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 import seemore


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"))
    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/eval_seemore_t_x4.yml"
MODEL_NAME = "checkpoints/SeemoRe_T/X4/net_g_latest.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 = seemore.SeemoRe(scale=cfg.model.scale, in_chans=cfg.model.in_chans,
                        num_experts=cfg.model.num_experts, num_layers=cfg.model.num_layers, embedding_dim=cfg.model.embedding_dim, 
                        img_range=cfg.model.img_range, use_shuffle=cfg.model.use_shuffle, global_kernel_size=cfg.model.global_kernel_size, 
                        recursive=cfg.model.recursive, lr_space=cfg.model.lr_space, topk=cfg.model.topk)

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




title = "See More Details"
description = ''' ### See More Details: Efficient Image Super-Resolution by Experts Mining

#### [Eduard Zamfir<sup>1</sup>](https://eduardzamfir.github.io), [Zongwei Wu<sup>1*</sup>](https://sites.google.com/view/zwwu/accueil), [Nancy Mehta<sup>1</sup>](https://scholar.google.com/citations?user=WwdYdlUAAAAJ&hl=en&oi=ao),  [Yulun Zhang<sup>2,3*</sup>](http://yulunzhang.com/) and [Radu Timofte<sup>1</sup>](https://www.informatik.uni-wuerzburg.de/computervision/)

#### **<sup>1</sup> University of Würzburg, Germany - <sup>2</sup> Shanghai Jiao Tong University, China - <sup>3</sup> ETH Zürich, Switzerland**
#### **<sup>*</sup> Corresponding authors**

<details>
<summary> <b> Abstract</b> (click me to read)</summary>
<p>
Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings
</p>
</details>


<br>

<code>
@inproceedings{zamfir2024details,
  title={See More Details: Efficient Image Super-Resolution by Experts Mining}, 
  author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte},
  booktitle={International Conference on Machine Learning},
  year={2024},
  organization={PMLR}
}
</code>
<br>
'''


article = "<p style='text-align: center'><a href='https://eduardzamfir.github.io/seemore' target='_blank'>See More Details: Efficient Image Super-Resolution by Experts Mining</a></p>"

#### Image,Prompts examples
examples = [['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/img002x4.png"),],
    outputs=ImageSlider(label="Super-Resolved Image", type="pil"), #[gr.Image(type="pil", label="Ouput", min_width=500)],
    title=title,
    description=description,
    article=article,
    examples=examples,
    css=css,
)

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