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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from model import TinyCNN | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| import torch | |
| import torchvision | |
| from torchvision import transforms | |
| from torch import nn | |
| # Setup class names | |
| with open("class_names.txt", "r") as f: # reading them in from class_names.txt | |
| class_names = [defects.strip() for defects in f.readlines()] | |
| ### 2. Model and transforms preparation ### | |
| # Create model | |
| TinyCNN_model = TinyCNN(input_shape=3, | |
| hidden_units=64, | |
| output_shape=len(class_names)) | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor() | |
| ]) | |
| # Load saved weights | |
| TinyCNN_model.load_state_dict( | |
| torch.load( | |
| f="cnn.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 = transform(img).unsqueeze(dim=0) | |
| # Put model into evaluation mode and turn on inference mode | |
| TinyCNN_model.eval() | |
| with torch.inference_mode(): | |
| # Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
| pred_probs = torch.softmax(TinyCNN_model(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 = "Wafer Defect Detection" | |
| description = "An app to predict Wafer Defects in semiconductors.[Center, Donut, Edge-Loc, Edge-Ring, Loc, Near-full, Random, Scratch, none]" | |
| # Create examples list from "examples/" directory | |
| example_list = [["example/" + example] for example in os.listdir("example")] | |
| # Create Gradio interface | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(sources=["upload"], type='pil'), | |
| outputs=[ | |
| gr.Label(num_top_classes=5, label="Predictions"), | |
| gr.Number(label="Prediction time (s)"), | |
| ], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| ) | |
| # Launch the app! | |
| demo.launch() |