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@ -124,15 +124,6 @@ def parse_args():
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args.data_root = os.path.join(args.data_dir, 'train2014')
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args.annFile = os.path.join(args.data_dir, 'annotations/instances_train2014.json')
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# init distributed
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if args.is_distributed:
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if args.device_target == "Ascend":
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init()
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else:
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init("nccl")
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args.rank = get_rank()
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args.group_size = get_group_size()
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# select for master rank save ckpt or all rank save, compatiable for model parallel
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args.rank_save_ckpt_flag = 0
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if args.is_save_on_master:
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@ -161,6 +152,14 @@ def train():
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devid = int(os.getenv('DEVICE_ID', '0'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target=args.device_target, save_graphs=True, device_id=devid)
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# init distributed
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if args.is_distributed:
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if args.device_target == "Ascend":
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init()
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else:
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init("nccl")
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args.rank = get_rank()
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args.group_size = get_group_size()
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if args.need_profiler:
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from mindspore.profiler.profiling import Profiler
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profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
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