def __init__(self,
checkpoint=None,
text_postproc=None,
data_preproc=None,
codec=None,
backend=None):
self.backend = backend
self.checkpoint = checkpoint
self.codec = codec
if checkpoint:
if backend:
raise Exception(
"Either a checkpoint or a backend can be provided")
with open(checkpoint + '.json', 'r') as f:
checkpoint_params = json_format.Parse(f.read(),
CheckpointParams())
self.model_params = checkpoint_params.model
self.network_params = self.model_params.network
self.backend = create_backend_from_proto(self.network_params,
restore=self.checkpoint)
self.text_postproc = text_postproc if text_postproc else text_processor_from_proto(
self.model_params.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(
self.model_params.data_preprocessor)
elif backend:
self.model_params = None
self.network_params = backend.network_proto
self.text_postproc = text_postproc
self.data_preproc = data_preproc
else:
raise Exception(
"Either a checkpoint or a existing backend must be provided")
def __init__(self, checkpoint=None, text_postproc=None, data_preproc=None, codec=None, network=None,
batch_size=1, processes=1,
auto_update_checkpoints=True,
with_gt=False,
):
""" Predicting a dataset based on a trained model
Parameters
----------
checkpoint : str, optional
filepath of the checkpoint of the network to load, alternatively you can directly use a loaded `network`
text_postproc : TextProcessor, optional
text processor to be applied on the predicted sentence for the final output.
If loaded from a checkpoint the text processor will be loaded from it.
data_preproc : DataProcessor, optional
data processor (must be the same as of the trained model) to be applied to the input image.
If loaded from a checkpoint the text processor will be loaded from it.
codec : Codec, optional
Codec of the deep net to use for decoding. This parameter is only required if a custom codec is used,
or a `network` has been provided instead of a `checkpoint`
network : ModelInterface, optional
DNN instance to used. Alternatively you can provide a `checkpoint` to load a network.
batch_size : int, optional
Batch size to use for prediction
processes : int, optional
The number of processes to use for prediction
auto_update_checkpoints : bool, optional
Update old models automatically (this will change the checkpoint files)
with_gt : bool, optional
The prediction will also output the ground truth if available else None
"""
self.network = network
self.checkpoint = checkpoint
self.processes = processes
self.auto_update_checkpoints = auto_update_checkpoints
self.with_gt = with_gt
if checkpoint:
if network:
raise Exception("Either a checkpoint or a network can be provided")
ckpt = Checkpoint(checkpoint, auto_update=self.auto_update_checkpoints)
self.checkpoint = ckpt.ckpt_path
checkpoint_params = ckpt.checkpoint
self.model_params = checkpoint_params.model
self.codec = codec if codec else Codec(self.model_params.codec.charset)
self.network_params = self.model_params.network
backend = create_backend_from_proto(self.network_params, restore=self.checkpoint, processes=processes)
self.text_postproc = text_postproc if text_postproc else text_processor_from_proto(self.model_params.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(self.model_params.data_preprocessor)
self.network = backend.create_net(
dataset=None,
codec=self.codec,
restore=self.checkpoint, weights=None, graph_type="predict", batch_size=batch_size)
elif network:
self.codec = codec
self.model_params = None
self.network_params = network.network_proto
self.text_postproc = text_postproc
self.data_preproc = data_preproc
if not codec:
raise Exception("A codec is required if preloaded network is used.")
else:
raise Exception("Either a checkpoint or a existing backend must be provided")
self.out_to_in_trans = OutputToInputTransformer(self.data_preproc, self.network)
def train(self, progress_bar=False):
checkpoint_params = self.checkpoint_params
train_start_time = time.time() + self.checkpoint_params.total_time
self.dataset.load_samples(processes=1, progress_bar=progress_bar)
datas, txts = self.dataset.train_samples(skip_empty=checkpoint_params.skip_invalid_gt)
if len(datas) == 0:
raise Exception("Empty dataset is not allowed. Check if the data is at the correct location")
if self.validation_dataset:
self.validation_dataset.load_samples(processes=1, progress_bar=progress_bar)
validation_datas, validation_txts = self.validation_dataset.train_samples(skip_empty=checkpoint_params.skip_invalid_gt)
if len(validation_datas) == 0:
raise Exception("Validation dataset is empty. Provide valid validation data for early stopping.")
