提交 a739abab 编写于 作者: W WenmuZhou

merge dygraph

......@@ -398,6 +398,7 @@ class MainWindow(QMainWindow, WindowMixin):
help = action(getStr('tutorial'), self.showTutorialDialog, None, 'help', getStr('tutorialDetail'))
showInfo = action(getStr('info'), self.showInfoDialog, None, 'help', getStr('info'))
showSteps = action(getStr('steps'), self.showStepsDialog, None, 'help', getStr('steps'))
showKeys = action(getStr('keys'), self.showKeysDialog, None, 'help', getStr('keys'))
zoom = QWidgetAction(self)
zoom.setDefaultWidget(self.zoomWidget)
......@@ -565,7 +566,7 @@ class MainWindow(QMainWindow, WindowMixin):
addActions(self.menus.file,
(opendir, None, saveLabel, saveRec, self.autoSaveOption, None, resetAll, deleteImg, quit))
addActions(self.menus.help, (showSteps, showInfo))
addActions(self.menus.help, (showKeys,showSteps, showInfo))
addActions(self.menus.view, (
self.displayLabelOption, self.labelDialogOption,
None,
......@@ -760,6 +761,10 @@ class MainWindow(QMainWindow, WindowMixin):
msg = stepsInfo(self.lang)
QMessageBox.information(self, u'Information', msg)
def showKeysDialog(self):
msg = keysInfo(self.lang)
QMessageBox.information(self, u'Information', msg)
def createShape(self):
assert self.beginner()
self.canvas.setEditing(False)
......
因为 它太大了无法显示 source diff 。你可以改为 查看blob
......@@ -174,6 +174,7 @@ def stepsInfo(lang='en'):
"10. 标注结果:关闭应用程序或切换文件路径后,手动保存过的标签将会被存放在所打开图片文件夹下的" \
"*Label.txt*中。在菜单栏点击 “PaddleOCR” - 保存识别结果后,会将此类图片的识别训练数据保存在*crop_img*文件夹下," \
"识别标签保存在*rec_gt.txt*中。\n"
else:
msg = "1. Build and launch using the instructions above.\n" \
"2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n"\
......@@ -188,4 +189,56 @@ def stepsInfo(lang='en'):
"9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n"\
"10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n"\
" Click PaddleOCR-Save Recognition Results in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*.\n"
return msg
def keysInfo(lang='en'):
if lang == 'ch':
msg = "快捷键\t\t\t说明\n" \
"———————————————————————\n"\
"Ctrl + shift + R\t\t对当前图片的所有标记重新识别\n" \
"W\t\t\t新建矩形框\n" \
"Q\t\t\t新建四点框\n" \
"Ctrl + E\t\t编辑所选框标签\n" \
"Ctrl + R\t\t重新识别所选标记\n" \
"Ctrl + C\t\t复制并粘贴选中的标记框\n" \
"Ctrl + 鼠标左键\t\t多选标记框\n" \
"Backspace\t\t删除所选框\n" \
"Ctrl + V\t\t确认本张图片标记\n" \
"Ctrl + Shift + d\t删除本张图片\n" \
"D\t\t\t下一张图片\n" \
"A\t\t\t上一张图片\n" \
"Ctrl++\t\t\t缩小\n" \
"Ctrl--\t\t\t放大\n" \
"↑→↓←\t\t\t移动标记框\n" \
"———————————————————————\n" \
"注:Mac用户Command键替换上述Ctrl键"
else:
msg = "Shortcut Keys\t\tDescription\n" \
"———————————————————————\n" \
"Ctrl + shift + R\t\tRe-recognize all the labels\n" \
"\t\t\tof the current image\n" \
"\n"\
"W\t\t\tCreate a rect box\n" \
"Q\t\t\tCreate a four-points box\n" \
"Ctrl + E\t\tEdit label of the selected box\n" \
"Ctrl + R\t\tRe-recognize the selected box\n" \
"Ctrl + C\t\tCopy and paste the selected\n" \
"\t\t\tbox\n" \
"\n"\
"Ctrl + Left Mouse\tMulti select the label\n" \
"Button\t\t\tbox\n" \
"\n"\
"Backspace\t\tDelete the selected box\n" \
"Ctrl + V\t\tCheck image\n" \
"Ctrl + Shift + d\tDelete image\n" \
"D\t\t\tNext image\n" \
"A\t\t\tPrevious image\n" \
"Ctrl++\t\t\tZoom in\n" \
"Ctrl--\t\t\tZoom out\n" \
"↑→↓←\t\t\tMove selected box" \
"———————————————————————\n" \
"Notice:For Mac users, use the 'Command' key instead of the 'Ctrl' key"
return msg
\ No newline at end of file
......@@ -89,6 +89,7 @@ saveRec=保存识别结果
tempLabel=待识别
nullLabel=无法识别
steps=操作步骤
keys=快捷键
choseModelLg=选择模型语言
cancel=取消
ok=确认
......
......@@ -89,6 +89,7 @@ saveRec=Save Recognition Result
tempLabel=TEMPORARY
nullLabel=NULL
steps=Steps
keys=Shortcut Keys
choseModelLg=Choose Model Language
cancel=Cancel
ok=OK
......
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Student2:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Teacher:
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
freeze_params: true
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 18
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDilaDBLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
- ["Student2", "Teacher"]
key: maps
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
- DistillationDMLLoss:
model_name_pairs:
- ["Student", "Student2"]
maps_name: "thrink_maps"
weight: 1.0
# act: None
model_name_pairs: ["Student", "Student2"]
key: maps
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Student2"]
# key: maps
# name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2", "Teacher"]
# key: maps
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Teacher:
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
freeze_params: true
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 18
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDilaDBLoss:
weight: 1.0
model_name_pairs:
- ["Student", "Teacher"]
key: maps
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Teacher"]
# key: maps
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2"]
key: head_out
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Student2:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
- DistillationDMLLoss:
model_name_pairs:
- ["Student", "Student2"]
maps_name: "thrink_maps"
weight: 1.0
act: "softmax"
model_name_pairs: ["Student", "Student2"]
key: maps
- DistillationDBLoss:
weight: 1.0
model_name_list: ["Student", "Student2"]
# key: maps
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DistillationDBPostProcess
model_name: ["Student", "Student2"]
key: head_out
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DistillationMetric
base_metric_name: DetMetric
main_indicator: hmean
key: "Student"
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
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: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
......@@ -13,7 +13,6 @@ SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
set(DEMO_NAME "ocr_system")
macro(safe_set_static_flag)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
......
