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| import torch | |
| import gradio as gr | |
| import torchvision.transforms as transforms | |
| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d') | |
| utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils') | |
| resneXt.eval().to(device) | |
| def inference(img): | |
| img_transforms = transforms.Compose( | |
| [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()] | |
| ) | |
| img = img_transforms(img) | |
| with torch.no_grad(): | |
| # mean and std are not multiplied by 255 as they are in training script | |
| # torch dataloader reads data into bytes whereas loading directly | |
| # through PIL creates a tensor with floats in [0,1] range | |
| mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1) | |
| std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1) | |
| img = img.float() | |
| img = img.unsqueeze(0).sub_(mean).div_(std) | |
| batch = torch.cat( | |
| [img] | |
| ).to(device) | |
| with torch.no_grad(): | |
| output = torch.nn.functional.softmax(resneXt(batch), dim=1) | |
| results = utils.pick_n_best(predictions=output, n=5) | |
| return results | |
| title="SE-ResNeXt101" | |
| description="Gradio demo for SE-ResNeXt101, ResNeXt with Squeeze-and-Excitation module added, trained with mixed precision using Tensor Cores. To use it, simply upload your image or click on one of the examples below. Read more at the links below" | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1709.01507'>Squeeze-and-Excitation Networks</a> | <a href='https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/se-resnext101-32x4d'>Github Repo</a></p>" | |
| examples=[['food.jpeg']] | |
| gr.Interface(inference,gr.inputs.Image(type="pil"),"text",title=title,description=description,article=article,examples=examples).launch(enable_queue=True) |