else:
validation_datas, validation_txts = [], []
# preprocessing steps
texts = self.txt_preproc.apply(txts, processes=checkpoint_params.processes, progress_bar=progress_bar)
datas = self.data_preproc.apply(datas, processes=checkpoint_params.processes, progress_bar=progress_bar)
validation_txts = self.txt_preproc.apply(validation_txts, processes=checkpoint_params.processes, progress_bar=progress_bar)
validation_datas = self.data_preproc.apply(validation_datas, processes=checkpoint_params.processes, progress_bar=progress_bar)
# compute the codec
codec = self.codec if self.codec else Codec.from_texts(texts, whitelist=self.codec_whitelist)
# data augmentation on preprocessed data
if self.data_augmenter:
datas, texts = self.data_augmenter.augment_datas(datas, texts, n_augmentations=self.n_augmentations,
processes=checkpoint_params.processes, progress_bar=progress_bar)
# TODO: validation data augmentation
# validation_datas, validation_txts = self.data_augmenter.augment_datas(validation_datas, validation_txts, n_augmentations=0,
# processes=checkpoint_params.processes, progress_bar=progress_bar)
# create backend
network_params = checkpoint_params.model.network
network_params.features = checkpoint_params.model.line_height
network_params.classes = len(codec)
if self.weights:
# if we load the weights, take care of codec changes as-well
with open(self.weights + '.json', 'r') as f:
restore_checkpoint_params = json_format.Parse(f.read(), CheckpointParams())
restore_model_params = restore_checkpoint_params.model
# checks
if checkpoint_params.model.line_height != network_params.features:
raise Exception("The model to restore has a line height of {} but a line height of {} is requested".format(
network_params.features, checkpoint_params.model.line_height
))
# create codec of the same type
restore_codec = codec.__class__(restore_model_params.codec.charset)
# the codec changes as tuple (deletions/insertions), and the new codec is the changed old one
codec_changes = restore_codec.align(codec)
codec = restore_codec
print("Codec changes: {} deletions, {} appends".format(len(codec_changes[0]), len(codec_changes[1])))
# The actual weight/bias matrix will be changed after loading the old weights
else:
codec_changes = None
# store the new codec
checkpoint_params.model.codec.charset[:] = codec.charset
print("CODEC: {}".format(codec.charset))
# compute the labels with (new/current) codec
labels = [codec.encode(txt) for txt in texts]
backend = create_backend_from_proto(network_params,
weights=self.weights,
)
backend.set_train_data(datas, labels)
backend.set_prediction_data(validation_datas)
if codec_changes:
backend.realign_model_labels(*codec_changes)
backend.prepare(train=True)
loss_stats = RunningStatistics(checkpoint_params.stats_size, checkpoint_params.loss_stats)
ler_stats = RunningStatistics(checkpoint_params.stats_size, checkpoint_params.ler_stats)
dt_stats = RunningStatistics(checkpoint_params.stats_size, checkpoint_params.dt_stats)
early_stopping_enabled = self.validation_dataset is not None \
and checkpoint_params.early_stopping_frequency > 0 \
and checkpoint_params.early_stopping_nbest > 1
early_stopping_best_accuracy = checkpoint_params.early_stopping_best_accuracy
early_stopping_best_cur_nbest = checkpoint_params.early_stopping_best_cur_nbest
early_stopping_best_at_iter = checkpoint_params.early_stopping_best_at_iter
early_stopping_predictor = Predictor(codec=codec, text_postproc=self.txt_postproc,
backend=backend)
# Start the actual training
# ====================================================================================
iter = checkpoint_params.iter
# helper function to write a checkpoint
def make_checkpoint(base_dir, prefix, version=None):
if version:
checkpoint_path = os.path.abspath(os.path.join(base_dir, "{}{}.ckpt".format(prefix, version)))
else:
checkpoint_path = os.path.abspath(os.path.join(base_dir, "{}{:08d}.ckpt".format(prefix, iter + 1)))
print("Storing checkpoint to '{}'".format(checkpoint_path))
backend.save_checkpoint(checkpoint_path)
checkpoint_params.iter = iter
checkpoint_params.loss_stats[:] = loss_stats.values
checkpoint_params.ler_stats[:] = ler_stats.values
checkpoint_params.dt_stats[:] = dt_stats.values
checkpoint_params.total_time = time.time() - train_start_time
checkpoint_params.early_stopping_best_accuracy = early_stopping_best_accuracy
checkpoint_params.early_stopping_best_cur_nbest = early_stopping_best_cur_nbest
checkpoint_params.early_stopping_best_at_iter = early_stopping_best_at_iter
with open(checkpoint_path + ".json", 'w') as f:
f.write(json_format.MessageToJson(checkpoint_params))
return checkpoint_path
try:
last_checkpoint = None
# Training loop, can be interrupted by early stopping
for iter in range(iter, checkpoint_params.max_iters):
checkpoint_params.iter = iter
iter_start_time = time.time()
result = backend.train_step(checkpoint_params.batch_size)
if not np.isfinite(result['loss']):
print("Error: Loss is not finite! Trying to restart from last checkpoint.")
