refactor: Renamed DataLoader arg "workers" into "num_workers" (#737)

* refactor: Renamed DataLoader arg

* refactor: Reflected changes

doctr/datasets/loader.py

@@ -46,7 +46,7 @@ class DataLoader:
         shuffle: whether the samples should be shuffled before passing it to the iterator
         batch_size: number of elements in each batch
         drop_last: if `True`, drops the last batch if it isn't full
-        workers: number of workers to use for data loading
+        num_workers: number of workers to use for data loading
     """
 
     def __init__(
@@ -55,7 +55,7 @@ def __init__(
         shuffle: bool = True,
         batch_size: int = 1,
         drop_last: bool = False,
-        workers: Optional[int] = None,
+        num_workers: Optional[int] = None,
         collate_fn: Optional[Callable] = None,
     ) -> None:
         self.dataset = dataset
@@ -67,7 +67,7 @@ def __init__(
             self.collate_fn = self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else default_collate
         else:
             self.collate_fn = collate_fn
-        self.workers = workers
+        self.num_workers = num_workers
         self.reset()
 
     def __len__(self) -> int:
@@ -90,7 +90,7 @@ def __next__(self):
             idx = self._num_yielded * self.batch_size
             indices = self.indices[idx: min(len(self.dataset), idx + self.batch_size)]
 
-            samples = multithread_exec(self.dataset.__getitem__, indices, threads=self.workers)
+            samples = multithread_exec(self.dataset.__getitem__, indices, threads=self.num_workers)
 
             batch_data = self.collate_fn(samples)
 

references/classification/train_tensorflow.py

@@ -102,7 +102,7 @@ def main(args):
         batch_size=args.batch_size,
         shuffle=False,
         drop_last=False,
-        workers=args.workers,
+        num_workers=args.workers,
         collate_fn=collate_fn,
     )
     print(f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in "
@@ -153,7 +153,7 @@ def main(args):
         batch_size=args.batch_size,
         shuffle=True,
         drop_last=True,
-        workers=args.workers,
+        num_workers=args.workers,
         collate_fn=collate_fn,
     )
     print(f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in "

references/detection/train_tensorflow.py

@@ -87,7 +87,13 @@ def main(args):
         label_path=os.path.join(args.val_path, 'labels.json'),
         sample_transforms=T.Resize((args.input_size, args.input_size)),
     )
-    val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, drop_last=False, workers=args.workers)
+    val_loader = DataLoader(
+        val_set,
+        batch_size=args.batch_size,
+        shuffle=False,
+        drop_last=False,
+        num_workers=args.workers,
+    )
     print(f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in "
           f"{val_loader.num_batches} batches)")
     with open(os.path.join(args.val_path, 'labels.json'), 'rb') as f:
@@ -133,7 +139,13 @@ def main(args):
             T.RandomBrightness(.3),
         ]),
     )
-    train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, workers=args.workers)
+    train_loader = DataLoader(
+        train_set,
+        batch_size=args.batch_size,
+        shuffle=True,
+        drop_last=True,
+        num_workers=args.workers,
+    )
     print(f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in "
           f"{train_loader.num_batches} batches)")
     with open(os.path.join(args.train_path, 'labels.json'), 'rb') as f:

references/recognition/train_tensorflow.py

@@ -90,7 +90,13 @@ def main(args):
         labels_path=os.path.join(args.val_path, 'labels.json'),
         sample_transforms=T.Resize((args.input_size, 4 * args.input_size), preserve_aspect_ratio=True),
     )
-    val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, drop_last=False, workers=args.workers)
+    val_loader = DataLoader(
+        val_set,
+        batch_size=args.batch_size,
+        shuffle=False,
+        drop_last=False,
+        num_workers=args.workers,
+    )
     print(f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in "
           f"{val_loader.num_batches} batches)")
     with open(os.path.join(args.val_path, 'labels.json'), 'rb') as f:
@@ -144,7 +150,13 @@ def main(args):
         for subfolder in parts[1:]:
             train_set.merge_dataset(RecognitionDataset(subfolder.joinpath('images'), subfolder.joinpath('labels.json')))
 
-    train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, workers=args.workers)
+    train_loader = DataLoader(
+        train_set,
+        batch_size=args.batch_size,
+        shuffle=True,
+        drop_last=True,
+        num_workers=args.workers,
+    )
     print(f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in "
           f"{train_loader.num_batches} batches)")
     with open(parts[0].joinpath('labels.json'), 'rb') as f: