app.py

import os
import yaml
import torch
import argparse
import numpy as np
import gradio as gr

from PIL import Image
from copy import deepcopy
from torch.nn.parallel import DataParallel, DistributedDataParallel

from huggingface_hub import hf_hub_download
from gradio_imageslider import ImageSlider

## local code
from models.smfanet_arch import  SMFANet


def dict2namespace(config):
    namespace = argparse.Namespace()
    for key, value in config.items():
        if isinstance(value, dict):
            new_value = dict2namespace(value)
        else:
            new_value = value
        setattr(namespace, key, new_value)
    return namespace

def load_img (filename, norm=True,):
    img = np.array(Image.open(filename).convert("RGB"))
    h, w = img.shape[:2]
    
    if w > 1920 or h > 1080:
        new_h, new_w = h // 4, w // 4
        img = np.array(Image.fromarray(img).resize((new_w, new_h), Image.BICUBIC))
 
    if norm:
        img = img / 255.
        img = img.astype(np.float32)
    return img

def process_img (image):
    img = np.array(image)
    img = img / 255.
    img = img.astype(np.float32)
    y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device)
    
    with torch.no_grad():
        x_hat = model(y)

    restored_img = x_hat.squeeze().permute(1,2,0).clamp_(0, 1).cpu().detach().numpy()
    restored_img = np.clip(restored_img, 0. , 1.)

    restored_img = (restored_img * 255.0).round().astype(np.uint8)  # float32 to uint8
    #return Image.fromarray(restored_img) #
    return (image, Image.fromarray(restored_img))

def load_network(net, load_path, strict=True, param_key='params'):
    if isinstance(net, (DataParallel, DistributedDataParallel)):
        net = net.module
    load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
    if param_key is not None:
        if param_key not in load_net and 'params' in load_net:
            param_key = 'params'
        load_net = load_net[param_key]
    # remove unnecessary 'module.'
    for k, v in deepcopy(load_net).items():
        if k.startswith('module.'):
            load_net[k[7:]] = v
            load_net.pop(k)
    net.load_state_dict(load_net, strict=strict)

CONFIG = "configs/SMFANet_plus_x4SR.yml"
MODEL_NAME = "pth/SMFANet_plus_DF2K_100w_x4SR.pth"

# parse config file
with open(os.path.join(CONFIG), "r") as f:
    config = yaml.safe_load(f)

cfg = dict2namespace(config)

device = torch.device("cpu")
model = SMFANet(dim=cfg.model.dim, n_blocks=cfg.model.n_blocks, ffn_scale=cfg.model.ffn_scale, upscaling_factor=cfg.model.upscaling_factor)

model = model.to(device)
print ("IMAGE MODEL CKPT:", MODEL_NAME)
load_network(model, MODEL_NAME, strict=True, param_key='params')




title = "[ECCV 2024] SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution"
description = '''  

#### [Mingjun Zheng](https://github.com/Zheng-MJ), [Long Sun](https://github.com/sunny2109), [Jiangxin Dong](https://scholar.google.com/citations?user=ruebFVEAAAAJ&hl=zh-CN&oi=ao), and [Jinshan Pan](https://jspan.github.io/)

#### [IMAG Lab](https://imag-njust.net/), Nanjing University of Science and Technology

####  Drag the slider on the super-resolution image left and right to see the changes in the image details. SeemoRe performs x4 upscaling on the input image.

<br>
<code>
@inproceedings{smfanet,
    title={SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution},
    author={Zheng, Mingjun and Sun, Long and Dong, Jiangxin and Pan, Jinshan},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    year={2024}
 }
</code>
<br>
'''


article = "<p style='text-align: center'><a href='https://raw.githubusercontent.com/Zheng-MJ/SMFANet' target='_blank'>SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution </a></p>"

#### Image,Prompts examples
examples = [
            ['images/0801x4.png'],
            ['images/0840x4.png'],
            ['images/0841x4.png'],
            ['images/0870x4.png'],
            ['images/0878x4.png'],
            ['images/0884x4.png'],
            ['images/0900x4.png'],
            ['images/img002x4.png'],
            ['images/img003x4.png'],
            ['images/img004x4.png'],
            ['images/img035x4.png'],
            ['images/img053x4.png'],
            ['images/img064x4.png'],
            ['images/img083x4.png'],
            ['images/img092x4.png'],
            ]

css = """
    .image-frame img, .image-container img {
        width: auto;
        height: auto;
        max-width: none;
    }
"""

demo = gr.Interface(
    fn=process_img,
    inputs=[gr.Image(type="pil", label="Input", value="images/0878x4.png"),],
    outputs=ImageSlider(label="Super-Resolved Image", 
                        type="pil",
                        show_download_button=True,
                        ), #[gr.Image(type="pil", label="Ouput", min_width=500)],
    title=title,
    description=description,
    article=article,
    examples=examples,
    css=css,
)

if __name__ == "__main__":
    demo.launch()