first commit

detection_train.py

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