Allow test to operate on batches
This commit is contained in:
James Betker
parent
8ead9ae183
commit
e98d92fc77
|
|
@ -21,7 +21,7 @@ def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
|
|||
num_workers=num_workers, sampler=sampler, drop_last=True,
|
||||
pin_memory=False)
|
||||
else:
|
||||
return torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0,
|
||||
return torch.utils.data.DataLoader(dataset, batch_size=12, shuffle=False, num_workers=3,
|
||||
pin_memory=False)
|
||||
|
||||
|
||||
|
|
@ -32,8 +32,9 @@ def create_dataset(dataset_opt):
|
|||
from data.LQ_dataset import LQDataset as D
|
||||
elif mode == 'LQGT':
|
||||
from data.LQGT_dataset import LQGTDataset as D
|
||||
elif mode == 'GTLQ':
|
||||
from data.GTLQ_dataset import GTLQDataset as D
|
||||
# datasets for image corruption
|
||||
elif mode == 'downsample':
|
||||
from data.Downsample_dataset import DownsampleDataset as D
|
||||
# datasets for video restoration
|
||||
elif mode == 'REDS':
|
||||
from data.REDS_dataset import REDSDataset as D
|
||||
|
|
|
|||
168
codes/test.py
168
codes/test.py
|
|
@ -9,97 +9,105 @@ import utils.util as util
|
|||
from data.util import bgr2ycbcr
|
||||
from data import create_dataset, create_dataloader
|
||||
from models import create_model
|
||||
from tqdm import tqdm
|
||||
|
||||
#### options
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_ESRGAN_vrp.yml')
|
||||
opt = option.parse(parser.parse_args().opt, is_train=False)
|
||||
opt = option.dict_to_nonedict(opt)
|
||||
if __name__ == "__main__":
|
||||
#### options
|
||||
want_just_images = True
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_corrupt_vixen_adrianna.yml')
|
||||
opt = option.parse(parser.parse_args().opt, is_train=False)
|
||||
opt = option.dict_to_nonedict(opt)
|
||||
|
||||
util.mkdirs(
|
||||
(path for key, path in opt['path'].items()
|
||||
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
|
||||
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
|
||||
screen=True, tofile=True)
|
||||
logger = logging.getLogger('base')
|
||||
logger.info(option.dict2str(opt))
|
||||
util.mkdirs(
|
||||
(path for key, path in opt['path'].items()
|
||||
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
|
||||
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
|
||||
screen=True, tofile=True)
|
||||
logger = logging.getLogger('base')
|
||||
logger.info(option.dict2str(opt))
|
||||
|
||||
#### Create test dataset and dataloader
|
||||
test_loaders = []
|
||||
for phase, dataset_opt in sorted(opt['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)
|
||||
#### Create test dataset and dataloader
|
||||
test_loaders = []
|
||||
for phase, dataset_opt in sorted(opt['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)
|
||||
|
||||
model = create_model(opt)
|
||||
for test_loader in test_loaders:
|
||||
test_set_name = test_loader.dataset.opt['name']
|
||||
logger.info('\nTesting [{:s}]...'.format(test_set_name))
|
||||
test_start_time = time.time()
|
||||
dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
|
||||
util.mkdir(dataset_dir)
|
||||
model = create_model(opt)
|
||||
for test_loader in test_loaders:
|
||||
test_set_name = test_loader.dataset.opt['name']
|
||||
logger.info('\nTesting [{:s}]...'.format(test_set_name))
|
||||
test_start_time = time.time()
|
||||
dataset_dir = osp.join(opt['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'] = []
|
||||
test_results = OrderedDict()
|
||||
test_results['psnr'] = []
|
||||
test_results['ssim'] = []
|
||||
test_results['psnr_y'] = []
|
||||
test_results['ssim_y'] = []
|
||||
|
||||
for data in test_loader:
|
||||
need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
|
||||
model.feed_data(data, need_GT=need_GT)
|
||||
img_path = data['GT_path'][0] if need_GT else data['LQ_path'][0]
|
||||
img_name = osp.splitext(osp.basename(img_path))[0]
|
||||
tq = tqdm(test_loader)
|
||||
for data in tq:
|
||||
need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
|
||||
model.feed_data(data, need_GT=need_GT)
|
||||
model.test()
|
||||
|
||||
model.test()
|
||||
visuals = model.get_current_visuals(need_GT=need_GT)
|
||||
visuals = model.fake_H.detach().float().cpu()
|
||||
for i in range(visuals.shape[0]):
|
||||
img_path = data['GT_path'][i] if need_GT else data['LQ_path'][i]
|
||||
img_name = osp.splitext(osp.basename(img_path))[0]
|
||||
|
||||
sr_img = util.tensor2img(visuals['rlt']) # uint8
|
||||
sr_img = util.tensor2img(visuals[i]) # uint8
|
||||
|
||||
# save images
|
||||
suffix = opt['suffix']
|
||||
if suffix:
|
||||
save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
|
||||
else:
|
||||
save_img_path = osp.join(dataset_dir, img_name + '.png')
|
||||
util.save_img(sr_img, save_img_path)
|
||||
# save images
|
||||
suffix = opt['suffix']
|
||||
if suffix:
|
||||
save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
|
||||
else:
|
||||
save_img_path = osp.join(dataset_dir, img_name + '.png')
|
||||
util.save_img(sr_img, save_img_path)
|
||||
|
||||
# calculate PSNR and SSIM
|
||||
if need_GT:
|
||||
gt_img = util.tensor2img(visuals['GT'])
|
||||
sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
|
||||
psnr = util.calculate_psnr(sr_img, gt_img)
|
||||
ssim = util.calculate_ssim(sr_img, gt_img)
|
||||
test_results['psnr'].append(psnr)
|
||||
test_results['ssim'].append(ssim)
|
||||
if want_just_images:
|
||||
continue
|
||||
|
||||
if gt_img.shape[2] == 3: # RGB image
|
||||
sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
|
||||
gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)
|
||||
# calculate PSNR and SSIM
|
||||
if need_GT:
|
||||
gt_img = util.tensor2img(visuals['GT'])
|
||||
sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
|
||||
psnr = util.calculate_psnr(sr_img, gt_img)
|
||||
ssim = util.calculate_ssim(sr_img, gt_img)
|
||||
test_results['psnr'].append(psnr)
|
||||
test_results['ssim'].append(ssim)
|
||||
|
||||
psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
|
||||
ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
|
||||
test_results['psnr_y'].append(psnr_y)
|
||||
test_results['ssim_y'].append(ssim_y)
|
||||
logger.info(
|
||||
'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
|
||||
format(img_name, psnr, ssim, psnr_y, ssim_y))
|
||||
else:
|
||||
logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
|
||||
else:
|
||||
logger.info(img_name)
|
||||
if gt_img.shape[2] == 3: # RGB image
|
||||
sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
|
||||
gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)
|
||||
|
||||
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'])
|
||||
psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
|
||||
ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
|
||||
test_results['psnr_y'].append(psnr_y)
|
||||
test_results['ssim_y'].append(ssim_y)
|
||||
logger.info(
|
||||
'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
|
||||
format(img_name, psnr, ssim, psnr_y, ssim_y))
|
||||
else:
|
||||
logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
|
||||
else:
|
||||
logger.info(img_name)
|
||||
|
||||
if not want_just_images and 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(
|
||||
'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
|
||||
format(ave_psnr_y, ave_ssim_y))
|
||||
'----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))
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user