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| import os | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| import torchvision.transforms as transforms | |
| from torch.autograd import Variable | |
| from network.Transformer import Transformer | |
| LOAD_SIZE = 1280 | |
| STYLE = "shinkai_makoto" | |
| MODEL_PATH = "models" | |
| COLOUR_MODEL = "RGB" | |
| model = Transformer() | |
| model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f"{STYLE}.pth"))) | |
| model.eval() | |
| disable_gpu = True | |
| def inference(img): | |
| # load image | |
| input_image = img.convert(COLOUR_MODEL) | |
| input_image = np.asarray(input_image) | |
| # RGB -> BGR | |
| input_image = input_image[:, :, [2, 1, 0]] | |
| input_image = transforms.ToTensor()(input_image).unsqueeze(0) | |
| # preprocess, (-1, 1) | |
| input_image = -1 + 2 * input_image | |
| if disable_gpu: | |
| input_image = Variable(input_image).float() | |
| else: | |
| input_image = Variable(input_image).cuda() | |
| # forward | |
| output_image = model(input_image) | |
| output_image = output_image[0] | |
| # BGR -> RGB | |
| output_image = output_image[[2, 1, 0], :, :] | |
| output_image = output_image.data.cpu().float() * 0.5 + 0.5 | |
| return transforms.ToPILImage()(output_image) | |
| title = "Anime Background GAN" | |
| description = "Gradio Demo for CartoonGAN by Chen Et. Al. Models are Shinkai Makoto, Hosoda Mamoru, Kon Satoshi, and Miyazaki Hayao." | |
| article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf' target='_blank'>CartoonGAN from Chen et.al</a></p><p style='text-align: center'><a href='https://github.com/venture-anime/cartoongan-pytorch' target='_blank'>Github Repo</a></p><p style='text-align: center'><a href='https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch' target='_blank'>Original Implementation from Yijunmaverick</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akiyamasho' alt='visitor badge'></center></p>" | |
| examples = [ | |
| ["examples/garden_in.jpg"], | |
| ["examples/library_in.jpg"], | |
| ] | |
| gr.Interface( | |
| fn=inference, | |
| inputs=[gr.inputs.Image(type="pil")], | |
| outputs=gr.outputs.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging=False, | |
| allow_screenshot=False, | |
| enable_queue=True, | |
| ).launch() | |