if not last_checkpoint:
raise Exception("No checkpoint written yet. Training must be stopped.")
else:
# reload also non trainable weights, such as solver-specific variables
backend.load_checkpoint_weights(last_checkpoint, restore_only_trainable=False)
continue
loss_stats.push(result['loss'])
ler_stats.push(result['ler'])
dt_stats.push(time.time() - iter_start_time)
if iter % checkpoint_params.display == 0:
pred_sentence = self.txt_postproc.apply("".join(codec.decode(result["decoded"][0])))
gt_sentence = self.txt_postproc.apply("".join(codec.decode(result["gt"][0])))
print("#{:08d}: loss={:.8f} ler={:.8f} dt={:.8f}s".format(iter, loss_stats.mean(), ler_stats.mean(), dt_stats.mean()))
print(" PRED: '{}'".format(pred_sentence))
print(" TRUE: '{}'".format(gt_sentence))
if (iter + 1) % checkpoint_params.checkpoint_frequency == 0:
last_checkpoint = make_checkpoint(checkpoint_params.output_dir, checkpoint_params.output_model_prefix)
if early_stopping_enabled and (iter + 1) % checkpoint_params.early_stopping_frequency == 0:
print("Checking early stopping model")
out = early_stopping_predictor.predict_raw(validation_datas, batch_size=checkpoint_params.batch_size,
progress_bar=progress_bar, apply_preproc=False)
pred_texts = [d.sentence for d in out]
result = Evaluator.evaluate(gt_data=validation_txts, pred_data=pred_texts, progress_bar=progress_bar)
accuracy = 1 - result["avg_ler"]
if accuracy > early_stopping_best_accuracy:
early_stopping_best_accuracy = accuracy
early_stopping_best_cur_nbest = 1
early_stopping_best_at_iter = iter + 1
# overwrite as best model
last_checkpoint = make_checkpoint(
checkpoint_params.early_stopping_best_model_output_dir,
prefix="",
version=checkpoint_params.early_stopping_best_model_prefix,
)
print("Found better model with accuracy of {:%}".format(early_stopping_best_accuracy))
else:
early_stopping_best_cur_nbest += 1
print("No better model found. Currently accuracy of {:%} at iter {} (remaining nbest = {})".
format(early_stopping_best_accuracy, early_stopping_best_at_iter,
checkpoint_params.early_stopping_nbest - early_stopping_best_cur_nbest))
if accuracy > 0 and early_stopping_best_cur_nbest >= checkpoint_params.early_stopping_nbest:
print("Early stopping now.")
break
except KeyboardInterrupt as e:
print("Storing interrupted checkpoint")
make_checkpoint(checkpoint_params.output_dir,
checkpoint_params.output_model_prefix,
"interrupted")
raise e
print("Total time {}s for {} iterations.".format(time.time() - train_start_time, iter))
def train(self, auto_compute_codec=False, progress_bar=False):
""" Launch the training
Parameters
----------
auto_compute_codec : bool
Compute the codec automatically based on the provided ground truth.
