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Commit 0d85119d authored by WenmuZhou's avatar WenmuZhou
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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into tipc

parents 010202ff 731a8406
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test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt
View file @ 0d85119d
===========================train_params===========================
model_name:ocr_det
model_name:ch_ppocr_mobile_v2.0_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
......
test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
View file @ 0d85119d
===========================kl_quant_params===========================
model_name:PPOCRv2_ocr_det
python:python3.7
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
infer_quant:True
......
test_tipc/configs/ch_ppocr_server_v2.0_det/train_infer_python.txt
View file @ 0d85119d
===========================train_params===========================
model_name:ocr_server_det
model_name:ch_ppocr_server_v2.0_det
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
......
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml 0 → 100644
View file @ 0d85119d
Global:
use_gpu: true
epoch_num: 5000
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/sast_r50_vd_ic15/
save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
Architecture:
model_type: det
algorithm: SAST
Transform:
Backbone:
name: ResNet_SAST
layers: 50
Neck:
name: SASTFPN
with_cab: True
Head:
name: SASTHead
Loss:
name: SASTLoss
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
# name: Cosine
learning_rate: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: SASTPostProcess
score_thresh: 0.5
sample_pts_num: 2
nms_thresh: 0.2
expand_scale: 1.0
shrink_ratio_of_width: 0.3
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: [0.1, 0.45, 0.3, 0.15]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- SASTProcessTrain:
image_shape: [512, 512]
min_crop_side_ratio: 0.3
min_crop_size: 24
min_text_size: 4
max_text_size: 512
- KeepKeys:
keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size_per_card: 4
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:
resize_long: 1536
- 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
test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt 0 → 100644
View file @ 0d85119d
===========================train_params===========================
model_name:det_r50_vd_sast_icdar15_v2.0
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/det_r50_vd_sast_icdar15_v2.0_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml 0 → 100644
View file @ 0d85119d
Global:
use_gpu: true
epoch_num: 5000
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/sast_r50_vd_tt/
save_epoch_step: 1000
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
save_res_path: ./output/sast_r50_vd_tt/predicts_sast.txt
Architecture:
model_type: det
algorithm: SAST
Transform:
Backbone:
name: ResNet_SAST
layers: 50
Neck:
name: SASTFPN
with_cab: True
Head:
name: SASTHead
Loss:
name: SASTLoss
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
# name: Cosine
learning_rate: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: SASTPostProcess
score_thresh: 0.5
sample_pts_num: 6
nms_thresh: 0.2
expand_scale: 1.2
shrink_ratio_of_width: 0.2
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/total_text/train
label_file_list: [./train_data/total_text/train/train.txt]
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- SASTProcessTrain:
image_shape: [512, 512]
min_crop_side_ratio: 0.3
min_crop_size: 24
min_text_size: 4
max_text_size: 512
- KeepKeys:
keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size_per_card: 4
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list:
- ./train_data/total_text/test/test.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
resize_long: 768
- 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
\ No newline at end of file
test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt 0 → 100644
View file @ 0d85119d
===========================train_params===========================
model_name:det_r50_vd_sast_totaltext_v2.0
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/det_r50_vd_sast_totaltext_v2.0/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
test_tipc/configs/en_server_pgnetA/train_infer_python.txt 0 → 100644
View file @ 0d85119d
===========================train_params===========================
model_name:en_server_pgnetA
python:python3.7
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=14
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/total_text/test/rgb/
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/en_server_pgnetA/best_accuracy
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:null
train_model:./inference/en_server_pgnetA/best_accuracy
infer_export:tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o
infer_quant:False
inference:tools/infer/predict_e2e.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
null:null
--benchmark:True
null:null
test_tipc/docs/test_lite_arm_cpp.md
View file @ 0d85119d
......@@ -16,7 +16,7 @@ Lite\_arm\_cpp预测功能测试的主程序为`test_lite_arm_cpp.sh`，可以
| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 | 测试硬件 |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 正常模型/量化模型 | 1 | 1/4 | 单/多 | 下载方式 | ARM\_CPU/ARM\_GPU_OPENCL |
| 正常模型/量化模型 | 1 | 1/4 | 单/多 | 下载方式/编译方式 | ARM\_CPU/ARM\_GPU_OPENCL |
## 2. 测试流程
......@@ -30,8 +30,11 @@ Lite\_arm\_cpp预测功能测试的主程序为`test_lite_arm_cpp.sh`，可以
```shell
# 数据和模型准备
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
# 数据、模型、Paddle-Lite预测库准备
#预测库为下载方式
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt download
#预测库为编译方式
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt compile
# 手机端测试:
bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
......@@ -42,8 +45,11 @@ bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
```shell
# 数据和模型准备
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
# 数据、模型、Paddle-Lite预测库准备
#预测库下载方式
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt download
#预测库编译方式
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt compile
# 手机端测试:
bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
......@@ -53,9 +59,7 @@ bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.
