123 lines
4.3 KiB
Python
Executable File
123 lines
4.3 KiB
Python
Executable File
'''
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author ： Hatcher W
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Email： hcwang@semi.ac.cn
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use: 目标检测训练
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voc 里面修改了label，全部都改成了bird
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'''
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import argparse
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import logging
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import os
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import torch
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import torch.distributed as dist
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from ssd.engine.inference import do_evaluation
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from ssd.config import cfg
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from ssd.data.build import make_data_loader
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from ssd.engine.trainer import do_train
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from ssd.modeling.detector import build_detection_model
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from ssd.solver.build import make_optimizer, make_lr_scheduler
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from ssd.utils import dist_util, mkdir
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from ssd.utils.checkpoint import CheckPointer
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from ssd.utils.dist_util import synchronize
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from ssd.utils.logger import setup_logger
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from ssd.utils.misc import str2bool
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os.environ['CUDA_VISIBLE_DEVICES'] = '3'
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def train(cfg, args):
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logger = logging.getLogger('SSD.trainer')
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model = build_detection_model(cfg)
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device = torch.device(cfg.MODEL.DEVICE)
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model.to(device)
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if args.distributed:
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank)
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lr = cfg.SOLVER.LR * args.num_gpus # scale by num gpus
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optimizer = make_optimizer(cfg, model, lr)
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milestones = [step // args.num_gpus for step in cfg.SOLVER.LR_STEPS]
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scheduler = make_lr_scheduler(cfg, optimizer, milestones)
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arguments = {"iteration": 0}
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save_to_disk = dist_util.get_rank() == 0
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checkpointer = CheckPointer(model, optimizer, scheduler, cfg.OUTPUT_DIR, save_to_disk, logger)
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extra_checkpoint_data = checkpointer.load()
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arguments.update(extra_checkpoint_data)
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max_iter = cfg.SOLVER.MAX_ITER // args.num_gpus
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train_loader = make_data_loader(cfg, is_train=True, distributed=args.distributed, max_iter=max_iter, start_iter=arguments['iteration'])
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model = do_train(cfg, model, train_loader, optimizer, scheduler, checkpointer, device, arguments, args)
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return model
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def main():
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parser = argparse.ArgumentParser(description='Single Shot MultiBox Detector Training With PyTorch')
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parser.add_argument(
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"--config-file",
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default="configs/hat-512.yaml",
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metavar="FILE",
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help="path to config file",
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type=str,
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)
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parser.add_argument("--local_rank", type=int, default=0)
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parser.add_argument('--log_step', default=10, type=int, help='Print logs every log_step')
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parser.add_argument('--save_step', default=2500, type=int, help='Save checkpoint every save_step')
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parser.add_argument('--eval_step', default=2500, type=int, help='Evaluate dataset every eval_step, disabled when eval_step < 0')
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parser.add_argument('--use_tensorboard', default=False, type=str2bool)
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parser.add_argument(
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"--skip-test",
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dest="skip_test",
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help="Do not test the final model",
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action="store_true",
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)
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parser.add_argument(
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"opts",
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help="Modify config options using the command-line",
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default=None,
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nargs=argparse.REMAINDER,
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)
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args = parser.parse_args()
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num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
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args.distributed = num_gpus > 1
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args.num_gpus = num_gpus
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print("GPU_num= ",num_gpus)
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if torch.cuda.is_available():
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# This flag allows you to enable the inbuilt cudnn auto-tuner to
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# find the best algorithm to use for your hardware.
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torch.backends.cudnn.benchmark = True
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if args.distributed:
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print("use multi GPU")
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torch.cuda.set_device(args.local_rank)
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torch.distributed.init_process_group(backend="nccl", init_method="env://",world_size=1)
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synchronize()
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(args.opts)
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cfg.freeze()
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if cfg.OUTPUT_DIR:
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mkdir(cfg.OUTPUT_DIR)
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logger = setup_logger("SSD", dist_util.get_rank(), cfg.OUTPUT_DIR)
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logger.info("Using {} GPUs".format(num_gpus))
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logger.info(args)
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logger.info("Loaded configuration file {}".format(args.config_file))
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with open(args.config_file, "r") as cf:
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config_str = "\n" + cf.read()
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logger.info(config_str)
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logger.info("Running with config:\n{}".format(cfg))
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model = train(cfg, args)
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if not args.skip_test:
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logger.info('Start evaluating...')
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torch.cuda.empty_cache() # speed up evaluating after training finished
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do_evaluation(cfg, model, distributed=args.distributed)
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if __name__ == '__main__':
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main()
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