CARE N2N Factory

Create a configuration for training CARE.

If "Z" is present in axes, then patch_size must be a list of length 3, otherwise 2.

If "C" is present in axes, then you need to set n_channels_in and n_channels_out to the number of input and output channels, respectively.

By default, all channels are trained independently. To train all channels together, set independent_channels to False.

By default, the augmentations applied are random flips along X or Y, and random 90 degrees rotations in the XY plane. To disable the augmentations, simply pass an empty list.

The parameters of the UNet can be specified in the model_params (passed as a parameter-value dictionary).

Note that num_workers is applied to all dataloaders unless explicitly overridden in the respective dataloader parameters.

Parameters:

• experiment_name (str) –

  Name of the experiment. A valid experiment name is a non-empty string that only contains letters, numbers, underscores, dashes and spaces.

• data_type (Literal['array', 'tiff', 'zarr', 'czi', 'custom']) –

  Type of the data.

• axes (str) –

  Axes of the data (e.g. SYX).

• patch_size (Sequence[int]) –

  Size of the patches along the spatial dimensions (e.g. [64, 64]).

• batch_size (int) –

  Batch size.

• num_epochs (int, default: 30 ) –

  Number of epochs to train for. If provided, this will be added to trainer_params.

• num_steps (int | None, default: None ) –

  Number of batches in 1 epoch. If provided, this will be added to trainer_params. Translates to limit_train_batches in PyTorch Lightning Trainer. See relevant documentation for more details.

• n_channels_in (int | None, default: None ) –

  Number of input channels. If channels is specified, then the number of channels is inferred from its length and this parameter is ignored.

• n_channels_out (int | None, default: None ) –

  Number of output channels. If not specified, but n_channels_in is specified, it will default to the same number as n_channels_in.

• augmentations (Sequence[{x_flip, y_flip, rotate_90}] | None, default: None ) –

  List of transforms to apply, either both or one of XYFlipConfig and XYRandomRotate90Config. By default, it applies both XYFlip (on X and Y) and XYRandomRotate90 (in XY) to the images.

• n_val_patches (int, default: 8, ) –

  The number of patches to set aside for validation during training. This parameter will be ignored if separate validation data is specified for training.

• in_memory (bool | None, default: None ) –

  Whether to load all data into memory. This is only supported for 'array', 'tiff' and 'custom' data types. If None, defaults to True for 'array', 'tiff' and custom, and False for 'zarr' and 'czi' data types. Must be True for array.

• channels (Sequence[int] | None, default: None ) –

  List of channels to use. If None, all channels are used.

• independent_channels (bool, default: False ) –

  Whether to train all channels independently.

• normalization ((mean_std, min_max, quantile, none), default: "mean_std" ) –

  Normalization strategy to use.

• normalization_params (dict[str, Any] | None, default: None ) –

  Strategy-specific normalization parameters. If None, default values are used.

  • For "mean_std": {"input_means": [...], "input_stds": [...]} (optional)
  • For "min_max": {"input_mins": [...], "input_maxes": [...]} (optional)
  • For "quantile": {"lower_quantiles": 0.01, "upper_quantiles": 0.99} (optional)
  • For "none": No parameters needed.
• patch_filter_config (SupportedPatchFilterConfig | None, default: None ) –

  Specify the configuration for patch filtering. Patch filtering reduces the probability of background patches being selected during training. If None, no patch filter is applied.

• num_workers (int, default: -1 ) –

  Number of workers for data loading. Use -1 to automatically choose based on the number of available CPUs. Unless explicitly overridden in train_dataloader_params and val_dataloader_params, this will be applied to all dataloaders.

• trainer_params (dict | None, default: None ) –

  Parameters for the trainer, see the relevant documentation.

• model_params (dict | None, default: None ) –

  UNetModel parameters.

• optimizer (Literal['Adam', 'Adamax', 'SGD'], default: "Adam" ) –

  Optimizer to use.

• optimizer_params (dict[str, Any] | None, default: None ) –

  Parameters for the optimizer, see PyTorch documentation for more details.

• lr_scheduler (Literal['ReduceLROnPlateau', 'StepLR'], default: "ReduceLROnPlateau" ) –

  Learning rate scheduler to use.

• lr_scheduler_params (dict[str, Any] | None, default: None ) –

  Parameters for the learning rate scheduler, see PyTorch documentation for more details.

• train_dataloader_params (dict[str, Any] | None, default: None ) –

  Parameters for the training dataloader, see the PyTorch docs for DataLoader. If left as None, {"shuffle": True} will be used.

• val_dataloader_params (dict[str, Any] | None, default: None ) –

  Parameters for the validation dataloader, see PyTorch the docs for DataLoader.

