Args: dataset_name (str): name of the dataset to be evaluated. distributed (bool): if True, will collect results from all ranks for evaluation. Otherwise, will evaluate the results in the current process. output_dir (str): an output direct
(
self,
dataset_name,
config,
distributed=True,
output_dir=None,
)
| 72 | """ |
| 73 | |
| 74 | def __init__( |
| 75 | self, |
| 76 | dataset_name, |
| 77 | config, |
| 78 | distributed=True, |
| 79 | output_dir=None, |
| 80 | ): |
| 81 | """ |
| 82 | Args: |
| 83 | dataset_name (str): name of the dataset to be evaluated. |
| 84 | distributed (bool): if True, will collect results from all ranks for evaluation. |
| 85 | Otherwise, will evaluate the results in the current process. |
| 86 | output_dir (str): an output directory to dump results. |
| 87 | num_classes, ignore_label: deprecated argument |
| 88 | """ |
| 89 | self._logger = logging.getLogger(__name__) |
| 90 | |
| 91 | self._dataset_name = dataset_name |
| 92 | self._distributed = distributed |
| 93 | self._output_dir = output_dir |
| 94 | |
| 95 | self._cpu_device = torch.device("cpu") |
| 96 | |
| 97 | self._class_names = config.dataset.kwargs.cfg.label_list[1:] |
| 98 | self._num_classes = len(self._class_names) |
| 99 | assert self._num_classes == config.dataset.kwargs.cfg.num_classes, f"{self._num_classes} != {config.dataset.kwargs.cfg.num_classes}" |
| 100 | self._contiguous_id_to_dataset_id = {i: k for i, k in enumerate(self._class_names)} # Dict that maps contiguous training ids to COCO category ids |
| 101 | self._ignore_label = config.dataset.kwargs.cfg.ignore_value |
| 102 | |
| 103 | def reset(self): |
| 104 | self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64) |