unitorch.cli.models.blip

BlipProcessor

Tip

core/process/blip is the section for configuration of BlipProcessor.

Bases: BlipProcessor

Processor for the BLIP model.

Initialize BlipProcessor.

Parameters:

Name	Type	Description	Default
vocab_path	str	The path to the vocabulary file.	required
vision_config_path	str	The path to the vision configuration file.	required
max_seq_length	int	The maximum sequence length. Defaults to 128.	128
max_gen_seq_length	int	The maximum generated sequence length. Defaults to 48.	48
position_start_id	int	The start position ID. Defaults to 0.	0

Source code in src/unitorch/cli/models/blip/processing.py

def __init__(
    self,
    vocab_path: str,
    vision_config_path: str,
    max_seq_length: Optional[int] = 128,
    max_gen_seq_length: Optional[int] = 48,
    position_start_id: Optional[int] = 0,
):
    """
    Initialize BlipProcessor.

    Args:
        vocab_path (str): The path to the vocabulary file.
        vision_config_path (str): The path to the vision configuration file.
        max_seq_length (int, optional): The maximum sequence length. Defaults to 128.
        max_gen_seq_length (int, optional): The maximum generated sequence length. Defaults to 48.
        position_start_id (int, optional): The start position ID. Defaults to 0.
    """
    super().__init__(
        vocab_path=vocab_path,
        vision_config_path=vision_config_path,
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
        position_start_id=position_start_id,
    )

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipProcessor from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
dict		A dictionary containing the processor parameters.

Source code in src/unitorch/cli/models/blip/processing.py

@classmethod
@add_default_section_for_init("core/process/blip")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipProcessor from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        dict: A dictionary containing the processor parameters.
    """
    config.set_default_section("core/process/blip")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )

    vocab_path = config.getoption("vocab_path", None)
    vocab_path = pop_value(
        vocab_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "vocab"),
    )
    vocab_path = cached_path(vocab_path)

    vision_config_path = config.getoption("vision_config_path", None)
    vision_config_path = pop_value(
        vision_config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "vision_config"),
    )
    vision_config_path = cached_path(vision_config_path)

    return {
        "vocab_path": vocab_path,
        "vision_config_path": vision_config_path,
    }

BlipForPretrain

Tip

core/model/pretrain/blip is the section for configuration of BlipForPretrain.

Bases: BlipForPretrain

BLIP model for pretraining.

Initialize BlipForPretrain.

Parameters:

Name	Type	Description	Default
config_path	str	The path to the model configuration file.	required
projection_dim	int	The dimension of the projection head. Defaults to 512.	512
freeze_base_model	bool	Whether to freeze the base model parameters. Defaults to True.	True
gradient_checkpointing	bool	Whether to use gradient checkpointing. Defaults to False.	False
use_all_gather	bool	Whether to use all_gather operation. Defaults to True.	True

Source code in src/unitorch/cli/models/blip/modeling.py

def __init__(
    self,
    config_path: str,
    projection_dim: Optional[int] = 512,
    freeze_base_model: Optional[bool] = True,
    gradient_checkpointing: Optional[bool] = False,
    use_all_gather: Optional[bool] = True,
):
    """
    Initialize BlipForPretrain.

    Args:
        config_path (str): The path to the model configuration file.
        projection_dim (int, optional): The dimension of the projection head. Defaults to 512.
        freeze_base_model (bool, optional): Whether to freeze the base model parameters. Defaults to True.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
        use_all_gather (bool, optional): Whether to use all_gather operation. Defaults to True.
    """
    super().__init__(
        config_path=config_path,
        projection_dim=projection_dim,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
        use_all_gather=use_all_gather,
    )

forward

forward(
    input_ids: Tensor,
    pixel_values: Tensor,
    attention_mask: Tensor = None,
    position_ids: Tensor = None,
)

Forward pass of the BlipForPretrain model.

Parameters:

Name	Type	Description	Default
input_ids	Tensor	The input token IDs.	required
pixel_values	Tensor	The pixel values of the images.	required
attention_mask	Tensor	The attention mask. Defaults to None.	None
position_ids	Tensor	The position IDs. Defaults to None.	None

Returns:

Name	Type	Description
LossOutputs		The loss outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    pixel_values: torch.Tensor,
    attention_mask: torch.Tensor = None,
    position_ids: torch.Tensor = None,
):
    """
    Forward pass of the BlipForPretrain model.

