add scannet dataset

mmdet3d/datasets/scannet_dataset.py

@@ -134,7 +134,7 @@ class ScannetDataset(torch_data.Dataset):
         return input_dict
 
     def _get_pts_filename(self, sample_idx):
-        pts_filename = os.path.join(self.data_path, sample_idx + '_vert.npy')
+        pts_filename = os.path.join(self.data_path, f'{sample_idx}_vert.npy')
         mmcv.check_file_exist(pts_filename)
         return pts_filename
 
@@ -150,9 +150,9 @@ class ScannetDataset(torch_data.Dataset):
             gt_labels = np.zeros(1, ).astype(np.bool)
             gt_bboxes_3d_mask = np.zeros(1, ).astype(np.bool)
         pts_instance_mask_path = osp.join(self.data_path,
-                                          sample_idx + '_ins_label.npy')
+                                          f'{sample_idx}_ins_label.npy')
         pts_semantic_mask_path = osp.join(self.data_path,
-                                          sample_idx + '_sem_label.npy')
+                                          f'{sample_idx}_sem_label.npy')
 
         anns_results = dict(
             gt_bboxes_3d=gt_bboxes_3d,
@@ -167,19 +167,13 @@ class ScannetDataset(torch_data.Dataset):
         return np.random.choice(pool)
 
     def _generate_annotations(self, output):
-        '''
-        transfer input_dict & pred_dicts to anno format
-        which is needed by AP calculator
-        return annos: a tuple (batch_pred_map_cls,batch_gt_map_cls)
-                        batch_pred_map_cls is a list: i=0,1..bs-1
-                            pred_list_i:[(pred_sem_cls,
-                            box_params, box_score)_j]
-                            j=0,1..num_pred_obj -1
-
-                        batch_gt_map_cls is a list: i=0,1..bs-1
-                            gt_list_i: [(sem_cls_label, box_params)_j]
-                            j=0,1..num_gt_obj -1
-        '''
+        """Generate Annotations.
+
+        Transform results of the model to the form of the evaluation.
+
+        Args:
+            output (List): The output of the model.
+        """
         result = []
         bs = len(output)
         for i in range(bs):
@@ -209,7 +203,15 @@ class ScannetDataset(torch_data.Dataset):
             results.append(result)
         return results
 
-    def evaluate(self, results, metric=None, logger=None, pklfile_prefix=None):
+    def evaluate(self, results, metric=None):
+        """Evaluate.
+
+        Evaluation in indoor protocol.
+
+        Args:
+            results (List): List of result.
+            metric (dict): AP_IOU_THRESHHOLDS.
+        """
         results = self._format_results(results)
         from mmdet3d.core.evaluation import indoor_eval
         assert ('AP_IOU_THRESHHOLDS' in metric)

tests/test_scannet_dataset.py

@@ -2,7 +2,7 @@ import numpy as np
 import pytest
 import torch
 
-from mmdet3d.datasets.scannet_dataset import ScannetDataset
+from mmdet3d.datasets import ScannetDataset
 
 
 def test_getitem():