50df
......@@ -28,7 +28,7 @@ Architecture:
algorithm: DB
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 18
Neck:
name: DBFPN
......
configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml
浏览文件 @ 140a50df
......@@ -45,7 +45,7 @@ Architecture:
algorithm: DB
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 18
Neck:
name: DBFPN
......
configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml
浏览文件 @ 140a50df
......@@ -61,7 +61,7 @@ Architecture:
model_type: det
algorithm: DB
Backbone:
name: ResNet
name: ResNet_vd
in_channels: 3
layers: 50
Neck:
......
configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml
浏览文件 @ 140a50df
......@@ -25,7 +25,7 @@ Architecture:
model_type: det
algorithm: DB
Backbone:
name: ResNet
name: ResNet_vd
in_channels: 3
layers: 50
Neck:
......@@ -40,7 +40,7 @@ Architecture:
model_type: det
algorithm: DB
Backbone:
name: ResNet
name: ResNet_vd
in_channels: 3
layers: 50
Neck:
......
configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml
浏览文件 @ 140a50df
......@@ -20,7 +20,7 @@ Architecture:
algorithm: DB
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 18
disable_se: True
Neck:
......
configs/det/det_r50_db++_ic15.yml 0 → 100644
浏览文件 @ 140a50df
Global:
debug: false
use_gpu: true
epoch_num: 1000
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/det_r50_icdar15/
save_epoch_step: 200
eval_batch_step:
- 0
- 2000
cal_metric_during_train: false
pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./checkpoints/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB++
Transform: null
Backbone:
name: ResNet
layers: 50
dcn_stage: [False, True, True, True]
Neck:
name: DBFPN
out_channels: 256
use_asf: True
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: BCELoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: DecayLearningRate
learning_rate: 0.007
epochs: 1000
factor: 0.9
end_lr: 0
weight_decay: 0.0001
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list:
- 1.0
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- IaaAugment:
augmenter_args:
- type: Fliplr
args:
p: 0.5
- type: Affine
args:
rotate:
- -10
- 10
- type: Resize
args:
size:
- 0.5
- 3
- EastRandomCropData:
size:
- 640
- 640
max_tries: 10
keep_ratio: true
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- NormalizeImage:
scale: 1./255.
mean:
- 0.48109378172549
- 0.45752457890196
- 0.40787054090196
std:
- 1.0
- 1.0
- 1.0
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- threshold_map
- threshold_mask
- shrink_map
- shrink_mask
loader:
shuffle: true
drop_last: false
batch_size_per_card: 4
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- DetResizeForTest:
image_shape:
- 1152
- 2048
- NormalizeImage:
scale: 1./255.
mean:
- 0.48109378172549
- 0.45752457890196
- 0.40787054090196
std:
- 1.0
- 1.0
- 1.0
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- shape
- polys
- ignore_tags
loader:
shuffle: false
drop_last: false
batch_size_per_card: 1
num_workers: 2
profiler_options: null
configs/det/det_r50_db++_td_tr.yml 0 → 100644
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Global:
debug: false
use_gpu: true
epoch_num: 1000
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/det_r50_td_tr/
save_epoch_step: 200
eval_batch_step:
- 0
- 2000
cal_metric_during_train: false
pretrained_model: ./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./checkpoints/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB++
Transform: null
Backbone:
name: ResNet
layers: 50
dcn_stage: [False, True, True, True]
Neck:
name: DBFPN
out_channels: 256
use_asf: True
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: BCELoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: DecayLearningRate
learning_rate: 0.007
epochs: 1000
factor: 0.9
end_lr: 0
weight_decay: 0.0001
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.5
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list:
- ./train_data/TD_TR/TD500/train_gt_labels.txt
- ./train_data/TD_TR/TR400/gt_labels.txt
ratio_list:
- 1.0
- 1.0
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- IaaAugment:
augmenter_args:
- type: Fliplr
args:
p: 0.5
- type: Affine
args:
rotate:
- -10
- 10
- type: Resize
args:
size:
- 0.5
- 3
- EastRandomCropData:
size:
- 640
- 640
max_tries: 10
keep_ratio: true
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- NormalizeImage:
scale: 1./255.
mean:
- 0.48109378172549
- 0.45752457890196
- 0.40787054090196
std:
- 1.0
- 1.0
- 1.0
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- threshold_map
- threshold_mask
- shrink_map
- shrink_mask
loader:
shuffle: true
drop_last: false
batch_size_per_card: 4
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list:
- ./train_data/TD_TR/TD500/test_gt_labels.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- DetResizeForTest:
image_shape:
- 736
- 736
keep_ratio: True
- NormalizeImage:
scale: 1./255.