    Args:
        input_ids (torch.Tensor): The input token IDs.
        pixel_values (torch.Tensor): The pixel values of the images.
        attention_mask (torch.Tensor, optional): The attention mask. Defaults to None.
        position_ids (torch.Tensor, optional): The position IDs. Defaults to None.

    Returns:
        LossOutputs: The loss outputs.
    """
    outputs = super().forward(
        input_ids=input_ids,
        pixel_values=pixel_values,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )
    return LossOutputs(loss=outputs)

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipForPretrain from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
BlipForPretrain		An instance of BlipForPretrain.

Source code in src/unitorch/cli/models/blip/modeling.py

@classmethod
@add_default_section_for_init("core/model/pretrain/blip")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipForPretrain from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        BlipForPretrain: An instance of BlipForPretrain.
    """
    config.set_default_section("core/model/pretrain/blip")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )

    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "config"),
    )
    config_path = cached_path(config_path)

    projection_dim = config.getoption("projection_dim", 512)
    freeze_base_model = config.getoption("freeze_base_model", True)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)
    use_all_gather = config.getoption("use_all_gather", True)

    inst = cls(
        config_path=config_path,
        projection_dim=projection_dim,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
        use_all_gather=use_all_gather,
    )
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

BlipForClassification

Tip

core/model/classification/blip is the section for configuration of BlipForClassification.

Bases: BlipForClassification

BLIP model for classification.

Initialize BlipForClassification.

Parameters:

Name	Type	Description	Default
config_path	str	The path to the model configuration file.	required
projection_dim	int	The dimension of the projection head. Defaults to 512.	512
num_classes	int	The number of classes for classification. Defaults to 1.	1
freeze_base_model	bool	Whether to freeze the base model parameters. Defaults to True.	True
gradient_checkpointing	bool	Whether to use gradient checkpointing. Defaults to False.	False

Source code in src/unitorch/cli/models/blip/modeling.py

def __init__(
    self,
    config_path: str,
    projection_dim: Optional[int] = 512,
    num_classes: Optional[int] = 1,
    freeze_base_model: Optional[bool] = True,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize BlipForClassification.

    Args:
        config_path (str): The path to the model configuration file.
        projection_dim (int, optional): The dimension of the projection head. Defaults to 512.
        num_classes (int, optional): The number of classes for classification. Defaults to 1.
        freeze_base_model (bool, optional): Whether to freeze the base model parameters. Defaults to True.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )

forward

forward(
    input_ids: Tensor,
    pixel_values: Tensor,
    attention_mask: Tensor = None,
    position_ids: Tensor = None,
)

Forward pass of the BlipForClassification model.

Parameters:

Name	Type	Description	Default
input_ids	Tensor	The input token IDs.	required
pixel_values	Tensor	The pixel values of the images.	required
attention_mask	Tensor	The attention mask. Defaults to None.	None
position_ids	Tensor	The position IDs. Defaults to None.	None

Returns:

Name	Type	Description
ClassificationOutputs		The classification outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    pixel_values: torch.Tensor,
    attention_mask: torch.Tensor = None,
    position_ids: torch.Tensor = None,
):
    """
    Forward pass of the BlipForClassification model.

    Args:
        input_ids (torch.Tensor): The input token IDs.
        pixel_values (torch.Tensor): The pixel values of the images.
        attention_mask (torch.Tensor, optional): The attention mask. Defaults to None.
        position_ids (torch.Tensor, optional): The position IDs. Defaults to None.

    Returns:
        ClassificationOutputs: The classification outputs.
    """
    outputs = super().forward(
        input_ids=input_ids,
        pixel_values=pixel_values,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )
    return ClassificationOutputs(outputs=outputs)

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipForClassification from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
BlipForClassification		An instance of BlipForClassification.

Source code in src/unitorch/cli/models/blip/modeling.py

@classmethod
@add_default_section_for_init("core/model/classification/blip")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipForClassification from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        BlipForClassification: An instance of BlipForClassification.
    """
    config.set_default_section("core/model/classification/blip")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)

    projection_dim = config.getoption("projection_dim", 512)
    num_classes = config.getoption("num_classes", 1)
    freeze_base_model = config.getoption("freeze_base_model", True)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

BlipForTextClassification

Tip

core/model/classification/blip/text is the section for configuration of BlipForTextClassification.

Bases: BlipForTextClassification

BLIP model for text classification.

