Configuration Factories

Create an algorithm model for training CAREamics.

Parameters:

Returns:

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 to the number of channels. Likewise, if you set the number of channels, then "C" must be present in axes.

To set the number of output channels, use the n_channels_out parameter. If it is not specified, it will be assumed to be equal to n_channels_in.

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

By setting augmentations to None, the default augmentations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default augmentations, a list of augmentations can be passed to the augmentations parameter. To disable the augmentations, simply pass an empty list.

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100
... )

You can also limit the number of batches per epoch:

>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_steps=100  # limit to 100 batches per epoch
... )

To disable augmentations, simply set augmentations to an empty list:

>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[]
... )

A list of augmentations can be passed to the augmentations parameter: to replace the default augmentations:

>>> from careamics.config.augmentations import XYFlipConfig
>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[
...         # No rotation and only Y flipping
...         XYFlipConfig(flip_x = False, flip_y = True)
...     ]
... )

If you are training multiple channels they will be trained independently by default, you simply need to specify the number of channels input (and optionally, the number of channels output):

>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YXC", # channels must be in the axes
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels_in=3, # number of input channels
...     n_channels_out=1 # if applicable
... )

If instead you want to train multiple channels together, you need to turn off the independent_channels parameter:

>>> config = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="array",
...     axes="YXC", # channels must be in the axes
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     independent_channels=False,
...     n_channels_in=3,
...     n_channels_out=1 # if applicable
... )

If you would like to train on CZI files, use "czi" as data_type and "SCYX" as axes for 2-D or "SCZYX" for 3-D denoising. Note that "SCYX" can also be used for 3-D data but spatial context along the Z dimension will then not be taken into account.

>>> config_2d = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="czi",
...     axes="SCYX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels_in=1,
... )
>>> config_3d = create_care_configuration(
...     experiment_name="care_experiment",
...     data_type="czi",
...     axes="SCZYX",
...     patch_size=[16, 64, 64],
...     batch_size=16,
...     num_epochs=100,
...     n_channels_in=1,
... )

Create a configuration for training HDN.

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 to the number of channels. Likewise, if you set the number of channels, then "C" must be present in axes.

To set the number of output channels, use the n_channels_out parameter. If it is not specified, it will be assumed to be equal to n_channels_in.

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

By setting augmentations to None, the default augmentations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default augmentations, a list of augmentations can be passed to the augmentations parameter. To disable the augmentations, simply pass an empty list.

TODO revisit the necessity of model_params

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_hdn_configuration(
...     experiment_name="hdn_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100
... )

You can also limit the number of batches per epoch:

>>> config = create_hdn_configuration(
...     experiment_name="hdn_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_steps=100  # limit to 100 batches per epoch
... )

Create a configuration for training MicroSplit.

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_microsplit_configuration(

... experiment_name="microsplit_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... num_epochs=100

... )

You can also limit the number of batches per epoch:

>>> config = create_microsplit_configuration(

... experiment_name="microsplit_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... num_steps=100 # limit to 100 batches per epoch

... )

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 to the number of channels. Likewise, if you set the number of channels, then "C" must be present in axes.

To set the number of output channels, use the n_channels_out parameter. If it is not specified, it will be assumed to be equal to n_channels_in.

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

By setting augmentations to None, the default augmentations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default augmentations, a list of augmentations can be passed to the augmentations parameter. To disable the augmentations, simply pass an empty list.

