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Lab/Lab4/code/tune-clip-in-stanford_cars/test.py

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+import torch
+import torch.nn
+import clip
+import numpy as np
+from tqdm import tqdm
+
+
+def test(net, test_dataset, test_loader, device):
+    net.eval()
+    total_accuracy = 0.0
+    texts = test_dataset.tokens.to(device)
+    with torch.no_grad():
+        for index, (images, tokens, targets) in tqdm(enumerate(test_loader), total=len(test_loader)):
+            images = images.to(device)
+            logits_per_image, logits_per_text = net(images, texts)
+            probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+            accuracy = np.sum(probs.argmax(1) == targets.numpy())
+            total_accuracy += accuracy
+    net.train()
+    return total_accuracy / len(test_dataset)