def __init__(self, training_callback, codec, data_gen, predict_func,
checkpoint_params, steps_per_epoch, text_post_proc):
self.training_callback = training_callback
self.codec = codec
self.data_gen = data_gen
self.predict_func = predict_func
self.checkpoint_params = checkpoint_params
self.steps_per_epoch = steps_per_epoch
self.text_post_proc = text_post_proc
self.loss_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.loss_stats)
self.ler_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
self.dt_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.dt_stats)
display = checkpoint_params.display
self.display_epochs = display <= 1
if display <= 0:
display = 0 # do not display anything
elif self.display_epochs:
display = max(1,
int(display * steps_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
self.display = display
self.iter_start_time = time.time()
self.train_start_time = time.time()
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))
class VisCallback(keras.callbacks.Callback):
def __init__(self, training_callback, codec, data_gen, predict_func,
checkpoint_params, steps_per_epoch, text_post_proc):
self.training_callback = training_callback
self.codec = codec
self.data_gen = data_gen
self.predict_func = predict_func
self.checkpoint_params = checkpoint_params
self.steps_per_epoch = steps_per_epoch
self.text_post_proc = text_post_proc
self.loss_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.loss_stats)
self.ler_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
self.dt_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.dt_stats)
display = checkpoint_params.display
self.display_epochs = display <= 1
if display <= 0:
display = 0 # do not display anything
elif self.display_epochs:
display = max(1,
int(display * steps_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
self.display = display
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_begin(self, logs):
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_end(self, logs):
self.training_callback.training_finished(
time.time() - self.train_start_time, self.checkpoint_params.iter)
def on_batch_end(self, batch, logs):
dt = time.time() - self.iter_start_time
self.iter_start_time = time.time()
self.dt_stats.push(dt)
self.loss_stats.push(logs['loss'])
self.checkpoint_params.iter += 1
if self.display > 0 and self.checkpoint_params.iter % self.display == 0:
# apply postprocessing to display the true output
cer, target, decoded = self._generate(1)
self.ler_stats.push(cer)
pred_sentence = self.text_post_proc.apply("".join(
self.codec.decode(decoded[0])))
gt_sentence = self.text_post_proc.apply("".join(
self.codec.decode(target[0])))
self.training_callback.display(self.ler_stats.mean(),
self.loss_stats.mean(),
self.dt_stats.mean(),
self.checkpoint_params.iter,
self.steps_per_epoch,
self.display_epochs, pred_sentence,
gt_sentence)
def on_epoch_end(self, epoch, logs):
pass
def _generate(self, count):
it = iter(self.data_gen)
cer, target, decoded = zip(
*[self.predict_func(next(it)) for _ in range(count)])
return np.mean(cer), sum(map(sparse_to_lists, target),
[]), sum(map(sparse_to_lists, decoded), [])
def _run_train(self, train_net, test_net, codec, train_start_time,
progress_bar):
checkpoint_params = self.checkpoint_params
validation_dataset = test_net.input_dataset
iters_per_epoch = max(
1,
int(train_net.input_dataset.epoch_size() /
checkpoint_params.batch_size))
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)
display = checkpoint_params.display
display_epochs = display <= 1
if display <= 0:
display = 0 # to not display anything
elif display_epochs:
display = max(1,
int(display * iters_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
checkpoint_frequency = checkpoint_params.checkpoint_frequency
early_stopping_frequency = checkpoint_params.early_stopping_frequency
if early_stopping_frequency < 0:
# set early stopping frequency to half epoch
early_stopping_frequency = int(0.5 * iters_per_epoch)
elif 0 < early_stopping_frequency <= 1:
early_stopping_frequency = int(
early_stopping_frequency *
iters_per_epoch) # relative to epochs
else:
early_stopping_frequency = int(early_stopping_frequency)
early_stopping_frequency = max(1, early_stopping_frequency)
if checkpoint_frequency < 0:
checkpoint_frequency = early_stopping_frequency
elif 0 < checkpoint_frequency <= 1:
checkpoint_frequency = int(checkpoint_frequency *
iters_per_epoch) # relative to epochs
else:
checkpoint_frequency = int(checkpoint_frequency)
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,
network=test_net)
# 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))
train_net.save_checkpoint(checkpoint_path)
checkpoint_params.version = Checkpoint.VERSION
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
n_infinite_losses = 0
n_max_infinite_losses = 5
# 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 = train_net.train_step()
if not np.isfinite(result['loss']):
n_infinite_losses += 1
if n_max_infinite_losses == n_infinite_losses:
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
train_net.load_weights(
last_checkpoint, restore_only_trainable=False)
continue
else:
continue
n_infinite_losses = 0
loss_stats.push(result['loss'])
ler_stats.push(result['ler'])
dt_stats.push(time.time() - iter_start_time)
if display > 0 and iter % display == 0:
# apply postprocessing to display the true output
pred_sentence = self.txt_postproc.apply("".join(
codec.decode(result["decoded"][0])))
gt_sentence = self.txt_postproc.apply("".join(
codec.decode(result["gt"][0])))
if display_epochs:
print("#{:08f}: loss={:.8f} ler={:.8f} dt={:.8f}s".
