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app.py
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app.py
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import sys
sys.path.append('SAFMN')
import os
import cv2
import argparse
import glob
import numpy as np
import os
import torch
import torch.nn.functional as F
import gradio as gr
from basicsr.utils.download_util import load_file_from_url
from basicsr.utils.colorfix import wavelet_reconstruction
from basicsr.archs.safmn_arch import SAFMN
pretrain_model_url = {
'safmn_x2': 'https://github.com/sunny2109/SAFMN/releases/download/v0.1.0/SAFMN_L_Real_LSDIR_x2-v2.pth',
'safmn_x4': 'https://github.com/sunny2109/SAFMN/releases/download/v0.1.0/SAFMN_L_Real_LSDIR_x4-v2.pth',
}
# download weights
if not os.path.exists('./experiments/pretrained_models/SAFMN_L_Real_LSDIR_x2-v2.pth'):
load_file_from_url(url=pretrain_model_url['safmn_x2'], model_dir='./experiments/pretrained_models/', progress=True, file_name=None)
if not os.path.exists('./experiments/pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'):
load_file_from_url(url=pretrain_model_url['safmn_x4'], model_dir='./experiments/pretrained_models/', progress=True, file_name=None)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def set_safmn(upscale):
model = SAFMN(dim=128, n_blocks=16, ffn_scale=2.0, upscaling_factor=upscale)
if upscale == 2:
model_path = './experiments/pretrained_models/SAFMN_L_Real_LSDIR_x2-v2.pth'
elif upscale == 4:
model_path = './experiments/pretrained_models/SAFMN_L_Real_LSDIR_x4-v2.pth'
else:
raise NotImplementedError('Only support x2/x4 upscaling!')
model.load_state_dict(torch.load(model_path)['params'], strict=True)
model.eval()
return model.to(device)
def img2patch(lq, scale=4, crop_size=512):
b, c, hl, wl = lq.size()
h, w = hl*scale, wl*scale
sr_size = (b, c, h, w)
assert b == 1
crop_size_h, crop_size_w = crop_size // scale * scale, crop_size // scale * scale
#adaptive step_i, step_j
num_row = (h - 1) // crop_size_h + 1
num_col = (w - 1) // crop_size_w + 1
import math
step_j = crop_size_w if num_col == 1 else math.ceil((w - crop_size_w) / (num_col - 1) - 1e-8)
step_i = crop_size_h if num_row == 1 else math.ceil((h - crop_size_h) / (num_row - 1) - 1e-8)
step_i = step_i // scale * scale
step_j = step_j // scale * scale
parts = []
idxes = []
i = 0 # 0~h-1
last_i = False
while i < h and not last_i:
j = 0
if i + crop_size_h >= h:
i = h - crop_size_h
last_i = True
last_j = False
while j < w and not last_j:
if j + crop_size_w >= w:
j = w - crop_size_w
last_j = True
parts.append(lq[:, :, i // scale :(i + crop_size_h) // scale, j // scale:(j + crop_size_w) // scale])
idxes.append({'i': i, 'j': j})
j = j + step_j
i = i + step_i
return torch.cat(parts, dim=0), idxes, sr_size
def patch2img(outs, idxes, sr_size, scale=4, crop_size=512):
preds = torch.zeros(sr_size).to(outs.device)
b, c, h, w = sr_size
count_mt = torch.zeros((b, 1, h, w)).to(outs.device)
crop_size_h, crop_size_w = crop_size // scale * scale, crop_size // scale * scale
for cnt, each_idx in enumerate(idxes):
i = each_idx['i']
j = each_idx['j']
preds[0, :, i: i + crop_size_h, j: j + crop_size_w] += outs[cnt]
count_mt[0, 0, i: i + crop_size_h, j: j + crop_size_w] += 1.
return (preds / count_mt).to(outs.device)
os.makedirs('./results', exist_ok=True)
def inference(image, upscale, large_input_flag, color_fix):
upscale = int(upscale) # convert type to int
if upscale > 4:
upscale = 4
if 0 < upscale < 3:
upscale = 2
model = set_safmn(upscale)
img = cv2.imread(str(image), cv2.IMREAD_COLOR)
print(f'input size: {img.shape}')
# img2tensor
img = img.astype(np.float32) / 255.
img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
img = img.unsqueeze(0).to(device)
# inference
if large_input_flag:
patches, idx, size = img2patch(img, scale=upscale)
with torch.no_grad():
n = len(patches)
outs = []
m = 1
i = 0
while i < n:
j = i + m
if j >= n:
j = n
pred = output = model(patches[i:j])
if isinstance(pred, list):
pred = pred[-1]
outs.append(pred.detach())
i = j
output = torch.cat(outs, dim=0)
output = patch2img(output, idx, size, scale=upscale)
else:
with torch.no_grad():
output = model(img)
# color fix
if color_fix:
img = F.interpolate(img, scale_factor=upscale, mode='bilinear')
output = wavelet_reconstruction(output, img)
# tensor2img
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
output = (output * 255.0).round().astype(np.uint8)
# save restored img
save_path = f'results/out.png'
cv2.imwrite(save_path, output)
output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
return output, save_path
title = "Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution"
description = r"""
<b>Official Gradio demo</b> for <a href='https://github.com/sunny2109/SAFMN' target='_blank'><b>Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution (ICCV 2023)</b></a>.<br>
"""
article = r"""
If SAFMN is helpful, please help to ⭐ the <a href='https://github.com/sunny2109/SAFMN' target='_blank'>Github Repo</a>. Thanks!
[](https://github.com/sunny2109/SAFMN)
---
📝 **Citation**
If our work is useful for your research, please consider citing:
```bibtex
@inproceedings{sun2023safmn,
title={Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution},
author={Sun, Long and Dong, Jiangxin and Tang, Jinhui and Pan, Jinshan},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2023}
}
```
<center><img src='https://visitor-badge.laobi.icu/badge?page_id=sunny2109/SAFMN' alt='visitors'></center>
"""
demo = gr.Interface(
inference, [
gr.inputs.Image(type="filepath", label="Input"),
gr.inputs.Number(default=2, label="Upscaling factor (up to 4)"),
gr.inputs.Checkbox(default=False, label="Memory-efficient inference"),
gr.inputs.Checkbox(default=False, label="Color correction"),
], [
gr.outputs.Image(type="numpy", label="Output"),
gr.outputs.File(label="Download the output")
],
title=title,
description=description,
article=article,
)
demo.queue(concurrency_count=2)
demo.launch()