**注意**：
1.由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
2.如果测试文本检测和识别完整的pipeline，在执行`prepare_lite_cpp.sh`时，配置文件需替换为`test_tipc/configs/ppocr_system_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`。在手机端测试阶段，配置文件同样修改为该文件。
由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
### 2.2 运行结果
......
test_tipc/prepare.sh
View file @ 0d85119d
......@@ -25,7 +25,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
# pretrain lite train data
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar --no-check-certificate
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
if [ ${model_name} == "ch_PPOCRv2_det" ]; then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
fi
......@@ -41,6 +41,18 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
ln -s ./icdar2015_lite ./icdar2015
cd ../
cd ./inference && tar xf rec_inference.tar && cd ../
if [ ${model_name} == "en_server_pgnetA" ]; then
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../
cd ./train_data && tar xf total_text_lite.tar && ln -s total_text && cd ../
fi
if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ] || [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
wget -nc -P ./train_data/ wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
cd ./train_data && tar xf total_text_lite.tar && ln -s total_text && cd ../
fi
elif [ ${MODE} = "whole_train_whole_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
rm -rf ./train_data/icdar2015
......@@ -48,10 +60,21 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
if [ ${model_name} == "ch_PPOCRv2_det" ]; then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
fi
if [ ${model_name} == "en_server_pgnetA" ]; then
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar --no-check-certificate
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../
cd ./train_data && tar xf total_text.tar && ln -s total_text && cd ../
fi
if [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar --no-check-certificate
cd ./train_data && tar xf total_text.tar && ln -s total_text && cd ../
fi
elif [ ${MODE} = "lite_train_whole_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
rm -rf ./train_data/icdar2015
......@@ -61,19 +84,20 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
if [ ${model_name} == "PPOCRv2_ocr_det" ]; then
if [ ${model_name} == "ch_PPOCRv2_det" ]; then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
fi
elif [ ${MODE} = "whole_infer" ];then
if [ ${model_name} = "ocr_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
rm -rf ./train_data/icdar2015
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ../
elif [ ${model_name} = "ocr_server_det" ]; then
elif [ ${model_name} = "ch_ppocr_server_v2.0_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
cd ./inference && tar xf ch_ppocr_server_v2.0_det_train.tar && tar xf ch_det_data_50.tar && cd ../
......@@ -100,21 +124,33 @@ elif [ ${MODE} = "whole_infer" ];then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
fi
elif [ ${model_name} = "PPOCRv2_ocr_det" ]; then
elif [ ${model_name} = "ch_PPOCRv2_det" ]; then
eval_model_name="ch_PP-OCRv2_det_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar --no-check-certificate
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
fi
elif [ ${model_name} = "ch_PPOCRv2_det" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar --no-check-certificate
cd ./inference && tar xf e2e_server_pgnetA_infer.tar && tar xf ch_det_data_50.tar && cd ../
fi
if [ ${model_name} == "en_server_pgnetA" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
cd ./inference && tar xf en_server_pgnetA.tar && cd ../
fi
if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar --no-check-certificate
cd ./inference/ && tar det_r50_vd_sast_icdar15_v2.0_train.tar && cd ../
fi
if [ ${MODE} = "klquant_whole_infer" ]; then
if [ ${model_name} = "ocr_det" ]; then
if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
fi
if [ ${model_name} = "PPOCRv2_ocr_det" ]; then
if [ ${model_name} = "ch_PPOCRv2_det" ]; then
eval_model_name="ch_PP-OCRv2_det_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar --no-check-certificate
......
test_tipc/prepare_lite_cpp.sh
View file @ 0d85119d
......@@ -6,6 +6,7 @@ dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
IFS=$'\n'
paddlelite_library_source=$2
inference_cmd=$(func_parser_value "${lines[1]}")
DEVICE=$(func_parser_value "${lines[2]}")
......@@ -13,40 +14,42 @@ det_lite_model_list=$(func_parser_value "${lines[3]}")
rec_lite_model_list=$(func_parser_value "${lines[4]}")
cls_lite_model_list=$(func_parser_value "${lines[5]}")
if [[ $inference_cmd =~ "det" ]];then
if [[ $inference_cmd =~ "det" ]]; then
lite_model_list=${det_lite_model_list}
elif [[ $inference_cmd =~ "rec" ]];then
elif [[ $inference_cmd =~ "rec" ]]; then
lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
elif [[ $inference_cmd =~ "system" ]];then
elif [[ $inference_cmd =~ "system" ]]; then
lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
else
echo "inference_cmd is wrong, please check."
