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import time
import numpy as np
import torch
from PIL import Image
import ray
from ray import serve
from clip_retrieval.load_clip import load_clip, get_tokenizer
# from clip_retrieval.clip_client import ClipClient, Modality
class CLIPModel:
def __init__(self):
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self._test_image_url = "https://static.wixstatic.com/media/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg/v1/fill/w_454,h_333,fp_0.50_0.50,q_90/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg"
self._clip_model="ViT-L/14"
self._clip_model_id ="laion5B-L-14"
self.model, self.preprocess = load_clip(self._clip_model, use_jit=True, device=self.device)
self.tokenizer = get_tokenizer(self._clip_model)
print ("using device", self.device)
def test_to_embeddings(self, prompt):
text = self.tokenizer([prompt]).to(self.device)
with torch.no_grad():
prompt_embededdings = self.model.encode_text(text)
prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
return(prompt_embededdings)
def image_to_embeddings(self, input_im):
input_im = Image.fromarray(input_im)
prepro = self.preprocess(input_im).unsqueeze(0).to(self.device)
with torch.no_grad():
image_embeddings = self.model.encode_image(prepro)
image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
return(image_embeddings)
def preprocessed_image_to_emdeddings(self, prepro):
with torch.no_grad():
image_embeddings = self.model.encode_image(prepro)
image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
return(image_embeddings)
# simple regression test
def regression_test(self):
text_embeddings = self.test_to_embeddings("Howdy!")
print("text embeddings", text_embeddings)
# download image from url
import requests
from io import BytesIO
response = requests.get(self._test_image_url)
input_image = Image.open(BytesIO(response.content))
input_image = input_image.convert('RGB')
# convert image to numpy array
input_image = np.array(input_image)
image_embeddings = self.image_to_embeddings(input_image)
print("image embeddings", image_embeddings)
input_im = Image.fromarray(input_image)
prepro = self.preprocess(input_im).unsqueeze(0).to(self.device)
image_embeddings = self.preprocessed_image_to_emdeddings(prepro)
print("image embeddings", image_embeddings)
# regression test
test_instance = CLIPModel()
test_instance.regression_test()
ray.init()
serve.start()
# Register the model with Ray Serve
serve.create_backend("clip_model", CLIPModel)
serve.create_endpoint("clip_model", backend="clip_model", route="/clip_model")
# You can now call the endpoint with your input
import requests
input_prompt = "Howdy!"
response = requests.get("http://localhost:8000/clip_model", json={"prompt": input_prompt})
print(response.json())
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