add local test for comparison

local_test.py

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+import time
+import numpy as np
+import torch
+
+num_steps = 1000
+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"
+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 test_text(prompt):
+    text = tokenizer([prompt]).to(device)
+    with torch.no_grad():
+        prompt_embededdings = model.encode_text(text)
+    prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
+    return(prompt_embededdings)
+
+def test_image(input_im):
+    input_im = Image.fromarray(input_im)
+    prepro = preprocess(input_im).unsqueeze(0).to(device)
+    with torch.no_grad():
+        image_embeddings = model.encode_image(prepro)
+    image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
+    return(image_embeddings)
+
+def test_preprocessed_image(prepro):
+    with torch.no_grad():
+        image_embeddings = model.encode_image(prepro)
+    image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
+    return(image_embeddings)
+
+
+# performance test for text
+start = time.time()
+for i in range(num_steps):
+    test_text("todo")
+end = time.time()
+average_time_seconds = (end - start) / num_steps
+average_time_seconds = average_time_seconds if average_time_seconds > 0 else 0.0000001
+print("Average time for text: ", average_time_seconds, "s")
+print("Average time for text: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for text: ", 1 / average_time_seconds)
+
+# 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)
+
+# performance test for image
+start = time.time()
+for i in range(num_steps):
+    test_image(input_image)
+end = time.time()
+average_time_seconds = (end - start) / num_steps
+print("Average time for image: ", average_time_seconds, "s")
+print("Average time for image: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for image: ", 1 / average_time_seconds)
+
+# performance test for preprocessed image
+input_im = Image.fromarray(input_image)
+prepro = preprocess(input_im).unsqueeze(0).to(device)
+
+start = time.time()
+for i in range(num_steps):
+    test_preprocessed_image(prepro)
+end = time.time()
+average_time_seconds = (end - start) / num_steps
+print("Average time for preprocessed image: ", average_time_seconds, "s")
+print("Average time for preprocessed image: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for preprocessed image: ", 1 / average_time_seconds)
+
+