format(iter / iters_per_epoch, loss_stats.mean(),
ler_stats.mean(), dt_stats.mean()))
else:
print("#{:08d}: loss={:.8f} ler={:.8f} dt={:.8f}s".
format(iter, loss_stats.mean(), ler_stats.mean(),
dt_stats.mean()))
# Insert utf-8 ltr/rtl direction marks for bidi support
lr = "\u202A\u202B"
print(" PRED: '{}{}{}'".format(
lr[bidi.get_base_level(pred_sentence)], pred_sentence,
"\u202C"))
print(" TRUE: '{}{}{}'".format(
lr[bidi.get_base_level(gt_sentence)], gt_sentence,
"\u202C"))
if checkpoint_frequency > 0 and (
iter + 1) % checkpoint_frequency == 0:
last_checkpoint = make_checkpoint(
checkpoint_params.output_dir,
checkpoint_params.output_model_prefix)
if early_stopping_enabled and (
iter + 1) % early_stopping_frequency == 0:
print("Checking early stopping model")
out_gen = early_stopping_predictor.predict_input_dataset(
validation_dataset, progress_bar=progress_bar)
result = Evaluator.evaluate_single_list(
map(
Evaluator.evaluate_single_args,
map(
lambda d: tuple(
self.txt_preproc.apply([
''.join(d.ground_truth), d.sentence
])), out_gen)))
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
if accuracy >= 1:
print(
"Reached perfect score on validation set. 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 __init__(self,
training_callback,
codec,
train_data_gen,
validation_data_gen: Union[tuple, None],
predict_func,
checkpoint_params,
steps_per_epoch,
text_post_proc,
log_dir='logs',
histogram_freq=0,
write_graph=True,
write_images=False,
update_freq='batch',
embeddings_freq=0,
embeddings_metadata=None,
**kwargs):
super().__init__(log_dir=log_dir,
histogram_freq=histogram_freq,
write_graph=write_graph,
write_images=write_images,
update_freq=update_freq,
embeddings_freq=embeddings_freq,
embeddings_metadata=embeddings_metadata,
**kwargs)
# override default folder structure
self._train_run_name = ''
self._validation_run_name = ''
self.training_callback = training_callback
self.codec = codec
self.train_data_gen = train_data_gen
self.validation_data_gen = validation_data_gen
self.predict_func = predict_func
self.checkpoint_params = checkpoint_params
self.steps_per_epoch = steps_per_epoch
self.text_post_proc = text_post_proc
self.loss_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.loss_stats)
self.ler_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
self.dt_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.dt_stats)
self.val_ler_stats = [
RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
for _ in range(len(self.validation_data_gen))
]
display = checkpoint_params.display
self.display_epochs = display <= 1
if display <= 0:
display = 0 # do not display anything
elif self.display_epochs:
display = max(1,
int(display * steps_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
self.display = display
self.iter_start_time = time.time()
self.train_start_time = time.time()
class CustomTensorBoard(TensorBoard):
"""
Custom TensorBoard Logging Class
Per display freq:
- training cer on 20 batches of population-wise data
- validation cer on 20 batches every subpopulation data
Per epoch:
- model weights
"""
def __init__(self,
training_callback,
codec,
train_data_gen,
validation_data_gen: Union[tuple, None],
predict_func,
checkpoint_params,
steps_per_epoch,
text_post_proc,
log_dir='logs',
histogram_freq=0,
write_graph=True,
write_images=False,
update_freq='batch',
embeddings_freq=0,
embeddings_metadata=None,
**kwargs):
super().__init__(log_dir=log_dir,
histogram_freq=histogram_freq,
write_graph=write_graph,
write_images=write_images,
update_freq=update_freq,
embeddings_freq=embeddings_freq,
embeddings_metadata=embeddings_metadata,
