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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()
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