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| import os | |
| import torch | |
| import gradio as gr | |
| from PIL import Image | |
| from torchvision import transforms | |
| """ | |
| Built following: | |
| https://huggingface.co/spaces/pytorch/ResNet/tree/main | |
| https://www.gradio.app/image_classification_in_pytorch/ | |
| """ | |
| # Get classes list | |
| os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt") | |
| # Load PyTorch model | |
| model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True) | |
| model.eval() | |
| # Download an example image from the pytorch website | |
| torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") | |
| # Inference! | |
| def inference(input_image): | |
| preprocess = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| ]) | |
| input_tensor = preprocess(input_image) | |
| input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model | |
| # Move the input and model to GPU for speed if available | |
| if torch.cuda.is_available(): | |
| input_batch = input_batch.to('cuda') | |
| model.to('cuda') | |
| with torch.no_grad(): | |
| output = model(input_batch) | |
| # The output has unnormalized scores. To get probabilities, you can run a softmax on it. | |
| probabilities = torch.nn.functional.softmax(output[0], dim=0) | |
| # Read the categories | |
| with open("imagenet_classes.txt", "r") as f: | |
| categories = [s.strip() for s in f.readlines()] | |
| # Show top categories per image | |
| top5_prob, top5_catid = torch.topk(probabilities, 5) | |
| result = {} | |
| for i in range(top5_prob.size(0)): | |
| result[categories[top5_catid[i]]] = top5_prob[i].item() | |
| return result | |
| # Define ins outs placeholders | |
| inputs = gr.inputs.Image(type='pil') | |
| outputs = gr.outputs.Label(type="confidences",num_top_classes=5) | |
| # Define style | |
| title = "Image Recognition Demo" | |
| description = "This is a prototype application which demonstrates how artifical intelligence based systems can recognize what object(s) is present in an image. This fundamental task in computer vision known as `Image Classification` has applications stretching from autonomous vehicles to medical imaging. To use it, simply upload your image, or click one of the examples images to load them, which I took at <a href='https://espacepourlavie.ca/en/biodome' target='_blank'>Montréal Biodôme</a>! Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>" | |
| # Run inference | |
| gr.Interface(inference, | |
| inputs, | |
| outputs, | |
| examples=["example1.jpg", "example2.jpg"], | |
| title=title, | |
| description=description, | |
| article=article, | |
| analytics_enabled=False).launch() | |