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Merge branch 'PaddlePaddle:dygraph' into dygraph

pull/4570/head
d2623587501 2021-11-10 16:33:15 +08:00 committed by GitHub
parent 376319d79f a8960021ed
commit be0da20e93
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25 changed files with 150 additions and 20 deletions
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2
configs/det/det_r50_vd_sast_icdar15.yml
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@ -8,7 +8,7 @@ Global:
# 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/
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False

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configs/det/det_r50_vd_sast_totaltext.yml
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@ -8,7 +8,7 @@ Global:
# 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/
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False

0
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 100644
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test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt 100644
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test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_mac.txt 100644
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test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_metal_arm_gpu.txt 100644
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test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_opencl_arm_gpu.txt 100644
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test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 100644
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@ -0,0 +1,12 @@
===========================paddle2onnx_params===========================
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/det_mobile_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_det.py
--use_gpu:True|False
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/

18
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 100644
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@ -0,0 +1,18 @@
===========================serving_params===========================
model_name:ocr_det_mobile
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs

0
test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 100644
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0
test_tipc/configs/ppocr_det_mobile/train_linux_dcu_normal_normal_infer_python_dcu.txt 100644
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0
test_tipc/configs/ppocr_det_mobile/train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt 100644
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12
test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 100644
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@ -0,0 +1,12 @@
===========================paddle2onnx_params===========================
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_server_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/det_server_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_det.py
--use_gpu:True|False
--det_model_dir:
--image_dir:./inference/det_inference

18
test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 100644
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@ -0,0 +1,18 @@
===========================serving_params===========================
model_name:ocr_det_server
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_server_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_server_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en

12
test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 100644
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@ -0,0 +1,12 @@
===========================paddle2onnx_params===========================
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/rec_mobile_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--rec_model_dir:
--image_dir:./inference/rec_inference

18
test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 100644
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@ -0,0 +1,18 @@
===========================serving_params===========================
model_name:ocr_rec_mobile
python:python3.7|cpp
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en

12
test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt 100644
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@ -0,0 +1,12 @@
===========================paddle2onnx_params===========================
2onnx: paddle2onnx
--model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--save_file:./inference/rec_server_onnx/model.onnx
--opset_version:10
--enable_onnx_checker:True
inference:tools/infer/predict_rec.py
--use_gpu:True|False
--rec_model_dir:
--image_dir:./inference/rec_inference

18
test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt 100644
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@ -0,0 +1,18 @@
===========================serving_params===========================
model_name:ocr_rec_server
python:python3.7
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
op.rec.local_service_conf.devices:null|0
op.rec.local_service_conf.use_mkldnn:True|False
op.rec.local_service_conf.thread_num:1|6
op.rec.local_service_conf.use_trt:False|True
op.rec.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_rpc_client.py|pipeline_http_client.py
--image_dir:../../doc/imgs_words_en

4
test_tipc/docs/test_paddle2onnx.md
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@ -18,10 +18,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测
先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。
```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "paddle2onnx_infer"
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt "paddle2onnx_infer"
# 用法:
bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
```
#### 运行结果

4
test_tipc/docs/test_serving.md
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@ -20,10 +20,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试
先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
```shell
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer"
bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
# 用法:
bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
```
#### 运行结果

3
test_tipc/prepare.sh
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@ -87,7 +87,8 @@ elif [ ${MODE} = "whole_infer" ];then
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
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
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
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

2
test_tipc/test_paddle2onnx.sh
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@ -11,7 +11,7 @@ python=$(func_parser_value "${lines[2]}")
# parser params
dataline=$(awk 'NR==111, NR==123{print}' $FILENAME)
dataline=$(awk 'NR==1, NR==12{print}' $FILENAME)
IFS=$'\n'
lines=(${dataline})

2
test_tipc/test_serving.sh
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@ -2,7 +2,7 @@
source test_tipc/common_func.sh
FILENAME=$1
dataline=$(awk 'NR==67, NR==84{print}' $FILENAME)
dataline=$(awk 'NR==1, NR==18{print}' $FILENAME)
# parser params
IFS=$'\n'

21
test_tipc/test_train_inference_python.sh
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@ -244,7 +244,7 @@ else
export Count=0
USE_GPU_KEY=(${train_use_gpu_value})
for gpu in ${gpu_list[*]}; do
use_gpu=${USE_GPU_KEY[Count]}
train_use_gpu=${USE_GPU_KEY[Count]}
Count=$(($Count + 1))
ips=""
if [ ${gpu} = "-1" ];then
@ -302,11 +302,20 @@ else
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
if [ ${#ips} -le 26 ];then
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
nodes=1
else
IFS=","
ips_array=(${ips})
IFS="|"
nodes=${#ips_array[@]}
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
fi
# load pretrain from norm training if current trainer is pact or fpgm trainer
if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
set_pretrain="${load_norm_train_model}"
fi
@ -325,7 +334,7 @@ else
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training
if [ ${trainer} = ${trainer_norm} ]; then
if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
load_norm_train_model=${set_eval_pretrain}
fi
# run eval

10
tools/infer/utility.py
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@ -205,7 +205,7 @@ def create_predictor(args, mode, logger):
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"x": [1, 3, 1280, 1280],
"conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
@ -255,16 +255,16 @@ def create_predictor(args, mode, logger):
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 500, 500]}
max_input_shape = {"x": [1, 3, 512, 512]}
opt_input_shape = {"x": [1, 3, 256, 256]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)