mean:
- 0.48109378172549
- 0.45752457890196
- 0.40787054090196
std:
- 1.0
- 1.0
- 1.0
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- shape
- polys
- ignore_tags
loader:
shuffle: false
drop_last: false
batch_size_per_card: 1
num_workers: 2
profiler_options: null
configs/det/det_r50_vd_db.yml
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......@@ -20,7 +20,7 @@ Architecture:
algorithm: DB
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 50
Neck:
name: DBFPN
......
configs/det/det_r50_vd_dcn_fce_ctw.yml
浏览文件 @ 140a50df
......@@ -21,7 +21,7 @@ Architecture:
algorithm: FCE
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 50
dcn_stage: [False, True, True, True]
out_indices: [1,2,3]
......
configs/det/det_r50_vd_east.yml
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......@@ -20,7 +20,7 @@ Architecture:
algorithm: EAST
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 50
Neck:
name: EASTFPN
......
configs/det/det_r50_vd_pse.yml
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......@@ -20,7 +20,7 @@ Architecture:
algorithm: PSE
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 50
Neck:
name: FPN
......
configs/det/det_res18_db_v2.0.yml
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......@@ -20,7 +20,7 @@ Architecture:
algorithm: DB
Transform:
Backbone:
name: ResNet
name: ResNet_vd
layers: 18
disable_se: True
Neck:
......
doc/doc_ch/algorithm_det_db.md
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# DB
# DB与DB++
- [1. 算法简介](#1)
- [2. 环境配置](#2)
......@@ -21,12 +21,24 @@
> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
> AAAI, 2020
> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)
> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang
> TPAMI, 2022
在ICDAR2015文本检测公开数据集上，算法复现效果如下：
|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- | --- |
|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|DB++|ResNet50|[configs/det/det_r50_db++_ic15.yml](../../configs/det/det_r50_db++_ic15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
在TD_TR文本检测公开数据集上，算法复现效果如下：
|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- | --- |
|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)|
<a name="2"></a>
......@@ -54,7 +66,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrai
DB文本检测模型推理，可以执行如下命令：
```shell
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" --det_algorithm="DB"
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
......@@ -96,4 +108,12 @@ DB模型还支持以下推理部署方式：
pages={11474--11481},
year={2020}
}
```
\ No newline at end of file
@article{liao2022real,
title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion},
author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
```
doc/doc_ch/dataset/ocr_datasets.md
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......@@ -34,6 +34,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
| ICDAR 2015 |https://rrc.cvc.uab.es/?ch=4&com=downloads| [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) |
| ctw1500 |https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip| 图片下载地址中已包含 |
| total text |https://paddleocr.bj.bcebos.com/dataset/total_text.tar| 图片下载地址中已包含 |
| td tr |https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar| 图片下载地址中已包含 |
#### 1.2.1 ICDAR 2015
ICDAR 2015 数据集包含1000张训练图像和500张测试图像。ICDAR 2015 数据集可以从上表中链接下载，首次下载需注册。
......
ppocr/data/imaug/operators.py
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......@@ -205,9 +205,12 @@ class DetResizeForTest(object):
def __init__(self, **kwargs):
super(DetResizeForTest, self).__init__()
self.resize_type = 0
self.keep_ratio = False
if 'image_shape' in kwargs:
self.image_shape = kwargs['image_shape']
self.resize_type = 1
if 'keep_ratio' in kwargs:
self.keep_ratio = kwargs['keep_ratio']
elif 'limit_side_len' in kwargs:
self.limit_side_len = kwargs['limit_side_len']
self.limit_type = kwargs.get('limit_type', 'min')
......@@ -237,6 +240,10 @@ class DetResizeForTest(object):
def resize_image_type1(self, img):
resize_h, resize_w = self.image_shape
ori_h, ori_w = img.shape[:2] # (h, w, c)
if self.keep_ratio is True:
resize_w = ori_w * resize_h / ori_h
N = math.ceil(resize_w / 32)
resize_w = N * 32
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
img = cv2.resize(img, (int(resize_w), int(resize_h)))
......
ppocr/modeling/backbones/__init__.py
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......@@ -18,9 +18,10 @@ __all__ = ["build_backbone"]
def build_backbone(config, model_type):
if model_type == "det" or model_type == "table":
from .det_mobilenet_v3 import MobileNetV3
from .det_resnet_vd import ResNet
from .det_resnet import ResNet
from .det_resnet_vd import ResNet_vd
from .det_resnet_vd_sast import ResNet_SAST
support_dict = ["MobileNetV3", "ResNet", "ResNet_SAST"]
support_dict = ["MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST"]
elif model_type == "rec" or model_type == "cls":
from .rec_mobilenet_v3 import MobileNetV3
from .rec_resnet_vd import ResNet
......
