def main():
logging.basicConfig(level=logging.INFO)
modules = load_config(MODULES_CONFIG_FILENAME)
arg_parser = build_arg_parser(modules['arg_parser'])
ote_args = vars(arg_parser.get_train_parser(MODEL_TEMPLATE_FILENAME).parse_args())
arg_converter = build_arg_converter(modules['arg_converter'])
train_args = arg_converter.convert_train_args(MODEL_TEMPLATE_FILENAME, ote_args)
# Note that compression args transformer is not applied here,
# since NNCF compression (if it is enabled) will be applied
# later, when the training is finished.
trainer = build_trainer(modules['trainer'])
trainer(**train_args)
if modules.get('compression') and is_compression_enabled_in_template(MODEL_TEMPLATE_FILENAME):
# TODO: think on the case if compression is enabled in template.yaml, but modules does not contain 'compression'
latest_snapshot = trainer.get_latest_snapshot()
if not latest_snapshot:
raise RuntimeError('Cannot find latest snapshot to make compression after training')
compress_args = arg_converter.convert_train_args_to_compress_args(MODEL_TEMPLATE_FILENAME, ote_args)
arg_converter.update_converted_args_to_load_from_snapshot(compress_args, latest_snapshot)
compression_arg_transformer = build_compression_arg_transformer(modules['compression'])
compress_args = compression_arg_transformer.process_args(MODEL_TEMPLATE_FILENAME, compress_args)
compress_trainer = build_trainer(modules['trainer'])
compress_trainer(**compress_args)
def main():
logging.basicConfig(level=logging.INFO)
modules = load_config(MODULES_CONFIG_FILENAME)
arg_parser = build_arg_parser(modules['arg_parser'])
ote_args = vars(arg_parser.get_compression_parser(MODEL_TEMPLATE_FILENAME).parse_args())
if 'compression' not in modules:
raise RuntimeError(f'Cannot make compression for the template that'
f' does not have "compression" field in its modules'
f' file {MODULES_CONFIG_FILENAME}')
if not is_optimisation_enabled_in_template(MODEL_TEMPLATE_FILENAME):
raise RuntimeError(f'Cannot make compression for the template that'
f' does not enable any of compression flags')
arg_converter = build_arg_converter(modules['arg_converter_map'])
compress_args = arg_converter.convert_compress_args(ote_args)
compression_arg_transformer = build_compression_arg_transformer(modules['compression'])
compress_args, is_optimisation_enabled = \
compression_arg_transformer.process_args(MODEL_TEMPLATE_FILENAME, compress_args)
if not is_optimisation_enabled:
logging.warning('Optimization flags are not set -- compression is not made')
return
# Note that compression in this tool will be made by the same trainer,
# as in the tool train.py
# The difference is only in the argparser and in the NNCFConfigTransformer used to
# transform the configuration file.
trainer = build_trainer(modules['trainer'])
trainer(**compress_args)
def main():
logging.basicConfig(level=logging.INFO)
modules = load_config(MODULES_CONFIG_FILENAME)
arg_parser = build_arg_parser(modules['arg_parser'])
ote_args = vars(arg_parser.get_train_parser(MODEL_TEMPLATE_FILENAME).parse_args())
arg_converter = build_arg_converter(modules['arg_converter_map'])
train_args = arg_converter.convert_train_args(ote_args)
# Note that compression args transformer is not applied here,
# since NNCF compression (if it is enabled) will be applied
# later, when the training is finished.
trainer = build_trainer(modules['trainer'])
trainer(**train_args)