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100
... )

You can also limit the number of batches per epoch:

>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_steps=100  # limit to 100 batches per epoch
... )

To disable augmentations, simply set augmentations to an empty list:

>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[]
... )

A list of augmentations can be passed to the augmentations parameter:

>>> from careamics.config.augmentations import XYFlipConfig
>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[
...         # No rotation and only Y flipping
...         XYFlipConfig(flip_x = False, flip_y = True)
...     ]
... )

If you are training multiple channels they will be trained independently by default, you simply need to specify the number of channels input (and optionally, the number of channels output):

>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YXC", # channels must be in the axes
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels_in=3, # number of input channels
...     n_channels_out=1 # if applicable
... )

If instead you want to train multiple channels together, you need to turn off the independent_channels parameter:

>>> config = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="array",
...     axes="YXC", # channels must be in the axes
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     independent_channels=False,
...     n_channels_in=3,
...     n_channels_out=1 # if applicable
... )

If you would like to train on CZI files, use "czi" as data_type and "SCYX" as axes for 2-D or "SCZYX" for 3-D denoising. Note that "SCYX" can also be used for 3-D data but spatial context along the Z dimension will then not be taken into account.

>>> config_2d = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="czi",
...     axes="SCYX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels_in=1,
... )
>>> config_3d = create_n2n_configuration(
...     experiment_name="n2n_experiment",
...     data_type="czi",
...     axes="SCZYX",
...     patch_size=[16, 64, 64],
...     batch_size=16,
...     num_epochs=100,
...     n_channels_in=1,
... )

Create a configuration for training Noise2Void.

N2V uses a UNet model to denoise images in a self-supervised manner. To use its variants structN2V and N2V2, set the struct_n2v_axis and struct_n2v_span (structN2V) parameters, or set use_n2v2 to True (N2V2).

N2V2 modifies the UNet architecture by adding blur pool layers and removes the skip connections, thus removing checkboard artefacts. StructN2V is used when vertical or horizontal correlations are present in the noise; it applies an additional mask to the manipulated pixel neighbors.

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 to the number of channels.

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

By default, the augmentations applied are a random flip along X or Y, and a random 90 degrees rotation in the XY plane. Normalization is always applied, as well as the N2V manipulation.

By setting augmentations to None, the default augmentations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default augmentations, a list of augmentations can be passed to the augmentations parameter. To disable the augmentations, simply pass an empty list.

The roi_size parameter specifies the size of the area around each pixel that will be manipulated by N2V. The masked_pixel_percentage parameter specifies how many pixels per patch will be manipulated.

The parameters of the UNet can be specified in the model_params (passed as a parameter-value dictionary). Note that use_n2v2 and 'n_channels' override the corresponding parameters passed in model_params.

If you pass "horizontal" or "vertical" to struct_n2v_axis, then structN2V mask will be applied to each manipulated pixel.

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100
... )

You can also limit the number of batches per epoch:

>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_steps=100  # limit to 100 batches per epoch
... )

To disable augmentations, simply set augmentations to an empty list:

>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[]
... )

A list of augmentations can be passed to the augmentations parameter:

>>> from careamics.config.augmentations import XYFlipConfig
>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     augmentations=[
...         # No rotation and only Y flipping
...         XYFlipConfig(flip_x = False, flip_y = True)
...     ]
... )

To use N2V2, simply pass the use_n2v2 parameter:

>>> config = create_n2v_configuration(
...     experiment_name="n2v2_experiment",
...     data_type="tiff",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     use_n2v2=True
... )

For structN2V, there are two parameters to set, struct_n2v_axis and struct_n2v_span:

>>> config = create_n2v_configuration(
...     experiment_name="structn2v_experiment",
...     data_type="tiff",
...     axes="YX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     struct_n2v_axis="horizontal",
...     struct_n2v_span=7
... )

If you are training multiple channels they will be trained independently by default, you simply need to specify the number of channels:

>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YXC",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels=3
... )

If instead you want to train multiple channels together, you need to turn off the independent_channels parameter:

>>> config = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="array",
...     axes="YXC",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     independent_channels=False,
...     n_channels=3
... )

If you would like to train on CZI files, use "czi" as data_type and "SCYX" as axes for 2-D or "SCZYX" for 3-D denoising. Note that "SCYX" can also be used for 3-D data but spatial context along the Z dimension will then not be taken into account.

