configuration_factories
Convenience functions to create configurations for training and inference.
algorithm_factory(algorithm)
#
Create an algorithm model for training CAREamics.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
algorithm | dict | Algorithm dictionary. | required |
Returns:
Type | Description |
---|---|
N2VAlgorithm or N2NAlgorithm or CAREAlgorithm | Algorithm model for training CAREamics. |
Source code in src/careamics/config/configuration_factories.py
create_care_configuration(experiment_name, data_type, axes, patch_size, batch_size, num_epochs, augmentations=None, independent_channels=True, loss='mae', n_channels_in=None, n_channels_out=None, logger='none', model_params=None, optimizer='Adam', optimizer_params=None, lr_scheduler='ReduceLROnPlateau', lr_scheduler_params=None, train_dataloader_params=None, val_dataloader_params=None, checkpoint_params=None)
#
Create a configuration for training CARE.
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 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 transformations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default transforms, a list of transforms can be passed to the augmentations
parameter. To disable the transforms, simply pass an empty list.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
experiment_name | str | Name of the experiment. | required |
data_type | Literal['array', 'tiff', 'czi', 'custom'] | Type of the data. | required |
axes | str | Axes of the data (e.g. SYX). | required |
patch_size | List[int] | Size of the patches along the spatial dimensions (e.g. [64, 64]). | required |
batch_size | int | Batch size. | required |
num_epochs | int | Number of epochs. | required |
augmentations | list of transforms | List of transforms to apply, either both or one of XYFlipModel and XYRandomRotate90Model. By default, it applies both XYFlip (on X and Y) and XYRandomRotate90 (in XY) to the images. | None |
independent_channels | bool | Whether to train all channels independently, by default False. | True |
loss | Literal['mae', 'mse'] | Loss function to use. | "mae" |
n_channels_in | int or None | Number of channels in. | None |
n_channels_out | int or None | Number of channels out. | None |
logger | Literal['wandb', 'tensorboard', 'none'] | Logger to use. | "none" |
model_params | dict | UNetModel parameters. | None |
optimizer | Literal['Adam', 'Adamax', 'SGD'] | Optimizer to use. | "Adam" |
optimizer_params | dict | Parameters for the optimizer, see PyTorch documentation for more details. | None |
lr_scheduler | Literal['ReduceLROnPlateau', 'StepLR'] | Learning rate scheduler to use. | "ReduceLROnPlateau" |
lr_scheduler_params | dict | Parameters for the learning rate scheduler, see PyTorch documentation for more details. | None |
train_dataloader_params | dict | Parameters for the training dataloader, see the PyTorch docs for | None |
val_dataloader_params | dict | Parameters for the validation dataloader, see PyTorch the docs for | None |
checkpoint_params | dict | Parameters for the checkpoint callback, see PyTorch Lightning documentation ( | None |
Returns:
Type | Description |
---|---|
Configuration | Configuration for training CARE. |
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
... )
To disable transforms, 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 transforms can be passed to the augmentations
parameter to replace the default augmentations:
>>> from careamics.config.transformations import XYFlipModel
>>> 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
... XYFlipModel(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,
... )
Source code in src/careamics/config/configuration_factories.py
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create_n2n_configuration(experiment_name, data_type, axes, patch_size, batch_size, num_epochs, augmentations=None, independent_channels=True, loss='mae', n_channels_in=None, n_channels_out=None, logger='none', model_params=None, optimizer='Adam', optimizer_params=None, lr_scheduler='ReduceLROnPlateau', lr_scheduler_params=None, train_dataloader_params=None, val_dataloader_params=None, checkpoint_params=None)
#
Create a configuration for training Noise2Noise.
