Update augmentations.py

augmentations.py

@@ -1,7 +1,9 @@
 import torch as pt
 import torch.nn.functional as F
 import numbers
+
 def resize_down_up(x, scale=0.5, mode="bilinear"):
+    # shrinks image then blows it back up to see what info was lost
     if isinstance(scale, pt.Tensor):
         scale = scale.item()
     _val_invars(x, scale=scale, mode=mode, type="resize")
@@ -16,6 +18,7 @@ def resize_down_up(x, scale=0.5, mode="bilinear"):
     return F.interpolate(x_down, size=(H,W), mode=mode, **kwargs)
 
 def _val_invars(x, **kwargs):
+    # validate invariants
     if x.ndim != 4:
         raise ValueError(f"x needs shape (B,C,H,W), got {x.shape}")
     op_type = kwargs.get("type")
@@ -55,6 +58,7 @@ def checkerboard_alias_attack(
     x: pt.Tensor,
     epsilon: float=0.5,
 ):
+    # injects high-frequency noise that mimics sampling artifacts
     _val_invars(x, epsilon=epsilon, type="attack")
     B, C, H, W = x.shape
     device = x.device