ppocr/modeling/backbones/det_resnet.py 0 → 100644
浏览文件 @ 140a50df
# copyright (c) 2022 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from paddle.vision.ops import DeformConv2D
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant, XavierUniform
from .det_resnet_vd import DeformableConvV2, ConvBNLayer
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
shortcut=True,
is_dcn=False):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=1,
act="relu", )
self.conv1 = ConvBNLayer(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
stride=stride,
act="relu",
is_dcn=is_dcn,
dcn_groups=1, )
self.conv2 = ConvBNLayer(
in_channels=num_filters,
out_channels=num_filters * 4,
kernel_size=1,
act=None, )
if not shortcut:
self.short = ConvBNLayer(
in_channels=num_channels,
out_channels=num_filters * 4,
kernel_size=1,
stride=stride, )
self.shortcut = shortcut
self._num_channels_out = num_filters * 4
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,
num_channels,
num_filters,
stride,
shortcut=True,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=3,
stride=stride,
act="relu")
self.conv1 = ConvBNLayer(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
act=None)
if not shortcut:
self.short = ConvBNLayer(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=1,
stride=stride)
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(nn.Layer):
def __init__(self,
in_channels=3,
layers=50,
out_indices=None,
dcn_stage=None):
super(ResNet, self).__init__()
self.layers = layers
self.input_image_channel = in_channels
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]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.dcn_stage = dcn_stage if dcn_stage is not None else [
False, False, False, False
]
self.out_indices = out_indices if out_indices is not None else [
0, 1, 2, 3
]
self.conv = ConvBNLayer(
in_channels=self.input_image_channel,
out_channels=64,
kernel_size=7,
stride=2,
act="relu", )
self.pool2d_max = MaxPool2D(
kernel_size=3,
stride=2,
padding=1, )
self.stages = []
self.out_channels = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
block_list = []
is_dcn = self.dcn_stage[block]
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(
conv_name,
BottleneckBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
is_dcn=is_dcn))
block_list.append(bottleneck_block)
shortcut = True
if block in self.out_indices:
self.out_channels.append(num_filters[block] * 4)
self.stages.append(nn.Sequential(*block_list))
else:
for block in range(len(depth)):
shortcut = False
block_list = []
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
basic_block = self.add_sublayer(
conv_name,
BasicBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block],
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut))
block_list.append(basic_block)
shortcut = True
if block in self.out_indices:
self.out_channels.append(num_filters[block])
self.stages.append(nn.Sequential(*block_list))
def forward(self, inputs):
y = self.conv(inputs)
y = self.pool2d_max(y)
out = []
for i, block in enumerate(self.stages):
y = block(y)
if i in self.out_indices:
out.append(y)
return out
ppocr/modeling/backbones/det_resnet_vd.py
浏览文件 @ 140a50df
......@@ -25,7 +25,7 @@ from paddle.vision.ops import DeformConv2D
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant, XavierUniform
__all__ = ["ResNet"]
__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
class DeformableConvV2(nn.Layer):
......@@ -104,6 +104,7 @@ class ConvBNLayer(nn.Layer):
kernel_size,
stride=1,
groups=1,
dcn_groups=1,
is_vd_mode=False,
act=None,
is_dcn=False):
......@@ -128,7 +129,7 @@ class ConvBNLayer(nn.Layer):
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=2, #groups,
groups=dcn_groups, #groups,
bias_attr=False)
self._batch_norm = nn.BatchNorm(out_channels, act=act)
......@@ -162,7 +163,8 @@ class BottleneckBlock(nn.Layer):
kernel_size=3,
stride=stride,
act='relu',
is_dcn=is_dcn)
is_dcn=is_dcn,
dcn_groups=2)
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
......@@ -238,14 +240,14 @@ class BasicBlock(nn.Layer):
return y
class ResNet(nn.Layer):
class ResNet_vd(nn.Layer):
def __init__(self,
in_channels=3,
layers=50,
dcn_stage=None,
out_indices=None,
**kwargs):
super(ResNet, self).__init__()
super(ResNet_vd, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
......@@ -321,7 +323,6 @@ class ResNet(nn.Layer):
for block in range(len(depth)):
block_list = []
shortcut = False
# is_dcn = self.dcn_stage[block]
for i in range(depth[block]):
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
......
