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unitorch.models.beit¤

BeitProcessor¤

Bases: HfImageClassificationProcessor

Initializes the BeitProcessor.

Parameters:

Name Type Description Default
vision_config_path str

The path to the vision configuration file.

 required
Source code in src/unitorch/models/beit/processing.py 
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def __init__(
    self,
    vision_config_path: str,
):
    """
    Initializes the BeitProcessor.

    Args:
        vision_config_path (str): The path to the vision configuration file.
    """
    vision_processor = BeitImageProcessor.from_json_file(vision_config_path)
    super().__init__(
        vision_processor=vision_processor,
    )

BeitForImageClassification¤

Bases: GenericModel

Initializes the BeitForImageClassification model.

Parameters:

Name Type Description Default
config_path str

The path to the configuration file.

 required
num_classes Optional[int]

The number of classes for classification. Defaults to 1.

 1
Source code in src/unitorch/models/beit/modeling.py 
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def __init__(
    self,
    config_path: str,
    num_classes: Optional[int] = 1,
):
    """
    Initializes the BeitForImageClassification model.

    Args:
        config_path (str): The path to the configuration file.
        num_classes (Optional[int], optional): The number of classes for classification. Defaults to 1.
    """
    super().__init__()
    config = BeitConfig.from_json_file(config_path)

    self.beit = BeitModel(config, add_pooling_layer=True)
    self.classifier = nn.Linear(config.hidden_size, num_classes)
    self.init_weights()

forward ¤

forward(pixel_values: Tensor)

Forward pass of the BeitForImageClassification model.

Parameters:

Name Type Description Default
pixel_values Tensor

The input tensor of pixel values.

 required

Returns:

Type Description
Tensor

The logits of the model output.
Source code in src/unitorch/models/beit/modeling.py 
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def forward(
    self,
    pixel_values: torch.Tensor,
):
    """
    Forward pass of the BeitForImageClassification model.

    Args:
        pixel_values (torch.Tensor): The input tensor of pixel values.

    Returns:
        (torch.Tensor):The logits of the model output.
    """
    outputs = self.beit(
        pixel_values=pixel_values,
    )

    pooled_output = outputs[1]
    logits = self.classifier(pooled_output)
    return logits