Rename mapping parameter

openfold/data/data_modules.py

@@ -34,7 +34,7 @@ class OpenFoldSingleDataset(torch.utils.data.Dataset):
         template_release_dates_cache_path: Optional[str] = None,
         shuffle_top_k_prefiltered: Optional[int] = None,
         treat_pdb_as_distillation: bool = True,
-        mapping_path: Optional[str] = None,
+        filter_path: Optional[str] = None,
         mode: str = "train", 
         alignment_index: Optional[Any] = None,
         _output_raw: bool = False,
@@ -102,10 +102,10 @@ class OpenFoldSingleDataset(torch.utils.data.Dataset):
 
         if(alignment_index is not None):
             self._chain_ids = list(alignment_index.keys())
-        elif(mapping_path is None):
+        elif(filter_path is None):
             self._chain_ids = list(os.listdir(alignment_dir))
         else:
-            with open(mapping_path, "r") as f:
+            with open(filter_path, "r") as f:
                 self._chain_ids = [l.strip() for l in f.readlines()]
        
         self._chain_id_to_idx_dict = {
@@ -496,8 +496,8 @@ class OpenFoldDataModule(pl.LightningDataModule):
         predict_data_dir: Optional[str] = None,
         predict_alignment_dir: Optional[str] = None,
         kalign_binary_path: str = '/usr/bin/kalign',
-        train_mapping_path: Optional[str] = None,
-        distillation_mapping_path: Optional[str] = None,
+        train_filter_path: Optional[str] = None,
+        distillation_filter_path: Optional[str] = None,
         obsolete_pdbs_file_path: Optional[str] = None,
         template_release_dates_cache_path: Optional[str] = None,
         batch_seed: Optional[int] = None,
@@ -525,8 +525,8 @@ class OpenFoldDataModule(pl.LightningDataModule):
         self.predict_data_dir = predict_data_dir
         self.predict_alignment_dir = predict_alignment_dir
         self.kalign_binary_path = kalign_binary_path
-        self.train_mapping_path = train_mapping_path
-        self.distillation_mapping_path = distillation_mapping_path
+        self.train_filter_path = train_filter_path
+        self.distillation_filter_path = distillation_filter_path
         self.template_release_dates_cache_path = (
             template_release_dates_cache_path
         )
@@ -589,7 +589,7 @@ class OpenFoldDataModule(pl.LightningDataModule):
             train_dataset = dataset_gen(
                 data_dir=self.train_data_dir,
                 alignment_dir=self.train_alignment_dir,
-                mapping_path=self.train_mapping_path,
+                filter_path=self.train_filter_path,
                 max_template_hits=self.config.train.max_template_hits,
                 shuffle_top_k_prefiltered=
                     self.config.train.shuffle_top_k_prefiltered,
@@ -603,7 +603,7 @@ class OpenFoldDataModule(pl.LightningDataModule):
                 distillation_dataset = dataset_gen(
                     data_dir=self.distillation_data_dir,
                     alignment_dir=self.distillation_alignment_dir,
-                    mapping_path=self.distillation_mapping_path,
+                    filter_path=self.distillation_filter_path,
                     max_template_hits=self.config.train.max_template_hits,
                     treat_pdb_as_distillation=True,
                     mode="train",
@@ -645,7 +645,7 @@ class OpenFoldDataModule(pl.LightningDataModule):
                 self.eval_dataset = dataset_gen(
                     data_dir=self.val_data_dir,
                     alignment_dir=self.val_alignment_dir,
-                    mapping_path=None,
+                    filter_path=None,
                     max_template_hits=self.config.eval.max_template_hits,
                     mode="eval",
                 )
@@ -655,7 +655,7 @@ class OpenFoldDataModule(pl.LightningDataModule):
             self.predict_dataset = dataset_gen(
                 data_dir=self.predict_data_dir,
                 alignment_dir=self.predict_alignment_dir,
-                mapping_path=None,
+                filter_path=None,
                 max_template_hits=self.config.predict.max_template_hits,
                 mode="predict",
             )

train_openfold.py

@@ -405,14 +405,14 @@ if __name__ == "__main__":
         help="Path to the kalign binary"
     )
     parser.add_argument(
-        "--train_mapping_path", type=str, default=None,
-        help='''Optional path to a .json file containing a mapping from
-                consecutive numerical indices to sample names. Used to filter
-                the training set'''
+        "--train_filter_path", type=str, default=None,
+        help='''Optional path to a text file containing names of training
+                examples to include, one per line. Used to filter the training 
+                set'''
     )
     parser.add_argument(
-        "--distillation_mapping_path", type=str, default=None,
-        help="""See --train_mapping_path"""
+        "--distillation_filter_path", type=str, default=None,
+        help="""See --train_filter_path"""
     )
     parser.add_argument(
         "--obsolete_pdbs_file_path", type=str, default=None,