Else provide a codec using a whitelist (faster).
progress_bar : bool
Show or hide any progress bar
"""
checkpoint_params = self.checkpoint_params
train_start_time = time.time() + self.checkpoint_params.total_time
# load training dataset
if self.preload_training:
self.dataset.preload(processes=checkpoint_params.processes,
progress_bar=progress_bar)
# load validation dataset
if self.validation_dataset and self.preload_validation:
self.validation_dataset.preload(
processes=checkpoint_params.processes,
progress_bar=progress_bar)
# compute the codec
if self.codec:
codec = self.codec
else:
if len(self.codec_whitelist) == 0 or auto_compute_codec:
codec = Codec.from_input_dataset(
[self.dataset, self.validation_dataset],
whitelist=self.codec_whitelist,
progress_bar=progress_bar)
else:
codec = Codec.from_texts([], whitelist=self.codec_whitelist)
# create backend
network_params = checkpoint_params.model.network
network_params.features = checkpoint_params.model.line_height
network_params.classes = len(codec)
if self.weights:
# if we load the weights, take care of codec changes as-well
ckpt = Checkpoint(self.weights + '.json',
auto_update=self.auto_update_checkpoints)
restore_checkpoint_params = ckpt.checkpoint
restore_model_params = restore_checkpoint_params.model
# checks
if checkpoint_params.model.line_height != network_params.features:
raise Exception(
"The model to restore has a line height of {} but a line height of {} is requested"
.format(network_params.features,
checkpoint_params.model.line_height))
# create codec of the same type
restore_codec = codec.__class__(restore_model_params.codec.charset)
# the codec changes as tuple (deletions/insertions), and the new codec is the changed old one
codec_changes = restore_codec.align(codec)
codec = restore_codec
print("Codec changes: {} deletions, {} appends".format(
len(codec_changes[0]), len(codec_changes[1])))
# The actual weight/bias matrix will be changed after loading the old weights
if all([c == 0 for c in codec_changes]):
codec_changes = None # No codec changes
else:
codec_changes = None
# store the new codec
checkpoint_params.model.codec.charset[:] = codec.charset
print("CODEC: {}".format(codec.charset))
backend = create_backend_from_proto(
network_params,
weights=self.weights,
)
train_net = backend.create_net(self.dataset,
codec,
restore=None,
weights=self.weights,
graph_type="train",
batch_size=checkpoint_params.batch_size)
test_net = backend.create_net(self.validation_dataset,
codec,
restore=None,
weights=self.weights,
graph_type="test",
batch_size=checkpoint_params.batch_size)
if codec_changes:
# only required on one net, since the other shares the same variables
train_net.realign_model_labels(*codec_changes)
train_net.prepare()
test_net.prepare()
if checkpoint_params.current_stage == 0:
self._run_train(train_net, test_net, codec, train_start_time,
progress_bar)
if checkpoint_params.data_aug_retrain_on_original and self.dataset.data_augmenter and self.dataset.data_augmentation_amount > 0:
print("Starting training on original data only")
if checkpoint_params.current_stage == 0:
checkpoint_params.current_stage = 1
checkpoint_params.iter = 0
checkpoint_params.early_stopping_best_at_iter = 0
checkpoint_params.early_stopping_best_cur_nbest = 0
checkpoint_params.early_stopping_best_accuracy = 0
self.dataset.generate_only_non_augmented = True # this is the important line!
train_net.prepare()
test_net.prepare()
self._run_train(train_net, test_net, codec, train_start_time,
progress_bar)
train_net.prepare() # reset the state
test_net.prepare() # to prevent blocking of tensorflow on shutdown
def train(self, progress_bar=False):
""" Launch the training
Parameters
----------
progress_bar : bool
Show or hide any progress bar
"""
checkpoint_params = self.checkpoint_params
train_start_time = time.time() + self.checkpoint_params.total_time
self.dataset.load_samples(processes=1, progress_bar=progress_bar)
datas, txts = self.dataset.train_samples(
skip_empty=checkpoint_params.skip_invalid_gt)
if len(datas) == 0:
raise Exception(
"Empty dataset is not allowed. Check if the data is at the correct location"
)
if self.validation_dataset:
self.validation_dataset.load_samples(processes=1,
progress_bar=progress_bar)
validation_datas, validation_txts = self.validation_dataset.train_samples(
skip_empty=checkpoint_params.skip_invalid_gt)
if len(validation_datas) == 0:
raise Exception(
"Validation dataset is empty. Provide valid validation data for early stopping."