ppocr/modeling/necks/db_fpn.py
浏览文件 @ 140a50df
......@@ -105,9 +105,10 @@ class DSConv(nn.Layer):
class DBFPN(nn.Layer):
def __init__(self, in_channels, out_channels, **kwargs):
def __init__(self, in_channels, out_channels, use_asf=False, **kwargs):
super(DBFPN, self).__init__()
self.out_channels = out_channels
self.use_asf = use_asf
weight_attr = paddle.nn.initializer.KaimingUniform()
self.in2_conv = nn.Conv2D(
......@@ -163,6 +164,9 @@ class DBFPN(nn.Layer):
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False)
if self.use_asf is True:
self.asf = ASFBlock(self.out_channels, self.out_channels // 4)
def forward(self, x):
c2, c3, c4, c5 = x
......@@ -187,6 +191,10 @@ class DBFPN(nn.Layer):
p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
if self.use_asf is True:
fuse = self.asf(fuse, [p5, p4, p3, p2])
return fuse
......@@ -356,3 +364,64 @@ class LKPAN(nn.Layer):
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
return fuse
class ASFBlock(nn.Layer):
"""
This code is refered from:
https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py
"""
def __init__(self, in_channels, inter_channels, out_features_num=4):
"""
Adaptive Scale Fusion (ASF) block of DBNet++
Args:
in_channels: the number of channels in the input data
inter_channels: the number of middle channels
out_features_num: the number of fused stages
"""
super(ASFBlock, self).__init__()
weight_attr = paddle.nn.initializer.KaimingUniform()
self.in_channels = in_channels
self.inter_channels = inter_channels
self.out_features_num = out_features_num
self.conv = nn.Conv2D(in_channels, inter_channels, 3, padding=1)
self.spatial_scale = nn.Sequential(
#Nx1xHxW
nn.Conv2D(
in_channels=1,
out_channels=1,
kernel_size=3,
bias_attr=False,
padding=1,
weight_attr=ParamAttr(initializer=weight_attr)),
nn.ReLU(),
nn.Conv2D(
in_channels=1,
out_channels=1,
kernel_size=1,
bias_attr=False,
weight_attr=ParamAttr(initializer=weight_attr)),
nn.Sigmoid())
self.channel_scale = nn.Sequential(
nn.Conv2D(
in_channels=inter_channels,
out_channels=out_features_num,
kernel_size=1,
bias_attr=False,
weight_attr=ParamAttr(initializer=weight_attr)),
nn.Sigmoid())
def forward(self, fuse_features, features_list):
fuse_features = self.conv(fuse_features)
spatial_x = paddle.mean(fuse_features, axis=1, keepdim=True)
attention_scores = self.spatial_scale(spatial_x) + fuse_features
attention_scores = self.channel_scale(attention_scores)
assert len(features_list) == self.out_features_num
out_list = []
for i in range(self.out_features_num):
out_list.append(attention_scores[:, i:i + 1] * features_list[i])
return paddle.concat(out_list, axis=1)
ppocr/optimizer/learning_rate.py
浏览文件 @ 140a50df
......@@ -308,3 +308,38 @@ class Const(object):
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
class DecayLearningRate(object):
"""
DecayLearningRate learning rate decay
new_lr = (lr - end_lr) * (1 - epoch/decay_steps)**power + end_lr
Args:
learning_rate(float): initial learning rate
step_each_epoch(int): steps each epoch
epochs(int): total training epochs
factor(float): Power of polynomial, should greater than 0.0 to get learning rate decay. Default: 0.9
end_lr(float): The minimum final learning rate. Default: 0.0.
"""
def __init__(self,
learning_rate,
step_each_epoch,
epochs,
factor=0.9,
end_lr=0,
**kwargs):
super(DecayLearningRate, self).__init__()
self.learning_rate = learning_rate
self.epochs = epochs + 1
self.factor = factor
self.end_lr = 0
self.decay_steps = step_each_epoch * epochs
def __call__(self):
learning_rate = lr.PolynomialDecay(
learning_rate=self.learning_rate,
decay_steps=self.decay_steps,
power=self.factor,
end_lr=self.end_lr)
return learning_rate
tools/infer/predict_det.py
浏览文件 @ 140a50df
......@@ -67,6 +67,23 @@ class TextDetector(object):
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = args.use_dilation
postprocess_params["score_mode"] = args.det_db_score_mode
elif self.det_algorithm == "DB++":
postprocess_params['name'] = 'DBPostProcess'
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = args.use_dilation
postprocess_params["score_mode"] = args.det_db_score_mode
pre_process_list[1] = {
'NormalizeImage': {
'std': [1.0, 1.0, 1.0],
'mean':
[0.48109378172549, 0.45752457890196, 0.40787054090196],
'scale': '1./255.',
'order': 'hwc'
}
}
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
......@@ -231,7 +248,7 @@ class TextDetector(object):
preds['f_score'] = outputs[1]
preds['f_tco'] = outputs[2]
preds['f_tvo'] = outputs[3]
elif self.det_algorithm in ['DB', 'PSE']:
elif self.det_algorithm in ['DB', 'PSE', 'DB++']:
preds['maps'] = outputs[0]
elif self.det_algorithm == 'FCE':
for i, output in enumerate(outputs):
......
tools/program.py
浏览文件 @ 140a50df
......@@ -577,7 +577,7 @@ def preprocess(is_train=False):
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
'SVTR', 'ViTSTR', 'ABINet'
'SVTR', 'ViTSTR', 'ABINet', 'DB++'
]
if use_xpu:
......
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