......@@ -93,3 +93,5 @@ cd D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release
### 注意
* 在Windows下的终端中执行文件exe时,可能会发生乱码的现象,此时需要在终端中输入`CHCP 65001`,将终端的编码方式由GBK编码(默认)改为UTF-8编码,更加具体的解释可以参考这篇博客:[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)
* 编译时,如果报错`错误:C1083 无法打开包括文件:"dirent.h":No such file or directory`,可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54),新建`dirent.h`文件,并添加到`VC++`的包含目录中。
......@@ -18,6 +18,7 @@ PaddleOCR模型部署。
* 首先需要从opencv官网上下载在Linux环境下源码编译的包,以opencv3.4.7为例,下载命令如下。
```
cd deploy/cpp_infer
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz
```
......@@ -184,7 +185,7 @@ cmake .. \
make -j
```
`OPENCV_DIR`为opencv编译安装的地址;`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹);`CUDA_LIB_DIR`为cuda库文件地址,在docker中为`/usr/local/cuda/lib64``CUDNN_LIB_DIR`为cudnn库文件地址,在docker中为`/usr/lib/x86_64-linux-gnu/`
`OPENCV_DIR`为opencv编译安装的地址;`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹);`CUDA_LIB_DIR`为cuda库文件地址,在docker中为`/usr/local/cuda/lib64``CUDNN_LIB_DIR`为cudnn库文件地址,在docker中为`/usr/lib/x86_64-linux-gnu/`**注意**:以上路径都写绝对路径,不要写相对路径。
* 编译完成之后,会在`build`文件夹下生成一个名为`ocr_system`的可执行文件。
......
......@@ -18,6 +18,7 @@ PaddleOCR model deployment.
* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
```
cd deploy/cpp_infer
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz
```
......
......@@ -668,7 +668,7 @@ void DisposeOutPts(OutPt *&pp) {
//------------------------------------------------------------------------------
inline void InitEdge(TEdge *e, TEdge *eNext, TEdge *ePrev, const IntPoint &Pt) {
std::memset(e, 0, sizeof(TEdge));
std::memset(e, int(0), sizeof(TEdge));
e->Next = eNext;
e->Prev = ePrev;
e->Curr = Pt;
......@@ -1895,17 +1895,17 @@ void Clipper::InsertLocalMinimaIntoAEL(const cInt botY) {
TEdge *rb = lm->RightBound;
OutPt *Op1 = 0;
if (!lb) {
if (!lb || !rb) {
// nb: don't insert LB into either AEL or SEL
InsertEdgeIntoAEL(rb, 0);
SetWindingCount(*rb);
if (IsContributing(*rb))
Op1 = AddOutPt(rb, rb->Bot);
} else if (!rb) {
InsertEdgeIntoAEL(lb, 0);
SetWindingCount(*lb);
if (IsContributing(*lb))
Op1 = AddOutPt(lb, lb->Bot);
//} else if (!rb) {
// InsertEdgeIntoAEL(lb, 0);
// SetWindingCount(*lb);
// if (IsContributing(*lb))
// Op1 = AddOutPt(lb, lb->Bot);
InsertScanbeam(lb->Top.Y);
} else {
InsertEdgeIntoAEL(lb, 0);
......@@ -2547,13 +2547,13 @@ void Clipper::ProcessHorizontal(TEdge *horzEdge) {
if (dir == dLeftToRight) {
maxIt = m_Maxima.begin();
while (maxIt != m_Maxima.end() && *maxIt <= horzEdge->Bot.X)
maxIt++;
++maxIt;
if (maxIt != m_Maxima.end() && *maxIt >= eLastHorz->Top.X)
maxIt = m_Maxima.end();
} else {
maxRit = m_Maxima.rbegin();
while (maxRit != m_Maxima.rend() && *maxRit > horzEdge->Bot.X)
maxRit++;
++maxRit;
if (maxRit != m_Maxima.rend() && *maxRit <= eLastHorz->Top.X)
maxRit = m_Maxima.rend();
}
......@@ -2576,13 +2576,13 @@ void Clipper::ProcessHorizontal(TEdge *horzEdge) {
while (maxIt != m_Maxima.end() && *maxIt < e->Curr.X) {
if (horzEdge->OutIdx >= 0 && !IsOpen)
AddOutPt(horzEdge, IntPoint(*maxIt, horzEdge->Bot.Y));
maxIt++;
++maxIt;
}
} else {
while (maxRit != m_Maxima.rend() && *maxRit > e->Curr.X) {
if (horzEdge->OutIdx >= 0 && !IsOpen)
AddOutPt(horzEdge, IntPoint(*maxRit, horzEdge->Bot.Y));
maxRit++;
++maxRit;
}
}
};
......
......@@ -21,10 +21,10 @@ std::vector<std::string> OCRConfig::split(const std::string &str,
std::vector<std::string> res;
if ("" == str)
return res;
char *strs = new char[str.length() + 1];
char strs[str.length() + 1];
std::strcpy(strs, str.c_str());
char *d = new char[delim.length() + 1];
char d[delim.length() + 1];
std::strcpy(d, delim.c_str());
char *p = std::strtok(strs, d);
......
