[107], "hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[27], "hccl_world_groupsum2")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[27], "hccl_world_groupsum3")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[27], "hccl_world_groupsum4")
auto_parallel_context().set_all_reduce_fusion_split_indices(
[27], "hccl_world_groupsum5")
init()
epoch_size = config.epoch_size
damping = get_model_damping(0, 0.03, 0.87, 50, 5004)
net = resnet50(class_num=config.class_num,
damping=damping,
loss_scale=config.loss_scale,
frequency=config.frequency)
if not config.label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropy(smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
if args_opt.do_train:
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
repeat_num=epoch_size,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale,
drop_overflow_update=False)
def create_network(name, *args, **kwargs):
if name == 'resnet50_thor':
return resnet50(*args, **kwargs)
raise NotImplementedError(f"{name} is not implemented in the repo")