♻️ [Refactor] the dataloader import and default aug

yolo/config/task/train.yaml

@@ -14,7 +14,9 @@ data:
   data_augment:
     Mosaic: 1
     # MixUp: 1
-    HorizontalFlip: 0.5
+    # HorizontalFlip: 0.5
+    RandomCrop: 1
+    RemoveOutliers: 1e-8
 
 optimizer:
   type: SGD

yolo/tools/data_loader.py

@@ -13,13 +13,8 @@ from torch.utils.data import DataLoader, Dataset
 from torch.utils.data.distributed import DistributedSampler
 
 from yolo.config.config import DataConfig, DatasetConfig
-from yolo.tools.data_augmentation import (
-    AugmentationComposer,
-    HorizontalFlip,
-    MixUp,
-    Mosaic,
-    VerticalFlip,
-)
+from yolo.tools.data_augmentation import *
+from yolo.tools.data_augmentation import AugmentationComposer
 from yolo.tools.dataset_preparation import prepare_dataset
 from yolo.utils.dataset_utils import (
     create_image_metadata,

yolo/utils/bounding_box_utils.py

@@ -217,6 +217,7 @@ class BoxMatcher:
         Returns:
             unique_indices [batch x anchors x 1]: The index of the best targets for each anchors
         """
+        # TODO: add a assert for no target on the image
         unique_indices = target_matrix.argmax(dim=1)
         return unique_indices[..., None]