fix bugs and adjust warmup times from 10 to 2 to speed up
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e450e32d1a
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09e7211082
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@ -138,8 +138,8 @@ infer_img_dir=$(func_parser_value "${lines[44]}")
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save_log_key=$(func_parser_key "${lines[45]}")
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benchmark_key=$(func_parser_key "${lines[46]}")
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benchmark_value=$(func_parser_value "${lines[46]}")
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infer_key2=$(func_parser_key "${lines[47]}")
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infer_value2=$(func_parser_value "${lines[47]}")
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infer_key1=$(func_parser_key "${lines[47]}")
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infer_value1=$(func_parser_value "${lines[47]}")
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LOG_PATH="./tests/output"
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mkdir -p ${LOG_PATH}
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@ -169,7 +169,8 @@ function func_inference(){
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set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
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set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
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set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
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set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
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command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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@ -285,7 +286,8 @@ for gpu in ${gpu_list[*]}; do
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
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# run eval
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if [ ${eval_py} != "null" ]; then
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eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu}"
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set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
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eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
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eval $eval_cmd
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status_check $? "${eval_cmd}" "${status_log}"
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fi
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@ -318,3 +320,4 @@ else
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#run inference
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func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
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fi
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@ -114,7 +114,7 @@ class TextDetector(object):
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time_keys=[
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'preprocess_time', 'inference_time', 'postprocess_time'
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],
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warmup=10,
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warmup=2,
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logger=logger)
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def order_points_clockwise(self, pts):
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@ -237,7 +237,7 @@ if __name__ == "__main__":
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if args.warmup:
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img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
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for i in range(10):
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for i in range(2):
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res = text_detector(img)
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if not os.path.exists(draw_img_save):
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@ -73,7 +73,7 @@ class TextRecognizer(object):
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model_precision=args.precision,
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batch_size=args.rec_batch_num,
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data_shape="dynamic",
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save_path=args.save_log_path,
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save_path=None, #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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@ -81,7 +81,8 @@ class TextRecognizer(object):
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time_keys=[
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'preprocess_time', 'inference_time', 'postprocess_time'
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],
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warmup=10)
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warmup=2,
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logger=logger)
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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@ -272,10 +273,10 @@ def main(args):
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valid_image_file_list = []
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img_list = []
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# warmup 10 times
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# warmup 2 times
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if args.warmup:
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img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
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for i in range(10):
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for i in range(2):
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res = text_recognizer([img])
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for image_file in image_file_list:
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