MissPenguin
GitHub
commit
b7c8bfb4a1
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@ -110,10 +110,12 @@ def main():
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# build dataloader
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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# start eval
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use_srn = config['Architecture']['algorithm'] == "SRN"
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model_type = config['Architecture']['model_type']
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metric = program.eval(model, valid_dataloader, post_process_class,
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eval_class, model_type)
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# start eval
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metirc = program.eval(model, valid_dataloader, post_process_class,
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eval_class, model_type, use_srn)
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logger.info('metric eval ***************')
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for k, v in metric.items():
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logger.info('{}:{}'.format(k, v))
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@ -1,7 +1,7 @@
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model_name:ocr_det
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python:python3.7
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gpu_list:-1|0|0,1
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Global.auto_cast:False|True
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gpu_list:0|0,1
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Global.auto_cast:False
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Global.epoch_num:10
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Global.save_model_dir:./output/
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Global.save_inference_dir:./output/
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@ -9,7 +9,7 @@ Train.loader.batch_size_per_card:
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Global.use_gpu
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Global.pretrained_model
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trainer:norm|pact|fpgm
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trainer:norm|pact
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norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
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quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
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fpgm_train:null
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@ -94,7 +94,7 @@ for train_model in ${train_model_list[*]}; do
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# eval
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for slim_trainer in ${trainer_list[*]}; do
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if [ ${slim_trainer} = "norm" ]; then
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if [ ${model_name} = "det" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -104,7 +104,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "pact" ]; then
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if [ ${model_name} = "det" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -114,7 +114,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "distill" ]; then
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if [ ${model_name} = "det" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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@ -124,7 +124,7 @@ for train_model in ${train_model_list[*]}; do
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "fpgm" ]; then
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if [ ${model_name} = "det" ]; then
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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10
test/test.sh
10
test/test.sh
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@ -110,7 +110,7 @@ function func_inference(){
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for threads in ${cpu_threads_list[*]}; do
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
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command="${_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}"
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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"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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@ -124,7 +124,7 @@ function func_inference(){
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fi
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
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command="${_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}"
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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"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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@ -138,9 +138,9 @@ if [ ${MODE} != "infer" ]; then
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IFS="|"
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for gpu in ${gpu_list[*]}; do
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use_gpu=True
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train_use_gpu=True
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if [ ${gpu} = "-1" ];then
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use_gpu=False
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train_use_gpu=False
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env=""
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elif [ ${#gpu} -le 1 ];then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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@ -181,7 +181,7 @@ for gpu in ${gpu_list[*]}; do
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save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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if [ ${#gpu} -le 2 ];then # epoch_num #TODO
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cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
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cmd="${python} ${run_train} ${train_use_gpu_key}=${train_use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
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elif [ ${#gpu} -le 15 ];then
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cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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else
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@ -106,7 +106,7 @@ class TextDetector(object):
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model_precision=args.precision,
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batch_size=1,
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data_shape="dynamic",
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save_path="./output/auto_log.lpg",
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save_path=args.save_log_path,
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inference_config=self.config,
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pids=pid,
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process_name=None,
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@ -174,7 +174,7 @@ class TextDetector(object):
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data = {'image': img}
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st = time.time()
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if args.benchmark:
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self.autolog.times.start()
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@ -212,7 +212,7 @@ class TextDetector(object):
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else:
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raise NotImplementedError
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self.predictor.try_shrink_memory()
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#self.predictor.try_shrink_memory()
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post_result = self.postprocess_op(preds, shape_list)
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dt_boxes = post_result[0]['points']
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if self.det_algorithm == "SAST" and self.det_sast_polygon:
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@ -262,7 +262,6 @@ if __name__ == "__main__":
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"det_res_{}".format(img_name_pure))
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cv2.imwrite(img_path, src_im)
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logger.info("The visualized image saved in {}".format(img_path))
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if args.benchmark:
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text_detector.autolog.report()
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@ -176,6 +176,7 @@ def create_predictor(args, mode, logger):
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"conv2d_59.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
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"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
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"conv2d_124.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
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"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
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"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
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@ -188,6 +189,7 @@ def create_predictor(args, mode, logger):
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"conv2d_91.tmp_0": [1, 96, 200, 200],
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"conv2d_59.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
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"conv2d_124.tmp_0": [1, 256, 400, 400],
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"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
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"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
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@ -202,6 +204,7 @@ def create_predictor(args, mode, logger):
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"conv2d_59.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
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"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
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"conv2d_124.tmp_0": [1, 256, 160, 160],
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"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
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"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
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"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
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@ -237,7 +240,7 @@ def create_predictor(args, mode, logger):
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# enable memory optim
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config.enable_memory_optim()
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config.disable_glog_info()
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#config.disable_glog_info()
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config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
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if mode == 'table':
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