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from sentence_transformers import SentenceTransformer
import torch.nn.functional as F
import torch
class Load_EmbeddingModels:
def __init__(self, model_name ='jinaai/jina-clip-v2'):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.img_model_ID = model_name
self.img_model = self.get_cp_img_model_info(self.img_model_ID)
def get_cp_img_model_info(self, model_name):
print('Loading SentenceTransformer model')
model =SentenceTransformer(model_name, trust_remote_code=True)
model = model.to(self.device)
return model
def get_single_image_embedding_cp_im(self, my_image):
embedding = self.img_model.encode(
my_image,
normalize_embeddings=True
)
values = embedding.tolist()
return values
class Get_EmbeddingModels:
def __init__(self, model_name='jinaai/jina-clip-v2'):
self.embed_model = Load_EmbeddingModels(model_name)
def get_dense_embd(self, img):
embd = self.embed_model.get_single_image_embedding_cp_im(img)
return embd
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