model

generate.py

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+import torch, time
+import clip
+from PIL import Image
+import lightning as L
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
+text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
+
+start_time = time.time()
+
+with torch.no_grad():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    
+    logits_per_image, logits_per_text = model(image, text)
+    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+end_time = time.time()
+
+print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]
+
+print(f"Prediction time: {end_time - start_time} seconds")