offer way for extractor to return latents without detaching them

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.35.7',
+  version = '0.35.8',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description_content_type = 'text/markdown',

vit_pytorch/extractor.py

@@ -4,6 +4,12 @@ from torch import nn
 def exists(val):
     return val is not None
 
+def identity(t):
+    return t
+
+def clone_and_detach(t):
+    return t.clone().detach()
+
 def apply_tuple_or_single(fn, val):
     if isinstance(val, tuple):
         return tuple(map(fn, val))
@@ -17,7 +23,8 @@ class Extractor(nn.Module):
         layer = None,
         layer_name = 'transformer',
         layer_save_input = False,
-        return_embeddings_only = False
+        return_embeddings_only = False,
+        detach = True
     ):
         super().__init__()
         self.vit = vit
@@ -34,9 +41,11 @@ class Extractor(nn.Module):
         self.layer_save_input = layer_save_input # whether to save input or output of layer
         self.return_embeddings_only = return_embeddings_only
 
+        self.detach_fn = clone_and_detach if detach else identity
+
     def _hook(self, _, inputs, output):
         layer_output = inputs if self.layer_save_input else output
-        self.latents = apply_tuple_or_single(lambda t: t.clone().detach(), layer_output)
+        self.latents = apply_tuple_or_single(self.detach_fn, layer_output)
 
     def _register_hook(self):
         if not exists(self.layer):