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import datetime
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import argparse, importlib
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from pytorch_lightning import seed_everything
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import torch
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import torch.distributed as dist
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def setup_dist(local_rank):
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if dist.is_initialized():
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return
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torch.cuda.set_device(local_rank)
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torch.distributed.init_process_group('nccl', init_method='env://')
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def get_dist_info():
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if dist.is_available():
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initialized = dist.is_initialized()
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else:
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initialized = False
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if initialized:
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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else:
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rank = 0
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world_size = 1
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return rank, world_size
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if __name__ == '__main__':
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now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
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parser = argparse.ArgumentParser()
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parser.add_argument("--module", type=str, help="module name", default="inference")
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parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0)
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args, unknown = parser.parse_known_args()
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inference_api = importlib.import_module(args.module, package=None)
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inference_parser = inference_api.get_parser()
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inference_args, unknown = inference_parser.parse_known_args()
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seed_everything(inference_args.seed)
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setup_dist(args.local_rank)
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torch.backends.cudnn.benchmark = True
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rank, gpu_num = get_dist_info()
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print("@DynamiCrafter Inference [rank%d]: %s"%(rank, now))
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inference_api.run_inference(inference_args, gpu_num, rank) |
