Remove cls_embed arg from forward/encode_image fns

src/open_clip/coca_model.py

@@ -133,8 +133,7 @@ def _encode_image(self, images, normalize: bool = True):
         image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent
         return image_latent, tokens_embs
 
-    def _encode_text(self, text, normalize: bool = True, embed_cls: bool = True):
-        text = text[:, :-1] if embed_cls else text # make space for CLS token
+    def _encode_text(self, text, normalize: bool = True):
         text_latent, token_emb = self.text(text)
         text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent
         return text_latent, token_emb
@@ -143,15 +142,14 @@ def encode_image(self, images, normalize: bool = True):
         image_latent, _ = self._encode_image(images, normalize=normalize)
         return image_latent
 
-    def encode_text(self, text, normalize: bool = True, embed_cls: bool = True):
-        text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls)
+    def encode_text(self, text, normalize: bool = True):
+        text_latent, _ = self._encode_text(text, normalize=normalize)
         return text_latent
 
     def forward(
             self,
             image,
             text: Optional[torch.Tensor] = None,
-            embed_cls: bool = True,
             image_latent: Optional[torch.Tensor] = None,
             image_embs: Optional[torch.Tensor] = None,
     ):
@@ -161,7 +159,7 @@ def forward(
         if text is None:
             return {"image_features": image_latent, "image_embs": image_embs}
 
-        text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls)
+        text_latent, token_embs = self._encode_text(text)
 
         # TODO: add assertion to avoid bugs?
         labels = text[:, -token_embs.shape[1]:]
@@ -222,7 +220,7 @@ def generate(
 
             if generation_type == "beam_search":
                 output = self._generate_beamsearch(
-                    image_inputs = image,
+                    image_inputs=image,
                     pad_token_id=pad_token_id,
                     eos_token_id=eos_token_id,
                     sot_token_id=sot_token_id,
@@ -267,7 +265,7 @@ def generate(
             while True:
                 x = out[:, -max_seq_len:]
                 cur_len = x.shape[1]
-                logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1]
+                logits = self(image, x, image_latent=image_latent, image_embs=image_embs)["logits"][:, -1]
                 mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)
                 sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id
 
@@ -362,7 +360,6 @@ def _generate_beamsearch(
             outputs = self(
                 model_inputs['images'],
                 model_inputs['text'],
-                embed_cls=False,
                 image_latent=image_latent,
                 image_embs=image_embs
             )