| |
|
|
| """ Work in progress |
| Plan: |
| Kinda a "temp" hack (maybe) |
| Prompt for TWO things. Subtract the second embed from the first. |
| Then see what is near |
| """ |
|
|
|
|
| import sys |
| import json |
| import torch |
| from safetensors import safe_open |
|
|
| from transformers import CLIPProcessor,CLIPModel |
|
|
| clipsrc="openai/clip-vit-large-patch14" |
| processor=None |
| model=None |
|
|
| device=torch.device("cuda") |
|
|
|
|
| def init(): |
| global processor |
| global model |
| |
| print("loading processor from "+clipsrc,file=sys.stderr) |
| processor = CLIPProcessor.from_pretrained(clipsrc) |
| print("done",file=sys.stderr) |
| print("loading model from "+clipsrc,file=sys.stderr) |
| model = CLIPModel.from_pretrained(clipsrc) |
| print("done",file=sys.stderr) |
|
|
| model = model.to(device) |
|
|
|
|
|
|
| embed_file="embeddings.safetensors" |
|
|
| device=torch.device("cuda") |
|
|
| print("reading words from dictionary now",file=sys.stderr) |
| with open("dictionary","r") as f: |
| tokendict = f.readlines() |
| wordlist = [token.strip() for token in tokendict] |
| print(len(wordlist),"lines read") |
|
|
|
|
| print("reading embeddings now",file=sys.stderr) |
| model = safe_open(embed_file,framework="pt",device="cuda") |
| embs=model.get_tensor("embeddings") |
| embs.to(device) |
| print("Shape of loaded embeds =",embs.shape) |
|
|
| def standard_embed_calc(text): |
| if processor == None: |
| init() |
|
|
| inputs = processor(text=text, return_tensors="pt") |
| inputs.to(device) |
| with torch.no_grad(): |
| text_features = model.get_text_features(**inputs) |
| embedding = text_features[0] |
| return embedding |
|
|
|
|
| def print_distances(targetemb): |
| targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2) |
|
|
| print("shape of distances...",targetdistances.shape) |
|
|
| smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False) |
|
|
| smallest_distances=smallest_distances.tolist() |
| smallest_indices=smallest_indices.tolist() |
| for d,i in zip(smallest_distances,smallest_indices): |
| print(wordlist[i],"(",d,")") |
|
|
|
|
|
|
|
|
| text1=input("First text? ") |
| text2=input("Second text? ") |
| emb1=standard_embed_calc(text1) |
| emb2=standard_embed_calc(text2) |
|
|
| result=torch.sub(emb1,emb2) |
| print_distances(result) |
|
|
