From b88349335bbfb651764a45353b15ac3d1696d784 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Mon, 28 Jun 2021 13:47:25 +0800 Subject: [PATCH] delete benchmark --- tools/infer/predict_cls.py | 14 +---- tools/infer/predict_rec.py | 51 +---------------- tools/infer/predict_system.py | 63 +------------------- tools/infer/utility.py | 104 ++-------------------------------- 4 files changed, 9 insertions(+), 223 deletions(-) diff --git a/tools/infer/predict_cls.py b/tools/infer/predict_cls.py index 0037b226..8f2a1361 100755 --- a/tools/infer/predict_cls.py +++ b/tools/infer/predict_cls.py @@ -48,8 +48,6 @@ class TextClassifier(object): self.predictor, self.input_tensor, self.output_tensors, _ = \ utility.create_predictor(args, 'cls', logger) - self.cls_times = utility.Timer() - def resize_norm_img(self, img): imgC, imgH, imgW = self.cls_image_shape h = img.shape[0] @@ -85,35 +83,28 @@ class TextClassifier(object): cls_res = [['', 0.0]] * img_num batch_num = self.cls_batch_num elapse = 0 - self.cls_times.total_time.start() for beg_img_no in range(0, img_num, batch_num): end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] max_wh_ratio = 0 + starttime = time.time() for ino in range(beg_img_no, end_img_no): h, w = img_list[indices[ino]].shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) - self.cls_times.preprocess_time.start() for ino in range(beg_img_no, end_img_no): norm_img = self.resize_norm_img(img_list[indices[ino]]) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() - starttime = time.time() - self.cls_times.preprocess_time.end() - self.cls_times.inference_time.start() self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.run() prob_out = self.output_tensors[0].copy_to_cpu() - self.cls_times.inference_time.end() - self.cls_times.postprocess_time.start() self.predictor.try_shrink_memory() cls_result = self.postprocess_op(prob_out) - self.cls_times.postprocess_time.end() elapse += time.time() - starttime for rno in range(len(cls_result)): label, score = cls_result[rno] @@ -121,9 +112,6 @@ class TextClassifier(object): if '180' in label and score > self.cls_thresh: img_list[indices[beg_img_no + rno]] = cv2.rotate( img_list[indices[beg_img_no + rno]], 1) - self.cls_times.total_time.end() - self.cls_times.img_num += img_num - elapse = self.cls_times.total_time.value() return img_list, cls_res, elapse diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 0d847046..405d829b 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -66,8 +66,6 @@ class TextRecognizer(object): self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'rec', logger) - self.rec_times = utility.Timer() - def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape assert imgC == img.shape[2] @@ -168,14 +166,13 @@ class TextRecognizer(object): width_list.append(img.shape[1] / float(img.shape[0])) # Sorting can speed up the recognition process indices = np.argsort(np.array(width_list)) - self.rec_times.total_time.start() rec_res = [['', 0.0]] * img_num batch_num = self.rec_batch_num + st = time.time() for beg_img_no in range(0, img_num, batch_num): end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] max_wh_ratio = 0 - self.rec_times.preprocess_time.start() for ino in range(beg_img_no, end_img_no): h, w = img_list[indices[ino]].shape[0:2] wh_ratio = w * 1.0 / h @@ -216,8 +213,6 @@ class TextRecognizer(object): gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list, ] - self.rec_times.preprocess_time.end() - self.rec_times.inference_time.start() input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[ @@ -241,15 +236,13 @@ class TextRecognizer(object): output = output_tensor.copy_to_cpu() outputs.append(output) preds = outputs[0] - self.rec_times.inference_time.end() - self.rec_times.postprocess_time.start() rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] self.rec_times.postprocess_time.end() self.rec_times.img_num += int(norm_img_batch.shape[0]) - self.rec_times.total_time.end() - return rec_res, self.rec_times.total_time.value() + + return rec_res, time.time() - st def main(args): @@ -278,12 +271,6 @@ def main(args): img_list.append(img) try: rec_res, _ = text_recognizer(img_list) - if args.benchmark: - cm, gm, gu = utility.get_current_memory_mb(0) - cpu_mem += cm - gpu_mem += gm - gpu_util += gu - count += 1 except Exception as E: logger.info(traceback.format_exc()) @@ -292,38 +279,6 @@ def main(args): for ino in range(len(img_list)): logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino])) - 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 recognizer module is as follows: ") - rec_time_dict = text_recognizer.rec_times.report(average=True) - rec_model_name = args.rec_model_dir - - if args.benchmark: - # construct log information - model_info = { - 'model_name': args.rec_model_dir.split('/')[-1], - 'precision': args.precision - } - data_info = { - 'batch_size': args.rec_batch_num, - 'shape': 'dynamic_shape', - 'data_num': rec_time_dict['img_num'] - } - perf_info = { - 'preprocess_time_s': rec_time_dict['preprocess_time'], - 'inference_time_s': rec_time_dict['inference_time'], - 'postprocess_time_s': rec_time_dict['postprocess_time'], - 'total_time_s': rec_time_dict['total_time'] - } - benchmark_log = benchmark_utils.PaddleInferBenchmark( - text_recognizer.config, model_info, data_info, perf_info, mems) - benchmark_log("Rec") if __name__ == "__main__": diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py index c008f967..c7e1c3cc 100755 --- a/tools/infer/predict_system.py +++ b/tools/infer/predict_system.py @@ -158,7 +158,7 @@ def main(args): img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(10): res = text_sys(img) - + total_time = 0 cpu_mem, gpu_mem, gpu_util = 0, 0, 0 _st = time.time() @@ -175,12 +175,6 @@ def main(args): dt_boxes, rec_res = text_sys(img) elapse = time.time() - starttime total_time += elapse - if args.benchmark and idx % 20 == 0: - cm, gm, gu = get_current_memory_mb(0) - cpu_mem += cm - gpu_mem += gm - gpu_util += gu - count += 1 logger.info( str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) @@ -215,61 +209,6 @@ def main(args): logger.info("\nThe predict total time is {}".format(total_time)) img_num = text_sys.text_detector.det_times.img_num - 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 - det_time_dict = text_sys.text_detector.det_times.report(average=True) - rec_time_dict = text_sys.text_recognizer.rec_times.report(average=True) - det_model_name = args.det_model_dir - rec_model_name = args.rec_model_dir - - # construct det 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_sys.text_detector.config, model_info, data_info, perf_info, mems, - args.save_log_path) - benchmark_log("Det") - - # construct rec log information - model_info = { - 'model_name': args.rec_model_dir.split('/')[-1], - 'precision': args.precision - } - data_info = { - 'batch_size': args.rec_batch_num, - 'shape': 'dynamic_shape', - 'data_num': rec_time_dict['img_num'] - } - perf_info = { - 'preprocess_time_s': rec_time_dict['preprocess_time'], - 'inference_time_s': rec_time_dict['inference_time'], - 'postprocess_time_s': rec_time_dict['postprocess_time'], - 'total_time_s': rec_time_dict['total_time'] - } - benchmark_log = benchmark_utils.PaddleInferBenchmark( - text_sys.text_recognizer.config, model_info, data_info, perf_info, mems, - args.save_log_path) - benchmark_log("Rec") if __name__ == "__main__": diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 90ac5aa5..5f4249ee 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -124,76 +124,6 @@ def parse_args(): return parser.parse_args() -class Times(object): - def __init__(self): - self.time = 0. - self.st = 0. - self.et = 0. - - def start(self): - self.st = time.time() - - def end(self, accumulative=True): - self.et = time.time() - if accumulative: - self.time += self.et - self.st - else: - self.time = self.et - self.st - - def reset(self): - self.time = 0. - self.st = 0. - self.et = 0. - - def value(self): - return round(self.time, 4) - - -class Timer(Times): - def __init__(self): - super(Timer, self).__init__() - self.total_time = Times() - self.preprocess_time = Times() - self.inference_time = Times() - self.postprocess_time = Times() - self.img_num = 0 - - def info(self, average=False): - logger.info("----------------------- Perf info -----------------------") - logger.info("total_time: {}, img_num: {}".format(self.total_time.value( - ), self.img_num)) - preprocess_time = round(self.preprocess_time.value() / self.img_num, - 4) if average else self.preprocess_time.value() - postprocess_time = round( - self.postprocess_time.value() / self.img_num, - 4) if average else self.postprocess_time.value() - inference_time = round(self.inference_time.value() / self.img_num, - 4) if average else self.inference_time.value() - - average_latency = self.total_time.value() / self.img_num - logger.info("average_latency(ms): {:.2f}, QPS: {:2f}".format( - average_latency * 1000, 1 / average_latency)) - logger.info( - "preprocess_latency(ms): {:.2f}, inference_latency(ms): {:.2f}, postprocess_latency(ms): {:.2f}". - format(preprocess_time * 1000, inference_time * 1000, - postprocess_time * 1000)) - - def report(self, average=False): - dic = {} - dic['preprocess_time'] = round( - self.preprocess_time.value() / self.img_num, - 4) if average else self.preprocess_time.value() - dic['postprocess_time'] = round( - self.postprocess_time.value() / self.img_num, - 4) if average else self.postprocess_time.value() - dic['inference_time'] = round( - self.inference_time.value() / self.img_num, - 4) if average else self.inference_time.value() - dic['img_num'] = self.img_num - dic['total_time'] = round(self.total_time.value(), 4) - return dic - - def create_predictor(args, mode, logger): if mode == "det": model_dir = args.det_model_dir @@ -212,11 +142,10 @@ def create_predictor(args, mode, logger): model_file_path = model_dir + "/inference.pdmodel" params_file_path = model_dir + "/inference.pdiparams" if not os.path.exists(model_file_path): - logger.info("not find model file path {}".format(model_file_path)) - sys.exit(0) + raise ValueError("not find model file path {}".format(model_file_path)) if not os.path.exists(params_file_path): - logger.info("not find params file path {}".format(params_file_path)) - sys.exit(0) + raise ValueError("not find params file path {}".format( + params_file_path)) config = inference.Config(model_file_path, params_file_path) @@ -332,7 +261,7 @@ def create_predictor(args, mode, logger): config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass") if mode == 'table': - config.delete_pass("fc_fuse_pass") # not supported for table + config.delete_pass("fc_fuse_pass") # not supported for table config.switch_use_feed_fetch_ops(False) config.switch_ir_optim(True) @@ -597,30 +526,5 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5): return image -def get_current_memory_mb(gpu_id=None): - """ - It is used to Obtain the memory usage of the CPU and GPU during the running of the program. - And this function Current program is time-consuming. - """ - import pynvml - import psutil - import GPUtil - pid = os.getpid() - p = psutil.Process(pid) - info = p.memory_full_info() - cpu_mem = info.uss / 1024. / 1024. - gpu_mem = 0 - gpu_percent = 0 - if gpu_id is not None: - GPUs = GPUtil.getGPUs() - gpu_load = GPUs[gpu_id].load - gpu_percent = gpu_load - pynvml.nvmlInit() - handle = pynvml.nvmlDeviceGetHandleByIndex(0) - meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle) - gpu_mem = meminfo.used / 1024. / 1024. - return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4) - - if __name__ == '__main__': pass -- GitLab