Initialize BlipForTextClassification.

Parameters:

Name	Type	Description	Default
config_path	str	The path to the model configuration file.	required
projection_dim	int	The dimension of the projection head. Defaults to 512.	512
num_classes	int	The number of classes for classification. Defaults to 1.	1
freeze_base_model	bool	Whether to freeze the base model parameters. Defaults to True.	True
gradient_checkpointing	bool	Whether to use gradient checkpointing. Defaults to False.	False

Source code in src/unitorch/cli/models/blip/modeling.py

def __init__(
    self,
    config_path: str,
    projection_dim: Optional[int] = 512,
    num_classes: Optional[int] = 1,
    freeze_base_model: Optional[bool] = True,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize BlipForTextClassification.

    Args:
        config_path (str): The path to the model configuration file.
        projection_dim (int, optional): The dimension of the projection head. Defaults to 512.
        num_classes (int, optional): The number of classes for classification. Defaults to 1.
        freeze_base_model (bool, optional): Whether to freeze the base model parameters. Defaults to True.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )

forward

forward(
    input_ids=None,
    attention_mask: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)

Forward pass of the BlipForTextClassification model.

Parameters:

Name	Type	Description	Default
input_ids	Tensor	The input token IDs. Defaults to None.	None
attention_mask	Tensor	The attention mask. Defaults to None.	None
position_ids	Tensor	The position IDs. Defaults to None.	None

Returns:

Name	Type	Description
ClassificationOutputs		The classification outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids=None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
):
    """
    Forward pass of the BlipForTextClassification model.

    Args:
        input_ids (torch.Tensor, optional): The input token IDs. Defaults to None.
        attention_mask (torch.Tensor, optional): The attention mask. Defaults to None.
        position_ids (torch.Tensor, optional): The position IDs. Defaults to None.

    Returns:
        ClassificationOutputs: The classification outputs.
    """
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )
    return ClassificationOutputs(outputs=outputs)

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipForTextClassification from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
BlipForTextClassification		An instance of BlipForTextClassification.

Source code in src/unitorch/cli/models/blip/modeling.py

@classmethod
@add_default_section_for_init("core/model/classification/blip/text")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipForTextClassification from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        BlipForTextClassification: An instance of BlipForTextClassification.
    """
    config.set_default_section("core/model/classification/blip/text")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)

    projection_dim = config.getoption("projection_dim", 512)
    num_classes = config.getoption("num_classes", 1)
    freeze_base_model = config.getoption("freeze_base_Truemodel", True)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

BlipForImageClassification

Tip

core/model/classification/blip/image is the section for configuration of BlipForImageClassification.

Bases: BlipForImageClassification

BLIP model for image classification.

Initialize BlipForImageClassification.

Parameters:

Name	Type	Description	Default
config_path	str	The path to the model configuration file.	required
projection_dim	int	The dimension of the projection head. Defaults to 512.	512
num_classes	int	The number of classes for classification. Defaults to 1.	1
freeze_base_model	bool	Whether to freeze the base model parameters. Defaults to True.	True
gradient_checkpointing	bool	Whether to use gradient checkpointing. Defaults to False.	False

Source code in src/unitorch/cli/models/blip/modeling.py

def __init__(
    self,
    config_path: str,
    projection_dim: Optional[int] = 512,
    num_classes: Optional[int] = 1,
    freeze_base_model: Optional[bool] = True,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize BlipForImageClassification.

    Args:
        config_path (str): The path to the model configuration file.
        projection_dim (int, optional): The dimension of the projection head. Defaults to 512.
        num_classes (int, optional): The number of classes for classification. Defaults to 1.
        freeze_base_model (bool, optional): Whether to freeze the base model parameters. Defaults to True.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )

forward

forward(pixel_values: Tensor)

Forward pass of the BlipForImageClassification model.

Parameters:

Name	Type	Description	Default
pixel_values	Tensor	The pixel values of the images.	required

Returns:

Name	Type	Description
ClassificationOutputs		The classification outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    pixel_values: torch.Tensor,
):
    """
    Forward pass of the BlipForImageClassification model.

    Args:
        pixel_values (torch.Tensor): The pixel values of the images.

    Returns:
        ClassificationOutputs: The classification outputs.
    """
    outputs = super().forward(pixel_values=pixel_values)
    return ClassificationOutputs(outputs=outputs)

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipForImageClassification from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
BlipForImageClassification		An instance of BlipForImageClassification.