**kwargs)
# override default folder structure
self._train_run_name = ''
self._validation_run_name = ''
self.training_callback = training_callback
self.codec = codec
self.train_data_gen = train_data_gen
self.validation_data_gen = validation_data_gen
self.predict_func = predict_func
self.checkpoint_params = checkpoint_params
self.steps_per_epoch = steps_per_epoch
self.text_post_proc = text_post_proc
self.loss_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.loss_stats)
self.ler_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
self.dt_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.dt_stats)
self.val_ler_stats = [
RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
for _ in range(len(self.validation_data_gen))
]
display = checkpoint_params.display
self.display_epochs = display <= 1
if display <= 0:
display = 0 # do not display anything
elif self.display_epochs:
display = max(1,
int(display * steps_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
self.display = display
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_begin(self, logs):
super().on_train_begin(logs)
if self.histogram_freq:
self._log_weights(0)
if self.embeddings_freq:
self._log_embeddings(0)
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_end(self, logs):
super().on_train_end(logs)
self.training_callback.training_finished(
time.time() - self.train_start_time, self.checkpoint_params.iter)
def on_train_batch_end(self, batch, logs=None):
assert self._total_batches_seen == self.checkpoint_params.iter
self.checkpoint_params.iter += 1
if self.update_freq == 'epoch' and self._profile_batch is None:
return
dt = time.time() - self.iter_start_time
self.iter_start_time = time.time()
self.dt_stats.push(dt)
self.loss_stats.push(logs['loss'])
logs = logs or {}
if (self.update_freq != 'epoch' and self.display > 0
and self.checkpoint_params.iter % self.display == 0):
cer, target, decoded = self._generate(
self.train_data_gen,
20) # 20 batches for generating training metrics
self.ler_stats.push(cer)
pred_sentence = self.text_post_proc.apply("".join(
self.codec.decode(decoded[0])))
gt_sentence = self.text_post_proc.apply("".join(
self.codec.decode(target[0])))
self._log_metrics({"loss": self.loss_stats.mean()},
prefix='training/batch_',
step=self.checkpoint_params.iter)
self._log_metrics({"cer": self.ler_stats.mean()},
prefix='training/batch_',
step=self.checkpoint_params.iter)
self._log_metrics({"lr": logs['lr']},
prefix='',
step=self.checkpoint_params.iter)
self.training_callback.display(self.ler_stats.mean(),
self.loss_stats.mean(),
self.dt_stats.mean(),
self.checkpoint_params.iter,
self.steps_per_epoch,
self.display_epochs, pred_sentence,
gt_sentence)
if self.validation_data_gen is not None:
for i, val_data in enumerate(self.validation_data_gen):
val_data_name = dataregistry.get_name(i)
val_cer, _, _ = self._generate(
val_data,
20) # 20 batches for generating training metrics
self.val_ler_stats[i].push(val_cer)
self._log_metrics(
{"cer": self.val_ler_stats[i].mean()},
prefix=f'{val_data_name}/validation_batch_',
step=self.checkpoint_params.iter)
self._total_batches_seen += 1
if context.executing_eagerly():
if self._is_tracing:
self._log_trace()
elif (not self._is_tracing and math_ops.equal(
self.checkpoint_params.iter, self._profile_batch - 1)):
self._enable_trace()
def on_epoch_end(self, epoch, logs=None):
self._log_metrics(logs, prefix='epoch_', step=epoch)
if self.histogram_freq and epoch % self.histogram_freq == 0:
self._log_weights(epoch)
if self.embeddings_freq and epoch % self.embeddings_freq == 0:
self._log_embeddings(epoch)
