main_test_tempest_drunet.py

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 (cskaizhang@gmail.com)
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: cskaizhang@gmail.com; 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()