def main():
enable_gpu_if_possible()
parser = argparse.ArgumentParser(description='Train networks.')
parser.add_argument('-s',
'--simple',
action='store_true',
help='Train simple conv net.')
parser.add_argument('-u',
'--unet',
action='store_true',
help='Train Unet net.')
parser.add_argument('-g',
'--gan',
action='store_true',
help='Train GAN net.')
parser.add_argument('training_name')
args = parser.parse_args()
if args.simple:
train(Models.SIMPLE_CONV, args.training_name)
if args.unet:
train(Models.UNET, args.training_name)
if args.gan:
train(Models.GAN, args.training_name)
def main():
enable_gpu_if_possible()
parser = argparse.ArgumentParser(description='Train networks.')
model_selection = parser.add_mutually_exclusive_group()
model_selection.add_argument('-s',
'--simple',
action='store_true',
help='Train simple conv net.')
model_selection.add_argument('-u',
'--unet',
action='store_true',
help='Train Unet et.')
model_selection.add_argument('-g',
'--gan',
action='store_true',
help='Train GAN net.')
parser.add_argument('weights_path')
parser.add_argument('output_dir')
args = parser.parse_args()
weights_path = Path(args.weights_path)
output_dir = Path(args.output_dir)
if args.simple:
test(Models.SIMPLE_CONV, weights_path, output_dir)
elif args.unet:
test(Models.UNET, weights_path, output_dir)
elif args.gan:
test(Models.GAN, weights_path, output_dir)
def main():
enable_gpu_if_possible()
parser = argparse.ArgumentParser(
description='Find best GAN weights in dir.')
parser.add_argument('weights_dir_path')
args = parser.parse_args()
weights_dir_path = Path(args.weights_dir_path)
find_best_gan(weights_dir_path)
def main():
enable_gpu_if_possible()
parser = argparse.ArgumentParser(description='Export Sentinel-2 dataset.')
model_selection = parser.add_mutually_exclusive_group()
model_selection.add_argument('-s', '--simple', action='store_true',
help='Export using simple conv net.')
model_selection.add_argument('-u', '--unet', action='store_true',
help='Export using Unet net.')
model_selection.add_argument('-g', '--gan', action='store_true',
help='Export using GAN net.')
parser.add_argument('-n', '--noise', action='store_true',
help='Add noise to HR before augmentation.')
parser.add_argument('-m', '--match_hist', action='store_true',
help='Match HR hist with real LR before augmentation.')
parser.add_argument('-d', '--demo', action='store_true',
help='Don\'t export dataset, demo inference.')
parser.add_argument('weights_path')
args = parser.parse_args()
if args.simple:
model_type = Models.SIMPLE_CONV
elif args.unet:
model_type = Models.UNET
elif args.gan:
model_type = Models.GAN
params = make_params(Path('params.yaml'), model_type)
weights_path = Path(args.weights_path)
add_noise = args.noise
match_hist = args.match_hist
if args.weights_path != '':
model = make_model(
model_type,
params['load']['input_shape'],
use_lr_masks=False)
model.load_weights(args.weights_path)
else:
model = model_transform_to_lr_bicubic
if args.demo:
demo_hr_path = Path(
'data/'
'proba-v_registered_b/'
'train/'
'NIR/'
'imgset0648/'
'HR000.png')
demo_export(model, demo_hr_path, add_noise)
else:
unregisterd_proba_path = Path('data/proba-v')
registerd_proba_path = Path('data/proba-v_registered_b')
if args.weights_path != '':
suffix = str(weights_path.stem)
else:
suffix = 'bicubic'
transform_proba_dataset_3xlrs(
model, unregisterd_proba_path, registerd_proba_path, suffix, add_noise, match_hist)
def main():
enable_gpu_if_possible()
parser = argparse.ArgumentParser(description='Export Sentinel-2 dataset.')
model_selection = parser.add_mutually_exclusive_group()
model_selection.add_argument('-s',
'--simple',
action='store_true',
help='Export using simple conv net.')
model_selection.add_argument('-u',
'--unet',
action='store_true',
help='Export using Unet net.')
model_selection.add_argument('-g',
'--gan',
action='store_true',
help='Export using GAN net.')
parser.add_argument('-r',
'--random_translations',
action='store_true',
help='Generate random LR translations instead of using'
' transaltions file.')
parser.add_argument('-d',
'--demo',
action='store_true',
help='Don\'t export dataset, demo inference.')
parser.add_argument('weights_path')
args = parser.parse_args()
if args.simple:
model_type = Models.SIMPLE_CONV
elif args.unet:
model_type = Models.UNET
elif args.gan:
model_type = Models.GAN
params = make_params(Path('params.yaml'), model_type)
weights_path = Path(args.weights_path)
model = make_model(model_type,
params['load']['input_shape'],
use_lr_masks=False)
model.load_weights(args.weights_path)
if args.demo:
demo_hr_path = Path(
'data/'
'sentinel-2_artificial/'
'S2B_MSIL1C_20200806T105619_N0209_R094_T30TWP_20200806T121751/'
'08280x10440/'
'b8/'
'hr.png')
demo_export(model, demo_hr_path)
else:
sentinel_root_path = Path('data/sentinel-2_artificial/')
suffix = str(weights_path.stem)
transform_sentinel_dataset_3xlrs(model, sentinel_root_path, suffix,
args.random_translations)