Pass train-crop-mode to create_loader/transforms from train.py args

timm/data/loader.py

@@ -196,6 +196,7 @@ def create_loader(
         re_mode: str = 'const',
         re_count: int = 1,
         re_split: bool = False,
+        train_crop_mode: Optional[str] = None,
         scale: Optional[Tuple[float, float]] = None,
         ratio: Optional[Tuple[float, float]] = None,
         hflip: float = 0.5,
@@ -280,6 +281,7 @@ def create_loader(
         input_size,
         is_training=is_training,
         no_aug=no_aug,
+        train_crop_mode=train_crop_mode,
         scale=scale,
         ratio=ratio,
         hflip=hflip,

timm/data/transforms_factory.py

@@ -83,6 +83,7 @@ def transforms_imagenet_train(
 
     Args:
         img_size: Target image size.
+        train_crop_mode: Training random crop mode ('rrc', 'rkrc', 'rkrr').
         scale: Random resize scale range (crop area, < 1.0 => zoom in).
         ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
         hflip: Horizontal flip probability.
@@ -112,6 +113,7 @@ def transforms_imagenet_train(
          * normalizes and converts the branches above with the third, final transform
     """
     train_crop_mode = train_crop_mode or 'rrc'
+    assert train_crop_mode in {'rrc', 'rkrc', 'rkrr'}
     if train_crop_mode in ('rkrc', 'rkrr'):
         # FIXME integration of RKR is a WIP
         scale = tuple(scale or (0.8, 1.00))
@@ -318,6 +320,7 @@ def create_transform(
         input_size: Union[int, Tuple[int, int], Tuple[int, int, int]] = 224,
         is_training: bool = False,
         no_aug: bool = False,
+        train_crop_mode: Optional[str] = None,
         scale: Optional[Tuple[float, float]] = None,
         ratio: Optional[Tuple[float, float]] = None,
         hflip: float = 0.5,
@@ -347,6 +350,7 @@ def create_transform(
         input_size: Target input size (channels, height, width) tuple or size scalar.
         is_training: Return training (random) transforms.
         no_aug: Disable augmentation for training (useful for debug).
+        train_crop_mode: Training random crop mode ('rrc', 'rkrc', 'rkrr').
         scale: Random resize scale range (crop area, < 1.0 => zoom in).
         ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
         hflip: Horizontal flip probability.
@@ -400,6 +404,7 @@ def create_transform(
         elif is_training:
             transform = transforms_imagenet_train(
                 img_size,
+                train_crop_mode=train_crop_mode,
                 scale=scale,
                 ratio=ratio,
                 hflip=hflip,

train.py

@@ -245,6 +245,8 @@
 group = parser.add_argument_group('Augmentation and regularization parameters')
 group.add_argument('--no-aug', action='store_true', default=False,
                    help='Disable all training augmentation, override other train aug args')
+group.add_argument('--train-crop-mode', type=str, default=None,
+                   help='Crop-mode in train'),
 group.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                    help='Random resize scale (default: 0.08 1.0)')
 group.add_argument('--ratio', type=float, nargs='+', default=[3. / 4., 4. / 3.], metavar='RATIO',
@@ -685,6 +687,7 @@ def main():
         re_mode=args.remode,
         re_count=args.recount,
         re_split=args.resplit,
+        train_crop_mode=args.train_crop_mode,
         scale=args.scale,
         ratio=args.ratio,
         hflip=args.hflip,