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Runtime error
| import torch | |
| model = torch.hub.load('pytorch/vision:v0.9.0', 'deeplabv3_resnet101', pretrained=True) | |
| model.eval() | |
| # Download an example image from the pytorch website | |
| import urllib | |
| url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") | |
| try: urllib.URLopener().retrieve(url, filename) | |
| except: urllib.request.urlretrieve(url, filename) | |
| # sample execution (requires torchvision) | |
| from PIL import Image | |
| from torchvision import transforms | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| def inference(input_image): | |
| preprocess = transforms.Compose([ | |
| 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)['out'][0] | |
| output_predictions = output.argmax(0) | |
| # create a color pallette, selecting a color for each class | |
| palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1]) | |
| colors = torch.as_tensor([i for i in range(21)])[:, None] * palette | |
| colors = (colors % 255).numpy().astype("uint8") | |
| # plot the semantic segmentation predictions of 21 classes in each color | |
| r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size) | |
| r.putpalette(colors) | |
| plt.imshow(r) | |
| return plt | |
| title = "DEEPLABV3-RESNET101" | |
| description = "demo for DEEPLABV3-RESNET101, DeepLabV3 model with a ResNet-101 backbone. 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/1706.05587'>Rethinking Atrous Convolution for Semantic Image Segmentation</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py'>Github Repo</a></p>" | |
| gr.Interface( | |
| inference, | |
| gr.inputs.Image(type="pil", label="Input"), | |
| gr.outputs.Image(type="plot", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ["dog.jpg"] | |
| ]).launch() |