else:
optimizer = 'adam'
if 'loss' in param:
loss = param['loss']
else:
loss = {'name': 'mse'}
if output_format.upper() == 'RGB':
export_bgr = False
else:
export_bgr = True
expt = Experiment(scale=param['scale'],
channel=channel,
build_model=build_model,
optimizer=optimizer,
loss=loss,
lr_sub_size=param['lr_sub_size'],
lr_sub_stride=param['lr_sub_stride'],
random=random,
save_dir=Path('./results') / save_dir)
if args.export_only:
expt.export_pb_model(['input_lr'], ['output_hr'],
Path('./results') / save_dir / 'model.pb', export_bgr)
exit()
# Training
expt.train(train_set=param['train_set'],
val_set=param['val_set'],
epochs=param['epochs'],
batch_size=param['batch'],
from toolbox.image import bicubic_resize
# Model
scale = 3
model = espcn(c=1, f1=9, f2=1, f3=5, n1=64, n2=32)
model.summary()
# Data
train_set = '91-image'
val_set = 'Set5'
test_sets = ['Set5', 'Set14']
preprocess = partial(bicubic_resize, size=scale)
load_set = partial(load_set,
sub_size=11,
sub_stride=5,
scale=scale,
channel=0,
preprocess=preprocess)
# Training
experiment = Experiment(scale=scale,
model=model,
preprocess=preprocess,
load_set=load_set,
save_dir='.')
experiment.train(train_set=train_set, val_set=val_set, epochs=500, resume=True)
# Evaluation
for test_set in test_sets:
experiment.test(test_set=test_set)
parser = argparse.ArgumentParser()
parser.add_argument('param_file', type=Path)
args = parser.parse_args()
param = json.load(args.param_file.open())
# Model
scale = param['scale']
build_model = partial(get_model(param['model']['name']),
**param['model']['params'])
if 'optimizer' in param:
optimizer = getattr(optimizers, param['optimizer']['name'].lower())
optimizer = optimizer(**param['optimizer']['params'])
else:
optimizer = 'adam'
# Data
load_set = partial(load_set,
lr_sub_size=param['lr_sub_size'],
lr_sub_stride=param['lr_sub_stride'])
# Training
expt = Experiment(scale=param['scale'], load_set=load_set,
build_model=build_model,optimizer=optimizer,
save_dir=param['save_dir'])
expt.train(train_set=param['train_set'], val_set=param['val_set'],
epochs=param['epochs'], resume=True)
# Evaluation
for test_set in param['test_sets']:
expt.test(test_set=test_set)
# Model
scale = param['scale']
build_model = partial(get_model(param['model']['name']),
**param['model']['params'])
if 'optimizer' in param:
optimizer = getattr(optimizers, param['optimizer']['name'].lower())
optimizer = optimizer(**param['optimizer']['params'])
else:
optimizer = 'adam'
# Data
load_set = partial(load_set_same_size,
lr_sub_size=param['lr_sub_size'],
lr_sub_stride=param['lr_sub_stride'])
# Training
expt = Experiment(scale=param['scale'],
load_set=load_set,
build_model=build_model,
optimizer=optimizer,
save_dir=param['save_dir'])
expt.train(train_set=param['train_set'],
val_set=param['val_set'],
epochs=param['epochs'],
resume=True)
# Evaluation
# for test_set in param['test_sets']:
# expt.test(test_set=test_set)
def get_experiment(parameters):
return Experiment(parameters)