def __init__(self,
checkpoint_params,
dataset,
validation_dataset=None,
txt_preproc=None,
txt_postproc=None,
data_preproc=None,
data_augmenter=None,
n_augmentations=0,
weights=None,
codec=None,
codec_whitelist=[]):
self.checkpoint_params = checkpoint_params
self.dataset = dataset
self.validation_dataset = validation_dataset
self.data_augmenter = data_augmenter
self.n_augmentations = n_augmentations
self.txt_preproc = txt_preproc if txt_preproc else text_processor_from_proto(
checkpoint_params.model.text_preprocessor, "pre")
self.txt_postproc = txt_postproc if txt_postproc else text_processor_from_proto(
checkpoint_params.model.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(
checkpoint_params.model.data_preprocessor)
self.weights = checkpoint_path(weights) if weights else None
self.codec = codec
self.codec_whitelist = codec_whitelist
def __init__(self, params: TrainerParams, scenario, restore=False):
"""Train a DNN using given preprocessing, weights, and data
The purpose of the Trainer is handle a default training mechanism.
As required input it expects a `dataset` and hyperparameters (`checkpoint_params`).
The steps are
1. Loading and preprocessing of the dataset
2. Computation of the codec
3. Construction of the DNN in the desired Deep Learning Framework
4. Launch of the training
During the training the Trainer will perform validation checks if a `validation_dataset` is given
to determine the best model.
Furthermore, the current status is printet and checkpoints are written.
"""
super(Trainer, self).__init__(params, scenario, restore)
self._params: TrainerParams = params
if not isinstance(self._params.checkpoint_save_freq,
str) and self._params.checkpoint_save_freq < 0:
self._params.checkpoint_save_freq = self._params.early_stopping_params.frequency
self._params.warmstart.model = (checkpoint_path(
self._params.warmstart.model) if self._params.warmstart.model else
None)
self.checkpoint = None
if self._params.warmstart.model:
# Manually handle loading
self.checkpoint = SavedCalamariModel(
self._params.warmstart.model,
auto_update=self._params.auto_upgrade_checkpoints,
)
self._params.warmstart.model = self.checkpoint.ckpt_path + ".h5"
self._params.warmstart.trim_graph_name = False
self._codec_changes = None
def __init__(
self,
checkpoint_params,
dataset,
validation_dataset=None,
txt_preproc=None,
txt_postproc=None,
data_preproc=None,
data_augmenter: DataAugmenter = None,
n_augmentations=0,
weights=None,
codec=None,
codec_whitelist=None,
auto_update_checkpoints=True,
preload_training=False,
preload_validation=False,
):
"""Train a DNN using given preprocessing, weights, and data
The purpose of the Trainer is handle a default training mechanism.
As required input it expects a `dataset` and hyperparameters (`checkpoint_params`).
The steps are
1. Loading and preprocessing of the dataset
2. Computation of the codec
3. Construction of the DNN in the desired Deep Learning Framework
4. Launch of the training
During the training the Trainer will perform validation checks if a `validation_dataset` is given
to determine the best model.
Furthermore, the current status is printet and checkpoints are written.
Parameters
----------
checkpoint_params : CheckpointParams
Proto parameter object that defines all hyperparameters of the model
dataset : Dataset
The Dataset used for training
validation_dataset : Dataset, optional
The Dataset used for validation, i.e. choosing the best model
txt_preproc : TextProcessor, optional
Text preprocessor that is applied on loaded text, before the Codec is computed
txt_postproc : TextProcessor, optional
Text processor that is applied on the loaded GT text and on the prediction to receive the final result
data_preproc : DataProcessor, optional
Preprocessing for the image lines (e. g. padding, inversion, deskewing, ...)
data_augmenter : DataAugmenter, optional
A DataAugmenter object to use for data augmentation. Count is set by `n_augmentations`
n_augmentations : int, optional
The number of augmentations performend by the `data_augmenter`
weights : str, optional
Path to a trained model for loading its weights
codec : Codec, optional
If provided the Codec will not be computed automaticall based on the GT, but instead `codec` will be used
codec_whitelist : obj:`list` of :obj:`str`
List of characters to be kept when the loaded `weights` have a different codec than the new one.
