Exceptions

EmptyDatasetException

Bases: DatasetValidationException

Exception raised when a dataset does not have any image for a specific config

Attributes: message -- explanation of the error

Source code in training/exceptions/dataset_exceptions.py

class EmptyDatasetException(DatasetValidationException):
    """
    Exception raised when a dataset does not have any image for a specific config

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, desc):
        self.message = "Empty Dataset: " + desc
        super().__init__(self.message)

IllegalDatasetParameterException

Bases: DatasetValidationException

Exception raised illegal dataset param.

Attributes: message -- explanation of the error

Source code in training/exceptions/dataset_exceptions.py

class IllegalDatasetParameterException(DatasetValidationException):
    """
    Exception raised illegal dataset param.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, desc):
        self.message = "Unsupported dataset parameter format: " + desc
        super().__init__(self.message)

UnsupportedBatchItemsFormat

Bases: ValueError

Exception raised illegal batch items returned from data loader.

Attributes: message -- explanation of the error

Source code in training/exceptions/dataset_exceptions.py

class UnsupportedBatchItemsFormat(ValueError):
    """Exception raised illegal batch items returned from data loader.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, batch_items: tuple):
        self.message = (
            f"The data loader is expected to return 2 to 3 items, but got {len(batch_items)} instead.\n"
            "Items expected:\n"
            "   - inputs = batch_items[0] # model input - The type might depend on the model you are using.\n"
            "   - targets = batch_items[1] # Target that will be used to compute loss/metrics - The type might depend on the function you are using.\n"
            "   - [OPTIONAL] additional_batch_items = batch_items[2] # Dict made of any additional item that you might want to use.\n"
            "To fix this, please change the implementation of your dataset __getitem__ method, so that it would return the items defined above.\n"
        )
        super().__init__(self.message)

ArchitectureKwargsException

Bases: KDModelException

Exception raised when subnet architectures are not defined.

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class ArchitectureKwargsException(KDModelException):
    """Exception raised when subnet architectures are not defined.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self):
        super().__init__("When architecture is not intialized both student_architecture and teacher_architecture must be passed " "through **kwargs")

InconsistentParamsException

Bases: KDModelException

Exception raised when values between arch_params/checkpoint_params should be equivalent.

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class InconsistentParamsException(KDModelException):
    """Exception raised when values between arch_params/checkpoint_params should be equivalent.

    Attributes:
        message -- explanation of the error
    """

    def __init__(
        self,
        inconsistent_key1: str,
        inconsistent_key1_container_name: str,
        inconsistent_key2: str,
        inconsistent_key2_container_name: str,
    ):
        super().__init__(
            f"{inconsistent_key1} in {inconsistent_key1_container_name} must be equal to " f"{inconsistent_key2} in {inconsistent_key2_container_name}"
        )

KDModelException

Bases: Exception

Exception raised illegal training param format.

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class KDModelException(Exception):
    """Exception raised illegal training param format.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, desc):
        self.message = "KDTrainer: " + desc
        super().__init__(self.message)

TeacherKnowledgeException

Bases: KDModelException

Exception raised when teacher net doesn't hold any knowledge (i.e weights are the initial ones).

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class TeacherKnowledgeException(KDModelException):
    """Exception raised when teacher net doesn't hold any knowledge (i.e weights are the initial ones).

    Attributes:
        message -- explanation of the error
    """

    def __init__(self):
        super().__init__("Expected: at least one of: teacher_pretrained_weights, teacher_checkpoint_path or load_kd_trainer_checkpoint=True")

UndefinedNumClassesException

Bases: KDModelException

Exception raised when num_classes is not defined for subnets (and cannot be derived).

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class UndefinedNumClassesException(KDModelException):
    """Exception raised when num_classes is not defined for subnets (and cannot be derived).

    Attributes:
        message -- explanation of the error
    """

    def __init__(self):
        super().__init__("Number of classes must be defined in students and teachers arch params or by connecting to a dataset interface")

UnsupportedKDArchitectureException

Bases: KDModelException

Exception raised for unsupported kd architecture.

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class UnsupportedKDArchitectureException(KDModelException):
    """Exception raised for unsupported kd architecture.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, architecture):
        super().__init__("Unsupported KD architecture: " + str(architecture))

UnsupportedKDModelArgException

Bases: KDModelException

Exception raised for unsupported args that might be supported for Trainer but not for KDTrainer.

Attributes: message -- explanation of the error

Source code in training/exceptions/kd_trainer_exceptions.py

class UnsupportedKDModelArgException(KDModelException):
    """Exception raised for unsupported args that might be supported for Trainer but not for KDTrainer.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, param_name: str, dict_name: str):
        super().__init__(param_name + " in " + dict_name + " not supported for KD models.")

IllegalRangeForLossAttributeException

Bases: Exception

Exception raised illegal value (i.e not in range) for _Loss attribute.

Attributes: message -- explanation of the error

Source code in training/exceptions/loss_exceptions.py

class IllegalRangeForLossAttributeException(Exception):
    """
    Exception raised illegal value (i.e not in range) for _Loss attribute.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, range_vals: tuple, attr_name: str):
        self.message = attr_name + " must be in range " + str(range_vals)
        super().__init__(self.message)

RequiredLossComponentReductionException

Bases: Exception

Exception raised illegal reduction for _Loss component.

Attributes: message -- explanation of the error

Source code in training/exceptions/loss_exceptions.py

class RequiredLossComponentReductionException(Exception):
    """
    Exception raised illegal reduction for _Loss component.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, component_name: str, reduction: str, required_reduction: str):
        self.message = component_name + ".reduction must be " + required_reduction + ", got" + reduction
        super().__init__(self.message)

GPUModeNotSetupError

Bases: Exception

Exception raised when the DDP should be setup but is not.

Source code in training/exceptions/sg_trainer_exceptions.py

class GPUModeNotSetupError(Exception):
    """Exception raised when the DDP should be setup but is not."""

    def __init__(self):
        super().__init__(
            "Your environment was not setup to support DDP. Please run at the beginning of your script:\n"
            ">>> from super_gradients.common.environment.env_helpers import init_trainer\n"
            ">>> setup_device(multi_gpu=..., num_gpus=...)\n"
        )

IllegalDataloaderInitialization

Bases: Exception

Exception raised illegal data loaders.

Source code in training/exceptions/sg_trainer_exceptions.py

class IllegalDataloaderInitialization(Exception):
    """Exception raised illegal data loaders."""

    def __init__(self):
        super().__init__("train_loader, valid_loader and class parameters are required when initializing Trainer with data loaders")

UnsupportedOptimizerFormat

Bases: UnsupportedTrainingParameterFormat

Exception raised illegal optimizer format.

Attributes: message -- explanation of the error

Source code in training/exceptions/sg_trainer_exceptions.py

class UnsupportedOptimizerFormat(UnsupportedTrainingParameterFormat):
    """Exception raised illegal optimizer format.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self):
        super().__init__("optimizer parameter expected one of ['Adam','SGD','RMSProp'], or torch.optim.Optimizer object")

UnsupportedTrainingParameterFormat

Bases: Exception

Exception raised illegal training param format.

Attributes: message -- explanation of the error

Source code in training/exceptions/sg_trainer_exceptions.py

class UnsupportedTrainingParameterFormat(Exception):
    """Exception raised illegal training param format.

    Attributes:
        message -- explanation of the error
    """

    def __init__(self, desc):
        self.message = "Unsupported training parameter format: " + desc
        super().__init__(self.message)