>>> config_2d = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="czi",
...     axes="SCYX",
...     patch_size=[64, 64],
...     batch_size=32,
...     num_epochs=100,
...     n_channels=1,
... )
>>> config_3d = create_n2v_configuration(
...     experiment_name="n2v_experiment",
...     data_type="czi",
...     axes="SCZYX",
...     patch_size=[16, 64, 64],
...     batch_size=16,
...     num_epochs=100,
...     n_channels=1,
... )

Create a configuration for training Probabilistic Noise2Void (PN2V).

PN2V extends N2V by incorporating a probabilistic noise model to estimate the posterior distibution of each pixel more precisely.

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

If "C" is present in axes, then you need to set num_in_channels to the number of channels.

By default, all channels are trained independently. To train all channels together, set independent_channels to False. When training independently, each input channel will have num_out_channels outputs (default 400). When training together, all input channels will share num_out_channels outputs.

By default, the augmentations applied are a random flip along X or Y, and a random 90 degrees rotation in the XY plane. Normalization is always applied, as well as the N2V manipulation.

By setting augmentations to None, the default augmentations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default augmentations, a list of augmentations can be passed to the augmentations parameter. To disable the augmentations, simply pass an empty list.

The roi_size parameter specifies the size of the area around each pixel that will be manipulated by N2V. The masked_pixel_percentage parameter specifies how many pixels per patch will be manipulated.

The parameters of the UNet can be specified in the model_params (passed as a parameter-value dictionary). Note that use_n2v2, num_in_channels, and num_out_channels override the corresponding parameters passed in model_params.

If you pass "horizontal" or "vertical" to struct_n2v_axis, then structN2V mask will be applied to each manipulated pixel.

Parameters:

Returns:

Examples:

Minimum example:

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100

... )

You can also limit the number of batches per epoch:

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_steps=100 # limit to 100 batches per epoch

... )

To disable augmentations, simply set augmentations to an empty list:

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... augmentations=[]

... )

A list of augmentations can be passed to the augmentations parameter:

>>> from careamics.config.augmentations import XYFlipModel

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... augmentations=[

... # No rotation and only Y flipping

... XYFlipModel(flip_x = False, flip_y = True)

... ]

... )

To use N2V2, simply pass the use_n2v2 parameter:

>>> config = create_pn2v_configuration(

... experiment_name="pn2v2_experiment",

... data_type="tiff",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... use_n2v2=True

... )

For structN2V, there are two parameters to set, struct_n2v_axis and

struct_n2v_span:

>>> config = create_pn2v_configuration(

... experiment_name="structpn2v_experiment",

... data_type="tiff",

... axes="YX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... struct_n2v_axis="horizontal",

... struct_n2v_span=7

... )

If you are training multiple channels they will be trained independently by

default, you simply need to specify the number of input channels. Each input

channel will correspond to num_out_channels outputs (1200 total for 3

channels with default num_out_channels=400):

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YXC",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... num_in_channels=3

... )

If instead you want to train multiple channels together, you need to turn

off the independent_channels parameter (resulting in 400 total output

channels regardless of the number of input channels):

>>> config = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="array",

... axes="YXC",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... independent_channels=False,

... num_in_channels=3

... )

>>> config_2d = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="czi",

... axes="SCYX",

... patch_size=[64, 64],

... batch_size=32,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... num_in_channels=1,

... )

>>> config_3d = create_pn2v_configuration(

... experiment_name="pn2v_experiment",

... data_type="czi",

... axes="SCZYX",

... patch_size=[16, 64, 64],

... batch_size=16,

... nm_path="path/to/noise_model.npz",

... num_epochs=100,

... num_in_channels=1,

... )

Get the likelihood configuration for split models.

Returns a tuple containing the following optional entries: - GaussianLikelihoodConfig: Gaussian likelihood configuration for musplit losses - MultiChannelNMConfig: Multi-channel noise model configuration for denoisplit losses - NMLikelihoodConfig: Noise model likelihood configuration for denoisplit losses

Parameters:

Returns:

Raises:

Update trainer parameters with num_epochs and num_steps.

Parameters:

Returns: