attept 1 of actual ranking

pipeline.py

@@ -1,6 +1,7 @@
 from typing import Dict, List, Any
 import numpy as np
 from transformers import CLIPTokenizer, CLIPModel
+import os
 
 
 class PreTrainedPipeline():
@@ -8,10 +9,15 @@ class PreTrainedPipeline():
         # Preload all the elements you are going to need at inference.
         # For instance your model, processors, tokenizer that might be needed.
         # This function is only called once, so do all the heavy processing I/O here"""
-        self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-        self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+        self.sign_ids = np.load(os.path.join(path, "sign_ids.npy"))
+        self.sign_embeddings = np.load(os.path.join(path, "vanilla_large-patch14_image_embeddings.npy"))
 
-    def __call__(self, inputs: str) -> List[float]:
+        hf_model_path = "openai/clip-vit-large-patch14"
+        self.model = CLIPModel.from_pretrained(hf_model_path)
+        self.tokenizer = CLIPTokenizer.from_pretrained(hf_model_path)
+
+
+    def __call__(self, inputs: str):
         """
         Args:
             inputs (:obj:`str`):
@@ -21,4 +27,12 @@ class PreTrainedPipeline():
         """
         token_inputs = self.tokenizer([inputs],  padding=True, return_tensors="pt")
         query_embed = self.model.get_text_features(**token_inputs)
-        return query_embed.detach().cpu().numpy()[0].tolist()
+        np_query_embed = query_embed.detach().cpu().numpy()[0]
+        
+        # Compute the cosine similarity; note the embeddings are normalized.
+        cos_similarites = self.sign_embeddings @ np_query_embed
+        sign_id_arg_rankings = np.argsort(cos_similarites)[::-1]
+        n = 50
+        top_sign_ids = self.sign_ids[sign_id_arg_rankings[:n]]
+        top_sign_similarities = cos_similarites[sign_id_arg_rankings[:n]]
+        return dict(zip(top_sign_ids.tolist(), top_sign_similarities.tolist()))