Nested navit

README.md

@@ -198,6 +198,38 @@ preds = v(
 ) # (5, 1000)
 ```
 
+Finally, if you would like to make use of a flavor of NaViT using <a href="https://pytorch.org/tutorials/prototype/nestedtensor.html">nested tensors</a> (which will omit a lot of the masking and padding altogether), make sure you are on version `2.4` and import as follows
+
+```python
+import torch
+from vit_pytorch.na_vit_nested_tensor import NaViT
+
+v = NaViT(
+    image_size = 256,
+    patch_size = 32,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 16,
+    mlp_dim = 2048,
+    dropout = 0.,
+    emb_dropout = 0.,
+    token_dropout_prob = 0.1
+)
+
+# 5 images of different resolutions - List[Tensor]
+
+images = [
+    torch.randn(3, 256, 256), torch.randn(3, 128, 128),
+    torch.randn(3, 128, 256), torch.randn(3, 256, 128),
+    torch.randn(3, 64, 256)
+]
+
+preds = v(images)
+
+assert preds.shape == (5, 1000)
+```
+
 ## Distillation
 
 <img src="./images/distill.png" width="300px"></img>

setup.py

@@ -6,7 +6,7 @@
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.7.5',
+  version = '1.7.6',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description=long_description,

vit_pytorch/na_vit.py

@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 from functools import partial
-from typing import List, Union
+from typing import List
 
 import torch
 import torch.nn.functional as F
@@ -245,7 +247,7 @@ def device(self):
 
     def forward(
         self,
-        batched_images: Union[List[Tensor], List[List[Tensor]]], # assume different resolution images already grouped correctly
+        batched_images: List[Tensor] | List[List[Tensor]], # assume different resolution images already grouped correctly
         group_images = False,
         group_max_seq_len = 2048
     ):
@@ -264,6 +266,11 @@ def forward(
                 max_seq_len = group_max_seq_len
             )
 
+        # if List[Tensor] is not grouped -> List[List[Tensor]]
+
+        if torch.is_tensor(batched_images[0]):
+            batched_images = [batched_images]
+
         # process images into variable lengthed sequences with attention mask
 
         num_images = []