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Update app.py
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app.py
CHANGED
@@ -68,43 +68,37 @@ resnet50.load_state_dict(
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def predict(img):
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"""Transforms and performs a prediction on img and returns prediction and time taken."""
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try:
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start_time = timer()
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# Ensure img is valid
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if img is None:
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return {"Error": "No image provided"}, 0.0
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# Transform the target image and add a batch dimension
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img_tensor = resnet50_transforms(img).unsqueeze(0)
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# Put model into evaluation mode and turn on inference mode
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resnet50.eval()
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with torch.inference_mode():
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pred_probs = torch.softmax(resnet50(img_tensor), dim=1)
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# Calculate entropy for OOD detection
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entropy = -torch.sum(pred_probs * torch.log(pred_probs + 1e-8)).item()
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max_prob = torch.max(pred_probs).item()
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# Create prediction dictionary
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pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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# OOD Detection
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if (max_prob > 0.95 and entropy < 0.2) or entropy > 2.0:
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# Calculate prediction time
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pred_time = round(timer() - start_time, 5)
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return pred_labels_and_probs, pred_time
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except Exception as e:
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# Return
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### 4. Gradio app ###
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def predict(img):
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"""Transforms and performs a prediction on img and returns prediction and time taken."""
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start_time = timer()
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try:
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img = resnet50_transforms(img).unsqueeze(0)
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resnet50.eval()
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with torch.inference_mode():
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pred_probs = torch.softmax(resnet50(img), dim=1)
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# Calculate entropy for OOD detection
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entropy = -torch.sum(pred_probs * torch.log(pred_probs + 1e-8)).item()
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max_prob = torch.max(pred_probs).item()
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# Create base prediction dictionary
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pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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# OOD Detection - modify existing probabilities instead of adding new keys
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if (max_prob > 0.95 and entropy < 0.2) or entropy > 2.0:
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# Boost the probability of the first class and add a marker
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pred_labels_and_probs[class_names[0]] = 0.99 # Use existing class
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# You could also just print a warning or log it
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print("May not be retina scan")
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pred_time = round(timer() - start_time, 5)
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return pred_labels_and_probs, pred_time
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except Exception as e:
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# Return dictionary with same structure as normal case
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pred_labels_and_probs = {class_names[i]: 0.0 for i in range(len(class_names))}
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pred_labels_and_probs[class_names[0]] = 1.0 # Show error in first class
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return pred_labels_and_probs, 0.0
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### 4. Gradio app ###
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