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main_test_tempest_drunet.py
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main_test_tempest_drunet.py
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import os.path
import logging
import argparse
import numpy as np
from datetime import datetime
from collections import OrderedDict
# from scipy.io import loadmat
import torch
from utils import utils_logger
from utils import utils_model
from utils import utils_image as util
from utils import utils_option as option
from models.select_model import define_Model
'''
Spyder (Python 3.6)
PyTorch 1.1.0
Windows 10 or Linux
Kai Zhang ([email protected])
github: https://github.com/cszn/KAIR
https://github.com/cszn/DnCNN
@article{zhang2017beyond,
title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising},
author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
journal={IEEE Transactions on Image Processing},
volume={26},
number={7},
pages={3142--3155},
year={2017},
publisher={IEEE}
}
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: [email protected]; github: https://github.com/cszn)
by Kai Zhang (12/Dec./2019)
'''
"""
# --------------------------------------------
|--model_zoo # model_zoo
|--dncnn_15 # model_name
|--dncnn_25
|--dncnn_50
|--dncnn_gray_blind
|--dncnn_color_blind
|--dncnn3
|--testset # testsets
|--set12 # testset_name
|--bsd68
|--cbsd68
|--results # results
|--set12_dncnn_15 # result_name = testset_name + '_' + model_name
|--set12_dncnn_25
|--bsd68_dncnn_15
# --------------------------------------------
"""
def main(json_path='options/train_drunet.json'):
# ----------------------------------------
# Preparation
# ----------------------------------------
parser = argparse.ArgumentParser()
parser.add_argument('--testsetH_name', type=str, default='web_images_test', help='test set, bsd68 | set12')
parser.add_argument('--testsetL_name', type=str, default='simulations', help='test set, bsd68 | set12')
parser.add_argument('--noise_level_img', type=int, default=15, help='noise level: 15, 25, 50')
parser.add_argument('--x8', type=bool, default=False, help='x8 to boost performance')
parser.add_argument('--show_img', type=bool, default=False, help='show the image')
parser.add_argument('--model_pool', type=str, default='model_zoo', help='path of model_zoo')
parser.add_argument('--testsets', type=str, default='testsets', help='path of testing folder')
parser.add_argument('--results', type=str, default='results', help='path of results')
parser.add_argument('--need_degradation', type=bool, default=True, help='add noise or not')
parser.add_argument('--task_current', type=str, default='dn', help='dn for denoising, fixed!')
parser.add_argument('--sf', type=int, default=1, help='unused for denoising')
parser.add_argument('--opt', type=str, default=json_path, help='Path to option JSON file.')
args = parser.parse_args()
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
opt = option.parse(args.opt, is_train=False)
border = 0
n_channels = 3
# --<--<--<--<--<--<--<--<--<--<--<--<--<-
# ----------------------------------------
# return None for missing key
# ----------------------------------------
opt = option.dict_to_nonedict(opt)
# if 'color' in args.model_name:
# n_channels = 3 # fixed, 1 for grayscale image, 3 for color image
# else:
# n_channels = 1 # fixed for grayscale image
# if args.model_name in ['dncnn_gray_blind', 'dncnn_color_blind', 'dncnn3']:
# nb = 20 # fixed
# else:
# nb = 17 # fixed
model_path = opt['path']['pretrained_netG']
pretrain_name = model_path.split('/')[-1].split('.')[0]
model_name = 'DRUNet_'+pretrain_name
result_name = f'eval_std{args.noise_level_img}_{model_name}' # fixed
border = args.sf if args.task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
# model_path = os.path.join(args.model_pool, args.model_name+'.pth')
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(args.testsets, args.testsetL_name) # L_path, for Low-quality images
H_path = os.path.join(args.testsets, args.testsetH_name) # H_path, for High-quality images
E_path = os.path.join(args.results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
if H_path == L_path:
args.need_degradation = True
logger_name = result_name
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
need_H = True if H_path is not None else False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
# from models.network_dncnn import DnCNN as net
# model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=nb, act_mode='R')
# # model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=nb, act_mode='BR') # use this if BN is not merged by utils_bnorm.merge_bn(model)
# model.load_state_dict(torch.load(model_path), strict=True)
model = define_Model(opt)
model.load()
# model.eval()
# for k, v in model.named_parameters():
# v.requires_grad = False
# model = model.to(device)
# logger.info('Model path: {:s}'.format(model_path))
# number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
# logger.info('Params number: {}'.format(number_parameters))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
logger.info('model_name:{}, image sigma:{}'.format(model_name, args.noise_level_img))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
H_paths = util.get_image_paths(H_path) if need_H else None
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
# logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
img_L = util.imread_uint(img, n_channels=n_channels)[:,:,:2]
img_L = util.uint2single(img_L)
if args.need_degradation: # degradation process
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, args.noise_level_img/255., img_L.shape)
util.imshow(util.single2uint(img_L), title='Noisy image with noise level {}'.format(args.noise_level_img)) if args.show_img else None
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
if not args.x8:
model.feed_data({'L':img_L}, need_H=False)
model.test()
visuals = model.current_visuals(need_H=False)
img_E = visuals['E']
else:
img_E = utils_model.test_mode(model, img_L, mode=3)
# img_E = util.tensor2uint(img_E)[:,:,0]
img_E = util.tensor2uint(img_E)
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
img_H = np.mean(img_H, axis=2)
img_H = img_H.squeeze()
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim))
util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if args.show_img else None
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name+ext))
if need_H:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
logger.info('Average PSNR/SSIM - {} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, ave_psnr, ave_ssim))
if __name__ == '__main__':
main()