get_detection_dataset_dicts(names=)

Summary: simplify argument name, otherwise `dataloader.train.datasets.dataset_names` is long and repetitive

Reviewed By: theschnitz

Differential Revision: D26667042

fbshipit-source-id: 92803ef87f4396513376f8a98d6787b0b424c10a

Author: ppwwyyxx

detectron2/data/build.py

@@ -206,32 +206,30 @@ def short_name(x):
     )
 
 
-def get_detection_dataset_dicts(
-    dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
-):
+def get_detection_dataset_dicts(names, filter_empty=True, min_keypoints=0, proposal_files=None):
     """
     Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
 
     Args:
-        dataset_names (str or list[str]): a dataset name or a list of dataset names
+        names (str or list[str]): a dataset name or a list of dataset names
         filter_empty (bool): whether to filter out images without instance annotations
         min_keypoints (int): filter out images with fewer keypoints than
             `min_keypoints`. Set to 0 to do nothing.
         proposal_files (list[str]): if given, a list of object proposal files
-            that match each dataset in `dataset_names`.
+            that match each dataset in `names`.
 
     Returns:
         list[dict]: a list of dicts following the standard dataset dict format.
     """
-    if isinstance(dataset_names, str):
-        dataset_names = [dataset_names]
-    assert len(dataset_names)
-    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
-    for dataset_name, dicts in zip(dataset_names, dataset_dicts):
+    if isinstance(names, str):
+        names = [names]
+    assert len(names), names
+    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
+    for dataset_name, dicts in zip(names, dataset_dicts):
         assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
 
     if proposal_files is not None:
-        assert len(dataset_names) == len(proposal_files)
+        assert len(names) == len(proposal_files)
         # load precomputed proposals from proposal files
         dataset_dicts = [
             load_proposals_into_dataset(dataset_i_dicts, proposal_file)
@@ -248,13 +246,13 @@ def get_detection_dataset_dicts(
 
     if has_instances:
         try:
-            class_names = MetadataCatalog.get(dataset_names[0]).thing_classes
-            check_metadata_consistency("thing_classes", dataset_names)
+            class_names = MetadataCatalog.get(names[0]).thing_classes
+            check_metadata_consistency("thing_classes", names)
             print_instances_class_histogram(dataset_dicts, class_names)
         except AttributeError:  # class names are not available for this dataset
             pass
 
-    assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names))
+    assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
     return dataset_dicts