)
else:
validation_datas, validation_txts = [], []
# preprocessing steps
texts = self.txt_preproc.apply(txts,
processes=checkpoint_params.processes,
progress_bar=progress_bar)
datas, params = [
list(a) for a in zip(
*self.data_preproc.apply(datas,
processes=checkpoint_params.processes,
progress_bar=progress_bar))
]
validation_txts = self.txt_preproc.apply(
validation_txts,
processes=checkpoint_params.processes,
progress_bar=progress_bar)
validation_data_params = self.data_preproc.apply(
validation_datas,
processes=checkpoint_params.processes,
progress_bar=progress_bar)
# compute the codec
codec = self.codec if self.codec else Codec.from_texts(
texts, whitelist=self.codec_whitelist)
# store original data in case data augmentation is used with a second step
original_texts = texts
original_datas = datas
# data augmentation on preprocessed data
if self.data_augmenter:
datas, texts = self.data_augmenter.augment_datas(
datas,
texts,
n_augmentations=self.n_augmentations,
processes=checkpoint_params.processes,
progress_bar=progress_bar)
# TODO: validation data augmentation
# validation_datas, validation_txts = self.data_augmenter.augment_datas(validation_datas, validation_txts, n_augmentations=0,
# processes=checkpoint_params.processes, progress_bar=progress_bar)
# create backend
network_params = checkpoint_params.model.network
network_params.features = checkpoint_params.model.line_height
network_params.classes = len(codec)
if self.weights:
# if we load the weights, take care of codec changes as-well
ckpt = Checkpoint(self.weights + '.json',
auto_update=self.auto_update_checkpoints)
restore_checkpoint_params = ckpt.checkpoint
restore_model_params = restore_checkpoint_params.model
# checks
if checkpoint_params.model.line_height != network_params.features:
raise Exception(
"The model to restore has a line height of {} but a line height of {} is requested"
.format(network_params.features,
checkpoint_params.model.line_height))
# create codec of the same type
restore_codec = codec.__class__(restore_model_params.codec.charset)
# the codec changes as tuple (deletions/insertions), and the new codec is the changed old one
codec_changes = restore_codec.align(codec)
codec = restore_codec
print("Codec changes: {} deletions, {} appends".format(
len(codec_changes[0]), len(codec_changes[1])))
# The actual weight/bias matrix will be changed after loading the old weights
if all([c == 0 for c in codec_changes]):
codec_changes = None # No codec changes
else:
codec_changes = None
# store the new codec
checkpoint_params.model.codec.charset[:] = codec.charset
print("CODEC: {}".format(codec.charset))
# compute the labels with (new/current) codec
labels = [codec.encode(txt) for txt in texts]
backend = create_backend_from_proto(
network_params,
weights=self.weights,
)
train_net = backend.create_net(restore=None,
weights=self.weights,
graph_type="train",
batch_size=checkpoint_params.batch_size)
test_net = backend.create_net(restore=None,
weights=self.weights,
graph_type="test",
batch_size=checkpoint_params.batch_size)
train_net.set_data(datas, labels)
test_net.set_data(validation_datas, validation_txts)
if codec_changes:
# only required on one net, since the other shares the same variables
train_net.realign_model_labels(*codec_changes)
train_net.prepare()
test_net.prepare()
if checkpoint_params.current_stage == 0:
self._run_train(train_net, test_net, codec, validation_data_params,
train_start_time, progress_bar)
if checkpoint_params.data_aug_retrain_on_original and self.data_augmenter and self.n_augmentations > 0:
print("Starting training on original data only")
if checkpoint_params.current_stage == 0:
checkpoint_params.current_stage = 1
checkpoint_params.iter = 0
checkpoint_params.early_stopping_best_at_iter = 0
checkpoint_params.early_stopping_best_cur_nbest = 0
checkpoint_params.early_stopping_best_accuracy = 0
train_net.set_data(original_datas,
[codec.encode(txt) for txt in original_texts])
test_net.set_data(validation_datas, validation_txts)
train_net.prepare()
test_net.prepare()
self._run_train(train_net, test_net, codec, validation_data_params,
train_start_time, progress_bar)
def __init__(self, checkpoint=None, text_postproc=None, data_preproc=None, codec=None, network=None,
batch_size=1, processes=1,
auto_update_checkpoints=True,
with_gt=False,
):
""" Predicting a dataset based on a trained model
Parameters
----------
checkpoint : str, optional
filepath of the checkpoint of the network to load, alternatively you can directly use a loaded `network`
text_postproc : TextProcessor, optional
text processor to be applied on the predicted sentence for the final output.