if self.update_freq == 'epoch':
train_cer, _, _ = self._generate(
self.train_data_gen,
20) # 20 batches for generating training metrics
self.ler_stats.push(train_cer)
self._log_metrics({"cer": self.ler_stats.mean()},
prefix='training/batch_',
step=epoch)
if self.validation_data_gen is not None:
for i, val_data in enumerate(self.validation_data_gen):
val_data_name = dataregistry.get_name(i)
val_cer, _, _ = self._generate(
val_data,
20) # 20 batches for generating training metrics
self.val_ler_stats[i].push(val_cer)
self._log_metrics(
{"cer": self.val_ler_stats[i].mean()},
prefix=f'{val_data_name}/validation_batch_',
step=self.checkpoint_params.iter)
def _generate(self, data_gen, count):
if data_gen is None:
pass
else:
it = iter(data_gen)
cer, target, decoded = zip(
*[self.predict_func(next(it)) for _ in range(count)])
return np.mean(cer), sum(map(sparse_to_lists, target),
[]), sum(map(sparse_to_lists, decoded),
[])
def _run_train(self, train_net, test_net, codec, train_start_time, progress_bar):
checkpoint_params = self.checkpoint_params
validation_dataset = test_net.input_dataset
iters_per_epoch = max(1, int(len(train_net.input_dataset) / checkpoint_params.batch_size))
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)
display = checkpoint_params.display
display_epochs = display <= 1
if display <= 0:
display = 0 # to not display anything
elif display_epochs:
display = max(1, int(display * iters_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
checkpoint_frequency = checkpoint_params.checkpoint_frequency
early_stopping_frequency = checkpoint_params.early_stopping_frequency
if early_stopping_frequency < 0:
# set early stopping frequency to half epoch
early_stopping_frequency = int(0.5 * iters_per_epoch)
elif 0 < early_stopping_frequency <= 1:
early_stopping_frequency = int(early_stopping_frequency * iters_per_epoch) # relative to epochs
else:
early_stopping_frequency = int(early_stopping_frequency)
if checkpoint_frequency < 0:
checkpoint_frequency = early_stopping_frequency
elif 0 < checkpoint_frequency <= 1:
checkpoint_frequency = int(checkpoint_frequency * iters_per_epoch) # relative to epochs
else:
checkpoint_frequency = int(checkpoint_frequency)
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,
network=test_net)
# 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))
train_net.save_checkpoint(checkpoint_path)
checkpoint_params.version = Checkpoint.VERSION
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
n_infinite_losses = 0
n_max_infinite_losses = 5
# 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 = train_net.train_step()
if not np.isfinite(result['loss']):
n_infinite_losses += 1
if n_max_infinite_losses == n_infinite_losses:
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
train_net.load_weights(last_checkpoint, restore_only_trainable=False)
continue
else:
continue
n_infinite_losses = 0
loss_stats.push(result['loss'])
ler_stats.push(result['ler'])
dt_stats.push(time.time() - iter_start_time)
if display > 0 and iter % display == 0:
# apply postprocessing to display the true output
pred_sentence = self.txt_postproc.apply("".join(codec.decode(result["decoded"][0])))
gt_sentence = self.txt_postproc.apply("".join(codec.decode(result["gt"][0])))
if display_epochs:
print("#{:08f}: loss={:.8f} ler={:.8f} dt={:.8f}s".format(
iter / iters_per_epoch, loss_stats.mean(), ler_stats.mean(), dt_stats.mean()))
else:
print("#{:08d}: loss={:.8f} ler={:.8f} dt={:.8f}s".format(
iter, loss_stats.mean(), ler_stats.mean(), dt_stats.mean()))
# Insert utf-8 ltr/rtl direction marks for bidi support
lr = "\u202A\u202B"
