def test_calculating_batch_size(self):
with self.cached_session():
# 64 is the number of input samples.
inputs = np.zeros((64, 3), dtype=np.float32)
targets = np.zeros((64, 4), dtype=np.float32)
model = get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
strategy = mirrored_strategy.MirroredStrategy(['/device:GPU:0',
'/device:CPU:0'])
strategy.extended._require_static_shapes = True
model.compile(optimizer, loss, distribute=strategy)
iterator = model._distribution_standardize_user_data(inputs,
targets,
batch_size=None,
check_steps=True,
steps_name='steps',
steps=3)
# The global batch size(21) across all replicas is the ratio of the input
# samples(64) to the steps(3).
# The batch size(10) per device is the ratio of the global batch size(21)
# to the number of replicas(2).
# The global batch size and batch size are rounded integer values.
self.assertEqual(10, distributed_training_utils.get_batch_dimension(
iterator))
def test_calculating_batch_size(self):
with self.cached_session():
# 64 is the number of input samples.
inputs = np.zeros((64, 3), dtype=np.float32)
targets = np.zeros((64, 4), dtype=np.float32)
model = get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
strategy = mirrored_strategy.MirroredStrategy(['/device:GPU:0',
'/device:CPU:0'])
strategy._require_static_shapes = True
model.compile(optimizer, loss, distribute=strategy)
iterator = model._distribution_standardize_user_data(inputs,
targets,
batch_size=None,
check_steps=True,
steps_name='steps',
steps=3)
# The global batch size(21) across all replicas is the ratio of the input
# samples(64) to the steps(3).
# The batch size(10) per device is the ratio of the global batch size(21)
# to the number of replicas(2).
# The global batch size and batch size are rounded integer values.
self.assertEqual(10, distributed_training_utils.get_batch_dimension(
iterator._iterator))
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
dataset_fully_shaped = (distributed_training_utils.
is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(padding_handler.padding_mask,
padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.PREDICT),))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
assert steps is not None
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
for step in range(steps):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
if verbose >= 1:
progbar.update(step + 1)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def experimental_tpu_predict_loop(model, dataset, verbose=0, steps=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
dataset_fully_shaped = (
distributed_training_utils.is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batched_dataset = input_lib._get_batched_dataset(dataset)
batch_size, _, prefetch_buffer = input_lib._get_batched_dataset_attributes(
batched_dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, ModeKeys.PREDICT, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, ModeKeys.PREDICT, inputs)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(model._distributed_model_predict, ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, ModeKeys.PREDICT)
distributed_training_utils._reset_metrics(model)
assert steps is not None
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
for step in range(steps):
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
if verbose >= 1:
progbar.update(step + 1)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def _experimental_predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
K.get_session().run(current_strategy.initialize())
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_replicas(model,
current_strategy,
make_callback_model=False,
inputs=inputs,
mode=_Mode.PREDICT)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function,
args=(model._grouped_model_predict, ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.run_steps_on_dataset(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(
model._grouped_model_predict)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
assert steps is not None
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
for step in range(steps):
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
if verbose >= 1:
progbar.update(step + 1)
K.get_session().run(current_strategy.finalize())
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def _experimental_predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
K.get_session().run(current_strategy.initialize())
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, *inputs):
"""Clones the model and calls make_predict_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_replicas(
model,
current_strategy,
make_callback_model=False,
inputs=inputs,
mode=_Mode.PREDICT)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function, args=(model._grouped_model_predict,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.run_steps_on_dataset(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model_predict)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
assert steps is not None
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
for step in range(steps):
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
if verbose >= 1:
progbar.update(step + 1)
K.get_session().run(current_strategy.finalize())
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
dataset_fully_shaped = (
distributed_training_utils.is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
out_labels = model.output_names
step_fn = _make_step_fn(model, ModeKeys.PREDICT, current_strategy,
out_labels)
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([predict_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
