157 lines
5.8 KiB
Python
Executable file
157 lines
5.8 KiB
Python
Executable file
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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# Set up custom environment before nearly anything else is imported
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# NOTE: this should be the first import (no not reorder)
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from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
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import argparse
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import os
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import torch
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from maskrcnn_benchmark.config import cfg
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from maskrcnn_benchmark.data import make_data_loader
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from maskrcnn_benchmark.engine.inference import inference
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from maskrcnn_benchmark.modeling.detector import build_detection_model
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from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
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from maskrcnn_benchmark.utils.collect_env import collect_env_info
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from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process
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from maskrcnn_benchmark.utils.logger import setup_logger
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from maskrcnn_benchmark.utils.miscellaneous import mkdir
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from maskrcnn_benchmark.utils.logger import format_step
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import dllogger
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def main():
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parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
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parser.add_argument(
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"--config-file",
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default="/workspace/object_detection/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml",
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metavar="FILE",
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help="path to config file",
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)
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parser.add_argument("--local_rank", type=int, default=os.getenv('LOCAL_RANK', 0))
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parser.add_argument("--json-summary",
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help="Out file for DLLogger",
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default="dllogger_inference.out",
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type=str)
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parser.add_argument(
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"--skip-eval",
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dest="skip_eval",
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help="Do not eval the predictions",
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action="store_true",
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)
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parser.add_argument(
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"--fp16",
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help="Mixed precision training",
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action="store_true",
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)
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parser.add_argument(
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"--amp",
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help="Mixed precision training",
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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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args.fp16 = args.fp16 or args.amp
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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 distributed:
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torch.cuda.set_device(args.local_rank)
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torch.distributed.init_process_group(
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backend="nccl", init_method="env://"
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)
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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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save_dir = ""
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logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
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if is_main_process():
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dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
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filename=args.json_summary),
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dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
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else:
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dllogger.init(backends=[])
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save_dir = ""
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dllogger.log(step="PARAMETER", data={"config":cfg})
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dllogger.log(step="PARAMETER", data={"gpu_count": num_gpus})
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# dllogger.log(step="PARAMETER", data={"env_info": collect_env_info()})
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model = build_detection_model(cfg)
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model.to(cfg.MODEL.DEVICE)
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# Initialize mixed-precision
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if args.fp16:
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use_mixed_precision = True
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else:
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use_mixed_precision = cfg.DTYPE == "float16"
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output_dir = cfg.OUTPUT_DIR
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checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
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_ = checkpointer.load(cfg.MODEL.WEIGHT)
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iou_types = ("bbox",)
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if cfg.MODEL.MASK_ON:
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iou_types = iou_types + ("segm",)
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output_folders = [None] * len(cfg.DATASETS.TEST)
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dataset_names = cfg.DATASETS.TEST
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if cfg.OUTPUT_DIR:
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for idx, dataset_name in enumerate(dataset_names):
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output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
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mkdir(output_folder)
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output_folders[idx] = output_folder
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data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
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results = []
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for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
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if use_mixed_precision:
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with torch.cuda.amp.autocast():
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result = inference(
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model,
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data_loader_val,
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dataset_name=dataset_name,
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iou_types=iou_types,
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box_only=cfg.MODEL.RPN_ONLY,
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device=cfg.MODEL.DEVICE,
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expected_results=cfg.TEST.EXPECTED_RESULTS,
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expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
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output_folder=output_folder,
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skip_eval=args.skip_eval,
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dllogger=dllogger,
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)
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else:
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result = inference(
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model,
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data_loader_val,
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dataset_name=dataset_name,
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iou_types=iou_types,
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box_only=cfg.MODEL.RPN_ONLY,
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device=cfg.MODEL.DEVICE,
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expected_results=cfg.TEST.EXPECTED_RESULTS,
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expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
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output_folder=output_folder,
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skip_eval=args.skip_eval,
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dllogger=dllogger,
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)
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synchronize()
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results.append(result)
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if is_main_process() and not args.skip_eval:
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map_results, raw_results = results[0]
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bbox_map = map_results.results["bbox"]['AP']
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segm_map = map_results.results["segm"]['AP']
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dllogger.log(step=tuple(), data={"BBOX_mAP": bbox_map, "MASK_mAP": segm_map})
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if __name__ == "__main__":
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main()
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dllogger.log(step=tuple(), data={})
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