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| import torch | |
| from PIL import Image | |
| from torchvision import datasets, models, transforms | |
| import gradio as gr | |
| import os | |
| import torch.nn as nn | |
| #os.system("wget https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/artist_classes.txt") | |
| #os.system("wget https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/model_weights_mobilenet_v2_valp1trainp2.pth") | |
| #model = torch.hub.load('pytorch/vision:v0.9.0', 'mobilenet_v2', pretrained=False) | |
| #checkpoint = 'https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/model_weights_mobilenet_v2_valp1trainp2.pth' | |
| #model.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=False)) | |
| #model = models.efficientnet_b0() | |
| #num_ftrs = model.classifier[1].in_features | |
| #model.classifier[1] = nn.Linear(num_ftrs, 6) | |
| #device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| #model = model.to(device) | |
| #model.load_state_dict(torch.load('model_weights_EfficientNetB0_final.pth',map_location=device)) | |
| model = models.vgg16() | |
| num_ftrs = model.classifier[6].in_features | |
| model.classifier[6] = nn.Linear(num_ftrs, 6) | |
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| model = model.to(device) | |
| model.load_state_dict(torch.load('VGG16_weights_May28.pth',map_location=device)) | |
| #torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") | |
| def inference(input_image): | |
| preprocess = transforms.Compose([ | |
| transforms.Resize(260), | |
| 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') | |
| else: | |
| model.to('cpu') | |
| 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 | |
| categories = { | |
| 0:"Vincent van Gogh", | |
| 1:"Monet", | |
| 2:"Leonardo da Vinci", | |
| 3:"Rembrandt", | |
| 4:"Pablo Picasso", | |
| 5:"Salvador Dali" | |
| } | |
| # Show top categories per image | |
| top5_prob, top5_catid = torch.topk(probabilities, 6) | |
| result = {} | |
| for i in range(top5_prob.size(0)): | |
| result[categories[top5_catid[i].item()]] = top5_prob[i].item() | |
| return result | |
| inputs = gr.Image(type='pil',label='Insert the image') | |
| outputs = gr.Label(type="confidences",num_top_classes=5,label='Prediction') | |
| title = "Artist Classifier" | |
| description = "The classifier is a demo classifier to predict the painter using fine-tuned VGG16. Transfer learning is adopted that significantly reduces the time/resource cost. It allows you to identify the creator of a painting among Vincent van Gogh, Claude Monet, Leonardo da Vinci, Rembrandt, Pablo Picasso, and Salvador Dali. Just upload the image to the left blank box and click the Submit button. A list of confidence will be displayed. Following the link below to find a related work that helps to create your own paintings following the style of painters" | |
| article = """ | |
| <p style='text-align: left'><a href='https://huggingface.co/spaces/breynolds1247/StarryNight_StyleTransfer'>Style Transfer: Create your own stylish paintings </a></p> | |
| <p style='text-align: left'>The app is based on <a href='https://arxiv.org/abs/1409.1556'>Very Deep Convolutional Networks</a></p> | |
| """ | |
| #examples = [ | |
| # ['dog.jpg'] | |
| #] | |
| #gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch() | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False).launch() |