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test_denoiser.py
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test_denoiser.py
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import numpy as np
import os
import argparse
from tqdm import tqdm
import torch.nn as nn
import torch
from torch.utils.data import DataLoader
import torch.nn.functional as F
import scipy.io as sio
from networks.MIRNet_model import MIRNet
from networks.MPRNet import MPRNet
from networks.RIDNet.ridnet import RIDNET
from dataloaders.data_rgb import get_validation_data_real
import utils
from skimage import img_as_ubyte
parser = argparse.ArgumentParser(description='noise_removal')
## --------------- input dir ----------------------------
# parser.add_argument('--input_dir', default='./datasets/SIDD/',
# type=str, help='Directory of validation images')
# parser.add_argument('--input_dir', default='./datasets/Nam/',
# type=str, help='Directory of validation images')
parser.add_argument('--input_dir', default='./datasets/PolyU/',
type=str, help='Directory of validation images')
# --------------------- selected method -----------------------
parser.add_argument('--method', default='no_model',
type=str, help='Model for testing')
# ----------------------- output dir ---------------------------
# parser.add_argument('--result_dir', default='./results/SIDD/noise_removal/',
# type=str, help='Directory for results')
# parser.add_argument('--result_dir', default='./results/Nam/noise_removal/',
# type=str, help='Directory for results')
parser.add_argument('--result_dir', default='./results/PolyU/noise_removal/',
type=str, help='Directory for results')
# ------------------------ weights dir ----------------------------
# parser.add_argument('--weights', default='./checkpoints_MIRNet/model_best.pth',
# type=str, help='Path to weights')
# parser.add_argument('--weights', default='/data/hxw/MIRNet_Codes/checkpoints_RIDNet_Nam/Denoising/models/RIDNet/model_best.pth',
# type=str, help='Path to weights')
parser.add_argument('--weights', default='./pre-trained/MIRNet_polyu.pth',
type=str, help='Path to weights')
# parser.add_argument('--weights', default='/data/hxw/MIRNet_Codes/checkpoints_finetune_MIRNet_official/Denoising/models/MIRNet/model_best.pth',
# type=str, help='Path to weights')
parser.add_argument('--gpus', default='0', type=str, help='CUDA_VISIBLE_DEVICES')
parser.add_argument('--bs', default=1, type=int, help='Batch size for dataloader')
parser.add_argument('--save_images', default=1, action='store_true', help='Save denoised images in result directory')
args = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
utils.mkdir(args.result_dir)
test_dataset = get_validation_data_real(args.input_dir)
test_loader = DataLoader(dataset=test_dataset, batch_size=args.bs, shuffle=False, num_workers=12, drop_last=False)
# model_restoration = SIMNet()
if args.method == 'mirnet':
model_restoration = MIRNet()
if args.method == 'ridnet':
model_restoration = RIDNET()
# model_restoration = RIDNET()
utils.load_checkpoint(model_restoration,args.weights)
print("===>Testing using weights: ", args.weights)
model_restoration.cuda()
model_restoration=nn.DataParallel(model_restoration)
model_restoration.eval()
with torch.no_grad():
psnr_val_rgb = []
ssim_val_rgb = []
for ii, data_test in enumerate(tqdm(test_loader), 0):
rgb_gt = data_test[0].cuda()
rgb_noisy = data_test[1].cuda()
filenames = data_test[2]
rgb_restored = model_restoration(rgb_noisy)
# rgb_restored = rgb_restored[0] # for MPRNet
rgb_restored = torch.clamp(rgb_restored,0,1)
psnr_val_rgb.append(utils.batch_PSNR(rgb_restored, rgb_gt, 1.))
ssim_val_rgb.append(utils.batch_SSIM(rgb_restored, rgb_gt))
rgb_gt = rgb_gt.permute(0, 2, 3, 1).cpu().detach().numpy()
rgb_noisy = rgb_noisy.permute(0, 2, 3, 1).cpu().detach().numpy()
rgb_restored = rgb_restored.permute(0, 2, 3, 1).cpu().detach().numpy()
if args.save_images:
for batch in range(len(rgb_gt)):
denoised_img = img_as_ubyte(rgb_restored[batch])
utils.save_img(args.result_dir + filenames[batch][:-4] + '.png', denoised_img)
psnr_val_rgb = sum(psnr_val_rgb)/len(psnr_val_rgb)
ssim_val_rgb = sum(ssim_val_rgb)/len(ssim_val_rgb)
print("PSNR: %.2f " %(psnr_val_rgb))
print("SSIM: %.3f " %(ssim_val_rgb))