Remove the unused/buggy --train-center-crop flag from Classification preset

references/classification/README.md

@@ -248,7 +248,7 @@ Note that `--val-resize-size` was optimized in a post-training step, see their `
 ### MaxViT
 ```
 torchrun --nproc_per_node=8 --n_nodes=4 train.py\
---model $MODEL --epochs 400 --batch-size 128 --opt adamw --lr 3e-3 --weight-decay 0.05 --lr-scheduler cosineannealinglr --lr-min 1e-5 --lr-warmup-method linear  --lr-warmup-epochs 32  --label-smoothing 0.1 --mixup-alpha 0.8 --clip-grad-norm 1.0 --interpolation bicubic --auto-augment ta_wide --policy-magnitude 15  --train-center-crop --model-ema --val-resize-size 224
+--model $MODEL --epochs 400 --batch-size 128 --opt adamw --lr 3e-3 --weight-decay 0.05 --lr-scheduler cosineannealinglr --lr-min 1e-5 --lr-warmup-method linear  --lr-warmup-epochs 32  --label-smoothing 0.1 --mixup-alpha 0.8 --clip-grad-norm 1.0 --interpolation bicubic --auto-augment ta_wide --policy-magnitude 15 --model-ema --val-resize-size 224\
 --val-crop-size 224 --train-crop-size 224 --amp  --model-ema-steps 32 --transformer-embedding-decay 0 --sync-bn
 ```
 Here `$MODEL` is `maxvit_t`.

references/classification/presets.py

@@ -16,13 +16,8 @@ def __init__(
         ra_magnitude=9,
         augmix_severity=3,
         random_erase_prob=0.0,
-        center_crop=False,
     ):
-        trans = (
-            [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
-            if center_crop
-            else [transforms.CenterCrop(crop_size)]
-        )
+        trans = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
         if hflip_prob > 0:
             trans.append(transforms.RandomHorizontalFlip(hflip_prob))
         if auto_augment_policy is not None:

references/classification/train.py

@@ -113,11 +113,10 @@ def _get_cache_path(filepath):
 def load_data(traindir, valdir, args):
     # Data loading code
     print("Loading data")
-    val_resize_size, val_crop_size, train_crop_size, center_crop = (
+    val_resize_size, val_crop_size, train_crop_size = (
         args.val_resize_size,
         args.val_crop_size,
         args.train_crop_size,
-        args.train_center_crop,
     )
     interpolation = InterpolationMode(args.interpolation)
 
@@ -136,7 +135,6 @@ def load_data(traindir, valdir, args):
         dataset = torchvision.datasets.ImageFolder(
             traindir,
             presets.ClassificationPresetTrain(
-                center_crop=center_crop,
                 crop_size=train_crop_size,
                 interpolation=interpolation,
                 auto_augment_policy=auto_augment_policy,
@@ -501,11 +499,6 @@ def get_args_parser(add_help=True):
     parser.add_argument(
         "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
     )
-    parser.add_argument(
-        "--train-center-crop",
-        action="store_true",
-        help="use center crop instead of random crop for training (default: False)",
-    )
     parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
     parser.add_argument("--ra-sampler", action="store_true", help="whether to use Repeated Augmentation in training")
     parser.add_argument(