File size: 4,910 Bytes
334dcac e4bcc80 55f430c 334dcac 55f430c 334dcac 55f430c 2afa949 55f430c 334dcac 2afa949 55f430c 2afa949 55f430c 2afa949 e4bcc80 2afa949 334dcac 55f430c 2afa949 55f430c 2afa949 55f430c 334dcac 55f430c 334dcac 2afa949 55f430c 334dcac 55f430c 334dcac 55f430c 2afa949 55f430c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
# 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}")
|