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import os |
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from collections import namedtuple |
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import tensorflow as tf |
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from model.unet import Unet |
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from runtime.run import train, evaluate, predict |
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from runtime.setup import get_logger, set_flags, prepare_model_dir |
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from runtime.arguments import parse_args |
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from data_loading.data_loader import Dataset |
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from TensorFlow.common.debug import dump_callback |
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from TensorFlow.common.tb_utils import TimeToTrainKerasHook |
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try: |
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import horovod.tensorflow as hvd |
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except ImportError: |
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hvd = None |
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def main(): |
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""" |
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Starting point of the application |
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""" |
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params = parse_args(description="UNet-medical") |
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if params.use_horovod: |
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if hvd is None: |
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raise RuntimeError( |
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"Problem encountered during Horovod import. Please make sure that habana-horovod package is installed.") |
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hvd.init() |
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set_flags(params) |
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model_dir = prepare_model_dir(params) |
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params.model_dir = model_dir |
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logger = get_logger(params) |
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tb_logger = None |
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ttt_callback = None |
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if params.tensorboard_logging: |
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log_dir = params.log_dir |
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if hvd is not None and hvd.is_initialized() and params.log_all_workers: |
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log_dir = os.path.join(log_dir, f'worker_{hvd.rank()}') |
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tb_logger = namedtuple('TBSummaryWriters', 'train_writer eval_writer')( |
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tf.summary.create_file_writer(log_dir), |
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tf.summary.create_file_writer(os.path.join(log_dir, 'eval'))) |
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ttt_callback = TimeToTrainKerasHook(os.path.join(log_dir, 'eval')) |
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model = Unet(seed=params.seed) |
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dataset = Dataset(data_dir=params.data_dir, |
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batch_size=params.batch_size, |
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fold=params.fold, |
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augment=params.augment, |
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hpu_id=hvd.rank() if hvd is not None and hvd.is_initialized() else 0, |
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num_hpus=hvd.size() if hvd is not None and hvd.is_initialized() else 1, |
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seed=params.seed, |
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gaudi_type=params.gaudi_type) |
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if 'train' in params.exec_mode: |
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with dump_callback(params.dump_config): |
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train(params, model, dataset, logger, tb_logger, ttt_callback) |
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if 'evaluate' in params.exec_mode: |
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evaluate(params, model, dataset, logger, tb_logger, ttt_callback) |
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if 'predict' in params.exec_mode: |
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predict(params, model, dataset, logger) |
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if __name__ == '__main__': |
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main() |
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