If loaded from a checkpoint the text processor will be loaded from it.
data_preproc : DataProcessor, optional
data processor (must be the same as of the trained model) to be applied to the input image.
If loaded from a checkpoint the text processor will be loaded from it.
codec : Codec, optional
Codec of the deep net to use for decoding. This parameter is only required if a custom codec is used,
or a `network` has been provided instead of a `checkpoint`
network : ModelInterface, optional
DNN instance to used. Alternatively you can provide a `checkpoint` to load a network.
batch_size : int, optional
Batch size to use for prediction
processes : int, optional
The number of processes to use for prediction
auto_update_checkpoints : bool, optional
Update old models automatically (this will change the checkpoint files)
with_gt : bool, optional
The prediction will also output the ground truth if available else None
"""
self.network = network
self.checkpoint = checkpoint
self.processes = processes
self.auto_update_checkpoints = auto_update_checkpoints
self.with_gt = with_gt
if checkpoint:
if network:
raise Exception("Either a checkpoint or a network can be provided")
ckpt = Checkpoint(checkpoint, auto_update=self.auto_update_checkpoints)
checkpoint_params = ckpt.checkpoint
self.model_params = checkpoint_params.model
self.codec = codec if codec else Codec(self.model_params.codec.charset)
self.network_params = self.model_params.network
backend = create_backend_from_proto(self.network_params, restore=self.checkpoint, processes=processes)
self.text_postproc = text_postproc if text_postproc else text_processor_from_proto(self.model_params.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(self.model_params.data_preprocessor)
self.network = backend.create_net(
dataset=None,
codec=self.codec,
restore=self.checkpoint, weights=None, graph_type="predict", batch_size=batch_size)
elif network:
self.codec = codec
self.model_params = None
self.network_params = network.network_proto
self.text_postproc = text_postproc
self.data_preproc = data_preproc
if not codec:
raise Exception("A codec is required if preloaded network is used.")
else:
raise Exception("Either a checkpoint or a existing backend must be provided")
self.out_to_in_trans = OutputToInputTransformer(self.data_preproc, self.network)
def __init__(self,
checkpoint=None,
text_postproc=None,
data_preproc=None,
codec=None,
network=None,
batch_size=1,
processes=1):
""" Predicting a dataset based on a trained model
Parameters
----------
checkpoint : str, optional
filepath of the checkpoint of the network to load, alternatively you can directly use a loaded `network`
text_postproc : TextProcessor, optional
text processor to be applied on the predicted sentence for the final output.
If loaded from a checkpoint the text processor will be loaded from it.
data_preproc : DataProcessor, optional
data processor (must be the same as of the trained model) to be applied to the input image.
If loaded from a checkpoint the text processor will be loaded from it.
codec : Codec, optional
Codec of the deep net to use for decoding. This parameter is only required if a custom codec is used,
or a `network` has been provided instead of a `checkpoint`
network : ModelInterface, optional
DNN instance to used. Alternatively you can provide a `checkpoint` to load a network.
batch_size : int, optional
Batch size to use for prediction
processes : int, optional
The number of processes to use for prediction
"""
self.network = network
self.checkpoint = checkpoint
self.processes = processes
if checkpoint:
if network:
raise Exception(
"Either a checkpoint or a network can be provided")
with open(checkpoint + '.json', 'r') as f:
checkpoint_params = json_format.Parse(f.read(),
CheckpointParams())
self.model_params = checkpoint_params.model
self.network_params = self.model_params.network
backend = create_backend_from_proto(self.network_params,
restore=self.checkpoint,
processes=processes)
self.network = backend.create_net(restore=self.checkpoint,
weights=None,
graph_type="predict",
batch_size=batch_size)
self.text_postproc = text_postproc if text_postproc else text_processor_from_proto(
self.model_params.text_postprocessor, "post")
self.data_preproc = data_preproc if data_preproc else data_processor_from_proto(
self.model_params.data_preprocessor)
elif network:
self.model_params = None
self.network_params = network.network_proto
self.text_postproc = text_postproc
self.data_preproc = data_preproc
if not codec:
raise Exception(
"A codec is required if preloaded network is used.")
