Spaces:
Build error
Build error
| import argparse | |
| import time | |
| import torch | |
| from mmcv import Config, DictAction | |
| from mmcv.cnn import fuse_conv_bn | |
| from mmcv.parallel import MMDataParallel | |
| from mmcv.runner import load_checkpoint, wrap_fp16_model | |
| from mmdet.datasets import (build_dataloader, build_dataset, | |
| replace_ImageToTensor) | |
| from mmdet.models import build_detector | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='MMDet benchmark a model') | |
| parser.add_argument('config', help='test config file path') | |
| parser.add_argument('checkpoint', help='checkpoint file') | |
| parser.add_argument( | |
| '--log-interval', default=50, help='interval of logging') | |
| parser.add_argument( | |
| '--fuse-conv-bn', | |
| action='store_true', | |
| help='Whether to fuse conv and bn, this will slightly increase' | |
| 'the inference speed') | |
| parser.add_argument( | |
| '--cfg-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='override some settings in the used config, the key-value pair ' | |
| 'in xxx=yyy format will be merged into config file. If the value to ' | |
| 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' | |
| 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' | |
| 'Note that the quotation marks are necessary and that no white space ' | |
| 'is allowed.') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| # import modules from string list. | |
| if cfg.get('custom_imports', None): | |
| from mmcv.utils import import_modules_from_strings | |
| import_modules_from_strings(**cfg['custom_imports']) | |
| # set cudnn_benchmark | |
| if cfg.get('cudnn_benchmark', False): | |
| torch.backends.cudnn.benchmark = True | |
| cfg.model.pretrained = None | |
| cfg.data.test.test_mode = True | |
| # build the dataloader | |
| samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) | |
| if samples_per_gpu > 1: | |
| # Replace 'ImageToTensor' to 'DefaultFormatBundle' | |
| cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) | |
| dataset = build_dataset(cfg.data.test) | |
| data_loader = build_dataloader( | |
| dataset, | |
| samples_per_gpu=1, | |
| workers_per_gpu=cfg.data.workers_per_gpu, | |
| dist=False, | |
| shuffle=False) | |
| # build the model and load checkpoint | |
| cfg.model.train_cfg = None | |
| model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) | |
| fp16_cfg = cfg.get('fp16', None) | |
| if fp16_cfg is not None: | |
| wrap_fp16_model(model) | |
| load_checkpoint(model, args.checkpoint, map_location='cpu') | |
| if args.fuse_conv_bn: | |
| model = fuse_conv_bn(model) | |
| model = MMDataParallel(model, device_ids=[0]) | |
| model.eval() | |
| # the first several iterations may be very slow so skip them | |
| num_warmup = 5 | |
| pure_inf_time = 0 | |
| # benchmark with 2000 image and take the average | |
| for i, data in enumerate(data_loader): | |
| torch.cuda.synchronize() | |
| start_time = time.perf_counter() | |
| with torch.no_grad(): | |
| model(return_loss=False, rescale=True, **data) | |
| torch.cuda.synchronize() | |
| elapsed = time.perf_counter() - start_time | |
| if i >= num_warmup: | |
| pure_inf_time += elapsed | |
| if (i + 1) % args.log_interval == 0: | |
| fps = (i + 1 - num_warmup) / pure_inf_time | |
| print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s') | |
| if (i + 1) == 2000: | |
| pure_inf_time += elapsed | |
| fps = (i + 1 - num_warmup) / pure_inf_time | |
| print(f'Overall fps: {fps:.1f} img / s') | |
| break | |
| if __name__ == '__main__': | |
| main() | |