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Function main

tools/test_waypoint.py:73–219  ·  view source on GitHub ↗
()

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71
72
73def main():
74 args = parse_args()
75
76 cfg = mmcv.Config.fromfile(args.config)
77 if args.cfg_options is not None:
78 cfg.merge_from_dict(args.cfg_options)
79 # set cudnn_benchmark
80 if cfg.get('cudnn_benchmark', False):
81 torch.backends.cudnn.benchmark = True
82 cfg.data.test.test_mode = True
83
84 # init distributed env first, since logger depends on the dist info.
85 if args.launcher == 'none':
86 distributed = False
87 else:
88 distributed = True
89 init_dist(args.launcher, **cfg.dist_params)
90
91 # build the dataloader
92 dataset = build_dataset(cfg.data.test)
93 # the extra round_up data will be removed during gpu/cpu collect
94 data_loader = build_dataloader(
95 dataset,
96 samples_per_gpu=cfg.data.samples_per_gpu,
97 workers_per_gpu=cfg.data.workers_per_gpu,
98 dist=distributed,
99 shuffle=False,
100 round_up=False)
101
102 # build the model and load checkpoint
103 cfg.model.model.waypoint = True
104 model = build_architecture(cfg.model)
105 fp16_cfg = cfg.get('fp16', None)
106 if fp16_cfg is not None:
107 wrap_fp16_model(model)
108 load_checkpoint(model, args.checkpoint, map_location='cpu')
109
110
111
112
113 # Compute the translation requirement
114
115 import numpy as np
116
117 phys_mode = args.physmode
118 phys_setting = 'kit' if 'kit' in args.config else 'human'
119
120 if phys_setting == 'kit':
121 mean_path = 'data/datasets/kit_pml/mean.npy'
122 std_path = 'data/datasets/kit_pml/std.npy'
123 else:
124 mean_path = 'data/datasets/human_pml3d/mean.npy'
125 std_path = 'data/datasets/human_pml3d/std.npy'
126 mean = np.load(mean_path)
127 std = np.load(std_path)
128
129 x_mean, x_std, y_mean, y_std = mean[0], std[0], mean[1], std[1]
130

Callers 1

test_waypoint.pyFile · 0.70

Calls 10

build_datasetFunction · 0.90
build_dataloaderFunction · 0.90
build_architectureFunction · 0.90
single_gpu_testFunction · 0.90
multi_gpu_testFunction · 0.90
add_scene_to_trajFunction · 0.90
execute_actionsFunction · 0.90
remove_scene_from_trajFunction · 0.90
parse_argsFunction · 0.70
evaluateMethod · 0.45

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