import torch from PIL import Image from torchvision import transforms import gradio as gr import os 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', 'shufflenet_v2_x1_0', pretrained=True) model.eval() os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt") 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 = "Shufflenet_v2 By Bekalu Nigus Dawit" description = " Thesis Title: The Development of an Image Classification Application based on Shufflenet" article = "

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design | Github Repo

" examples = [ ['dog.jpg'],['pizza.jpeg'],['mushroom.jpeg'] ] gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()