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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| import torchvision | |
| from torch import nn | |
| from torchvision.models import densenet121 | |
| def create_densenet121_model(num_classes: int = 2, seed: int = 42): | |
| """Creates a DenseNet121 model and transforms.""" | |
| # Create DenseNet121 model | |
| model = densenet121(weights=None) # Set to None since we will be loading our own weights | |
| # Freeze all layers in base model | |
| for param in model.parameters(): | |
| param.requires_grad = False | |
| # Change classifier head with random seed for reproducibility | |
| torch.manual_seed(seed) | |
| model.classifier = nn.Linear(model.classifier.in_features, num_classes) | |
| transforms = torchvision.transforms.Compose([ | |
| torchvision.transforms.Resize(224), | |
| torchvision.transforms.ToTensor(), | |
| torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| return model, transforms | |
| # Create densenet121 model | |
| densenet, densenet_transforms = create_densenet121_model() | |
| # Load saved weights | |
| state_dict = torch.load("model/FL_global_model_4be885f7-8d33-4498-a5ef-85aa301706bd.pt", map_location=torch.device("cpu")) | |
| model_weights = state_dict["model"] | |
| densenet.load_state_dict(model_weights,strict=False) # Set strict to True since we now expect it to match | |
| 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 = densenet_transforms(img).unsqueeze(0) | |
| # Put model into evaluation mode and turn on inference mode | |
| densenet.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(densenet(img), dim=1).squeeze() | |
| pred_labels_and_probs = { | |
| 'Nodules': pred_probs[0].item(), | |
| 'Normal': pred_probs[1].item() | |
| } | |
| # 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 | |
| example_list = [[f"examples/example{i+1}.jpg"] for i in range(3)] | |
| title = "ChestXray Classification" | |
| description = "A Densenet121 computer vision model to classify images of Xray Chest images as Normal or Nodules." | |
| article = "model train by hamsteryang0" | |
| # article = "Created at (https://github.com/azizche/chest_xray_Classification)." | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=[gr.Label(num_top_classes=2, label="Predictions"), gr.Number(label="Prediction time (s)")], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article | |
| ) | |
| # Launch the demo! | |
| demo.launch() | |