()
| 111 | |
| 112 | assert args.out or args.eval or args.format_only or args.show \ |
| 113 | or args.show_dir, \ |
| 114 | ('Please specify at least one operation (save/eval/format/show the ' |
| 115 | 'results / save the results) with the argument "--out", "--eval"' |
| 116 | ', "--format-only", "--show" or "--show-dir"') |
| 117 | |
| 118 | if args.eval and args.format_only: |
| 119 | raise ValueError('--eval and --format_only cannot be both specified') |
| 120 | |
| 121 | if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): |
| 122 | raise ValueError('The output file must be a pkl file.') |
| 123 | |
| 124 | cfg = Config.fromfile(args.config) |
| 125 | if args.cfg_options is not None: |
| 126 | cfg.merge_from_dict(args.cfg_options) |
| 127 | # import modules from string list. |
| 128 | if cfg.get('custom_imports', None): |
| 129 | from mmcv.utils import import_modules_from_strings |
| 130 | import_modules_from_strings(**cfg['custom_imports']) |
| 131 | |
| 132 | # import modules from plguin/xx, registry will be updated |
| 133 | if hasattr(cfg, 'plugin'): |
| 134 | if cfg.plugin: |
| 135 | import importlib |
| 136 | if hasattr(cfg, 'plugin_dir'): |
| 137 | plugin_dir = cfg.plugin_dir |
| 138 | _module_dir = os.path.dirname(plugin_dir) |
| 139 | _module_dir = _module_dir.split('/') |
| 140 | _module_path = _module_dir[0] |
| 141 | |
| 142 | for m in _module_dir[1:]: |
| 143 | _module_path = _module_path + '.' + m |
| 144 | print(_module_path) |
| 145 | plg_lib = importlib.import_module(_module_path) |
| 146 | else: |
| 147 | # import dir is the dirpath for the config file |
| 148 | _module_dir = os.path.dirname(args.config) |
| 149 | _module_dir = _module_dir.split('/') |
| 150 | _module_path = _module_dir[0] |
| 151 | for m in _module_dir[1:]: |
| 152 | _module_path = _module_path + '.' + m |
| 153 | print(_module_path) |
| 154 | plg_lib = importlib.import_module(_module_path) |
| 155 | |
| 156 | # set cudnn_benchmark |
| 157 | if cfg.get('cudnn_benchmark', False): |
| 158 | torch.backends.cudnn.benchmark = True |
| 159 | |
| 160 | cfg.model.pretrained = None |
| 161 | # in case the test dataset is concatenated |
| 162 | samples_per_gpu = 1 |
| 163 | if isinstance(cfg.data.test, dict): |
| 164 | cfg.data.test.test_mode = True |
| 165 | samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) |
| 166 | if samples_per_gpu > 1: |
| 167 | # Replace 'ImageToTensor' to 'DefaultFormatBundle' |
| 168 | cfg.data.test.pipeline = replace_ImageToTensor( |
| 169 | cfg.data.test.pipeline) |
| 170 | elif isinstance(cfg.data.test, list): |
no test coverage detected