Source code in src/unitorch/cli/models/blip/modeling.py

@classmethod
@add_default_section_for_init("core/model/classification/blip/image")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipForImageClassification from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        BlipForImageClassification: An instance of BlipForImageClassification.
    """
    config.set_default_section("core/model/classification/blip/image")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)

    projection_dim = config.getoption("projection_dim", 512)
    num_classes = config.getoption("num_classes", 1)
    freeze_base_model = config.getoption("freeze_base_model", True)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(
        config_path=config_path,
        projection_dim=projection_dim,
        num_classes=num_classes,
        freeze_base_model=freeze_base_model,
        gradient_checkpointing=gradient_checkpointing,
    )
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

BlipForImageCaption

Tip

core/model/caption/blip is the section for configuration of BlipForImageCaption.

Bases: BlipForImageCaption

BLIP model for image captioning.

Initialize BlipForImageCaption.

Parameters:

Name	Type	Description	Default
config_path	str	The path to the model configuration file.	required
gradient_checkpointing	bool	Whether to use gradient checkpointing. Defaults to False.	False

Source code in src/unitorch/cli/models/blip/modeling.py

def __init__(
    self,
    config_path: str,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize BlipForImageCaption.

    Args:
        config_path (str): The path to the model configuration file.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        gradient_checkpointing=gradient_checkpointing,
    )

forward

forward(
    pixel_values: Tensor,
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
)

Forward pass of the BlipForImageCaption model.

Parameters:

Name	Type	Description	Default
pixel_values	Tensor	The pixel values of the images.	required
input_ids	Tensor	The input captions.	required

Returns:

Name	Type	Description
GenerationOutputs		The generation outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    pixel_values: torch.Tensor,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
):
    """
    Forward pass of the BlipForImageCaption model.

    Args:
        pixel_values (torch.Tensor): The pixel values of the images.
        input_ids (torch.Tensor): The input captions.

    Returns:
        GenerationOutputs: The generation outputs.
    """
    outputs = super().forward(
        pixel_values=pixel_values,
        input_ids=input_ids,
        attention_mask=attention_mask,
    )
    return GenerationOutputs(sequences=outputs)

from_core_configure classmethod

from_core_configure(config, **kwargs)

Create an instance of BlipForImageCaption from a core configuration.

Parameters:

Name	Type	Description	Default
config		The core configuration.	required
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
BlipForImageCaption		An instance of BlipForImageCaption.

Source code in src/unitorch/cli/models/blip/modeling.py

@classmethod
@add_default_section_for_init("core/model/caption/blip")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BlipForImageCaption from a core configuration.

    Args:
        config: The core configuration.
        **kwargs: Additional keyword arguments.

    Returns:
        BlipForImageCaption: An instance of BlipForImageCaption.
    """
    config.set_default_section("core/model/caption/blip")
    pretrained_name = config.getoption(
        "pretrained_name", "blip-image-captioning-base"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(
        config_path=config_path,
        gradient_checkpointing=gradient_checkpointing,
    )
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_blip_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

generate

generate(
    pixel_values: Tensor,
    num_beams: Optional[int] = 5,
    decoder_start_token_id: Optional[int] = 101,
    decoder_end_token_id: Optional[
        Union[int, List[int]]
    ] = 102,
    num_return_sequences: Optional[int] = 1,
    min_gen_seq_length: Optional[int] = 0,
    max_gen_seq_length: Optional[int] = 48,
    repetition_penalty: Optional[float] = 1.0,
    no_repeat_ngram_size: Optional[int] = 0,
    early_stopping: Optional[bool] = True,
    length_penalty: Optional[float] = 1.0,
    num_beam_groups: Optional[int] = 1,
    diversity_penalty: Optional[float] = 0.0,
    do_sample: Optional[bool] = False,
    temperature: Optional[float] = 1.0,
    top_k: Optional[int] = 50,
    top_p: Optional[float] = 1.0,
)

Generate captions using the BlipForImageCaption model.

