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test_IKC.py
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test_IKC.py
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import os.path
import logging
import time
import argparse
from collections import OrderedDict
import numpy as np
import torch
import options.options as option
import utils.util as util
from data.util import bgr2ycbcr
from data import create_dataset, create_dataloader
from models import create_model
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt_F', type=str, required=True, help='Path to options YMAL file.')
parser.add_argument('-opt_P', type=str, required=True, help='Path to options YMAL file.')
parser.add_argument('-opt_C', type=str, required=True, help='Path to options YMAL file.')
opt_F = option.parse(parser.parse_args().opt_F, is_train=False)
opt_P = option.parse(parser.parse_args().opt_P, is_train=False)
opt_C = option.parse(parser.parse_args().opt_C, is_train=False)
opt_F = option.dict_to_nonedict(opt_F)
opt_P = option.dict_to_nonedict(opt_P)
opt_C = option.dict_to_nonedict(opt_C)
#### mkdir and logger
util.mkdirs((path for key, path in opt_P['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
util.mkdirs((path for key, path in opt_C['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt_P['path']['log'], 'test_' + opt_P['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt_P))
logger.info(option.dict2str(opt_C))
#### Create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt_P['datasets'].items()):
test_set = create_dataset(dataset_opt)
test_loader = create_dataloader(test_set, dataset_opt)
logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
test_loaders.append(test_loader)
# load pretrained model by default
model_F = create_model(opt_F)
model_P = create_model(opt_P)
model_C = create_model(opt_C)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']#path opt['']
logger.info('\nTesting [{:s}]...'.format(test_set_name))
test_start_time = time.time()
dataset_dir = os.path.join(opt_P['path']['results_root'], test_set_name)
util.mkdir(dataset_dir)
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
for test_data in test_loader:
single_img_psnr = []
single_img_ssim = []
single_img_psnr_y = []
single_img_ssim_y = []
need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
img_path = test_data['GT_path'][0] if need_GT else test_data['LQ_path'][0]
img_name = os.path.splitext(os.path.basename(img_path))[0]
#### input dataset_LQ
LR_img = test_data['LQ']
# Predictor test
model_P.feed_data(LR_img)
model_P.test()
P_visuals = model_P.get_current_visuals()
est_ker_map = P_visuals['Batch_est_ker_map']
# Corrector test
for step in range(opt_C['step']):
step += 1
# Test SFTMD to produce SR images
model_F.feed_data(test_data, LR_img, est_ker_map)
model_F.test()
F_visuals = model_F.get_current_visuals()
SR_img = F_visuals['Batch_SR']
model_C.feed_data(SR_img, est_ker_map)
model_C.test()
C_visuals = model_C.get_current_visuals()
est_ker_map = C_visuals['Batch_est_ker_map']
sr_img = util.tensor2img(F_visuals['SR']) # uint8
# save images
suffix = opt_P['suffix']
if suffix:
save_img_path = os.path.join(dataset_dir, img_name + suffix + '_' + str(step) + '.png')
else:
save_img_path = os.path.join(dataset_dir, img_name + '_' + str(step) + '.png')
util.save_img(sr_img, save_img_path)
# calculate PSNR and SSIM
if need_GT:
gt_img = util.tensor2img(F_visuals['GT'])
gt_img = gt_img / 255.
sr_img = sr_img / 255.
crop_border = opt_P['crop_border'] if opt_P['crop_border'] else opt_P['scale']
if crop_border == 0:
cropped_sr_img = sr_img
cropped_gt_img = gt_img
else:
cropped_sr_img = sr_img[crop_border:-crop_border, crop_border:-crop_border, :]
cropped_gt_img = gt_img[crop_border:-crop_border, crop_border:-crop_border, :]
psnr = util.calculate_psnr(cropped_sr_img * 255, cropped_gt_img * 255)
ssim = util.calculate_ssim(cropped_sr_img * 255, cropped_gt_img * 255)
#test_results['psnr'].append(psnr)
#test_results['ssim'].append(ssim)
if gt_img.shape[2] == 3: # RGB image
sr_img_y = bgr2ycbcr(sr_img, only_y=True)
gt_img_y = bgr2ycbcr(gt_img, only_y=True)
if crop_border == 0:
cropped_sr_img_y = sr_img_y
cropped_gt_img_y = gt_img_y
else:
cropped_sr_img_y = sr_img_y[crop_border:-crop_border, crop_border:-crop_border]
cropped_gt_img_y = gt_img_y[crop_border:-crop_border, crop_border:-crop_border]
psnr_y = util.calculate_psnr(cropped_sr_img_y * 255, cropped_gt_img_y * 255)
ssim_y = util.calculate_ssim(cropped_sr_img_y * 255, cropped_gt_img_y * 255)
#test_results['psnr_y'].append(psnr_y)
#test_results['ssim_y'].append(ssim_y)
single_img_psnr += psnr
single_img_ssim += ssim
single_img_psnr_y += psnr_y
single_img_ssim_y += ssim_y
logger.info(
'step:{:3d}, img:{:15s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
format(step, img_name, psnr, ssim, psnr_y, ssim_y))
else:
logger.info('step:{:3d}, img:{:15s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(step, img_name, psnr, ssim))
else:
logger.info(img_name)
if need_GT:
max_img_index = np.argmax(single_img_psnr)
test_results['psnr'].append(single_img_psnr[max_img_index])
test_results['ssim'].append(single_img_ssim[max_img_index])
test_results['psnr_y'].append(single_img_psnr_y[max_img_index])
test_results['ssim_y'].append(single_img_ssim_y[max_img_index])
avg_signle_img_psnr = single_img_psnr / step
avg_signle_img_ssim = single_img_ssim / step
avg_signle_img_psnr_y = single_img_psnr_y / step
avg_signle_img_ssim_y = single_img_ssim_y / step
logger.info(
'step:{:3d}, img:{:15s} - average PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
format(step, img_name, avg_signle_img_psnr, avg_signle_img_ssim, avg_signle_img_psnr_y, avg_signle_img_ssim_y))
max_signle_img_psnr = single_img_psnr[max_img_index]
max_signle_img_ssim = single_img_ssim[max_img_index]
max_signle_img_psnr_y = single_img_psnr_y[max_img_index]
max_signle_img_ssim_y = single_img_ssim_y[max_img_index]
logger.info(
'step:{:3d}, img:{:15s} - max PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
format(step, img_name, max_signle_img_psnr, max_signle_img_ssim, max_signle_img_psnr_y, max_signle_img_ssim_y))
if need_GT: # metrics
# Average PSNR/SSIM results
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 results for {}----\n\tPSNR: {:.6f} dB; SSIM: {:.6f}\n'.format(
test_set_name, ave_psnr, ave_ssim))
if test_results['psnr_y'] and test_results['ssim_y']:
ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
logger.info(
'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
format(ave_psnr_y, ave_ssim_y))