• checkpoint_params (dict[str, Any] | None, default: None ) –

  Parameters for the checkpoint callback, see PyTorch Lightning documentation (ModelCheckpoint) for the list of available parameters.

• early_stopping_params (dict[str, Any] | None, default: None ) –

  Parameters for the early stopping callback, see PyTorch Lightning documentation (EarlyStopping) for the list of available parameters.

• logger (Literal['wandb', 'tensorboard', 'none'], default: "none" ) –

  Logger to use.

• seed (int | None, default: None ) –

  Random seed for reproducibility.

Returns:

• Configuration –

  Configuration for training CARE.

Create a configuration for training Noise2Noise.

If "Z" is present in axes, then patch_size must be a list of length 3, otherwise 2.

If "C" is present in axes, then you need to set n_channels_in and n_channels_out to the number of input and output channels, respectively.

By default, all channels are trained independently. To train all channels together, set independent_channels to False.

By default, the augmentations applied are random flips along X or Y, and random 90 degrees rotations in the XY plane. To disable the augmentations, simply pass an empty list.

The parameters of the UNet can be specified in the model_params (passed as a parameter-value dictionary).

Note that num_workers is applied to all dataloaders unless explicitly overridden in the respective dataloader parameters.

Parameters:

• experiment_name (str) –

  Name of the experiment. A valid experiment name is a non-empty string that only contains letters, numbers, underscores, dashes and spaces.

• data_type (Literal['array', 'tiff', 'zarr', 'czi', 'custom']) –

  Type of the data.

• axes (str) –

  Axes of the data (e.g. SYX).

• patch_size (Sequence[int]) –

  Size of the patches along the spatial dimensions (e.g. [64, 64]).

• batch_size (int) –

  Batch size.

• num_epochs (int, default: 30 ) –

  Number of epochs to train for. If provided, this will be added to trainer_params.

• num_steps (int | None, default: None ) –

  Number of batches in 1 epoch. If provided, this will be added to trainer_params. Translates to limit_train_batches in PyTorch Lightning Trainer. See relevant documentation for more details.

• n_channels_in (int | None, default: None ) –

  Number of input channels. If channels is specified, then the number of channels is inferred from its length and this parameter is ignored.

• n_channels_out (int | None, default: None ) –

  Number of output channels. If not specified, but n_channels_in is specified, it will default to the same number as n_channels_in.

• augmentations (Sequence[{x_flip, y_flip, rotate_90}] | None, default: None ) –

  List of transforms to apply, either both or one of XYFlipConfig and XYRandomRotate90Config. By default, it applies both XYFlip (on X and Y) and XYRandomRotate90 (in XY) to the images.

• n_val_patches (int, default: 8, ) –

  The number of patches to set aside for validation during training. This parameter will be ignored if separate validation data is specified for training.

• in_memory (bool | None, default: None ) –

  Whether to load all data into memory. This is only supported for 'array', 'tiff' and 'custom' data types. If None, defaults to True for 'array', 'tiff' and custom, and False for 'zarr' and 'czi' data types. Must be True for array.

• channels (Sequence[int] | None, default: None ) –

  List of channels to use. If None, all channels are used.

• independent_channels (bool, default: True ) –

  Whether to train all channels independently.

• normalization ((mean_std, min_max, quantile, none), default: "mean_std" ) –

  Normalization strategy to use.

• normalization_params (dict[str, Any] | None, default: None ) –

  Strategy-specific normalization parameters. If None, default values are used.

  • For "mean_std": {"input_means": [...], "input_stds": [...]} (optional)
  • For "min_max": {"input_mins": [...], "input_maxes": [...]} (optional)
  • For "quantile": {"lower_quantiles": 0.01, "upper_quantiles": 0.99} (optional)
  • For "none": No parameters needed.
• patch_filter_config (SupportedPatchFilterConfig | None, default: None ) –

  Specify the configuration for patch filtering. Patch filtering reduces the probability of background patches being selected during training. If None, no patch filter is applied.

• num_workers (int, default: -1 ) –

  Number of workers for data loading. Use -1 to automatically choose based on the number of available CPUs. Unless explicitly overridden in train_dataloader_params and val_dataloader_params, this will be applied to all dataloaders.

• trainer_params (dict | None, default: None ) –

  Parameters for the trainer, see the relevant documentation.

• model_params (dict | None, default: None ) –

  UNetModel parameters.

• optimizer (Literal['Adam', 'Adamax', 'SGD'], default: "Adam" ) –

  Optimizer to use.

• optimizer_params (dict[str, Any] | None, default: None ) –

  Parameters for the optimizer, see PyTorch documentation for more details.

• lr_scheduler (Literal['ReduceLROnPlateau', 'StepLR'], default: "ReduceLROnPlateau" ) –

  Learning rate scheduler to use.

