app.py

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()