print(" PRED: '{}{}{}'".format(lr[bidi.get_base_level(pred_sentence)], pred_sentence, "\u202C"))
print(" TRUE: '{}{}{}'".format(lr[bidi.get_base_level(gt_sentence)], gt_sentence, "\u202C"))
if checkpoint_frequency > 0 and (iter + 1) % checkpoint_frequency == 0:
last_checkpoint = make_checkpoint(checkpoint_params.output_dir, checkpoint_params.output_model_prefix)
if early_stopping_enabled and (iter + 1) % early_stopping_frequency == 0:
print("Checking early stopping model")
out_gen = early_stopping_predictor.predict_input_dataset(validation_dataset,
progress_bar=progress_bar)
result = Evaluator.evaluate_single_list(map(
Evaluator.evaluate_single_args,
map(lambda d: tuple(self.txt_preproc.apply([''.join(d.ground_truth), d.sentence])), out_gen)))
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
if accuracy >= 1:
print("Reached perfect score on validation set. 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))
class VisCallback(keras.callbacks.Callback):
def __init__(self, codec, data_gen, predict_func, checkpoint_params,
steps_per_epoch, text_post_proc):
self.codec = codec
self.data_gen = data_gen
self.predict_func = predict_func
self.checkpoint_params = checkpoint_params
self.steps_per_epoch = steps_per_epoch
self.text_post_proc = text_post_proc
self.loss_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.loss_stats)
self.ler_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.ler_stats)
self.dt_stats = RunningStatistics(checkpoint_params.stats_size,
checkpoint_params.dt_stats)
display = checkpoint_params.display
self.display_epochs = display <= 1
if display <= 0:
display = 0 # do not display anything
elif self.display_epochs:
display = max(1,
int(display * steps_per_epoch)) # relative to epochs
else:
display = max(1, int(display)) # iterations
self.display = display
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_begin(self, logs):
self.iter_start_time = time.time()
self.train_start_time = time.time()
def on_train_end(self, logs):
print("Total training time {}s for {} iterations.".format(
time.time() - self.train_start_time, self.checkpoint_params.iter))
def on_batch_end(self, batch, logs):
dt = time.time() - self.iter_start_time
self.iter_start_time = time.time()
self.dt_stats.push(dt)
self.loss_stats.push(logs['loss'])
self.checkpoint_params.iter += 1
if self.display > 0 and self.checkpoint_params.iter % self.display == 0:
# apply postprocessing to display the true output
cer, target, decoded = self._generate(1)
self.ler_stats.push(cer)
pred_sentence = self.text_post_proc.apply("".join(
self.codec.decode(decoded[0])))
gt_sentence = self.text_post_proc.apply("".join(
self.codec.decode(target[0])))
if self.display_epochs:
print("#{:08f}: loss={:.8f} ler={:.8f} dt={:.8f}s".format(
self.checkpoint_params.iter / self.steps_per_epoch,
self.loss_stats.mean(), self.ler_stats.mean(),
self.dt_stats.mean()))
else:
print("#{:08d}: loss={:.8f} ler={:.8f} dt={:.8f}s".format(
self.checkpoint_params.iter, self.loss_stats.mean(),
self.ler_stats.mean(), self.dt_stats.mean()))
# Insert utf-8 ltr/rtl direction marks for bidi support
lr = "\u202A\u202B"
print(" PRED: '{}{}{}'".format(
lr[bidi.get_base_level(pred_sentence)], pred_sentence,
"\u202C"))
print(" TRUE: '{}{}{}'".format(
lr[bidi.get_base_level(gt_sentence)], gt_sentence, "\u202C"))
def on_epoch_end(self, epoch, logs):
pass
def _generate(self, count):
it = iter(self.data_gen)
cer, target, decoded = zip(
*[self.predict_func(next(it)) for _ in range(count)])
return np.mean(cer), sum(map(sparse_to_lists, target),
[]), sum(map(sparse_to_lists, decoded), [])