exit 1
fi
if [ ${DEVICE} = "ARM_CPU" ];then
if [ ${DEVICE} = "ARM_CPU" ]; then
valid_targets="arm"
paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
paddlelite_library_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
end_index="66"
elif [ ${DEVICE} = "ARM_GPU_OPENCL" ];then
compile_with_opencl="OFF"
elif [ ${DEVICE} = "ARM_GPU_OPENCL" ]; then
valid_targets="opencl"
paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
paddlelite_library_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
end_index="71"
compile_with_opencl="ON"
else
echo "DEVICE only suport ARM_CPU, ARM_GPU_OPENCL."
echo "DEVICE only support ARM_CPU, ARM_GPU_OPENCL."
exit 2
fi
# prepare lite .nb model
# prepare paddlelite model
pip install paddlelite==2.10-rc
current_dir=${PWD}
IFS="|"
model_path=./inference_models
for model in ${lite_model_list[*]}; do
if [[ $model =~ "PP-OCRv2" ]];then
if [[ $model =~ "PP-OCRv2" ]]; then
inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
elif [[ $model =~ "v2.0" ]];then
elif [[ $model =~ "v2.0" ]]; then
inference_model_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/${model}.tar
else
echo "Model is wrong, please check."
......@@ -63,31 +66,42 @@ done
# prepare test data
data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
model_path=./inference_models
inference_model=${inference_model_url##*/}
data_file=${data_url##*/}
wget -nc -P ./inference_models ${inference_model_url}
wget -nc -P ./test_data ${data_url}
cd ./inference_models && tar -xf ${inference_model} && cd ../
cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
# prepare lite env
paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
paddlelite_file=${paddlelite_zipfile:0:${end_index}}
wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
mkdir -p ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
cd ${paddlelite_file}/demo/cxx/ocr/
# prepare paddlelite predict library
if [[ ${paddlelite_library_source} = "download" ]]; then
paddlelite_library_zipfile=$(echo $paddlelite_library_url | awk -F "/" '{print $NF}')
paddlelite_library_file=${paddlelite_library_zipfile:0:${end_index}}
wget ${paddlelite_library_url} && tar -xf ${paddlelite_library_zipfile}
cd ${paddlelite_library_zipfile}
elif [[ ${paddlelite_library_source} = "compile" ]]; then
git clone -b release/v2.10 https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
./lite/tools/build_android.sh --arch=armv8 --with_cv=ON --with_extra=ON --toolchain=clang --with_opencl=${compile_with_opencl}
cd ../
cp -r Paddle-Lite/build.lite.android.armv8.clang/inference_lite_lib.android.armv8/ .
paddlelite_library_file=inference_lite_lib.android.armv8
else
echo "paddlelite_library_source only support 'download' and 'compile'"
exit 3
fi
# organize the required files
mkdir -p ${paddlelite_library_file}/demo/cxx/ocr/test_lite
cp -r ${model_path}/*_opt.nb test_data ${paddlelite_library_file}/demo/cxx/ocr/test_lite
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_library_file}/demo/cxx/ocr/test_lite
cp -r ./deploy/lite/* ${paddlelite_library_file}/demo/cxx/ocr/
cp ${paddlelite_library_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_library_file}/demo/cxx/ocr/test_lite
cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_library_file}/demo/cxx/ocr/test_lite
cd ${paddlelite_library_file}/demo/cxx/ocr/
git clone https://github.com/cuicheng01/AutoLog.git
# make
# compile and do some postprocess
make -j
sleep 1
make -j
cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
rm -rf ${paddlelite_file}* && rm -rf ${model_path}
rm -rf ${paddlelite_library_file}* && rm -rf ${model_path}
test_tipc/test_train_inference_python.sh
View file @ 0d85119d
......@@ -20,10 +20,10 @@ train_use_gpu_value=$(func_parser_value "${lines[4]}")
autocast_list=$(func_parser_value "${lines[5]}")
autocast_key=$(func_parser_key "${lines[5]}")
epoch_key=$(func_parser_key "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}")
epoch_num=$(func_parser_params "${lines[6]}" "${MODE}")
save_model_key=$(func_parser_key "${lines[7]}")
train_batch_key=$(func_parser_key "${lines[8]}")
train_batch_value=$(func_parser_params "${lines[8]}")
train_batch_value=$(func_parser_params "${lines[8]}" "${MODE}")
pretrain_model_key=$(func_parser_key "${lines[9]}")
pretrain_model_value=$(func_parser_value "${lines[9]}")
train_model_name=$(func_parser_value "${lines[10]}")
......
tools/infer/utility.py 100755 → 100644
View file @ 0d85119d
......@@ -190,6 +190,7 @@ def create_predictor(args, mode, logger):
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
workspace_size=1 << 30,
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
......@@ -310,10 +311,6 @@ def create_predictor(args, mode, logger):
def get_infer_gpuid():
cmd = "nvidia-smi"
res = os.popen(cmd).readlines()
if len(res) == 0:
return None
cmd = "env | grep CUDA_VISIBLE_DEVICES"
env_cuda = os.popen(cmd).readlines()
if len(env_cuda) == 0:
......
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