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import argparse |
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import os |
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import os.path as osp |
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import mmcv |
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from mmcv import Config, DictAction |
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from mmcv.runner import init_dist |
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from terminaltables import GithubFlavoredMarkdownTable |
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from tools.analysis_tools.benchmark import repeat_measure_inference_speed |
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def parse_args(): |
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parser = argparse.ArgumentParser( |
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description='MMDet benchmark a model of FPS') |
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parser.add_argument('config', help='test config file path') |
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parser.add_argument('checkpoint_root', help='Checkpoint file root path') |
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parser.add_argument( |
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'--round-num', |
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type=int, |
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default=1, |
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help='round a number to a given precision in decimal digits') |
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parser.add_argument( |
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'--repeat-num', |
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type=int, |
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default=1, |
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help='number of repeat times of measurement for averaging the results') |
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parser.add_argument( |
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'--out', type=str, help='output path of gathered fps to be stored') |
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parser.add_argument( |
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'--max-iter', type=int, default=2000, help='num of max iter') |
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parser.add_argument( |
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'--log-interval', type=int, default=50, help='interval of logging') |
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parser.add_argument( |
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'--fuse-conv-bn', |
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action='store_true', |
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help='Whether to fuse conv and bn, this will slightly increase' |
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'the inference speed') |
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parser.add_argument( |
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'--cfg-options', |
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nargs='+', |
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action=DictAction, |
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help='override some settings in the used config, the key-value pair ' |
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'in xxx=yyy format will be merged into config file. If the value to ' |
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' |
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' |
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'Note that the quotation marks are necessary and that no white space ' |
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'is allowed.') |
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parser.add_argument( |
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'--launcher', |
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choices=['none', 'pytorch', 'slurm', 'mpi'], |
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default='none', |
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help='job launcher') |
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parser.add_argument('--local_rank', type=int, default=0) |
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args = parser.parse_args() |
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if 'LOCAL_RANK' not in os.environ: |
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os.environ['LOCAL_RANK'] = str(args.local_rank) |
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return args |
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def results2markdown(result_dict): |
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table_data = [] |
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is_multiple_results = False |
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for cfg_name, value in result_dict.items(): |
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name = cfg_name.replace('configs/', '') |
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fps = value['fps'] |
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ms_times_pre_image = value['ms_times_pre_image'] |
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if isinstance(fps, list): |
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is_multiple_results = True |
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mean_fps = value['mean_fps'] |
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mean_times_pre_image = value['mean_times_pre_image'] |
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fps_str = ','.join([str(s) for s in fps]) |
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ms_times_pre_image_str = ','.join( |
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[str(s) for s in ms_times_pre_image]) |
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table_data.append([ |
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name, fps_str, mean_fps, ms_times_pre_image_str, |
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mean_times_pre_image |
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]) |
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else: |
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table_data.append([name, fps, ms_times_pre_image]) |
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if is_multiple_results: |
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table_data.insert(0, [ |
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'model', 'fps', 'mean_fps', 'times_pre_image(ms)', |
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'mean_times_pre_image(ms)' |
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]) |
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else: |
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table_data.insert(0, ['model', 'fps', 'times_pre_image(ms)']) |
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table = GithubFlavoredMarkdownTable(table_data) |
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print(table.table, flush=True) |
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if __name__ == '__main__': |
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args = parse_args() |
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assert args.round_num >= 0 |
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assert args.repeat_num >= 1 |
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config = Config.fromfile(args.config) |
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if args.launcher == 'none': |
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raise NotImplementedError('Only supports distributed mode') |
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else: |
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init_dist(args.launcher) |
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result_dict = {} |
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for model_key in config: |
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model_infos = config[model_key] |
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if not isinstance(model_infos, list): |
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model_infos = [model_infos] |
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for model_info in model_infos: |
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record_metrics = model_info['metric'] |
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cfg_path = model_info['config'].strip() |
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cfg = Config.fromfile(cfg_path) |
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checkpoint = osp.join(args.checkpoint_root, |
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model_info['checkpoint'].strip()) |
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try: |
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fps = repeat_measure_inference_speed(cfg, checkpoint, |
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args.max_iter, |
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args.log_interval, |
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args.fuse_conv_bn, |
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args.repeat_num) |
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if args.repeat_num > 1: |
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fps_list = [round(fps_, args.round_num) for fps_ in fps] |
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times_pre_image_list = [ |
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round(1000 / fps_, args.round_num) for fps_ in fps |
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] |
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mean_fps = round( |
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sum(fps_list) / len(fps_list), args.round_num) |
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mean_times_pre_image = round( |
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sum(times_pre_image_list) / len(times_pre_image_list), |
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args.round_num) |
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print( |
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f'{cfg_path} ' |
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f'Overall fps: {fps_list}[{mean_fps}] img / s, ' |
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f'times per image: ' |
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f'{times_pre_image_list}[{mean_times_pre_image}] ' |
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f'ms / img', |
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flush=True) |
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result_dict[cfg_path] = dict( |
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fps=fps_list, |
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mean_fps=mean_fps, |
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ms_times_pre_image=times_pre_image_list, |
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mean_times_pre_image=mean_times_pre_image) |
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else: |
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print( |
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f'{cfg_path} fps : {fps:.{args.round_num}f} img / s, ' |
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f'times per image: {1000 / fps:.{args.round_num}f} ' |
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f'ms / img', |
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flush=True) |
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result_dict[cfg_path] = dict( |
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fps=round(fps, args.round_num), |
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ms_times_pre_image=round(1000 / fps, args.round_num)) |
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except Exception as e: |
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print(f'{cfg_path} error: {repr(e)}') |
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if args.repeat_num > 1: |
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result_dict[cfg_path] = dict( |
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fps=[0], |
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mean_fps=0, |
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ms_times_pre_image=[0], |
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mean_times_pre_image=0) |
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else: |
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result_dict[cfg_path] = dict(fps=0, ms_times_pre_image=0) |
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if args.out: |
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mmcv.mkdir_or_exist(args.out) |
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mmcv.dump(result_dict, osp.join(args.out, 'batch_inference_fps.json')) |
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results2markdown(result_dict) |
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