Inference on one image and one text

[GabrielSoranzoUPEC]

Hi,
Thanks for your work.
I made a code to make inference for one image and one text, if it helps someone:

Getting the model:

def get_model(model_path):
    model = CLIPModel().to(CFG.device)
    model.load_state_dict(torch.load(model_path, map_location=CFG.device))
    model.eval()
    return model

Getting an image embedding:

def get_image_embedding(model,image_path):
  image = cv2.imread(image_path)
  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  transforms=get_transforms(mode='valid')
  image = transforms(image=image)['image']
  image_tensor = torch.tensor(image).permute(2, 0, 1).float().unsqueeze(0)
  image_features = model.image_encoder(image_tensor.to(CFG.device))
  image_embeddings = model.image_projection(image_features)
  return image_embeddings

Getting text embedding:

def get_text_embedding(model,query):
    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)
    encoded_query = tokenizer([query])
    batch = {
        key: torch.tensor(values).to(CFG.device)
        for key, values in encoded_query.items()
    }
    with torch.no_grad():
        text_features = model.text_encoder(
            input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
        )
        text_embeddings = model.text_projection(text_features)
    return text_embeddings

Make an inference:

model=get_model("best.pt")

image_embedding=get_image_embedding(model,"cuillere.jpg")
text_embedding=get_text_embedding(model,"a spoon")

image_embeddings_n = F.normalize(image_embedding, p=2, dim=-1)
text_embeddings_n = F.normalize(text_embedding, p=2, dim=-1)
dot_similarity = text_embeddings_n @ image_embeddings_n.T
print(dot_similarity.item())

Hope this help