def main():
"""
Starting point of the application
"""
hvd.init()
params = parse_args(PARSER.parse_args())
set_flags(params)
model_dir = prepare_model_dir(params)
params.model_dir = model_dir
logger = get_logger(params)
model = Unet()
dataset = Dataset(data_dir=params.data_dir,
batch_size=params.batch_size,
fold=params.fold,
augment=params.augment,
gpu_id=hvd.rank(),
num_gpus=hvd.size(),
seed=params.seed)
if 'train' in params.exec_mode:
train(params, model, dataset, logger)
if 'evaluate' in params.exec_mode:
if hvd.rank() == 0:
evaluate(params, model, dataset, logger)
if 'predict' in params.exec_mode:
if hvd.rank() == 0:
predict(params, model, dataset, logger)
def main():
params = parse_args()
tf.random.set_seed(params.seed)
tf.get_logger().setLevel(logging.ERROR)
params = setup_horovod(params)
set_flags(params)
model_dir = prepare_model_dir(params)
logger = get_logger(params)
dataset = Dataset(data_dir=params.data_dir,
batch_size=params.batch_size,
fold_idx=params.fold,
n_folds=params.num_folds,
params=params,
seed=params.seed)
estimator = build_estimator(params, model_dir)
if params.tensorboard_logging and (params.worker_id == 0
or params.log_all_workers):
from TensorFlow.common.tb_utils import write_hparams_v1
write_hparams_v1(params.log_dir, vars(params))
if not params.benchmark:
params.max_steps = params.max_steps // params.num_workers
if 'train' in params.exec_mode:
with dump_callback(params.dump_config):
training_hooks = get_hooks(params, logger)
dataset_fn = dataset.synth_train_fn if params.synth_data else dataset.train_fn
estimator.train(input_fn=dataset_fn,
steps=params.max_steps,
hooks=training_hooks)
if 'evaluate' in params.exec_mode:
result = estimator.evaluate(input_fn=dataset.eval_fn,
steps=dataset.eval_size)
data = parse_evaluation_results(result)
if params.worker_id == 0:
logger.log(step=(), data=data)
if 'predict' == params.exec_mode:
inference_hooks = get_hooks(params, logger)
if params.worker_id == 0:
count = 1 if not params.benchmark else 2 * params.warmup_steps * params.batch_size // dataset.test_size
predictions = estimator.predict(input_fn=lambda: dataset.test_fn(
count=count, drop_remainder=params.benchmark),
hooks=inference_hooks)
for idx, p in enumerate(predictions):
volume = p['predictions']
if not params.benchmark:
np.save(
os.path.join(params.model_dir,
"vol_{}.npy".format(idx)), volume)
def main():
"""
Starting point of the application
"""
params = parse_args(description="UNet-medical")
if params.use_horovod:
hvd_init()
set_flags(params)
model_dir = prepare_model_dir(params)
params.model_dir = model_dir
logger = get_logger(params)
tb_logger = None
if params.tensorboard_logging:
log_dir = params.log_dir
if horovod_enabled() and params.log_all_workers:
log_dir = os.path.join(log_dir, f'worker_{hvd_rank()}')
tb_logger = namedtuple('TBSummaryWriters', 'train_writer eval_writer')(
tf.summary.create_file_writer(log_dir),
tf.summary.create_file_writer(os.path.join(log_dir, 'eval')))
model = Unet()
dataset = Dataset(data_dir=params.data_dir,
batch_size=params.batch_size,
fold=params.fold,
augment=params.augment,
hpu_id=hvd_rank() if horovod_enabled() else 0,
num_hpus=hvd_size() if horovod_enabled() else 1,
seed=params.seed)
if 'train' in params.exec_mode:
with dump_callback(params.dump_config):
train(params, model, dataset, logger, tb_logger)
if 'evaluate' in params.exec_mode:
evaluate(params, model, dataset, logger, tb_logger)
if 'predict' in params.exec_mode:
predict(params, model, dataset, logger)
def main():
tf.get_logger().setLevel(logging.ERROR)
hvd.init()
params = PARSER.parse_args()
model_dir = prepare_model_dir(params)
logger = get_logger(params)
dataset = Dataset(data_dir=params.data_dir,
batch_size=params.batch_size,
fold_idx=params.fold,
n_folds=params.num_folds,
params=params)
estimator = build_estimator(params=params, model_dir=model_dir)
max_steps = params.max_steps // (1 if params.benchmark else hvd.size())
if 'train' in params.exec_mode:
training_hooks = get_hooks(params, logger)
estimator.train(input_fn=dataset.train_fn,
steps=max_steps,
hooks=training_hooks)
if 'evaluate' in params.exec_mode:
result = estimator.evaluate(input_fn=dataset.eval_fn,
steps=dataset.eval_size)
data = parse_evaluation_results(result)
if hvd.rank() == 0:
logger.log(step=(), data=data)
if 'predict' == params.exec_mode:
inference_hooks = get_hooks(params, logger)
if hvd.rank() == 0:
count = 1 if not params.benchmark else 2 * params.warmup_steps * params.batch_size // dataset.test_size
predictions = estimator.predict(input_fn=lambda: dataset.test_fn(
count=count, drop_remainder=params.benchmark),
hooks=inference_hooks)
for idx, p in enumerate(predictions):
volume = p['predictions']
if not params.benchmark:
np.save(
os.path.join(params.model_dir,
"vol_{}.npy".format(idx)), volume)
if 'debug_train' == params.exec_mode:
hooks = [hvd.BroadcastGlobalVariablesHook(0)]
if hvd.rank() == 0:
hooks += [
TrainingHook(log_every=params.log_every,
logger=logger,
tensor_names=['total_loss_ref:0']),
ProfilingHook(warmup_steps=params.warmup_steps,
global_batch_size=hvd.size() * params.batch_size,
logger=logger,
mode='train')
]
estimator.train(input_fn=dataset.synth_train_fn,
steps=max_steps,
hooks=hooks)
if 'debug_predict' == params.exec_mode:
if hvd.rank() == 0:
hooks = [
ProfilingHook(warmup_steps=params.warmup_steps,
global_batch_size=params.batch_size,
logger=logger,
mode='inference')
]
count = 2 * params.warmup_steps
predictions = estimator.predict(
input_fn=lambda: dataset.synth_predict_fn(count=count),
hooks=hooks)
for p in predictions:
_ = p['predictions']