......@@ -29,7 +29,8 @@ deploy/hubserving/ocr_system/
### 1. 准备环境
```shell
# 安装paddlehub
pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# paddlehub 需要 python>3.6.2
pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 2. 下载推理模型
......
......@@ -30,7 +30,8 @@ The following steps take the 2-stage series service as an example. If only the d
### 1. Prepare the environment
```shell
# Install paddlehub
pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# python>3.6.2 is required bt paddlehub
pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 2. Download inference model
......
......@@ -101,7 +101,7 @@ def main():
quanter = QAT(config=quant_config)
quanter.quantize(model)
init_model(config, model, logger)
init_model(config, model)
model.eval()
# build metric
......@@ -113,7 +113,7 @@ def main():
use_srn = config['Architecture']['algorithm'] == "SRN"
model_type = config['Architecture']['model_type']
# start eval
metirc = program.eval(model, valid_dataloader, post_process_class,
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, use_srn)
logger.info('metric eval ***************')
......
......@@ -18,9 +18,9 @@ PaddleOCR 也提供了数据格式转换脚本,可以将官网 label 转换支
```
# 将官网下载的标签文件转换为 train_icdar2015_label.txt
python gen_label.py --mode="det" --root_path="icdar_c4_train_imgs/" \
--input_path="ch4_training_localization_transcription_gt" \
--output_label="train_icdar2015_label.txt"
python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/" \
--input_path="/path/to/ch4_training_localization_transcription_gt" \
--output_label="/path/to/train_icdar2015_label.txt"
```
解压数据集和下载标注文件后,PaddleOCR/train_data/ 有两个文件夹和两个文件,分别是:
......
......@@ -147,12 +147,12 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_m
如果输入图片的分辨率比较大,而且想使用更大的分辨率预测,可以设置det_limit_side_len 为想要的值,比如1216:
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
```
如果想使用CPU进行预测,执行命令如下
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
```
<a name="DB文本检测模型推理"></a>
......@@ -221,7 +221,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Gl
```
**SAST文本检测模型推理,需要设置参数`--det_algorithm="SAST"`,同时,还需要增加参数`--det_sast_polygon=True`,**可以执行如下命令:
SAST文本检测模型推理,需要设置参数`--det_algorithm="SAST"`,同时,还需要增加参数`--det_sast_polygon=True`可以执行如下命令:
```
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
```
......
......@@ -330,6 +330,8 @@ PaddleOCR目前已支持80种(除中文外)语种识别,`configs/rec/multi
```
意大利文由拉丁字母组成,因此执行完命令后会得到名为 rec_latin_lite_train.yml 的配置文件。
2. 手动修改配置文件
您也可以手动修改模版中的以下几个字段:
......@@ -375,7 +377,9 @@ PaddleOCR目前已支持80种(除中文外)语种识别,`configs/rec/multi
更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
多语言模型训练方式与中文模型一致,训练数据集均为100w的合成数据,少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载,提取码:frgi。
多语言模型训练方式与中文模型一致,训练数据集均为100w的合成数据,少量的字体可以通过下面两种方式下载。
* [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA)。提取码:frgi。
* [google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
如您希望在现有模型效果的基础上调优,请参考下列说明修改配置文件:
......
......@@ -154,12 +154,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
```
If you want to use the CPU for prediction, execute the command as follows
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
```
<a name="DB_DETECTION"></a>
......@@ -230,7 +230,7 @@ First, convert the model saved in the SAST text detection training process into
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt
```
**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`, run the following command:
```
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
......
......@@ -329,6 +329,7 @@ There are two ways to create the required configuration file::
...
```
Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.
2. Manually modify the configuration file
......@@ -375,7 +376,9 @@ Currently, the multi-language algorithms supported by PaddleOCR are:
For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
If you want to finetune on the basis of the existing model effect, please refer to the following instructions to modify the configuration file:
......
......@@ -15,8 +15,6 @@
- 2020.6.8 Add [datasets](./datasets_en.md) and keep updating
- 2020.6.5 Support exporting `attention` model to `inference_model`
- 2020.6.5 Support separate prediction and recognition, output result score
- 2020.6.5 Support exporting `attention` model to `inference_model`
- 2020.6.5 Support separate prediction and recognition, output result score
- 2020.5.30 Provide Lightweight Chinese OCR online experience
- 2020.5.30 Model prediction and training support on Windows system
- 2020.5.30 Open source general Chinese OCR model
......
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  • 2-up
  • Swipe
  • Onion skin
......@@ -14,7 +14,6 @@
import numpy as np
import os
import random
import traceback
from paddle.io import Dataset
from .imaug import transform, create_operators
......@@ -46,7 +45,6 @@ class SimpleDataSet(Dataset):
self.seed = seed
logger.info("Initialize indexs of datasets:%s" % label_file_list)
self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
self.check_data()
self.data_idx_order_list = list(range(len(self.data_lines)))
if self.mode == "train" and self.do_shuffle:
self.shuffle_data_random()
......@@ -103,18 +101,25 @@ class SimpleDataSet(Dataset):
def __getitem__(self, idx):
file_idx = self.data_idx_order_list[idx]
data = self.data_lines[file_idx]
data_line = self.data_lines[file_idx]
try:
data_line = data_line.decode('utf-8')
substr = data_line.strip("\n").split(self.delimiter)
file_name = substr[0]
label = substr[1]
img_path = os.path.join(self.data_dir, file_name)
data = {'img_path': img_path, 'label': label}
if not os.path.exists(img_path):
raise Exception("{} does not exist!".format(img_path))
with open(data['img_path'], 'rb') as f:
img = f.read()
data['image'] = img
data['ext_data'] = self.get_ext_data()
outs = transform(data, self.ops)
except:
error_meg = traceback.format_exc()
except Exception as e:
self.logger.error(
"When parsing file {} and label {}, error happened with msg: {}".format(
data['img_path'],data['label'], error_meg))
"When parsing line {}, error happened with msg: {}".format(
data_line, e))
outs = None
if outs is None:
