Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
55 lines
1.9 KiB
Python
Executable File
55 lines
1.9 KiB
Python
Executable File
import torch, os
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from model.model import parsingNet
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from utils.common import merge_config
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from utils.dist_utils import dist_print
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from evaluation.eval_wrapper import eval_lane
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import torch
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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args, cfg = merge_config()
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print(cfg)
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distributed = False
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if 'WORLD_SIZE' in os.environ:
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distributed = int(os.environ['WORLD_SIZE']) > 1
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if distributed:
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torch.cuda.set_device(args.local_rank)
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torch.distributed.init_process_group(backend='nccl', init_method='env://')
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dist_print('start testing...')
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assert cfg.backbone in ['18','34','50','101','152','50next','101next','50wide','101wide']
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if cfg.dataset == 'CULane':
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cls_num_per_lane = 18
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elif cfg.dataset == 'Tusimple':
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cls_num_per_lane = 56
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else:
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raise NotImplementedError
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net = parsingNet(pretrained=False, backbone=cfg.backbone, cls_dim=(cfg.griding_num+1, cls_num_per_lane,
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cfg.num_lanes), use_aux=False).to(device)
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# cfg.num_lanes), use_aux=False).cuda()
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# we don't need auxiliary segmentation in testing
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state_dict = torch.load(cfg.test_model, map_location='cuda')['model']
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compatible_state_dict = {}
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for k, v in state_dict.items():
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if 'module.' in k:
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compatible_state_dict[k[7:]] = v
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else:
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compatible_state_dict[k] = v
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net.load_state_dict(compatible_state_dict, strict=False)
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if distributed:
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net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.local_rank])
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if not os.path.exists(cfg.test_work_dir):
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os.mkdir(cfg.test_work_dir)
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eval_lane(net, cfg.dataset, cfg.data_root, cfg.test_work_dir, cfg.griding_num, False, distributed)
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