replace all function names

AllenCellModeling · Aug 23, 2024 · 316a93a · 316a93a
1 parent 84717b7
commit 316a93a
Show file tree

Hide file tree

Showing 4 changed files with 9 additions and 9 deletions.
diff --git a/cyto_dl/image/transforms/generate_jepa_masks.py b/cyto_dl/image/transforms/generate_jepa_masks.py
@@ -5,7 +5,7 @@
 from monai.transforms import RandomizableTransform
 from skimage.segmentation import find_boundaries
 
-from cyto_dl.nn.vits.utils import validate_spatial_dims
+from cyto_dl.nn.vits.utils import match_tuple_dimensions
 
 
 class JEPAMaskGenerator(RandomizableTransform):
@@ -39,7 +39,7 @@ def __init__(
         """
         assert 0 < mask_ratio < 1, "mask_ratio must be between 0 and 1"
 
-        num_patches = validate_spatial_dims(spatial_dims, [num_patches])[0]
+        num_patches = match_tuple_dimensions(spatial_dims, [num_patches])[0]
         assert mask_size * max(block_aspect_ratio) < min(
             num_patches[-2:]
         ), "mask_size * max mask aspect ratio must be less than the smallest dimension of num_patches"

diff --git a/cyto_dl/nn/vits/decoder.py b/cyto_dl/nn/vits/decoder.py
@@ -12,8 +12,8 @@
 from cyto_dl.nn.vits.blocks import CrossAttentionBlock
 from cyto_dl.nn.vits.utils import (
     get_positional_embedding,
+    match_tuple_dimensions,
     take_indexes,
-    validate_spatial_dims,
 )
 
 
@@ -51,7 +51,7 @@ def __init__(
             If True, learnable positional embeddings are used. If False, fixed sin/cos positional embeddings. Empirically, fixed positional embeddings work better for brightfield images.
         """
         super().__init__()
-        num_patches, patch_size = validate_spatial_dims(spatial_dims, [num_patches, patch_size])
+        num_patches, patch_size = match_tuple_dimensions(spatial_dims, [num_patches, patch_size])
 
         self.has_cls_token = has_cls_token
 

diff --git a/cyto_dl/nn/vits/mae.py b/cyto_dl/nn/vits/mae.py
@@ -8,15 +8,15 @@
 
 from cyto_dl.nn.vits.decoder import CrossMAE_Decoder, MAE_Decoder
 from cyto_dl.nn.vits.encoder import HieraEncoder, MAE_Encoder
-from cyto_dl.nn.vits.utils import validate_spatial_dims
+from cyto_dl.nn.vits.utils import match_tuple_dimensions
 
 
 class MAE_Base(torch.nn.Module, ABC):
     def __init__(
         self, spatial_dims, num_patches, patch_size, mask_ratio, features_only, context_pixels
     ):
         super().__init__()
-        num_patches, patch_size, context_pixels = validate_spatial_dims(
+        num_patches, patch_size, context_pixels = match_tuple_dimensions(
             spatial_dims, [num_patches, patch_size, context_pixels]
         )
 
@@ -213,7 +213,7 @@ def __init__(
             features_only=features_only,
             context_pixels=context_pixels,
         )
-        num_mask_units = validate_spatial_dims(self.spatial_dims, [num_mask_units])[0]
+        num_mask_units = match_tuple_dimensions(self.spatial_dims, [num_mask_units])[0]
 
         self._encoder = HieraEncoder(
             num_patches=self.num_patches,

diff --git a/cyto_dl/nn/vits/predictor.py b/cyto_dl/nn/vits/predictor.py
@@ -8,8 +8,8 @@
 from cyto_dl.nn.vits.blocks import CrossAttentionBlock
 from cyto_dl.nn.vits.utils import (
     get_positional_embedding,
+    match_tuple_dimensions,
     take_indexes,
-    validate_spatial_dims,
 )
 
 
@@ -56,7 +56,7 @@ def __init__(
             ]
         )
 
-        num_patches = validate_spatial_dims(spatial_dims, [num_patches])[0]
+        num_patches = match_tuple_dimensions(spatial_dims, [num_patches])[0]
 
         self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, emb_dim))
         self.pos_embedding = get_positional_embedding(