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Commit ac98415b authored by WenmuZhou's avatar WenmuZhou
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merge dygraph

parents af34d785 29929ac6
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tests/readme.md
View file @ ac98415b
......@@ -25,34 +25,44 @@ test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从
tests/
├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
├── ocr_ppocr_mobile_params.txt # 测试OCR检测+识别模型串联的参数配置文件
└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
└── test.sh # 根据
└── test.sh # 测试主程序
```
# 使用方法
test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
- 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```shell
bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
```
- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```
- 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
```
- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```
- 模式3：infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
用法1:
# 用法1:
bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
```
模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
```
- 模式4：whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
```
- 模式5：cpp_infer , CE： 验证inference model的c++预测是否走通；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer'
```
tests/results/det_results_gpu_fp32.txt
View file @ ac98415b
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tests/test.sh
View file @ ac98415b
......@@ -144,6 +144,32 @@ benchmark_key=$(func_parser_key "${lines[49]}")
benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
# parser serving
trans_model_py=$(func_parser_value "${lines[67]}")
infer_model_dir_key=$(func_parser_key "${lines[68]}")
infer_model_dir_value=$(func_parser_value "${lines[68]}")
model_filename_key=$(func_parser_key "${lines[69]}")
model_filename_value=$(func_parser_value "${lines[69]}")
params_filename_key=$(func_parser_key "${lines[70]}")
params_filename_value=$(func_parser_value "${lines[70]}")
serving_server_key=$(func_parser_key "${lines[71]}")
serving_server_value=$(func_parser_value "${lines[71]}")
serving_client_key=$(func_parser_key "${lines[72]}")
serving_client_value=$(func_parser_value "${lines[72]}")
serving_dir_value=$(func_parser_value "${lines[73]}")
web_service_py=$(func_parser_value "${lines[74]}")
web_use_gpu_key=$(func_parser_key "${lines[75]}")
web_use_gpu_list=$(func_parser_value "${lines[75]}")
web_use_mkldnn_key=$(func_parser_key "${lines[76]}")
web_use_mkldnn_list=$(func_parser_value "${lines[76]}")
web_cpu_threads_key=$(func_parser_key "${lines[77]}")
web_cpu_threads_list=$(func_parser_value "${lines[77]}")
web_use_trt_key=$(func_parser_key "${lines[78]}")
web_use_trt_list=$(func_parser_value "${lines[78]}")
web_precision_key=$(func_parser_key "${lines[79]}")
web_precision_list=$(func_parser_value "${lines[79]}")
pipeline_py=$(func_parser_value "${lines[80]}")
if [ ${MODE} = "cpp_infer" ]; then
# parser cpp inference model
......@@ -166,7 +192,8 @@ if [ ${MODE} = "cpp_infer" ]; then
cpp_infer_model_key=$(func_parser_key "${lines[62]}")
cpp_image_dir_key=$(func_parser_key "${lines[63]}")
cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
cpp_save_log_key=$(func_parser_key "${lines[64]}")
cpp_infer_key1=$(func_parser_key "${lines[64]}")
cpp_infer_value1=$(func_parser_value "${lines[64]}")
cpp_benchmark_key=$(func_parser_key "${lines[65]}")
cpp_benchmark_value=$(func_parser_value "${lines[65]}")
fi
......@@ -244,6 +271,81 @@ function func_inference(){
fi
done
}
function func_serving(){
IFS='|'
_python=$1
_script=$2
_model_dir=$3
# pdserving
set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $trans_model_cmd
cd ${serving_dir_value}
echo $PWD
unset https_proxy
unset http_proxy
for use_gpu in ${web_use_gpu_list[*]}; do
echo ${ues_gpu}
if [ ${use_gpu} = "null" ]; then
for use_mkldnn in ${web_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ]; then
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
_save_log_path="${_log_path}/server_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &>${_save_log_path} &"
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py}"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "Falg_quantse" || ${precision} =~ "int8" ]]; then
continue
fi
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} &>${_save_log_path} & "
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py}"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
function func_cpp_inference(){
IFS='|'
......@@ -267,7 +369,8 @@ function func_cpp_inference(){
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
......@@ -295,7 +398,8 @@ function func_cpp_inference(){
set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
......@@ -332,9 +436,7 @@ if [ ${MODE} = "infer" ]; then
export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_export=$?
