readme parameter sample_aug_params correction

README.md

@@ -51,7 +51,7 @@ patch_embeddings = torch.randn((num_patches, embedding_dim), device="cuda")
 # mode="wsi_wise" applies the same transformation across the whole slide
 # mode="instance_wise" applies different transformations per patch
 aug_params = model.sample_aug_params(
-    n=num_patches,
+    batch_size=num_patches,
     device=patch_embeddings.device,
     mode="wsi_wise"
 )
@@ -115,7 +115,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 num_to_sample = 5
 
 # start: sample once and inspect current config
-sample_1 = model.sample_aug_params(n=num_to_sample, device=device, mode="wsi_wise")
+sample_1 = model.sample_aug_params(batch_size=num_to_sample, device=device, mode="wsi_wise")
 print("initial sample:\n", sample_1, "\n")
 
 print("initial transforms_parameters:\n", model.histaug.transforms_parameters, "\n")
@@ -127,7 +127,7 @@ model.histaug.transforms_parameters.pop("hue", None)
 # pop a discrete transform: remove "rotation" (probability-based)
 model.histaug.transforms_parameters.pop("rotation", None)
 
-sample_2 = model.sample_aug_params(n=num_to_sample, device=device, mode="wsi_wise")
+sample_2 = model.sample_aug_params(batch_size=num_to_sample, device=device, mode="wsi_wise")
 print("after popping 'hue' (continuous) and 'rotation' (discrete):\n", sample_2, "\n")
 
 # change param examples
@@ -137,7 +137,7 @@ model.histaug.transforms_parameters["brightness"] = [-0.25, 0.25]
 # change a discrete transform probability: lower 'h_flip' from 0.75 to 0.10
 model.histaug.transforms_parameters["h_flip"] = 0.10
 
-sample_3 = model.sample_aug_params(n=num_to_sample, device=device, mode="wsi_wise")
+sample_3 = model.sample_aug_params(batch_size=num_to_sample, device=device, mode="wsi_wise")
 print("after changing 'brightness' interval and 'h_flip' probability:\n", sample_3, "\n")
 
 ````
@@ -171,7 +171,7 @@ def maybe_augment_bag(bag_features: torch.Tensor,
         return bag_features
     with torch.no_grad():
         aug_params = histaug.sample_aug_params(
-            n=bag_features.size(0),
+            batch_size=bag_features.size(0),
             device=bag_features.device,
             mode=mode  # "wsi_wise" or "instance_wise"
         )