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Config

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MicroSplitDataConfig

Bases: BaseModel

data_type instance-attribute

Type of the dataset, should be one of DataType

datasplit_type = None class-attribute instance-attribute

Whether to return training, validation or test split, should be one of DataSplitType

depth3D = 1 class-attribute instance-attribute

Number of slices in 3D. If data is 2D depth3D is equal to 1

empty_patch_replacement_enabled = False class-attribute instance-attribute

Whether to replace the content of one of the channels with background with given probability

enable_gaussian_noise = False class-attribute instance-attribute

Whether to enable gaussian noise

grid_size = None class-attribute instance-attribute

Frame is divided into square grids of this size. A patch centered on a grid having size image_size is returned. Grid size not used in training, used only during val / test, grid size controls the overlap of the patches

image_size instance-attribute

Size of one patch of data

input_idx = None class-attribute instance-attribute

Index of the channel where the input is stored in the data

input_is_sum = False class-attribute instance-attribute

Whether the input is the sum or average of channels

max_val = None class-attribute instance-attribute

Maximum data in the dataset. Is calculated for train split, and should be externally set for val and test splits.

mode_3D = False class-attribute instance-attribute

If training in 3D mode or not

multiscale_lowres_count = None class-attribute instance-attribute

Number of LC scales

normalized_input = True class-attribute instance-attribute

If this is set to true, then one mean and stdev is used for both channels. Otherwise, two different mean and stdev are used.

num_channels = 2 class-attribute instance-attribute

Number of channels in the input

overlapping_padding_kwargs = None class-attribute instance-attribute

Parameters for np.pad method

poisson_noise_factor = -1 class-attribute instance-attribute

The added poisson noise factor

target_idx_list = None class-attribute instance-attribute

Indices of the channels where the targets are stored in the data

uncorrelated_channels = False class-attribute instance-attribute

Replace the content in one of the channels with given probability to make channel content 'uncorrelated'