else:
raise Exception(
"Either a checkpoint or a existing backend must be provided")
self.codec = codec if codec else Codec(self.model_params.codec.charset)
self.out_to_in_trans = OutputToInputTransformer(
self.data_preproc, self.network)
def train(self, auto_compute_codec=False, progress_bar=False):
""" Launch the training
Parameters
----------
progress_bar : bool
Show or hide any progress bar
"""
checkpoint_params = self.checkpoint_params
train_start_time = time.time() + self.checkpoint_params.total_time
# load training dataset
if self.preload_training:
self.dataset.preload(processes=checkpoint_params.processes, progress_bar=progress_bar)
# load validation dataset
if self.validation_dataset and self.preload_validation:
self.validation_dataset.preload(processes=checkpoint_params.processes, progress_bar=progress_bar)
# compute the codec
if self.codec:
codec = self.codec
else:
if len(self.codec_whitelist) == 0 or auto_compute_codec:
codec = Codec.from_input_dataset([self.dataset, self.validation_dataset],
whitelist=self.codec_whitelist, progress_bar=True)
else:
codec = Codec.from_texts([], whitelist=self.codec_whitelist)
# create backend
network_params = checkpoint_params.model.network
network_params.features = checkpoint_params.model.line_height
network_params.classes = len(codec)
if self.weights:
# if we load the weights, take care of codec changes as-well
ckpt = Checkpoint(self.weights + '.json', auto_update=self.auto_update_checkpoints)
restore_checkpoint_params = ckpt.checkpoint
restore_model_params = restore_checkpoint_params.model
# checks
if checkpoint_params.model.line_height != network_params.features:
raise Exception("The model to restore has a line height of {} but a line height of {} is requested".format(
network_params.features, checkpoint_params.model.line_height
))
# create codec of the same type
restore_codec = codec.__class__(restore_model_params.codec.charset)
# the codec changes as tuple (deletions/insertions), and the new codec is the changed old one
codec_changes = restore_codec.align(codec)
codec = restore_codec
print("Codec changes: {} deletions, {} appends".format(len(codec_changes[0]), len(codec_changes[1])))
# The actual weight/bias matrix will be changed after loading the old weights
if all([c == 0 for c in codec_changes]):
codec_changes = None # No codec changes
else:
codec_changes = None
# store the new codec
checkpoint_params.model.codec.charset[:] = codec.charset
print("CODEC: {}".format(codec.charset))
backend = create_backend_from_proto(network_params,
weights=self.weights,
)
train_net = backend.create_net(self.dataset, codec, restore=None, weights=self.weights, graph_type="train", batch_size=checkpoint_params.batch_size)
test_net = backend.create_net(self.validation_dataset, codec, restore=None, weights=self.weights, graph_type="test", batch_size=checkpoint_params.batch_size)
if codec_changes:
# only required on one net, since the other shares the same variables
train_net.realign_model_labels(*codec_changes)
train_net.prepare()
test_net.prepare()
if checkpoint_params.current_stage == 0:
self._run_train(train_net, test_net, codec, train_start_time, progress_bar)
if checkpoint_params.data_aug_retrain_on_original and self.dataset.data_augmenter and self.dataset.data_augmentation_amount > 0:
print("Starting training on original data only")
if checkpoint_params.current_stage == 0:
checkpoint_params.current_stage = 1
checkpoint_params.iter = 0
checkpoint_params.early_stopping_best_at_iter = 0
checkpoint_params.early_stopping_best_cur_nbest = 0
checkpoint_params.early_stopping_best_accuracy = 0
self.dataset.generate_only_non_augmented = True # this is the important line!
train_net.prepare()
test_net.prepare()
self._run_train(train_net, test_net, codec, train_start_time, progress_bar)
train_net.prepare() # reset the state
test_net.prepare() # to prevent blocking of tensorflow on shutdown