# during evaluation, we should fix the idx to get same results for many times of evaluation.
......@@ -125,17 +130,3 @@ class SimpleDataSet(Dataset):
def __len__(self):
return len(self.data_idx_order_list)
def check_data(self):
new_data_lines = []
for data_line in self.data_lines:
data_line = data_line.decode('utf-8')
substr = data_line.strip("\n").strip("\r").split(self.delimiter)
file_name = substr[0]
label = substr[1]
img_path = os.path.join(self.data_dir, file_name)
if os.path.exists(img_path):
new_data_lines.append({'img_path': img_path, 'label': label})
else:
self.logger.info("{} does not exist!".format(img_path))
self.data_lines = new_data_lines
\ No newline at end of file
......@@ -54,6 +54,27 @@ class CELoss(nn.Layer):
return loss
class KLJSLoss(object):
def __init__(self, mode='kl'):
assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
self.mode = mode
def __call__(self, p1, p2, reduction="mean"):
loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5))
if self.mode.lower() == "js":
loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5))
loss *= 0.5
if reduction == "mean":
loss = paddle.mean(loss, axis=[1,2])
elif reduction=="none" or reduction is None:
return loss
else:
loss = paddle.sum(loss, axis=[1,2])
return loss
class DMLLoss(nn.Layer):
"""
DMLLoss
......@@ -70,16 +91,20 @@ class DMLLoss(nn.Layer):
else:
self.act = None
self.jskl_loss = KLJSLoss(mode="js")
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
out2 = self.act(out2)
if len(out1.shape) < 2:
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (F.kl_div(
log_out1, out2, reduction='batchmean') + F.kl_div(
log_out2, out1, reduction='batchmean')) / 2.0
else:
loss = self.jskl_loss(out1, out2)
return loss
......
......@@ -17,7 +17,7 @@ import paddle.nn as nn
from .distillation_loss import DistillationCTCLoss
from .distillation_loss import DistillationDMLLoss
from .distillation_loss import DistillationDistanceLoss
from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss
class CombinedLoss(nn.Layer):
......@@ -44,15 +44,16 @@ class CombinedLoss(nn.Layer):
def forward(self, input, batch, **kargs):
loss_dict = {}
loss_all = 0.
for idx, loss_func in enumerate(self.loss_func):
loss = loss_func(input, batch, **kargs)
if isinstance(loss, paddle.Tensor):
loss = {"loss_{}_{}".format(str(loss), idx): loss}
weight = self.loss_weight[idx]
loss = {
"{}_{}".format(key, idx): loss[key] * weight
for key in loss
}
loss_dict.update(loss)
loss_dict["loss"] = paddle.add_n(list(loss_dict.values()))
for key in loss.keys():
if key == "loss":
loss_all += loss[key] * weight
else:
loss_dict["{}_{}".format(key, idx)] = loss[key]
loss_dict["loss"] = loss_all
return loss_dict
......@@ -14,23 +14,76 @@
import paddle
import paddle.nn as nn
import numpy as np
import cv2
from .rec_ctc_loss import CTCLoss
from .basic_loss import DMLLoss
from .basic_loss import DistanceLoss
from .det_db_loss import DBLoss
from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
def _sum_loss(loss_dict):
if "loss" in loss_dict.keys():
return loss_dict
else:
loss_dict["loss"] = 0.
for k, value in loss_dict.items():
if k == "loss":
continue
else:
loss_dict["loss"] += value
return loss_dict
class DistillationDMLLoss(DMLLoss):
"""
"""
def __init__(self, model_name_pairs=[], act=None, key=None,
name="loss_dml"):
def __init__(self,
model_name_pairs=[],
act=None,
key=None,
maps_name=None,
name="dml"):
super().__init__(act=act)
assert isinstance(model_name_pairs, list)
self.key = key
self.model_name_pairs = model_name_pairs
self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
self.name = name
self.maps_name = self._check_maps_name(maps_name)
def _check_model_name_pairs(self, model_name_pairs):
if not isinstance(model_name_pairs, list):
return []
elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str):
return model_name_pairs
else:
return [model_name_pairs]
def _check_maps_name(self, maps_name):
if maps_name is None:
return None
elif type(maps_name) == str:
return [maps_name]
elif type(maps_name) == list:
return [maps_name]
else:
return None
def _slice_out(self, outs):
new_outs = {}
for k in self.maps_name:
if k == "thrink_maps":
new_outs[k] = outs[:, 0, :, :]
elif k == "threshold_maps":
new_outs[k] = outs[:, 1, :, :]
elif k == "binary_maps":
new_outs[k] = outs[:, 2, :, :]
else:
continue
return new_outs
def forward(self, predicts, batch):
loss_dict = dict()
......@@ -40,6 +93,8 @@ class DistillationDMLLoss(DMLLoss):
if self.key is not None:
out1 = out1[self.key]
out2 = out2[self.key]
if self.maps_name is None:
loss = super().forward(out1, out2)
if isinstance(loss, dict):
for key in loss:
......@@ -47,6 +102,21 @@ class DistillationDMLLoss(DMLLoss):
idx)] = loss[key]
else:
loss_dict["{}_{}".format(self.name, idx)] = loss
else:
outs1 = self._slice_out(out1)
outs2 = self._slice_out(out2)
for _c, k in enumerate(outs1.keys()):
loss = super().forward(outs1[k], outs2[k])
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}_{}_{}_{}".format(key, pair[
0], pair[1], map_name, idx)] = loss[key]
else:
loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c],
idx)] = loss
loss_dict = _sum_loss(loss_dict)
return loss_dict
......@@ -73,6 +143,98 @@ class DistillationCTCLoss(CTCLoss):
return loss_dict
class DistillationDBLoss(DBLoss):
def __init__(self,
model_name_list=[],
balance_loss=True,
main_loss_type='DiceLoss',
alpha=5,
beta=10,
ohem_ratio=3,
eps=1e-6,
name="db",
**kwargs):
super().__init__()
self.model_name_list = model_name_list
self.name = name
self.key = None
def forward(self, predicts, batch):
loss_dict = {}
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.key is not None:
out = out[self.key]
loss = super().forward(out, batch)
if isinstance(loss, dict):
