100 lines
3.1 KiB
Python
Executable File
100 lines
3.1 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: 目标检测测试结果
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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.utils.data
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from ssd.config import cfg
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from ssd.engine.inference import do_evaluation,do_evaluation_byHatcherW
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from ssd.modeling.detector import build_detection_model
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from ssd.utils import dist_util
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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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def evaluation_byHatcherW(cfg, ckpt, distributed) :
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logger = logging.getLogger("SSD.inference")
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model = build_detection_model(cfg)
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checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR, logger=logger)
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device = torch.device(cfg.MODEL.DEVICE)
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model.to(device)
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checkpointer.load(ckpt, use_latest=ckpt is None)
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do_evaluation_byHatcherW(cfg, model, distributed)
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def evaluation(cfg, ckpt, distributed):
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logger = logging.getLogger("SSD.inference")
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model = build_detection_model(cfg)
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checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR, logger=logger)
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device = torch.device(cfg.MODEL.DEVICE)
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model.to(device)
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checkpointer.load(ckpt, use_latest=ckpt is None)
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do_evaluation(cfg, model, distributed)
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def main():
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parser = argparse.ArgumentParser(description='SSD Evaluation on VOC and COCO dataset.')
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parser.add_argument(
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"--config-file",
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default="configs/vgg_ssd300_voc0712.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(
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"--ckpt",
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help="The path to the checkpoint for test, default is the latest checkpoint.",
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default=None,
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type=str,
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)
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parser.add_argument("--output_dir", default="eval_results", type=str, help="The directory to store evaluation results.")
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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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distributed = num_gpus > 1
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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 distributed:
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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://")
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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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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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evaluation_byHatcherW(cfg, ckpt=args.ckpt, distributed=distributed)
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if __name__ == '__main__':
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main()
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