address #304

setup.py

@@ -6,7 +6,7 @@
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.6.5',
+  version = '1.6.6',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description=long_description,

vit_pytorch/cross_vit.py

@@ -170,12 +170,13 @@ def __init__(
         dim,
         image_size,
         patch_size,
-        dropout = 0.
+        dropout = 0.,
+        channels = 3
     ):
         super().__init__()
         assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
         num_patches = (image_size // patch_size) ** 2
-        patch_dim = 3 * patch_size ** 2
+        patch_dim = channels * patch_size ** 2
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
@@ -223,11 +224,12 @@ def __init__(
         cross_attn_dim_head = 64,
         depth = 3,
         dropout = 0.1,
-        emb_dropout = 0.1
+        emb_dropout = 0.1,
+        channels = 3
     ):
         super().__init__()
-        self.sm_image_embedder = ImageEmbedder(dim = sm_dim, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
-        self.lg_image_embedder = ImageEmbedder(dim = lg_dim, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
+        self.sm_image_embedder = ImageEmbedder(dim = sm_dim, channels= channels, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
+        self.lg_image_embedder = ImageEmbedder(dim = lg_dim, channels = channels, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
 
         self.multi_scale_encoder = MultiScaleEncoder(
             depth = depth,