Train

Main code interface for training.

biom3d.train.main_pretrain_seg_pred_eval(pretrain_config: str | dict[str, Any] | AttrDict | None = None, train_config: str | None = None, log: str | None = None, path_encoder: str | None = None, freeze_encoder: bool = False, model_encoder: bool = False, path_in: str | None = None, path_out: str | None = None, path_lab: str | None = None) → None

Run a full pipeline including pretraining, training, inference, and evaluation.

This function is designed to execute the four main stages of a segmentation workflow: 1. Pretrain a model or encoder using a pretraining configuration. 2. Train a segmentation model using a training configuration. Optionally initialize with a pretrained encoder. 3. Generate predictions on a test set (from path_in) and save the predicted segmentations to path_out. 4. If ground truth labels are available at path_lab, compute evaluation metrics on the predictions.

Parameters:

• pretrain_config (str, dict or biom3d.utils.AttrDict, optional) – Path to the configuration file or dictionary for pretraining the encoder or model.

• train_config (str) – Path to the training configuration file (python or yaml).

• log (str, optional) – Existing model directory.

• path_encoder (str, optional) – Path to the pretrained encoder to be used in training.

• freeze_encoder (bool, default=False) – Whether to freeze the encoder during training (useful for transfer learning).

• model_encoder (bool, default=False) – If True, path_encoder points to a full model checkpoint. If False, it is an encoder checkpoint only.

• path_in (str, optional) – Path to collection containing images for inference (test set).

• path_out (str, optional) – Path to collection to store the predicted segmentation masks.

• path_lab (str, optional) – Path to collection containing the ground truth masks for evaluation after inference.

Return type:

None

Notes

• If path_in or path_out is None, then this function is equivalent to train

• If path_lab is None, then evaluation will not be done.

• Evaluation result is only printed in terminal.

biom3d.train.main_seg_pred_eval(config_path: str | None = None, path: str | None = None, path_in: str | None = None, path_out: str | None = None, path_lab: str | None = None, freeze_encoder: bool = False)

Interface function to do a training., then prediction and evaluation.

Do 3 tasks:

• train the model

• compute predictions on the test set (path_in) and store the results in path_out

• evaluate the prediction stored in path_out and print the result

Parameters:

• config_path (str, optional) – Path to a Python configuration file (in either .py or .yaml format).

• path (str , optional) – Path to a builder folder which contains the model folder, the model configuration and the training logs.

• path_in (str, optional) – Path to raw image collection used for prediction.

• path_out (str, optional) – Path to prediction output.

• path_lab (str, optional) – Path to mask collection corresponding to the raws, for evaluation.

• freeze_encoder (bool, default=False) – Whether to freeze the encoder during training (useful for transfer learning).

Raises:

same as Builder.__init__ –

Return type:

None

Notes

• If path_in or path_out is None, then this function is equivalent to train

• If path_lab is None, then evaluation will not be done.

• Altough all parameter can be written as None, it would not work if config_path and path are both None.

• Evaluation result is only printed in terminal.

biom3d.train.train(config: str | dict[str, Any] | AttrDict | None = None, path: str | list[str] | None = None) → Builder

Interface function to do a training.

Parameters:

• config (str, dict or biom3d.utils.AttrDict, optional) – Path to a Python configuration file (in either .py or .yaml format) or dictionary of a configuration file. Please refer to biom3d.config_default.py to see the default configuration file format.

• path (str, list of str, optional) – Path to a builder folder which contains the model folder, the model configuration and the training logs. If path is a list of strings, then it is considered that it is intended to run multi-model predictions. Training is not compatible with this mode.

Raises:

same as Builder.__init__ –

Returns:

builder – Builder used for the training.

Return type:

biom3d.builder.Builder