From 7588fc082a2bcbeddc0bcf52db557943f7746aca Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 17 Jun 2021 02:24:00 +0000 Subject: [PATCH] delete benchmark utils --- tools/infer/predict_det.py | 60 ++++---------------------------------- 1 file changed, 5 insertions(+), 55 deletions(-) diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index baa89be1..38dd9d2f 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -31,7 +31,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process -import tools.infer.benchmark_utils as benchmark_utils +# import tools.infer.benchmark_utils as benchmark_utils logger = get_logger() @@ -100,8 +100,6 @@ class TextDetector(object): self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor( args, 'det', logger) - self.det_times = utility.Timer() - def order_points_clockwise(self, pts): """ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py @@ -158,8 +156,8 @@ class TextDetector(object): def __call__(self, img): ori_im = img.copy() data = {'image': img} - self.det_times.total_time.start() - self.det_times.preprocess_time.start() + + st = time.time() data = transform(data, self.preprocess_op) img, shape_list = data if img is None: @@ -168,16 +166,12 @@ class TextDetector(object): shape_list = np.expand_dims(shape_list, axis=0) img = img.copy() - self.det_times.preprocess_time.end() - self.det_times.inference_time.start() - self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) - self.det_times.inference_time.end() preds = {} if self.det_algorithm == "EAST": @@ -193,8 +187,6 @@ class TextDetector(object): else: raise NotImplementedError - self.det_times.postprocess_time.start() - self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]['points'] @@ -203,10 +195,8 @@ class TextDetector(object): else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) - self.det_times.postprocess_time.end() - self.det_times.total_time.end() - self.det_times.img_num += 1 - return dt_boxes, self.det_times.total_time.value() + et = time.time() + return dt_boxes, et - st if __name__ == "__main__": @@ -216,7 +206,6 @@ if __name__ == "__main__": count = 0 total_time = 0 draw_img_save = "./inference_results" - cpu_mem, gpu_mem, gpu_util = 0, 0, 0 # warmup 10 times fake_img = np.random.uniform(-1, 1, [640, 640, 3]).astype(np.float32) @@ -239,12 +228,6 @@ if __name__ == "__main__": total_time += elapse count += 1 - if args.benchmark: - cm, gm, gu = utility.get_current_memory_mb(0) - cpu_mem += cm - gpu_mem += gm - gpu_util += gu - logger.info("Predict time of {}: {}".format(image_file, elapse)) src_im = utility.draw_text_det_res(dt_boxes, image_file) img_name_pure = os.path.split(image_file)[-1] @@ -252,36 +235,3 @@ if __name__ == "__main__": "det_res_{}".format(img_name_pure)) logger.info("The visualized image saved in {}".format(img_path)) - # print the information about memory and time-spent - if args.benchmark: - mems = { - 'cpu_rss_mb': cpu_mem / count, - 'gpu_rss_mb': gpu_mem / count, - 'gpu_util': gpu_util * 100 / count - } - else: - mems = None - logger.info("The predict time about detection module is as follows: ") - det_time_dict = text_detector.det_times.report(average=True) - det_model_name = args.det_model_dir - - if args.benchmark: - # construct log information - model_info = { - 'model_name': args.det_model_dir.split('/')[-1], - 'precision': args.precision - } - data_info = { - 'batch_size': 1, - 'shape': 'dynamic_shape', - 'data_num': det_time_dict['img_num'] - } - perf_info = { - 'preprocess_time_s': det_time_dict['preprocess_time'], - 'inference_time_s': det_time_dict['inference_time'], - 'postprocess_time_s': det_time_dict['postprocess_time'], - 'total_time_s': det_time_dict['total_time'] - } - benchmark_log = benchmark_utils.PaddleInferBenchmark( - text_detector.config, model_info, data_info, perf_info, mems) - benchmark_log("Det") -- GitLab