for key in loss.keys():
if key == "loss":
continue
name = "{}_{}_{}".format(self.name, model_name, key)
loss_dict[name] = loss[key]
else:
loss_dict["{}_{}".format(self.name, model_name)] = loss
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationDilaDBLoss(DBLoss):
def __init__(self,
model_name_pairs=[],
key=None,
balance_loss=True,
main_loss_type='DiceLoss',
alpha=5,
beta=10,
ohem_ratio=3,
eps=1e-6,
name="dila_dbloss"):
super().__init__()
self.model_name_pairs = model_name_pairs
self.name = name
self.key = key
def forward(self, predicts, batch):
loss_dict = dict()
for idx, pair in enumerate(self.model_name_pairs):
stu_outs = predicts[pair[0]]
tch_outs = predicts[pair[1]]
if self.key is not None:
stu_preds = stu_outs[self.key]
tch_preds = tch_outs[self.key]
stu_shrink_maps = stu_preds[:, 0, :, :]
stu_binary_maps = stu_preds[:, 2, :, :]
# dilation to teacher prediction
dilation_w = np.array([[1, 1], [1, 1]])
th_shrink_maps = tch_preds[:, 0, :, :]
th_shrink_maps = th_shrink_maps.numpy() > 0.3 # thresh = 0.3
dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
for i in range(th_shrink_maps.shape[0]):
dilate_maps[i] = cv2.dilate(
th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
th_shrink_maps = paddle.to_tensor(dilate_maps)
label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
1:]
# calculate the shrink map loss
bce_loss = self.alpha * self.bce_loss(
stu_shrink_maps, th_shrink_maps, label_shrink_mask)
loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
label_shrink_mask)
# k = f"{self.name}_{pair[0]}_{pair[1]}"
k = "{}_{}_{}".format(self.name, pair[0], pair[1])
loss_dict[k] = bce_loss + loss_binary_maps
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationDistanceLoss(DistanceLoss):
"""
"""
......
......@@ -55,6 +55,7 @@ class DetMetric(object):
result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
self.results.append(result)
def get_metric(self):
"""
return metrics {
......
......@@ -24,8 +24,8 @@ from .cls_metric import ClsMetric
class DistillationMetric(object):
def __init__(self,
key=None,
base_metric_name="RecMetric",
main_indicator='acc',
base_metric_name=None,
main_indicator=None,
**kwargs):
self.main_indicator = main_indicator
self.key = key
......@@ -42,16 +42,13 @@ class DistillationMetric(object):
main_indicator=self.main_indicator, **self.kwargs)
self.metrics[key].reset()
def __call__(self, preds, *args, **kwargs):
def __call__(self, preds, batch, **kwargs):
assert isinstance(preds, dict)
if self.metrics is None:
self._init_metrcis(preds)
output = dict()
for key in preds:
metric = self.metrics[key].__call__(preds[key], *args, **kwargs)
for sub_key in metric:
output["{}_{}".format(key, sub_key)] = metric[sub_key]
return output
self.metrics[key].__call__(preds[key], batch, **kwargs)
def get_metric(self):
"""
......
......@@ -79,6 +79,9 @@ class BaseModel(nn.Layer):
x = self.neck(x)
y["neck_out"] = x
x = self.head(x, targets=data)
if isinstance(x, dict):
y.update(x)
else:
y["head_out"] = x
if self.return_all_feats:
return y
......
......@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
from ppocr.modeling.necks import build_neck
from ppocr.modeling.heads import build_head
from .base_model import BaseModel
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import init_model, load_pretrained_params
__all__ = ['DistillationModel']
......@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
pretrained = model_config.pop("pretrained")
model = BaseModel(model_config)
if pretrained is not None:
init_model(model, path=pretrained)
load_pretrained_params(model, pretrained)
if freeze_params:
for param in model.parameters():
param.trainable = False
......
......@@ -21,7 +21,7 @@ import copy
__all__ = ['build_post_process']
from .db_postprocess import DBPostProcess
from .db_postprocess import DBPostProcess, DistillationDBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
......@@ -33,9 +33,10 @@ from .pse_postprocess import PSEPostProcess
def build_post_process(config, global_config=None):
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'DBPostProcess','PSEPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
'DistillationCTCLabelDecode', 'TableLabelDecode', 'PSEPostProcess'
'DistillationCTCLabelDecode', 'TableLabelDecode',
'DistillationDBPostProcess'
]
config = copy.deepcopy(config)
......
......@@ -187,3 +187,29 @@ class DBPostProcess(object):
boxes_batch.append({'points': boxes})
return boxes_batch
class DistillationDBPostProcess(object):
def __init__(self, model_name=["student"],
key=None,
thresh=0.3,
box_thresh=0.6,
max_candidates=1000,
unclip_ratio=1.5,
use_dilation=False,
score_mode="fast",
**kwargs):
self.model_name = model_name
self.key = key
self.post_process = DBPostProcess(thresh=thresh,
box_thresh=box_thresh,
max_candidates=max_candidates,
unclip_ratio=unclip_ratio,
use_dilation=use_dilation,
score_mode=score_mode)
def __call__(self, predicts, shape_list):
results = {}
for k in self.model_name:
results[k] = self.post_process(predicts[k], shape_list=shape_list)
return results
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# 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
# 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.
# 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.
import os
import argparse
import json
......@@ -31,7 +31,9 @@ def gen_det_label(root_path, input_dir, out_label):
for label_file in os.listdir(input_dir):
img_path = root_path + label_file[3:-4] + ".jpg"
label = []
with open(os.path.join(input_dir, label_file), 'r') as f:
with open(
os.path.join(input_dir, label_file), 'r',
encoding='utf-8-sig') as f:
for line in f.readlines():
tmp = line.strip("\n\r").replace("\xef\xbb\xbf",
"").split(',')
......
