import torch import torchvision import gradio as gr import pathlib import random from torch import nn from typing import Tuple, Dict from PIL import Image from timeit import default_timer as timer from typing import Tuple, Dict device = 'cuda' if torch.cuda.is_available() else 'cpu' with open('class-names.txt', 'r') as f: class_names = f.read().split('\n')[:-1] def load_model() -> Tuple[torch.nn.Module, torchvision.transforms.Compose]: weights = torchvision.models.ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 shufflenet_transforms = weights.transforms() shufflenet = torchvision.models.shufflenet_v2_x1_5(weights=weights) shufflenet.fc = nn.Linear(in_features=1024, out_features=len(class_names), bias=True) state_dict = torch.load('ShuffleNetV2.pt', map_location=device) shufflenet.load_state_dict(state_dict) return shufflenet, shufflenet_transforms model, transforms = load_model() def predict(img) -> Tuple[Dict, float]: start = timer() model.to(device) model.eval() with torch.inference_mode(): transformed_img = transforms(img).to(device) logits = model(transformed_img.unsqueeze(dim=0)) pred_prob = torch.softmax(logits, dim=1) pred_dict = {class_names[i]:pred_prob.squeeze(0)[i].item() for i in range(len(class_names))} pred_time = round(timer() - start, 5) return pred_dict, pred_time example_paths = list(pathlib.Path('examples').glob("*/*.jpg")) example_list = [[str(filepath)] for filepath in random.sample(example_paths, k=6)] title = 'Birds Species Classifier 🐦' description = 'A [ShuffleNetV2](https://pytorch.org/vision/main/models/shufflenetv2.html) feature extractor computer vision model to classify images of [525 bird species](https://www.kaggle.com/datasets/gpiosenka/100-bird-species/).' article = 'Made with ❤️🤗 by [me](https://www.linkedin.com/in/taufiq-dwi-purnomo/).' demo = gr.Interface( fn=predict, inputs=gr.Image(type='pil'), outputs=[gr.Label(num_top_classes=3, label='Predictions'), gr.Number(label="Prediction time (s)")], description=description, title=title, allow_flagging='never', examples=example_list, article=article ) demo.launch(debug=False)