• lr_scheduler_params (dict[str, Any] | None, default: None ) –

  Parameters for the learning rate scheduler, see PyTorch documentation for more details.

• train_dataloader_params (dict[str, Any] | None, default: None ) –

  Parameters for the training dataloader, see the PyTorch docs for DataLoader. If left as None, {"shuffle": True} will be used.

• val_dataloader_params (dict[str, Any] | None, default: None ) –

  Parameters for the validation dataloader, see PyTorch the docs for DataLoader.

• checkpoint_params (dict[str, Any] | None, default: None ) –

  Parameters for the checkpoint callback, see PyTorch Lightning documentation (ModelCheckpoint) for the list of available parameters.

• logger (Literal['wandb', 'tensorboard', 'none'], default: "none" ) –

  Logger to use.

• seed (int | None, default: None ) –

  Random seed for reproducibility.

Returns:

• Configuration –

  Configuration for training Noise2Noise.

Create a configuration for training CARE.

The axes parameters must reflect the actual axes and axis order from the data, and should be the same throughout all images. The accepted axes are STCZYX. If "C" is in axes, then you need to set n_channels_in and n_channels_out to the number of channels expected in the input and output, respectively.

By default, CAREamics will go through the entire training data once per epoch. For large datasets, this can lead to very long epochs. To limit the number of batches per epoch, set the num_steps parameter to the desired number of batches.

If the content of your data is expected to always have the same orientation, consider disabling certain augmentations. By default augmentations=None will apply random flips along X and Y, and random 90 degrees rotations in the XY plane. To disable augmentations, set augmentations=[].

See create_advanced_care_config for more parameters.

Parameters:

• experiment_name (str) –

  Name of the experiment. A valid experiment name is a non-empty string that only contains letters, numbers, underscores, dashes and spaces.

• data_type (Literal['array', 'tiff', 'zarr', 'czi', 'custom']) –

  Type of the data.

• axes (str) –

  Axes of the data (e.g. SYX).

• patch_size (Sequence[int]) –

  Size of the patches along the spatial dimensions (e.g. [64, 64]).

• batch_size (int) –

  Batch size.

• num_epochs (int, default: 30 ) –

  Number of epochs to train for.

• num_steps (int, default: None ) –

  Number of batches in 1 epoch.

• augmentations (Sequence of {"x_flip", "y_flip", "rotate_90"}, default: None ) –

  List of augmentations to apply. If None, all augmentations are applied.

• n_val_patches (int, default: 8, ) –

  The number of patches to set aside for validation during training. This parameter will be ignored if separate validation data is specified for training.

• n_channels_in (int or None, default: None ) –

  Number of input channels.

• n_channels_out (int or None, default: None ) –

  Number of output channels.

Returns:

• Configuration –

  Configuration for training CARE.

Create a configuration for training Noise2Noise.

The axes parameters must reflect the actual axes and axis order from the data, and should be the same throughout all images. The accepted axes are STCZYX. If "C" is in axes, then you need to set n_channels_in and n_channels_out to the number of channels expected in the input and output, respectively.

By default, CAREamics will go through the entire training data once per epoch. For large datasets, this can lead to very long epochs. To limit the number of batches per epoch, set the num_steps parameter to the desired number of batches.

If the content of your data is expected to always have the same orientation, consider disabling certain augmentations. By default augmentations=None will apply random flips along X and Y, and random 90 degrees rotations in the XY plane. To disable augmentations, set augmentations=[].

See create_advanced_care_config for more parameters.

Parameters:

• experiment_name (str) –

  Name of the experiment. A valid experiment name is a non-empty string that only contains letters, numbers, underscores, dashes and spaces.

• data_type (Literal['array', 'tiff', 'zarr', 'czi', 'custom']) –

  Type of the data.

• axes (str) –

  Axes of the data (e.g. SYX).

• patch_size (Sequence[int]) –

  Size of the patches along the spatial dimensions (e.g. [64, 64]).

• batch_size (int) –

  Batch size.

• num_epochs (int, default: 30 ) –

  Number of epochs to train for.

• num_steps (int, default: None ) –

  Number of batches in 1 epoch.

• augmentations (Sequence of {"x_flip", "y_flip", "rotate_90"}, default: None ) –

  List of augmentations to apply. If None, all augmentations are applied.

• n_val_patches (int, default: 8, ) –

  The number of patches to set aside for validation during training. This parameter will be ignored if separate validation data is specified for training.

• n_channels_in (int or None, default: None ) –

  Number of input channels.

• n_channels_out (int or None, default: None ) –

  Number of output channels.

Returns:

• Configuration –

  Configuration for training Noise2Noise.