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from typing import Dict, List, Any
import numpy as np
from transformers import CLIPTokenizer, CLIPModel
import os
class PreTrainedPipeline():
def __init__(self, path=""):
# 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.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"))
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`):
a string to get the features from.
Return:
A :obj:`list` of floats: The features computed by the model.
"""
token_inputs = self.tokenizer([inputs], padding=True, return_tensors="pt")
query_embed = self.model.get_text_features(**token_inputs)
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 [top_sign_ids.tolist(), top_sign_similarities.tolist()]
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