Args: cfg (CfgNode):
(self, cfg, resume=False, reuse_ckpt=False)
| 58 | |
| 59 | class Trainer(DefaultTrainer): |
| 60 | def __init__(self, cfg, resume=False, reuse_ckpt=False): |
| 61 | """ |
| 62 | Args: |
| 63 | cfg (CfgNode): |
| 64 | """ |
| 65 | super(DefaultTrainer, self).__init__() |
| 66 | |
| 67 | logger = logging.getLogger("detectron2") |
| 68 | if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 |
| 69 | setup_logger() |
| 70 | cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) |
| 71 | |
| 72 | # Assume these objects must be constructed in this order. |
| 73 | model = self.build_model(cfg) |
| 74 | |
| 75 | ckpt = DetectionCheckpointer(model) |
| 76 | self.start_iter = 0 |
| 77 | self.start_iter = ckpt.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1 |
| 78 | self.iter =self.start_iter |
| 79 | |
| 80 | optimizer = self.build_optimizer(cfg, model) |
| 81 | data_loader = self.build_train_loader(cfg) |
| 82 | |
| 83 | # For training, wrap with DDP. But don't need this for inference. |
| 84 | if comm.get_world_size() > 1: |
| 85 | model = DistributedDataParallel( |
| 86 | model, device_ids=[comm.get_local_rank()], broadcast_buffers=False |
| 87 | ) |
| 88 | self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)( |
| 89 | model, data_loader, optimizer |
| 90 | ) |
| 91 | |
| 92 | self.scheduler = self.build_lr_scheduler(cfg, optimizer) |
| 93 | self.checkpointer = DetectionCheckpointer( |
| 94 | model, |
| 95 | cfg.OUTPUT_DIR, |
| 96 | optimizer=optimizer, |
| 97 | scheduler=self.scheduler, |
| 98 | ) |
| 99 | self.start_iter = 0 |
| 100 | self.max_iter = cfg.SOLVER.MAX_ITER |
| 101 | self.cfg = cfg |
| 102 | self.register_hooks(self.build_hooks()) |
| 103 | |
| 104 | @classmethod |
| 105 | def build_evaluator(cls, cfg, dataset_name, output_folder=None): |
nothing calls this directly
no test coverage detected