......@@ -116,6 +116,27 @@ def load_dygraph_params(config, model, logger, optimizer):
logger.info(f"loaded pretrained_model successful from {pm}")
return {}
def load_pretrained_params(model, path):
if path is None:
return False
if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
print(f"The pretrained_model {path} does not exists!")
return False
path = path if path.endswith('.pdparams') else path + '.pdparams'
params = paddle.load(path)
state_dict = model.state_dict()
new_state_dict = {}
for k1, k2 in zip(state_dict.keys(), params.keys()):
if list(state_dict[k1].shape) == list(params[k2].shape):
new_state_dict[k1] = params[k2]
else:
print(
f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
)
model.set_state_dict(new_state_dict)
print(f"load pretrain successful from {path}")
return model
def save_model(model,
optimizer,
......
model_name:ocr_det
python:python3.7
gpu_list:0|0,1
Global.auto_cast:False
Global.auto_cast:null
Global.epoch_num:10
Global.save_model_dir:./output/
Global.save_inference_dir:./output/
Train.loader.batch_size_per_card:
Global.use_gpu
Global.pretrained_model
Global.use_gpu:
Global.pretrained_model:null
trainer:norm|pact
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
......@@ -17,6 +16,8 @@ distill_train:null
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py
......@@ -29,7 +30,6 @@ inference:tools/infer/predict_det.py
--rec_batch_num:1
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--det_model_dir
--image_dir
--save_log_path
--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:./test/output/
model_name:ocr_rec
python:python
gpu_list:0|0,1
Global.auto_cast:null
Global.epoch_num:10
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:
Global.use_gpu:
Global.pretrained_model:null
trainer:norm|pact
norm_train:tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
quant_train:deploy/slim/quantization/quant.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
fpgm_train:null
distill_train:null
eval:tools/eval.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml -o
fpgm_export:null
distill_export:null
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
\ No newline at end of file
......@@ -26,20 +26,24 @@ IFS=$'\n'
# The training params
model_name=$(func_parser_value "${lines[0]}")
train_model_list=$(func_parser_value "${lines[0]}")
trainer_list=$(func_parser_value "${lines[10]}")
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
MODE=$2
# prepare pretrained weights and dataset
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
if [ ${train_model_list[*]} = "ocr_det" ]; then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
fi
if [ ${MODE} = "lite_train_infer" ];then
# pretrain lite train data
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
cd ./train_data/ && tar xf icdar2015_lite.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
epoch=10
......@@ -47,13 +51,15 @@ if [ ${MODE} = "lite_train_infer" ];then
elif [ ${MODE} = "whole_train_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
cd ./train_data/ && tar xf icdar2015.tar && cd ../
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
epoch=500
eval_batch_step=200
elif [ ${MODE} = "whole_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
cd ./train_data/ && tar xf icdar2015_infer.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
epoch=10
......@@ -62,8 +68,8 @@ else
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
else
eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
......@@ -86,15 +92,17 @@ for train_model in ${train_model_list[*]}; do
elif [ ${train_model} = "ocr_rec" ];then
model_name="ocr_rec"
yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_rec_data_200.tar
cd ./inference && tar xf ch_rec_data_200.tar && cd ../
img_dir="./inference/ch_rec_data_200/"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
cd ./inference && tar xf rec_inference.tar && cd ../
img_dir="./inference/rec_inference/"
data_dir=./inference/rec_inference
data_label_file=[./inference/rec_inference/rec_gt_test.txt]
fi
# eval
for slim_trainer in ${trainer_list[*]}; do
if [ ${slim_trainer} = "norm" ]; then
if [ ${model_name} = "ocr_det" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
......@@ -104,7 +112,7 @@ for train_model in ${train_model_list[*]}; do
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "pact" ]; then
if [ ${model_name} = "ocr_det" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
......@@ -114,7 +122,7 @@ for train_model in ${train_model_list[*]}; do
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "distill" ]; then
if [ ${model_name} = "ocr_det" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
......@@ -124,7 +132,7 @@ for train_model in ${train_model_list[*]}; do
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
fi
elif [ ${slim_trainer} = "fpgm" ]; then
if [ ${model_name} = "ocr_det" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && cd ../
......
