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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 | |
| # 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 | |
| ### 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() | |