add ability to only return embeddings, without human and mouse head c…

…alculation

enformer_pytorch/enformer_pytorch.py

@@ -373,6 +373,7 @@ def forward(
         target = None,
         return_corr_coef = False,
         return_embeddings = False,
+        return_only_embeddings = False,
         head = None
     ):
         dtype = x.dtype
@@ -386,12 +387,15 @@ def forward(
             x = rearrange(x, '... -> () ...')
 
         x = self._trunk(x)
-        out = map_values(lambda fn: fn(x), self._heads)
 
         if no_batch:
-            out = map_values(lambda t: rearrange(t, '() ... -> ...'), out)
             x = rearrange(x, '() ... -> ...')
 
+        if return_only_embeddings:
+            return x
+
+        out = map_values(lambda fn: fn(x), self._heads)
+
         if exists(head):
             assert head in self._heads, f'head {head} not found'
             out = out[head]

enformer_pytorch/finetune.py

@@ -53,7 +53,7 @@ def forward(
         enformer_context = freeze_batchnorm_context(self.enformer) if not freeze_enformer else torch.no_grad()
 
         with enformer_context:
-            _, embeddings = self.enformer(seq, return_embeddings = True)
+            embeddings = self.enformer(seq, return_only_embeddings = True)
 
             if freeze_enformer:
                 embeddings.detach_()
@@ -90,7 +90,7 @@ def forward(
         enformer_context = freeze_batchnorm_context(self.enformer) if not freeze_enformer else torch.no_grad()
 
         with enformer_context:
-            _, embeddings = self.enformer(seq, return_embeddings = True)
+            embeddings = self.enformer(seq, return_only_embeddings = True)
 
             if freeze_enformer:
                 embeddings.detach_()

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'enformer-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.1.5',
+  version = '0.1.6',
   license='MIT',
   description = 'Enformer - Pytorch',
   author = 'Phil Wang',