"""
self.checkpoint_params = checkpoint_params
self.txt_preproc = txt_preproc if txt_preproc else text_processor_from_proto(
checkpoint_params.model.text_preprocessor, "pre")
self.txt_postproc = txt_postproc if txt_postproc else text_processor_from_proto(
checkpoint_params.model.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(
checkpoint_params.model.data_preprocessor)
self.weights = checkpoint_path(weights) if weights else None
self.codec = codec
self.codec_whitelist = [] if codec_whitelist is None else codec_whitelist
self.auto_update_checkpoints = auto_update_checkpoints
self.dataset = InputDataset(dataset, self.data_preproc,
self.txt_preproc, data_augmenter,
n_augmentations)
self.validation_dataset = InputDataset(
validation_dataset, self.data_preproc,
self.txt_preproc) if validation_dataset else None
self.preload_training = preload_training
self.preload_validation = preload_validation
if len(self.dataset) == 0:
raise Exception("Dataset is empty.")
if self.validation_dataset and len(self.validation_dataset) == 0:
raise Exception(
"Validation dataset is empty. Provide valid validation data for early stopping."
)
def __init__(self, checkpoint_params,
dataset,
validation_dataset=None,
txt_preproc=None,
txt_postproc=None,
data_preproc=None,
data_augmenter: DataAugmenter = None,
n_augmentations=0,
weights=None,
codec=None,
codec_whitelist=[],
auto_update_checkpoints=True,
preload_training=False,
preload_validation=False,
):
"""Train a DNN using given preprocessing, weights, and data
The purpose of the Trainer is handle a default training mechanism.
As required input it expects a `dataset` and hyperparameters (`checkpoint_params`).
The steps are
1. Loading and preprocessing of the dataset
2. Computation of the codec
3. Construction of the DNN in the desired Deep Learning Framework
4. Launch of the training
During the training the Trainer will perform validation checks if a `validation_dataset` is given
to determine the best model.
Furthermore, the current status is printet and checkpoints are written.
Parameters
----------
checkpoint_params : CheckpointParams
Proto parameter object that defines all hyperparameters of the model
dataset : Dataset
The Dataset used for training
validation_dataset : Dataset, optional
The Dataset used for validation, i.e. choosing the best model
txt_preproc : TextProcessor, optional
Text preprocessor that is applied on loaded text, before the Codec is computed
txt_postproc : TextProcessor, optional
Text processor that is applied on the loaded GT text and on the prediction to receive the final result
data_preproc : DataProcessor, optional
Preprocessing for the image lines (e. g. padding, inversion, deskewing, ...)
data_augmenter : DataAugmenter, optional
A DataAugmenter object to use for data augmentation. Count is set by `n_augmentations`
n_augmentations : int, optional
The number of augmentations performend by the `data_augmenter`
weights : str, optional
Path to a trained model for loading its weights
codec : Codec, optional
If provided the Codec will not be computed automaticall based on the GT, but instead `codec` will be used
codec_whitelist : obj:`list` of :obj:`str`
List of characters to be kept when the loaded `weights` have a different codec than the new one.
"""
self.checkpoint_params = checkpoint_params
self.txt_preproc = txt_preproc if txt_preproc else text_processor_from_proto(checkpoint_params.model.text_preprocessor, "pre")
self.txt_postproc = txt_postproc if txt_postproc else text_processor_from_proto(checkpoint_params.model.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(checkpoint_params.model.data_preprocessor)
self.weights = checkpoint_path(weights) if weights else None
self.codec = codec
self.codec_whitelist = codec_whitelist
self.auto_update_checkpoints = auto_update_checkpoints
self.dataset = InputDataset(dataset, self.data_preproc, self.txt_preproc, data_augmenter, n_augmentations)
self.validation_dataset = InputDataset(validation_dataset, self.data_preproc, self.txt_preproc) if validation_dataset else None
self.preload_training = preload_training
self.preload_validation = preload_validation
if len(self.dataset) == 0:
raise Exception("Dataset is empty.")
if self.validation_dataset and len(self.validation_dataset) == 0:
raise Exception("Validation dataset is empty. Provide valid validation data for early stopping.")
