dacapo.experiments.starts.cosem_start

Attributes

logger

Classes

CosemStart

A class to represent the starting point for tasks. This class inherits

Functions

get_model_setup(run)

Loads the model setup from the dacapo store for the specified run. The

Module Contents

dacapo.experiments.starts.cosem_start.logger

dacapo.experiments.starts.cosem_start.get_model_setup(run)

Loads the model setup from the dacapo store for the specified run. The model setup includes the classes_channels, voxel_size_input and voxel_size_output.

Parameters:

run – str The run for which the model setup is to be loaded.

Returns:

list: The classes_channels of the model.
voxel_size_inputlist: The voxel_size_input of the model.
voxel_size_outputlist: The voxel_size_output of the model.

Return type:

classes_channels

Raises:

Exception – If the model setup cannot be loaded, an Exception is thrown which is logged and handled by training the model without head matching.

Examples

>>> classes_channels, voxel_size_input, voxel_size_output = get_model_setup(run)

Notes

This function is called by the CosemStart class to load the model setup from the dacapo store for the specified run.

class dacapo.experiments.starts.cosem_start.CosemStart(start_config)

A class to represent the starting point for tasks. This class inherits from the Start class and is used to load the weights of the starter model used for finetuning. The weights are loaded from the dacapo store for the specified run and criterion.

run: str The run to be used as a starting point for tasks.

criterion: str The criterion to be used for choosing weights from run.

name: str The name of the run and criterion.

channels: list The classes_channels of the model.

__init__(start_config): Initializes the CosemStart class with specified config to run the initialization of weights for a model associated with a specific criterion.

check(): Checks if the checkpoint for the specified run and criterion exists.

initialize_weights(model, new_head=None): Retrieves the weights from the dacapo store and load them into the model.

Notes

This class is used to represent the starting point for tasks. The weights of the starter model used for finetuning are loaded from the dacapo store.

run

criterion

name

channels

check()

Checks if the checkpoint for the specified run and criterion exists.

Raises:: Exception – If the checkpoint does not exist, an Exception is thrown which is logged and handled by training the model without head matching.

Examples

>>> check()

Notes

This function is called by the CosemStart class to check if the checkpoint for the specified run and criterion exists.

initialize_weights(model, new_head=None)

Retrieves the weights from the dacapo store and load them into the model.

Parameters:

model – obj The model to which the weights are to be loaded.
new_head – list The labels of the new head.

Returns:

obj: The model with the weights loaded from the dacapo store.

Return type:

model

Raises:

RuntimeError – If weights of a non-existing or mismatched layer are being loaded, a RuntimeError exception is thrown which is logged and handled by loading only the common layers from weights.

Examples

>>> model = initialize_weights(model, new_head)

Notes

This function is called by the CosemStart class to retrieve the weights from the dacapo store and load them into the model.