......@@ -41,59 +41,51 @@ gpu_list=$(func_parser_value "${lines[2]}")
autocast_list=$(func_parser_value "${lines[3]}")
autocast_key=$(func_parser_key "${lines[3]}")
epoch_key=$(func_parser_key "${lines[4]}")
epoch_num=$(func_parser_value "${lines[4]}")
save_model_key=$(func_parser_key "${lines[5]}")
save_infer_key=$(func_parser_key "${lines[6]}")
train_batch_key=$(func_parser_key "${lines[7]}")
train_use_gpu_key=$(func_parser_key "${lines[8]}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
trainer_list=$(func_parser_value "${lines[10]}")
norm_trainer=$(func_parser_value "${lines[11]}")
pact_trainer=$(func_parser_value "${lines[12]}")
fpgm_trainer=$(func_parser_value "${lines[13]}")
distill_trainer=$(func_parser_value "${lines[14]}")
eval_py=$(func_parser_value "${lines[15]}")
norm_export=$(func_parser_value "${lines[16]}")
pact_export=$(func_parser_value "${lines[17]}")
fpgm_export=$(func_parser_value "${lines[18]}")
distill_export=$(func_parser_value "${lines[19]}")
inference_py=$(func_parser_value "${lines[20]}")
use_gpu_key=$(func_parser_key "${lines[21]}")
use_gpu_list=$(func_parser_value "${lines[21]}")
use_mkldnn_key=$(func_parser_key "${lines[22]}")
use_mkldnn_list=$(func_parser_value "${lines[22]}")
cpu_threads_key=$(func_parser_key "${lines[23]}")
cpu_threads_list=$(func_parser_value "${lines[23]}")
batch_size_key=$(func_parser_key "${lines[24]}")
batch_size_list=$(func_parser_value "${lines[24]}")
use_trt_key=$(func_parser_key "${lines[25]}")
use_trt_list=$(func_parser_value "${lines[25]}")
precision_key=$(func_parser_key "${lines[26]}")
precision_list=$(func_parser_value "${lines[26]}")
model_dir_key=$(func_parser_key "${lines[27]}")
image_dir_key=$(func_parser_key "${lines[28]}")
save_log_key=$(func_parser_key "${lines[29]}")
train_batch_key=$(func_parser_key "${lines[6]}")
train_use_gpu_key=$(func_parser_key "${lines[7]}")
pretrain_model_key=$(func_parser_key "${lines[8]}")
pretrain_model_value=$(func_parser_value "${lines[8]}")
trainer_list=$(func_parser_value "${lines[9]}")
norm_trainer=$(func_parser_value "${lines[10]}")
pact_trainer=$(func_parser_value "${lines[11]}")
fpgm_trainer=$(func_parser_value "${lines[12]}")
distill_trainer=$(func_parser_value "${lines[13]}")
eval_py=$(func_parser_value "${lines[14]}")
save_infer_key=$(func_parser_key "${lines[15]}")
export_weight=$(func_parser_key "${lines[16]}")
norm_export=$(func_parser_value "${lines[17]}")
pact_export=$(func_parser_value "${lines[18]}")
fpgm_export=$(func_parser_value "${lines[19]}")
distill_export=$(func_parser_value "${lines[20]}")
inference_py=$(func_parser_value "${lines[21]}")
use_gpu_key=$(func_parser_key "${lines[22]}")
use_gpu_list=$(func_parser_value "${lines[22]}")
use_mkldnn_key=$(func_parser_key "${lines[23]}")
use_mkldnn_list=$(func_parser_value "${lines[23]}")
cpu_threads_key=$(func_parser_key "${lines[24]}")
cpu_threads_list=$(func_parser_value "${lines[24]}")
batch_size_key=$(func_parser_key "${lines[25]}")
batch_size_list=$(func_parser_value "${lines[25]}")
use_trt_key=$(func_parser_key "${lines[26]}")
use_trt_list=$(func_parser_value "${lines[26]}")
precision_key=$(func_parser_key "${lines[27]}")
precision_list=$(func_parser_value "${lines[27]}")
infer_model_key=$(func_parser_key "${lines[28]}")
infer_model=$(func_parser_value "${lines[28]}")
image_dir_key=$(func_parser_key "${lines[29]}")
infer_img_dir=$(func_parser_value "${lines[29]}")
save_log_key=$(func_parser_key "${lines[30]}")
LOG_PATH="./test/output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
if [ ${MODE} = "lite_train_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=10
elif [ ${MODE} = "whole_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=10
elif [ ${MODE} = "whole_train_infer" ]; then
export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
export epoch_num=300
else
export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
fi
function func_inference(){
IFS='|'
......@@ -109,8 +101,8 @@ function func_inference(){
for use_mkldnn in ${use_mkldnn_list[*]}; do
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
eval $command
status_check $? "${command}" "${status_log}"
done
......@@ -123,8 +115,8 @@ function func_inference(){
continue
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir} ${save_log_key}=${_save_log_path} --benchmark=True"
eval $command
status_check $? "${command}" "${status_log}"
done
......@@ -138,12 +130,13 @@ if [ ${MODE} != "infer" ]; then
IFS="|"
for gpu in ${gpu_list[*]}; do
train_use_gpu=True
use_gpu=True
if [ ${gpu} = "-1" ];then
train_use_gpu=False
use_gpu=False
env=""
elif [ ${#gpu} -le 1 ];then
env="export CUDA_VISIBLE_DEVICES=${gpu}"
eval ${env}
elif [ ${#gpu} -le 15 ];then
IFS=","
array=(${gpu})
......@@ -155,6 +148,7 @@ for gpu in ${gpu_list[*]}; do
ips=${array[0]}
gpu=${array[1]}
IFS="|"
env=" "
fi
for autocast in ${autocast_list[*]}; do
for trainer in ${trainer_list[*]}; do
......@@ -179,13 +173,32 @@ for gpu in ${gpu_list[*]}; do
continue
fi
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
if [ ${#gpu} -le 2 ];then # epoch_num #TODO
cmd="${python} ${run_train} ${train_use_gpu_key}=${train_use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
elif [ ${#gpu} -le 15 ];then
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
# not set autocast when autocast is null
if [ ${autocast} = "null" ]; then
set_autocast=" "
else
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
set_autocast="${autocast_key}=${autocast}"
fi
# not set epoch when whole_train_infer
if [ ${MODE} != "whole_train_infer" ]; then
set_epoch="${epoch_key}=${epoch_num}"
else
set_epoch=" "
fi
# set pretrain
if [ ${pretrain_model_value} != "null" ]; then
set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
else
set_pretrain=" "
fi
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
fi
# run train
eval $cmd
......@@ -198,24 +211,27 @@ for gpu in ${gpu_list[*]}; do
# run export model
save_infer_path="${save_log}"
export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
#run inference
eval $env
save_infer_path="${save_log}"
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
eval "unset CUDA_VISIBLE_DEVICES"
done
done
done
else
save_infer_path="${LOG_PATH}/${MODE}"
run_export=${norm_export}
export_cmd="${python} ${run_export} ${save_model_key}=${save_infer_path} ${pretrain_model_key}=${infer_model_dir} ${save_infer_key}=${save_infer_path}"
eval $export_cmd
status_check $? "${export_cmd}" "${status_log}"
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
echo $env
#run inference
func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
fi
......@@ -19,7 +19,29 @@
### 2.1 训练
TBD
#### 数据准备
训练数据使用公开数据集[PubTabNet](https://arxiv.org/abs/1911.10683),可以从[官网](https://github.com/ibm-aur-nlp/PubTabNet)下载。PubTabNet数据集包含约50万张表格数据的图像,以及图像对应的html格式的注释。
#### 启动训练
*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false*
```shell
# 单机单卡训练
python3 tools/train.py -c configs/table/table_mv3.yml
# 单机多卡训练,通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
```
上述指令中,通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释,请参考[链接](./config.md)
#### 断点训练
如果训练程序中断,如果希望加载训练中断的模型从而恢复训练,可以通过指定Global.checkpoints指定要加载的模型路径:
```shell
python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
```
**注意**`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级,即同时指定两个参数时,优先加载`Global.checkpoints`指定的模型,如果`Global.checkpoints`指定的模型路径有误,会加载`Global.pretrain_weights`指定的模型。
### 2.2 评估
先cd到PaddleOCR/ppstructure目录下
......
