226 строки
7.4 KiB
Python
226 строки
7.4 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
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r"""
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Basic training script for PyTorch
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"""
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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 scene_graph_benchmark.config import sg_cfg
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from maskrcnn_benchmark.data import make_data_loader
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from maskrcnn_benchmark.data.datasets.utils.load_files import config_dataset_file
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from maskrcnn_benchmark.solver import make_lr_scheduler
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from maskrcnn_benchmark.solver import make_optimizer, make_optimizer_d2
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from maskrcnn_benchmark.engine.inference import inference
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from maskrcnn_benchmark.engine.trainer import do_train
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from maskrcnn_benchmark.modeling.detector import build_detection_model
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from scene_graph_benchmark.scene_parser import SceneParser
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from scene_graph_benchmark.AttrRCNN import AttrRCNN
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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
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from maskrcnn_benchmark.utils.imports import import_file
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from maskrcnn_benchmark.utils.logger import setup_logger
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from maskrcnn_benchmark.utils.metric_logger import MetricLogger
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from maskrcnn_benchmark.utils.miscellaneous import mkdir, save_config
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from tools.test_sg_net import run_test
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import random
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import numpy as np
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torch.manual_seed(1000)
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torch.cuda.manual_seed(1000)
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torch.cuda.manual_seed_all(1000) # if you are using multi-GPU.
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np.random.seed(1000) # Numpy module.
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random.seed(1000) # Python random module.
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torch.manual_seed(1000)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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def train(cfg, local_rank, distributed):
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if cfg.MODEL.META_ARCHITECTURE == "SceneParser":
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model = SceneParser(cfg)
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elif cfg.MODEL.META_ARCHITECTURE == "AttrRCNN":
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model = AttrRCNN(cfg)
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device = torch.device(cfg.MODEL.DEVICE)
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model.to(device)
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if cfg.MODEL.BACKBONE.CONV_BODY.startswith("ViL"):
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optimizer = make_optimizer_d2(cfg, model)
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else:
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optimizer = make_optimizer(cfg, model)
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scheduler = make_lr_scheduler(cfg, optimizer)
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if distributed:
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[local_rank], output_device=local_rank,
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# this should be removed if we update BatchNorm stats
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broadcast_buffers=False, find_unused_parameters=True
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)
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arguments = {}
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arguments["iteration"] = 0
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output_dir = cfg.OUTPUT_DIR
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save_to_disk = get_rank() == 0
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checkpointer = DetectronCheckpointer(
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cfg, model, optimizer, scheduler, output_dir, save_to_disk
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)
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extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
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arguments.update(extra_checkpoint_data)
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data_loader = make_data_loader(
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cfg,
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is_train=True,
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is_distributed=distributed,
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start_iter=arguments["iteration"],
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)
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test_period = cfg.SOLVER.TEST_PERIOD
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if test_period > 0:
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data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed, is_for_period=True)
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else:
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data_loader_val = None
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checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
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meters = MetricLogger(delimiter=" ")
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do_train(
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cfg,
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model,
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data_loader,
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data_loader_val,
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optimizer,
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scheduler,
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checkpointer,
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device,
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checkpoint_period,
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test_period,
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arguments,
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meters
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)
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return model
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def run_test(cfg, model, distributed):
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if distributed:
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model = model.module
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torch.cuda.empty_cache() # TODO check if it helps
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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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if cfg.MODEL.KEYPOINT_ON:
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iou_types = iou_types + ("keypoints",)
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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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labelmap_file = config_dataset_file(cfg.DATA_DIR, cfg.DATASETS.LABELMAP_FILE)
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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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inference(
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model,
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cfg,
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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=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
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bbox_aug=cfg.TEST.BBOX_AUG.ENABLED,
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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_performance_eval=cfg.TEST.SKIP_PERFORMANCE_EVAL,
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labelmap_file=labelmap_file,
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save_predictions=cfg.TEST.SAVE_PREDICTIONS,
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)
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synchronize()
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def main():
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parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
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parser.add_argument(
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"--config-file",
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default="",
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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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"--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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if args.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.set_new_allowed(True)
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cfg.merge_from_other_cfg(sg_cfg)
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cfg.set_new_allowed(False)
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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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output_dir = cfg.OUTPUT_DIR
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if output_dir:
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mkdir(output_dir)
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logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
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logger.info("Using {} GPUs".format(num_gpus))
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logger.info(args)
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logger.info("Collecting env info (might take some time)")
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logger.info("\n" + collect_env_info())
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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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output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml')
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logger.info("Saving config into: {}".format(output_config_path))
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# save overloaded model config in the output directory
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save_config(cfg, output_config_path)
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model = train(cfg, args.local_rank, args.distributed)
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if not args.skip_test:
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run_test(cfg, model, args.distributed, model_name="model_final")
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if __name__ == "__main__":
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main()
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