def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.hparams = context.get_hparams()
self.criterion = torch.nn.functional.cross_entropy
# The last epoch is only used for logging.
self._last_epoch = -1
def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.hparams = context.get_hparams()
self.criterion = torch.nn.functional.cross_entropy
# The last epoch is only used for logging.
self._last_epoch = -1
self.results = {
"loss": float("inf"),
"top1_accuracy": 0,
"top5_accuracy": 0
}
def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.hparams = AttrDict(context.get_hparams())
# Create a unique download directory for each rank so they don't overwrite each other.
self.download_directory = self.data_config["data_download_dir"]
data.download_data(self.download_directory)
corpus = data_util.Corpus(self.download_directory)
self.corpus = corpus
self.ntokens = len(corpus.dictionary)
self.hidden = None
# This is used to store eval history and will switch to ASGD
# once validation perplexity stops improving.
self._last_loss = None
self._eval_history = []
self._last_epoch = -1
def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.hparams = context.get_hparams()
self.data_config = context.get_data_config()
self.cfg = Config.fromfile(self.hparams["config_file"])
self.cfg.data.train.ann_file = self.data_config["train_ann_file"]
self.cfg.data.val.ann_file = self.data_config["val_ann_file"]
self.cfg.data.val.test_mode = True
self.cfg.data.workers_per_gpu = self.data_config["workers_per_gpu"]
if self.data_config["backend"] in ["gcs", "fake"]:
sub_backend(self.data_config["backend"], self.cfg)
print(self.cfg)
self.model = self.context.wrap_model(
build_detector(self.cfg.model,
train_cfg=self.cfg.train_cfg,
test_cfg=self.cfg.test_cfg))
self.optimizer = self.context.wrap_optimizer(
build_optimizer(self.model, self.cfg.optimizer))
scheduler_cls = WarmupWrapper(MultiStepLR)
scheduler = scheduler_cls(
self.hparams["warmup"], # warmup schedule
self.hparams["warmup_iters"], # warmup_iters
self.hparams["warmup_ratio"], # warmup_ratio
self.optimizer,
[self.hparams["step1"], self.hparams["step2"]], # milestones
self.hparams["gamma"], # gamma
)
self.scheduler = self.context.wrap_lr_scheduler(
scheduler, step_mode=LRScheduler.StepMode.MANUAL_STEP)
self.clip_grads_fn = (lambda x: torch.nn.utils.clip_grad_norm_(
x, self.hparams["clip_grads_norm"])
if self.hparams["clip_grads"] else None)