power_cogs.base package¶
Submodules¶
power_cogs.base.base_torch_dataset module¶
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class
power_cogs.base.base_torch_dataset.
BaseTorchDataset
[source]¶ Bases:
torch.utils.data.dataset.Dataset
power_cogs.base.base_torch_model module¶
power_cogs.base.base_torch_trainer module¶
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class
power_cogs.base.base_torch_trainer.
BaseTorchTrainer
(name: Optional[str] = None, pretrained_path: Optional[str] = None, visualize_output: bool = False, use_cuda: bool = False, device_id: int = 0, early_stoppage: bool = False, loss_threshold: float = -inf, batch_size: int = 32, epochs: int = 100, checkpoint_interval: int = 100, num_samples: Optional[int] = None, model_config: Dict[str, Any] = {}, dataset_config: Dict[str, Any] = {}, optimizer_config: Dict[str, Any] = {}, scheduler_config: Dict[str, Any] = {}, logging_config: Dict[str, Any] = {}, dataloader_config: Dict[str, Any] = {}, tune_config: Dict[str, Any] = {}, config: Dict[str, Any] = {})[source]¶ Bases:
object
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classmethod
from_config
(config_path: Optional[str] = None, config: Dict[str, Any] = {}) → power_cogs.base.base_torch_trainer.BaseTorchTrainer[source]¶
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save
(base_path: Optional[str] = None, step: Optional[int] = None, path_name: Optional[str] = None) → str[source]¶
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train
(batch_size=None, epochs=None, checkpoint_interval=None, visualize=None) → Dict[str, Any][source]¶ Main training function, should call train_iter
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classmethod
Module contents¶
-
class
power_cogs.base.
BaseTorchDataset
[source]¶ Bases:
torch.utils.data.dataset.Dataset
-
class
power_cogs.base.
BaseTorchTrainer
(name: Optional[str] = None, pretrained_path: Optional[str] = None, visualize_output: bool = False, use_cuda: bool = False, device_id: int = 0, early_stoppage: bool = False, loss_threshold: float = -inf, batch_size: int = 32, epochs: int = 100, checkpoint_interval: int = 100, num_samples: Optional[int] = None, model_config: Dict[str, Any] = {}, dataset_config: Dict[str, Any] = {}, optimizer_config: Dict[str, Any] = {}, scheduler_config: Dict[str, Any] = {}, logging_config: Dict[str, Any] = {}, dataloader_config: Dict[str, Any] = {}, tune_config: Dict[str, Any] = {}, config: Dict[str, Any] = {})[source]¶ Bases:
object
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classmethod
from_config
(config_path: Optional[str] = None, config: Dict[str, Any] = {}) → power_cogs.base.base_torch_trainer.BaseTorchTrainer[source]¶
-
save
(base_path: Optional[str] = None, step: Optional[int] = None, path_name: Optional[str] = None) → str[source]¶
-
train
(batch_size=None, epochs=None, checkpoint_interval=None, visualize=None) → Dict[str, Any][source]¶ Main training function, should call train_iter
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classmethod