if [ ${status_export} = 0 ];then
status_check $status_export "${export_cmd}" "${status_log}"
fi
else
save_infer_dir=${infer_model}
fi
......@@ -363,6 +465,20 @@ elif [ ${MODE} = "cpp_infer" ]; then
Count=$(($Count + 1))
done
elif [ ${MODE} = "serving_infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
# set CUDA_VISIBLE_DEVICES
eval $env
export Count=0
IFS="|"
#run serving
func_serving "${web_service_cmd}"
else
IFS="|"
export Count=0
......
tools/eval.py
View file @ ac98415b
......@@ -55,6 +55,7 @@ def main():
model = build_model(config['Architecture'])
use_srn = config['Architecture']['algorithm'] == "SRN"
use_sar = config['Architecture']['algorithm'] == "SAR"
if "model_type" in config['Architecture'].keys():
model_type = config['Architecture']['model_type']
else:
......@@ -71,7 +72,7 @@ def main():
# start eval
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, use_srn)
eval_class, model_type, use_srn, use_sar)
logger.info('metric eval ***************')
for k, v in metric.items():
logger.info('{}:{}'.format(k, v))
......
tools/infer/predict_e2e.py
View file @ ac98415b
......@@ -141,6 +141,7 @@ if __name__ == "__main__":
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
img = img[:, :, ::-1]
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
......
tools/infer/predict_system.py
View file @ ac98415b
......@@ -173,6 +173,9 @@ def main(args):
logger.info("The predict total time is {}".format(time.time() - _st))
logger.info("\nThe predict total time is {}".format(total_time))
if args.benchmark:
text_sys.text_detector.autolog.report()
text_sys.text_recognizer.autolog.report()
if __name__ == "__main__":
......
tools/infer/utility.py
View file @ ac98415b
......@@ -243,11 +243,11 @@ def create_predictor(args, mode, logger):
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
......
tools/infer_rec.py
View file @ ac98415b
......@@ -74,6 +74,10 @@ def main():
'image', 'encoder_word_pos', 'gsrm_word_pos',
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
]
elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = [
'image', 'valid_ratio'
]
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
......@@ -106,11 +110,16 @@ def main():
paddle.to_tensor(gsrm_slf_attn_bias1_list),
paddle.to_tensor(gsrm_slf_attn_bias2_list)
]
if config['Architecture']['algorithm'] == "SAR":
valid_ratio = np.expand_dims(batch[-1], axis=0)
img_metas = [paddle.to_tensor(valid_ratio)]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
if config['Architecture']['algorithm'] == "SRN":
preds = model(images, others)
elif config['Architecture']['algorithm'] == "SAR":
preds = model(images, img_metas)
else:
preds = model(images)
post_result = post_process_class(preds)
......
tools/program.py
View file @ ac98415b
......@@ -187,7 +187,7 @@ def train(config,
use_srn = config['Architecture']['algorithm'] == "SRN"
use_nrtr = config['Architecture']['algorithm'] == "NRTR"
use_sar = config['Architecture']['algorithm'] == 'SAR'
try:
model_type = config['Architecture']['model_type']
except:
......@@ -215,7 +215,7 @@ def train(config,
images = batch[0]
if use_srn:
model_average = True
if use_srn or model_type == 'table' or use_nrtr:
if use_srn or model_type == 'table' or use_nrtr or use_sar:
preds = model(images, data=batch[1:])
else:
preds = model(images)
......@@ -279,7 +279,8 @@ def train(config,
post_process_class,
eval_class,
model_type,
use_srn=use_srn)
use_srn=use_srn,
use_sar=use_sar)
cur_metric_str = 'cur metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
logger.info(cur_metric_str)
......@@ -351,7 +352,8 @@ def eval(model,
post_process_class,
eval_class,
model_type,
use_srn=False):
use_srn=False,
use_sar=False):
model.eval()
with paddle.no_grad():
total_frame = 0.0
......@@ -364,7 +366,7 @@ def eval(model,
break
images = batch[0]
start = time.time()
if use_srn or model_type == 'table':
if use_srn or model_type == 'table' or use_sar:
preds = model(images, data=batch[1:])
else:
preds = model(images)
......@@ -400,7 +402,7 @@ def preprocess(is_train=False):
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'PSE'
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
......
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