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| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| import gradio as gr | |
| import os | |
| os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt") | |
| torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") | |
| model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg11', pretrained=True) | |
| # or any of these variants | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg11_bn', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg13', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg13_bn', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg16', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg16_bn', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg19', pretrained=True) | |
| # model = torch.hub.load('pytorch/vision:v0.9.0', 'vgg19_bn', pretrained=True) | |
| model.eval() | |
| 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 | |
| inputs = gr.inputs.Image(type='pil') | |
| outputs = gr.outputs.Label(type="confidences",num_top_classes=5) | |
| title = "VGG-NETS" | |
| description = "Gradio demo for VGG-NETS, Award winning ConvNets from 2014 Imagenet ILSVRC challenge. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1409.1556'>Very Deep Convolutional Networks for Large-Scale Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py'>Github Repo</a></p>" | |
| examples = [ | |
| ['dog.jpg'] | |
| ] | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch() |