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ModelConfig

Model Configuration.

Parameters

num_fmaps:

    The number of feature maps in the first level of the U-Net.

fmap_inc_factor:

    The factor by which to increase the number of feature maps between
    levels of the U-Net.

features_in_last_layer (default = 64):

    The number of feature channels in the last layer of the U-Net

downsampling_factors (default = [[2,2]]):

    A list of downsampling factors, each given per dimension (e.g.,
    [[2,2], [3,3]] would correspond to two downsample layers, one with
    an isotropic factor of 2, and another one with 3). This parameter
    will also determine the number of levels in the U-Net.

checkpoint (default = None):

    A path to a checkpoint of the network. Needs to be set for networks
    that are used for prediction. If set during training, the
    checkpoint will be used to resume training, otherwise the network
    will be trained from scratch.

initialize (default = True)

    If True, initialize the model weights with Kaiming Normal.
Source code in cellulus/configs/model_config.py 
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@attrs.define
class ModelConfig:
    """Model Configuration.

    Parameters
    ----------

        num_fmaps:

            The number of feature maps in the first level of the U-Net.

        fmap_inc_factor:

            The factor by which to increase the number of feature maps between
            levels of the U-Net.

        features_in_last_layer (default = 64):

            The number of feature channels in the last layer of the U-Net

        downsampling_factors (default = [[2,2]]):

            A list of downsampling factors, each given per dimension (e.g.,
            [[2,2], [3,3]] would correspond to two downsample layers, one with
            an isotropic factor of 2, and another one with 3). This parameter
            will also determine the number of levels in the U-Net.

        checkpoint (default = None):

            A path to a checkpoint of the network. Needs to be set for networks
            that are used for prediction. If set during training, the
            checkpoint will be used to resume training, otherwise the network
            will be trained from scratch.

        initialize (default = True)

            If True, initialize the model weights with Kaiming Normal.

    """

    num_fmaps: int = attrs.field(validator=instance_of(int))
    fmap_inc_factor: int = attrs.field(validator=instance_of(int))
    features_in_last_layer: int = attrs.field(default=64)
    downsampling_factors: List[List[int]] = attrs.field(
        default=[
            [2, 2],
        ]
    )
    checkpoint: Path = attrs.field(default=None, converter=to_path)
    initialize: bool = attrs.field(default=True, validator=instance_of(bool))