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 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 transformations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default transforms, a list of transforms can be passed to the augmentations
parameter. To disable the transforms, simply pass an empty list.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
experiment_name | str | Name of the experiment. | required |
data_type | Literal['array', 'tiff', 'czi', 'custom'] | Type of the data. | required |
axes | str | Axes of the data (e.g. SYX). | required |
patch_size | List[int] | Size of the patches along the spatial dimensions (e.g. [64, 64]). | required |
batch_size | int | Batch size. | required |
num_epochs | int | Number of epochs. | required |
augmentations | list of transforms | List of transforms to apply, either both or one of XYFlipModel and XYRandomRotate90Model. By default, it applies both XYFlip (on X and Y) and XYRandomRotate90 (in XY) to the images. | None |
independent_channels | bool | Whether to train all channels independently, by default False. | True |
loss | Literal['mae', 'mse'] | Loss function to use, by default "mae". | 'mae' |
n_channels_in | int or None | Number of channels in. | None |
n_channels_out | int or None | Number of channels out. | None |
logger | Literal['wandb', 'tensorboard', 'none'] | Logger to use, by default "none". | 'none' |
model_params | dict | UNetModel parameters. | None |
optimizer | Literal['Adam', 'Adamax', 'SGD'] | Optimizer to use. | "Adam" |
optimizer_params | dict | Parameters for the optimizer, see PyTorch documentation for more details. | None |
lr_scheduler | Literal['ReduceLROnPlateau', 'StepLR'] | Learning rate scheduler to use. | "ReduceLROnPlateau" |
lr_scheduler_params | dict | Parameters for the learning rate scheduler, see PyTorch documentation for more details. | None |
train_dataloader_params | dict | Parameters for the training dataloader, see the PyTorch docs for | None |
val_dataloader_params | dict | Parameters for the validation dataloader, see PyTorch the docs for | None |
checkpoint_params | dict | Parameters for the checkpoint callback, see PyTorch Lightning documentation ( | None |
Returns:
Type | Description |
---|---|
Configuration | Configuration for training Noise2Noise. |
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
... )
To disable transforms, 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 transforms can be passed to the augmentations
parameter to replace the default augmentations:
>>> from careamics.config.transformations import XYFlipModel
>>> 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
... XYFlipModel(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,
... )
Source code in src/careamics/config/configuration_factories.py
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create_n2v_configuration(experiment_name, data_type, axes, patch_size, batch_size, num_epochs, augmentations=None, independent_channels=True, use_n2v2=False, n_channels=None, roi_size=11, masked_pixel_percentage=0.2, struct_n2v_axis='none', struct_n2v_span=5, logger='none', model_params=None, optimizer='Adam', optimizer_params=None, lr_scheduler='ReduceLROnPlateau', lr_scheduler_params=None, train_dataloader_params=None, val_dataloader_params=None, checkpoint_params=None)
#
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 path_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 transformations 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 transformations (flip in X and Y, rotations by 90 degrees in the XY plane) are applied. Rather than the default transforms, a list of transforms can be passed to the augmentations
parameter. To disable the transforms, 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:
Name | Type | Description | Default |
---|---|---|---|
experiment_name | str | Name of the experiment. | required |
data_type | Literal['array', 'tiff', 'czi', 'custom'] | Type of the data. | required |
axes | str | Axes of the data (e.g. SYX). | required |
patch_size | List[int] | Size of the patches along the spatial dimensions (e.g. [64, 64]). | required |
batch_size | int | Batch size. | required |
num_epochs | int | Number of epochs. | required |
augmentations | list of transforms | List of transforms to apply, either both or one of XYFlipModel and XYRandomRotate90Model. By default, it applies both XYFlip (on X and Y) and XYRandomRotate90 (in XY) to the images. | None |
independent_channels | bool | Whether to train all channels together, by default True. | True |
use_n2v2 | bool | Whether to use N2V2, by default False. | False |
n_channels | int or None | Number of channels (in and out). | None |
roi_size | int | N2V pixel manipulation area, by default 11. | 11 |
masked_pixel_percentage | float | Percentage of pixels masked in each patch, by default 0.2. | 0.2 |
struct_n2v_axis | Literal['horizontal', 'vertical', 'none'] | Axis along which to apply structN2V mask, by default "none". | 'none' |
struct_n2v_span | int | Span of the structN2V mask, by default 5. | 5 |
logger | Literal['wandb', 'tensorboard', 'none'] | Logger to use, by default "none". | 'none' |
model_params | dict | UNetModel parameters. | None |
optimizer | Literal['Adam', 'Adamax', 'SGD'] | Optimizer to use. | "Adam" |
optimizer_params | dict | Parameters for the optimizer, see PyTorch documentation for more details. | None |
lr_scheduler | Literal['ReduceLROnPlateau', 'StepLR'] | Learning rate scheduler to use. | "ReduceLROnPlateau" |
lr_scheduler_params | dict | Parameters for the learning rate scheduler, see PyTorch documentation for more details. | None |
train_dataloader_params | dict | Parameters for the training dataloader, see the PyTorch docs for | None |
val_dataloader_params | dict | Parameters for the validation dataloader, see PyTorch the docs for | None |
checkpoint_params | dict | Parameters for the checkpoint callback, see PyTorch Lightning documentation ( | None |
Returns:
Type | Description |
---|---|
Configuration | Configuration for training N2V. |
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
... )
To disable transforms, 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 transforms can be passed to the augmentations
parameter:
>>> from careamics.config.transformations import XYFlipModel
>>> 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
... XYFlipModel(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,
... )
Source code in src/careamics/config/configuration_factories.py
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