def __init__(self, config: Dict) -> None:
super().__init__(config)
self.name = "config_default"
self.train = trainer_configs.BaseTrainConfig(config.pop(Conf.TRAIN))
self.val = trainer_configs.BaseValConfig(config.pop(Conf.VAL))
self.dataset_train = data.BaseDatasetConfig(
config.pop(Conf.DATASET_TRAIN))
self.dataset_val = data.BaseDatasetConfig(config.pop(Conf.DATASET_VAL))
self.logging = utils.BaseLoggingConfig(config.pop(Conf.LOGGING))
self.saving = trainer_configs.BaseSavingConfig(config.pop(Conf.SAVING))
self.optimizer = optimization.OptimizerConfig(
config.pop(Conf.OPTIMIZER))
self.lr_scheduler = lr_scheduler.SchedulerConfig(
config.pop(Conf.LR_SCHEDULER))
self.mlp = MLPNetConfig(config.pop("mlp"))
def __init__(self,
config: Dict[str, Any],
*,
is_train: bool = True) -> None:
super().__init__(config)
self.name = "config_ret"
self.dim_feat_global: int = config.pop("dim_feat_global", 768)
self.dim_feat_local: int = config.pop("dim_feat_local", 384)
if not is_train:
# Disable dataset caching
logger = logging.getLogger(utils.LOGGER_NAME)
logger.debug("Disable dataset caching during validation.")
config["dataset_val"]["preload_vid_feat"] = False
config["dataset_val"]["preload_text_feat"] = False
try:
self.train = RetrievalTrainConfig(config.pop(Conf.TRAIN))
self.val = RetrievalValConfig(config.pop(Conf.VAL))
self.dataset_train = RetrievalDatasetConfig(
config.pop(Conf.DATASET_TRAIN))
self.dataset_val = RetrievalDatasetConfig(
config.pop(Conf.DATASET_VAL))
self.logging = trainer_configs.BaseLoggingConfig(
config.pop(Conf.LOGGING))
self.saving = trainer_configs.BaseSavingConfig(
config.pop(Conf.SAVING))
self.optimizer = optimization.OptimizerConfig(
config.pop(Conf.OPTIMIZER))
self.lr_scheduler = lr_scheduler.SchedulerConfig(
config.pop(Conf.LR_SCHEDULER))
self.model_cfgs = {}
for key in RetrievalNetworksConst.values():
self.model_cfgs[key] = models.TransformerConfig(
config.pop(key))
except KeyError as e:
print()
print(traceback.format_exc())
print(
f"ERROR: {e} not defined in config {self.__class__.__name__}\n"
)
raise e
self.post_init()
def __init__(self, config: Dict[str, Any]) -> None:
super().__init__(config)
self.name = "config_ret"
# mandatory groups, needed for nntrainer to work correctly
self.train = trainer_configs.BaseTrainConfig(config.pop("train"))
self.val = trainer_configs.BaseValConfig(config.pop("val"))
self.dataset_train = MartDatasetConfig(config.pop("dataset_train"))
self.dataset_val = MartDatasetConfig(config.pop("dataset_val"))
self.logging = trainer_configs.BaseLoggingConfig(config.pop("logging"))
self.saving = trainer_configs.BaseSavingConfig(config.pop("saving"))
# more training
self.label_smoothing: float = config.pop("label_smoothing")
# more validation
self.save_mode: str = config.pop("save_mode")
self.use_beam: bool = config.pop("use_beam")
self.beam_size: int = config.pop("beam_size")
self.n_best: int = config.pop("n_best")
self.min_sen_len: int = config.pop("min_sen_len")
self.max_sen_len: int = config.pop("max_sen_len")
self.block_ngram_repeat: int = config.pop("block_ngram_repeat")
self.length_penalty_name: str = config.pop("length_penalty_name")
self.length_penalty_alpha: float = config.pop("length_penalty_alpha")
# dataset
self.max_n_sen: int = config.pop("max_n_sen")
self.max_n_sen_add_val: int = config.pop("max_n_sen_add_val")
self.max_t_len: int = config.pop("max_t_len")
self.max_v_len: int = config.pop("max_v_len")
self.type_vocab_size: int = config.pop("type_vocab_size")
self.word_vec_size: int = config.pop("word_vec_size")
# dataset: coot features
self.coot_model_name: Optional[str] = config.pop("coot_model_name")
self.coot_dim_clip: int = config.pop("coot_dim_clip")
self.coot_dim_vid: int = config.pop("coot_dim_vid")
self.coot_mode: str = config.pop("coot_mode")
self.video_feature_size: int = config.pop("video_feature_size")
# technical
self.debug: bool = config.pop("debug")
# model
self.attention_probs_dropout_prob: float = config.pop(
"attention_probs_dropout_prob")
self.hidden_dropout_prob: float = config.pop("hidden_dropout_prob")
self.hidden_size: int = config.pop("hidden_size")
self.intermediate_size: int = config.pop("intermediate_size")
self.layer_norm_eps: float = config.pop("layer_norm_eps")
self.memory_dropout_prob: float = config.pop("memory_dropout_prob")
self.num_attention_heads: int = config.pop("num_attention_heads")
self.num_hidden_layers: int = config.pop("num_hidden_layers")
self.n_memory_cells: int = config.pop("n_memory_cells")
self.share_wd_cls_weight: bool = config.pop("share_wd_cls_weight")
self.recurrent: bool = config.pop("recurrent")
self.untied: bool = config.pop("untied")
self.mtrans: bool = config.pop("mtrans")
self.xl: bool = config.pop("xl")
self.xl_grad: bool = config.pop("xl_grad")
self.use_glove: bool = config.pop("use_glove")
self.freeze_glove: bool = config.pop("freeze_glove")
# optimization
self.ema_decay: float = config.pop("ema_decay")
self.initializer_range: float = config.pop("initializer_range")
self.lr: float = config.pop("lr")
self.lr_warmup_proportion: float = config.pop("lr_warmup_proportion")
self.infty: int = config.pop("infty", 0)
self.eps: float = config.pop("eps", 1e-6)
# max position embeddings is calculated as the max joint sequence length
self.max_position_embeddings: int = self.max_v_len + self.max_t_len
# must be set manually as it depends on the dataset
self.vocab_size: Optional[int] = None
# assert the config is valid
if self.xl:
assert self.recurrent, "recurrent must be True if TransformerXL is used."
if self.xl_grad:
assert self.xl, "xl must be True when using xl_grad"
assert not (self.recurrent and self.untied), "cannot be True for both"
assert not (self.recurrent and self.mtrans), "cannot be True for both"
assert not (self.untied and self.mtrans), "cannot be True for both"
if self.share_wd_cls_weight:
assert self.word_vec_size == self.hidden_size, (
"hidden size has to be the same as word embedding size when "
"sharing the word embedding weight and the final classifier weight"
)
# infer model type
if self.recurrent: # recurrent paragraphs
if self.xl:
if self.xl_grad:
self.model_type = "xl_grad"
else:
self.model_type = "xl"
else:
self.model_type = "re"
else: # single sentence
if self.untied:
self.model_type = "untied_single"
elif self.mtrans:
self.model_type = "mtrans_single"
else:
self.model_type = "single"
self.post_init()