# File name: graph_client.py from concurrent.futures import ThreadPoolExecutor import json import os import numpy as np import requests from concurrent.futures import ThreadPoolExecutor, as_completed import time import torch # hack for debugging, set HTTP_ADDRESS to "http://127.0.0.1:8000/" # os.environ["HTTP_ADDRESS"] = "http://192.168.7.79:8000" 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" english_text = ( "It was the best of times, it was the worst of times, it was the age " "of wisdom, it was the age of foolishness, it was the epoch of belief" ) clip_model="ViT-L/14" clip_model_id ="laion5B-L-14" device = "cuda:0" if torch.cuda.is_available() else "cpu" print ("using device", device) from clip_retrieval.load_clip import load_clip, get_tokenizer # from clip_retrieval.clip_client import ClipClient, Modality model, preprocess = load_clip(clip_model, use_jit=True, device=device) tokenizer = get_tokenizer(clip_model) def preprocess_image(image_url): # download image from url import requests from PIL import Image from io import BytesIO response = requests.get(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) input_im = Image.fromarray(input_image) prepro = preprocess(input_im).unsqueeze(0).cpu() return prepro preprocessed_image = preprocess_image(test_image_url) def send_text_request(number): payload = { "text": ('str', english_text, 'application/octet-stream'), } url = os.environ.get("HTTP_ADDRESS", "http://127.0.0.1:8000/") response = requests.post(url, files=payload) embeddings = response.text return number, embeddings def send_image_url_request(number): payload = { "image_url": ('str', test_image_url, 'application/octet-stream'), } url = os.environ.get("HTTP_ADDRESS", "http://127.0.0.1:8000/") response = requests.post(url, files=payload) embeddings = response.text return number, embeddings def send_preprocessed_image_request(number): key = "preprocessed_image" data_bytes = preprocessed_image.numpy().tobytes() shape_bytes = np.array(preprocessed_image.shape).tobytes() dtype_bytes = str(preprocessed_image.dtype).encode() payload = { key: ('tensor', data_bytes, 'application/octet-stream'), 'shape': ('shape', shape_bytes, 'application/octet-stream'), 'dtype': ('dtype', dtype_bytes, 'application/octet-stream'), } url = os.environ.get("HTTP_ADDRESS", "http://127.0.0.1:8000/") response = requests.post(url, files=payload) embeddings = response.text return number, embeddings def process(numbers, send_func, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(send_func, number) for number in numbers] for future in as_completed(futures): n_result, result = future.result() result = json.loads(result) print (f"{n_result} : {len(result[0])}") # def process_text(numbers, max_workers=10): # for n in numbers: # n_result, result = send_text_request(n) # result = json.loads(result) # print (f"{n_result} : {len(result[0])}") if __name__ == "__main__": n_calls = 300 # test text # n_calls = 1 numbers = list(range(n_calls)) start_time = time.monotonic() process(numbers, send_text_request) end_time = time.monotonic() total_time = end_time - start_time avg_time_ms = total_time / n_calls * 1000 calls_per_sec = n_calls / total_time print(f"Text...") print(f" Average time taken: {avg_time_ms:.2f} ms") print(f" Number of calls per second: {calls_per_sec:.2f}") # test image url # n_calls = 1 numbers = list(range(n_calls)) start_time = time.monotonic() process(numbers, send_image_url_request) end_time = time.monotonic() total_time = end_time - start_time avg_time_ms = total_time / n_calls * 1000 calls_per_sec = n_calls / total_time print(f"Image passing url...") print(f" Average time taken: {avg_time_ms:.2f} ms") print(f" Number of calls per second: {calls_per_sec:.2f}") # test image as vector # n_calls = 1 numbers = list(range(n_calls)) start_time = time.monotonic() process(numbers, send_preprocessed_image_request) end_time = time.monotonic() total_time = end_time - start_time avg_time_ms = total_time / n_calls * 1000 calls_per_sec = n_calls / total_time print(f"Preprocessed image...") print(f" Average time taken: {avg_time_ms:.2f} ms") print(f" Number of calls per second: {calls_per_sec:.2f}")