......@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import init_model, load_pretrained_params
from ppocr.utils.utility import print_dict
import tools.program as program
......@@ -55,7 +55,10 @@ def main():
model = build_model(config['Architecture'])
use_srn = config['Architecture']['algorithm'] == "SRN"
if "model_type" in config['Architecture'].keys():
model_type = config['Architecture']['model_type']
else:
model_type = None
best_model_dict = init_model(config, model)
if len(best_model_dict):
......
......@@ -112,7 +112,6 @@ class TextClassifier(object):
if '180' in label and score > self.cls_thresh:
img_list[indices[beg_img_no + rno]] = cv2.rotate(
img_list[indices[beg_img_no + rno]], 1)
elapse = time.time() - starttime
return img_list, cls_res, elapse
......@@ -146,7 +145,6 @@ def main(args):
cls_res[ino]))
logger.info(
"The predict time about text angle classify module is as follows: ")
text_classifier.cls_times.info(average=False)
if __name__ == "__main__":
......
......@@ -175,7 +175,7 @@ class TextDetector(object):
st = time.time()
if args.benchmark:
if self.args.benchmark:
self.autolog.times.start()
data = transform(data, self.preprocess_op)
......@@ -186,7 +186,7 @@ class TextDetector(object):
shape_list = np.expand_dims(shape_list, axis=0)
img = img.copy()
if args.benchmark:
if self.args.benchmark:
self.autolog.times.stamp()
self.input_tensor.copy_from_cpu(img)
......@@ -195,7 +195,7 @@ class TextDetector(object):
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if args.benchmark:
if self.args.benchmark:
self.autolog.times.stamp()
preds = {}
......@@ -220,7 +220,7 @@ class TextDetector(object):
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
if args.benchmark:
if self.args.benchmark:
self.autolog.times.end(stamp=True)
et = time.time()
return dt_boxes, et - st
......
......@@ -64,6 +64,24 @@ class TextRecognizer(object):
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger)
self.benchmark = args.benchmark
if args.benchmark:
import auto_log
pid = os.getpid()
self.autolog = auto_log.AutoLogger(
model_name="rec",
model_precision=args.precision,
batch_size=args.rec_batch_num,
data_shape="dynamic",
save_path=args.save_log_path,
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0 if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=10)
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
......@@ -168,6 +186,8 @@ class TextRecognizer(object):
rec_res = [['', 0.0]] * img_num
batch_num = self.rec_batch_num
st = time.time()
if self.benchmark:
self.autolog.times.start()
for beg_img_no in range(0, img_num, batch_num):
end_img_no = min(img_num, beg_img_no + batch_num)
norm_img_batch = []
......@@ -196,6 +216,8 @@ class TextRecognizer(object):
norm_img_batch.append(norm_img[0])
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
if self.benchmark:
self.autolog.times.stamp()
if self.rec_algorithm == "SRN":
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
......@@ -222,6 +244,8 @@ class TextRecognizer(object):
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = {"predict": outputs[2]}
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
......@@ -231,11 +255,14 @@ class TextRecognizer(object):
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if self.benchmark:
self.autolog.times.stamp()
preds = outputs[0]
rec_result = self.postprocess_op(preds)
for rno in range(len(rec_result)):
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
if self.benchmark:
self.autolog.times.end(stamp=True)
return rec_res, time.time() - st
......@@ -251,9 +278,6 @@ def main(args):
for i in range(10):
res = text_recognizer([img])
cpu_mem, gpu_mem, gpu_util = 0, 0, 0
count = 0
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
......@@ -273,6 +297,8 @@ def main(args):
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
rec_res[ino]))
if args.benchmark:
text_recognizer.autolog.report()
if __name__ == "__main__":
......
......@@ -174,8 +174,6 @@ def main(args):
logger.info("The predict total time is {}".format(time.time() - _st))
logger.info("\nThe predict total time is {}".format(total_time))
img_num = text_sys.text_detector.det_times.img_num
if __name__ == "__main__":
args = utility.parse_args()
......
......@@ -34,7 +34,7 @@ def init_args():
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--min_subgraph_size", type=int, default=3)
parser.add_argument("--min_subgraph_size", type=int, default=10)
parser.add_argument("--precision", type=str, default="fp32")
parser.add_argument("--gpu_mem", type=int, default=500)
......@@ -161,7 +161,7 @@ def create_predictor(args, mode, logger):
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=inference.PrecisionType.Float32,
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
......
......@@ -186,7 +186,10 @@ def train(config,
model.train()
use_srn = config['Architecture']['algorithm'] == "SRN"
try:
model_type = config['Architecture']['model_type']
except:
model_type = None
if 'start_epoch' in best_model_dict:
start_epoch = best_model_dict['start_epoch']
......
......@@ -98,7 +98,6 @@ def main(config, device, logger, vdl_writer):
eval_class = build_metric(config['Metric'])
# load pretrain model
pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format(
......
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