Parameters:

Name	Type	Description	Default
pixel_values	Tensor	The pixel values of the images.	required
num_beams	int	The number of beams for beam search. Defaults to 5.	5
decoder_start_token_id	int	The start token ID for the decoder. Defaults to 30522.	101
decoder_end_token_id	int or List[int]	The end token ID for the decoder. Defaults to 2.	102
num_return_sequences	int	The number of sequences to return. Defaults to 1.	1
min_gen_seq_length	int	The minimum length of generated sequences. Defaults to 0.	0
max_gen_seq_length	int	The maximum length of generated sequences. Defaults to 48.	48
repetition_penalty	float	The repetition penalty. Defaults to 1.0.	1.0
no_repeat_ngram_size	int	The size of n-grams to avoid repeating. Defaults to 0.	0
early_stopping	bool	Whether to perform early stopping. Defaults to True.	True
length_penalty	float	The length penalty. Defaults to 1.0.	1.0
num_beam_groups	int	The number of beam groups. Defaults to 1.	1
diversity_penalty	float	The diversity penalty. Defaults to 0.0.	0.0
do_sample	bool	Whether to use sampling for generation. Defaults to False.	False
temperature	float	The temperature value for sampling. Defaults to 1.0.	1.0
top_k	int	The top-k value for sampling. Defaults to 50.	50
top_p	float	The top-p value for sampling. Defaults to 1.0.	1.0

Returns:

Name	Type	Description
GenerationOutputs		The generation outputs.

Source code in src/unitorch/cli/models/blip/modeling.py

@torch.no_grad()
@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def generate(
    self,
    pixel_values: torch.Tensor,
    num_beams: Optional[int] = 5,
    decoder_start_token_id: Optional[int] = 101,
    decoder_end_token_id: Optional[Union[int, List[int]]] = 102,
    num_return_sequences: Optional[int] = 1,
    min_gen_seq_length: Optional[int] = 0,
    max_gen_seq_length: Optional[int] = 48,
    repetition_penalty: Optional[float] = 1.0,
    no_repeat_ngram_size: Optional[int] = 0,
    early_stopping: Optional[bool] = True,
    length_penalty: Optional[float] = 1.0,
    num_beam_groups: Optional[int] = 1,
    diversity_penalty: Optional[float] = 0.0,
    do_sample: Optional[bool] = False,
    temperature: Optional[float] = 1.0,
    top_k: Optional[int] = 50,
    top_p: Optional[float] = 1.0,
):
    """
    Generate captions using the BlipForImageCaption model.

    Args:
        pixel_values (torch.Tensor): The pixel values of the images.
        num_beams (int, optional): The number of beams for beam search. Defaults to 5.
        decoder_start_token_id (int, optional): The start token ID for the decoder. Defaults to 30522.
        decoder_end_token_id (int or List[int], optional): The end token ID for the decoder. Defaults to 2.
        num_return_sequences (int, optional): The number of sequences to return. Defaults to 1.
        min_gen_seq_length (int, optional): The minimum length of generated sequences. Defaults to 0.
        max_gen_seq_length (int, optional): The maximum length of generated sequences. Defaults to 48.
        repetition_penalty (float, optional): The repetition penalty. Defaults to 1.0.
        no_repeat_ngram_size (int, optional): The size of n-grams to avoid repeating. Defaults to 0.
        early_stopping (bool, optional): Whether to perform early stopping. Defaults to True.
        length_penalty (float, optional): The length penalty. Defaults to 1.0.
        num_beam_groups (int, optional): The number of beam groups. Defaults to 1.
        diversity_penalty (float, optional): The diversity penalty. Defaults to 0.0.
        do_sample (bool, optional): Whether to use sampling for generation. Defaults to False.
        temperature (float, optional): The temperature value for sampling. Defaults to 1.0.
        top_k (int, optional): The top-k value for sampling. Defaults to 50.
        top_p (float, optional): The top-p value for sampling. Defaults to 1.0.

    Returns:
        GenerationOutputs: The generation outputs.
    """
    outputs = super().generate(
        pixel_values=pixel_values,
        num_beams=num_beams,
        decoder_start_token_id=decoder_start_token_id,
        decoder_end_token_id=decoder_end_token_id,
        num_return_sequences=num_return_sequences,
        min_gen_seq_length=min_gen_seq_length,
        max_gen_seq_length=max_gen_seq_length,
        repetition_penalty=repetition_penalty,
        no_repeat_ngram_size=no_repeat_ngram_size,
        early_stopping=early_stopping,
        length_penalty=length_penalty,
        num_beam_groups=num_beam_groups,
        diversity_penalty=diversity_penalty,
        do_sample=do_sample,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
    )

    return GenerationOutputs(
        sequences=outputs.sequences,
        sequences_scores=outputs.sequences_scores,
    )