def training():
c = get_config()
dataset_name = 'Task04_Hippocampus'
# dataset_name = 'Task02_Heart'
# download_dataset(dest_path=c.data_root_dir, dataset=dataset_name, id=c.google_drive_id)
# c.do_load_checkpoint = True
# c.checkpoint_dir = c.base_dir + '/20190801-_unet_experiment' + '/checkpoint/checkpoint_current'
# c.checkpoint_file = "checkpoint_last.pth.tar"
if not exists(os.path.join(os.path.join(c.data_root_dir, dataset_name), 'preprocessed')):
print('Preprocessing data. [STARTED]')
preprocess_data(root_dir=os.path.join(c.data_root_dir, dataset_name))
create_splits(output_dir=c.split_dir, image_dir=c.data_dir)
print('Preprocessing data. [DONE]')
else:
print('The data has already been preprocessed. It will not be preprocessed again. Delete the folder to enforce it.')
exp = UNetExperiment(config=c, name='unet_experiment', n_epochs=c.n_epochs,
seed=42, append_rnd_to_name=c.append_rnd_string) # visdomlogger_kwargs={"auto_start": c.start_visdom}
exp.run()
exp.run_test(setup=False)
c = get_config()
#download_dataset(dest_path=c.data_root_dir, dataset=c.dataset_name, id=c.google_drive_id)
if not exists(
os.path.join(os.path.join(c.data_root_dir, c.dataset_name),
'preprocessed')):
print('Preprocessing data. [STARTED]')
#preprocess_data(root_dir=os.path.join(c.data_root_dir, c.dataset_name), y_shape=c.patch_size, z_shape=c.patch_size)
#create_splits(output_dir=c.split_dir, image_dir=c.data_dir)
print('Preprocessing data. [DONE]')
else:
print(
'The data has already been preprocessed. It will not be preprocessed again. Delete the folder to enforce it.'
)
exp = UNetExperiment(
config=c,
name=c.name,
n_epochs=c.n_epochs,
seed=42,
append_rnd_to_name=c.append_rnd_string,
globs=globals(),
# visdomlogger_kwargs={"auto_start": c.start_visdom},
loggers={"visdom": ("visdom", {
"auto_start": c.start_visdom
})})
exp.run()
exp.run_test(setup=False)