Extension of the Trainer class adapted to DiffusionInst.
| 35 | |
| 36 | |
| 37 | class Trainer(DefaultTrainer): |
| 38 | """ Extension of the Trainer class adapted to DiffusionInst. """ |
| 39 | |
| 40 | def __init__(self, cfg): |
| 41 | """ |
| 42 | Args: |
| 43 | cfg (CfgNode): |
| 44 | """ |
| 45 | super(DefaultTrainer, self).__init__() # call grandfather's `__init__` while avoid father's `__init()` |
| 46 | logger = logging.getLogger("detectron2") |
| 47 | if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 |
| 48 | setup_logger() |
| 49 | cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) |
| 50 | |
| 51 | # Assume these objects must be constructed in this order. |
| 52 | model = self.build_model(cfg) |
| 53 | optimizer = self.build_optimizer(cfg, model) |
| 54 | data_loader = self.build_train_loader(cfg) |
| 55 | |
| 56 | model = create_ddp_model(model, broadcast_buffers=False) |
| 57 | self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)( |
| 58 | model, data_loader, optimizer |
| 59 | ) |
| 60 | |
| 61 | self.scheduler = self.build_lr_scheduler(cfg, optimizer) |
| 62 | |
| 63 | ########## EMA ############ |
| 64 | kwargs = { |
| 65 | 'trainer': weakref.proxy(self), |
| 66 | } |
| 67 | kwargs.update(may_get_ema_checkpointer(cfg, model)) |
| 68 | self.checkpointer = DetectionCheckpointer( |
| 69 | # Assume you want to save checkpoints together with logs/statistics |
| 70 | model, |
| 71 | cfg.OUTPUT_DIR, |
| 72 | **kwargs, |
| 73 | # trainer=weakref.proxy(self), |
| 74 | ) |
| 75 | self.start_iter = 0 |
| 76 | self.max_iter = cfg.SOLVER.MAX_ITER |
| 77 | self.cfg = cfg |
| 78 | |
| 79 | self.register_hooks(self.build_hooks()) |
| 80 | |
| 81 | @classmethod |
| 82 | def build_model(cls, cfg): |
| 83 | """ |
| 84 | Returns: |
| 85 | torch.nn.Module: |
| 86 | |
| 87 | It now calls :func:`detectron2.modeling.build_model`. |
| 88 | Overwrite it if you'd like a different model. |
| 89 | """ |
| 90 | model = build_model(cfg) |
| 91 | logger = logging.getLogger(__name__) |
| 92 | logger.info("Model:\n{}".format(model)) |
| 93 | # setup EMA |
| 94 | may_build_model_ema(cfg, model) |