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import numpy as np
import os
import argparse
from tqdm import tqdm
import torch
import torch.nn as nn
from skimage import img_as_ubyte
import utils
from basicsr.models.archs.mairunet_arch import MaIRUNet
import scipy.io as sio
parser = argparse.ArgumentParser(description='Real Image Denoising')
parser.add_argument('--input_dir', default='/xlearning/boyun/datasets/RealDN/val/', type=str, help='Directory of validation images')
parser.add_argument('--result_dir', default='/xlearning/boyun/codes/MaIR/realDenoising/results/Real_Denoising/SIDD/', type=str, help='Directory for results')
parser.add_argument('--weights', default='/xlearning/boyun/codes/MaIR/realDenoising/experiments/MaIR_RealDN/models/MaIR_RealDN.pth', type=str, help='Path to weights')
parser.add_argument('--save_images', action='store_true', help='Save denoised images in result directory')
args = parser.parse_args()
####### Load yaml #######
opt_str = r"""
type: MaIRUNet
inp_channels: 3
out_channels: 3
dim: 48
num_blocks: [4, 6, 6, 8]
num_refinement_blocks: 4
ssm_ratio: 2.0
flp_ratio: 4.0
mlp_ratio: 1.5
bias: False
dual_pixel_task: False
img_size: 128
scan_len: 4
batch_size: 8
dynamic_ids: False
"""
import yaml
x = yaml.safe_load(opt_str)
s = x.pop('type')
##########################
result_dir_mat = os.path.join(args.result_dir, 'mat')
os.makedirs(result_dir_mat, exist_ok=True)
if args.save_images:
result_dir_png = os.path.join(args.result_dir, 'png')
os.makedirs(result_dir_png, exist_ok=True)
model_restoration = MaIRUNet(**x)
device = torch.device('cuda:7')
# torch.cuda.set_device(7)
checkpoint = torch.load(args.weights, map_location=device)
model_restoration.load_state_dict(checkpoint['params'])
print("===>Testing using weights: ",args.weights)
model_restoration.cuda()
model_restoration = nn.DataParallel(model_restoration)
model_restoration.eval()
# Process data
filepath = os.path.join(args.input_dir, 'ValidationNoisyBlocksSrgb.mat')
img = sio.loadmat(filepath)
Inoisy = np.float32(np.array(img['ValidationNoisyBlocksSrgb']))
Inoisy /=255.
restored = np.zeros_like(Inoisy)
with torch.no_grad():
for i in tqdm(range(40)):
for k in range(32):
noisy_patch = torch.from_numpy(Inoisy[i,k,:,:,:]).unsqueeze(0).permute(0,3,1,2).cuda()
restored_patch = model_restoration(noisy_patch)
restored_patch = torch.clamp(restored_patch,0,1).cpu().detach().permute(0, 2, 3, 1).squeeze(0)
restored[i,k,:,:,:] = restored_patch
if args.save_images:
save_file = os.path.join(result_dir_png, '%04d_%02d.png'%(i+1,k+1))
utils.save_img(save_file, img_as_ubyte(restored_patch))
# save denoised data
sio.savemat(os.path.join(result_dir_mat, 'Idenoised.mat'), {"Idenoised": restored,})
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