Upload graph-byclip.py

graph-byclip.py

@@ -5,7 +5,7 @@ Plan:
    Modded version of graph-embeddings.py
    Just to see if using different CLIP module changes values significantly
    (It does not)
-   This requires
+   This code requires
        pip install git+https://github.com/openai/CLIP.git
 """
 
@@ -21,7 +21,11 @@ matplotlib.use('QT5Agg')  # Set the backend to TkAgg
 
 import matplotlib.pyplot as plt
 
-CLIPname= "ViT-L/14"
+# Available models:
+#  'RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'
+#CLIPname= "ViT-L/14"
+CLIPname= "ViT-B/16"
+#CLIPname= "ViT-L/14@336px"
 
 device=torch.device("cuda")
 print("loading CLIP model")
@@ -29,22 +33,28 @@ model, processor = clip.load(CLIPname,device=device)
 model.cuda().eval()
 print("done")
 
-def embed_from_text(text):
-    tokens = clip.tokenize(text).to(device)
+def embed_from_tokenid(num):
+    # A bit sleazy, but, eh.
+    tokens = clip.tokenize("dummy").to(device)
+    tokens[0][1]=num
 
     with torch.no_grad():
         embed = model.encode_text(tokens)
         return embed
 
 
-# Expect SINGLE WORD ONLY
-def standard_embed_calc(text):
-    inputs = processor(text=text, return_tensors="pt")
-    inputs.to(device)
+
+def embed_from_text(text):
+    if text[0]=="#":
+        print("Converting string to number")
+        return embed_from_tokenid(int(text[1:]))
+
+    tokens = clip.tokenize(text).to(device)
+    print("Tokens for",text,"=",tokens)
+
     with torch.no_grad():
-        text_features = model.get_text_features(**inputs)
-    embedding = text_features[0]
-    return embedding
+        embed = model.encode_text(tokens)
+        return embed
 
 
 fig, ax = plt.subplots()