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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from torch import nn | |
| from model import create_resnet50_model | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| import torch.nn.functional as F | |
| # Setup class names | |
| class_names = ['CRVO', | |
| 'Choroidal Nevus', | |
| 'Diabetic Retinopathy', | |
| 'Laser Spots', | |
| 'Macular Degeneration', | |
| 'Macular Hole', | |
| 'Myelinated Nerve Fiber', | |
| 'Normal', | |
| 'Pathological Mypoia', | |
| 'Retinitis Pigmentosa'] | |
| ### 2. Model and transforms preparation ### | |
| # Create ResNet50 model | |
| resnet50, resnet50_transforms = create_resnet50_model( | |
| num_classes=len(class_names), # actual value would also work | |
| ) | |
| resnet50.fc = nn.Linear(2048, 10) | |
| # Load saved weights | |
| resnet50.load_state_dict( | |
| torch.load( | |
| f="pretrained_resnet50_feature_extractor_drappcompressed.pth", | |
| map_location=torch.device("cpu"), # load to CPU | |
| ) | |
| ) | |
| ### 3. Predict function ### | |
| # Create predict function | |
| # def predict(img) -> Tuple[Dict, float]: | |
| # """Transforms and performs a prediction on img and returns prediction and time taken. | |
| # """ | |
| # # Start the timer | |
| # start_time = timer() | |
| # # Transform the target image and add a batch dimension | |
| # img = resnet50_transforms(img).unsqueeze(0) | |
| # # Put model into evaluation mode and turn on inference mode | |
| # resnet50.eval() | |
| # with torch.inference_mode(): | |
| # # Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
| # pred_probs = torch.softmax(resnet50(img), dim=1) | |
| # # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter) | |
| # pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
| # # Calculate the prediction time | |
| # pred_time = round(timer() - start_time, 5) | |
| # # Return the prediction dictionary and prediction time | |
| # return pred_labels_and_probs, pred_time | |
| def predict(img): | |
| """Transforms and performs a prediction on img and returns prediction and time taken.""" | |
| start_time = timer() | |
| try: | |
| img = resnet50_transforms(img).unsqueeze(0) | |
| resnet50.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(resnet50(img), dim=1) | |
| # Calculate entropy for OOD detection | |
| entropy = -torch.sum(pred_probs * torch.log(pred_probs + 1e-8)).item() | |
| max_prob = torch.max(pred_probs).item() | |
| # Create base prediction dictionary | |
| pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
| # OOD Detection - modify existing probabilities instead of adding new keys | |
| if (max_prob > 0.95 and entropy < 0.2) or entropy > 2.0: | |
| # Boost the probability of the first class and add a marker | |
| pred_labels_and_probs[class_names[0]] = 0.99 # Use existing class | |
| # You could also just print a warning or log it | |
| print("May not be retina scan") | |
| pred_time = round(timer() - start_time, 5) | |
| return pred_labels_and_probs, pred_time | |
| except Exception as e: | |
| # Return dictionary with same structure as normal case | |
| pred_labels_and_probs = {class_names[i]: 0.0 for i in range(len(class_names))} | |
| pred_labels_and_probs[class_names[0]] = 1.0 # Show error in first class | |
| return pred_labels_and_probs, 0.0 | |
| ### 4. Gradio app ### | |
| # Create title, description and article strings | |
| #title = "DeepFundus 👀" | |
| #description = "A ResNet50 feature extractor computer vision model to classify funduscopic images." | |
| #article = "Created with the help from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| # Create the Gradio demo | |
| demo = gr.Interface(fn=predict, # mapping function from input to output | |
| inputs=gr.Image(type="pil"), # what are the inputs? | |
| outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
| # Create examples list from "examples/" directory | |
| examples=example_list) | |
| #title=title, | |
| #description=description, | |
| #article=article) | |
| # Launch the demo! | |
| demo.launch() | |