first commit

detection_test.py

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+'''
+author ：  Hatcher W
+Email： hcwang@semi.ac.cn
+use: 目标检测测试结果
+
+'''
+import argparse
+import logging
+import os
+
+import torch
+import torch.utils.data
+
+from ssd.config import cfg
+from ssd.engine.inference import do_evaluation,do_evaluation_byHatcherW
+from ssd.modeling.detector import build_detection_model
+from ssd.utils import dist_util
+from ssd.utils.checkpoint import CheckPointer
+from ssd.utils.dist_util import synchronize
+from ssd.utils.logger import setup_logger
+
+
+def evaluation_byHatcherW(cfg, ckpt, distributed) :
+    logger = logging.getLogger("SSD.inference")
+
+    model = build_detection_model(cfg)
+    checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR, logger=logger)
+    device = torch.device(cfg.MODEL.DEVICE)
+    model.to(device)
+    checkpointer.load(ckpt, use_latest=ckpt is None)
+    do_evaluation_byHatcherW(cfg, model, distributed)
+def evaluation(cfg, ckpt, distributed):
+    logger = logging.getLogger("SSD.inference")
+
+    model = build_detection_model(cfg)
+    checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR, logger=logger)
+    device = torch.device(cfg.MODEL.DEVICE)
+    model.to(device)
+    checkpointer.load(ckpt, use_latest=ckpt is None)
+    do_evaluation(cfg, model, distributed)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='SSD Evaluation on VOC and COCO dataset.')
+    parser.add_argument(
+        "--config-file",
+        default="configs/vgg_ssd300_voc0712.yaml",
+        metavar="FILE",
+        help="path to config file",
+        type=str,
+    )
+    parser.add_argument("--local_rank", type=int, default=0)
+    parser.add_argument(
+        "--ckpt",
+        help="The path to the checkpoint for test, default is the latest checkpoint.",
+        default=None,
+        type=str,
+    )
+
+    parser.add_argument("--output_dir", default="eval_results", type=str, help="The directory to store evaluation results.")
+
+    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
+    distributed = num_gpus > 1
+
+    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 distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend="nccl", init_method="env://")
+        synchronize()
+
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+
+    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))
+    evaluation_byHatcherW(cfg, ckpt=args.ckpt, distributed=distributed)
+
+
+if __name__ == '__main__':
+    main()