def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.::
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict, is_training)
.. caution::
Note it squeezes the output tensor of 6 dims
``[batch, x, y, z, time, modality]``
by removing all dims along which length is one.
:return: a tensorflow graph op
"""
assert all([thread.isAlive() for thread in self._threads]), \
"input sampling threads are not running"
if self._window.has_dynamic_shapes:
data_output = self._dequeue_func()
else:
data_output = self._dequeue_func(self._batch_size)
for (name, shape) in self._window.shapes.items():
# set first dim as the batch size
data_output[name].set_shape([self._batch_size] + list(shape[1:]))
for name in data_output:
data_output[name] = squeeze_spatial_temporal_dim(data_output[name])
# keep a copy of the sampler's output tensors
self.output_tensor = data_output
return data_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.::
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict['image'], is_training)
.. caution::
Note it squeezes the output tensor of 6 dims
``[batch, x, y, z, time, modality]``
by removing all dims along which length is one.
:return: a dictionary of image window tensors.
"""
if self.dataset is None or self.iterator is None:
# in case `run_threads` is not called,
# here we initialise the dataset and iterator
self.init_dataset()
self.iterator = self.dataset.make_one_shot_iterator()
# self.iterator = tf.data.Iterator.from_structure(
# self.dataset.output_types, self.dataset.output_shapes)
window_output = self.iterator.get_next()
for name in window_output:
window_output[name] = squeeze_spatial_temporal_dim(
window_output[name])
return window_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.::
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict['image'], is_training)
.. caution::
Note it squeezes the output tensor of 6 dims
``[batch, x, y, z, time, modality]``
by removing all dims along which length is one.
:return: a dictionary of image window tensors.
"""
if self.dataset is None or self.iterator is None:
self.init_dataset()
self.iterator = self.dataset.make_initializable_iterator()
window_output = self.iterator.get_next()
if not self.from_generator:
window_output = window_output[0]
# for name in list(self.shapes):
# window_output[name].set_shape(
# [self.batch_size] + list(self.shapes[name][1:]))
for name in window_output:
window_output[name] = squeeze_spatial_temporal_dim(
window_output[name])
return window_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.::
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict, is_training)
.. caution::
Note it squeezes the output tensor of 6 dims
``[batch, x, y, z, time, modality]``
by removing all dims along which length is one.
:return: a tensorflow graph op
"""
assert all([thread.isAlive() for thread in self._threads]), \
"input sampling threads are not running"
if self._window.has_dynamic_shapes:
data_output = self._dequeue_func()
else:
data_output = self._dequeue_func(self._batch_size)
for (name, shape) in self._window.shapes.items():
data_output[name].set_shape([self._batch_size] + list(shape))
for name in data_output:
data_output[name] = squeeze_spatial_temporal_dim(data_output[name])
# keep a copy of the sampler's output tensors
self.output_tensor = data_output
return data_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.,
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict, is_training)
Note it squeezes the output tensor of 6 dims
[batch, x, y, z, time, modality]
by removing all dims along which length is one.
:param as_numpy_array: a boolean value indicating numpy outputs
:return: a tensorflow graph op if not as_numpy_array,
otherwise returns a numpy array
"""
assert all([thread.isAlive() for thread in self._threads]), \
"input sampling threads are not running"
if self._window.has_dynamic_shapes:
data_output = self._dequeue_func()
else:
data_output = self._dequeue_func(self._batch_size)
for (name, shape) in self._window.shapes.items():
data_output[name].set_shape([self._batch_size] + list(shape))
for name in data_output:
data_output[name] = squeeze_spatial_temporal_dim(data_output[name])
# keep a copy of the sampler's output tensors
self.output_tensor = data_output
return data_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.::
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict['image'], is_training)
.. caution::
Note it squeezes the output tensor of 6 dims
``[batch, x, y, z, time, modality]``
by removing all dims along which length is one.
:return: a dictionary of image window tensors.
"""
if self.dataset is None or self.iterator is None:
self.init_dataset()
self.iterator = self.dataset.make_initializable_iterator()
window_output = self.iterator.get_next()
if not self.from_generator:
window_output = window_output[0]
# for name in list(self.shapes):
# window_output[name].set_shape(
# [self.batch_size] + list(self.shapes[name][1:]))
for name in window_output:
window_output[name] = squeeze_spatial_temporal_dim(
window_output[name])
return window_output
def pop_batch_op(self):
"""
This function is used when connecting a sampler output
to a network. e.g.,
data_dict = self.get_sampler()[0].pop_batch_op(device_id)
net_output = net_model(data_dict, is_training)
Note it squeezes the output tensor of 6 dims
[batch, x, y, z, time, modality]
by removing all dims along which length is one.
:param device_id: integer representing the GPU
:return: a tensorflow graph op
"""
if self._window.has_dynamic_shapes:
data_output = self._dequeue_func()
else:
data_output = self._dequeue_func(self._batch_size)
for (name, shape) in self._window.shapes.items():
data_output[name].set_shape([self._batch_size] + list(shape))
for name in data_output:
data_output[name] = squeeze_spatial_temporal_dim(data_output[name])
return data_output
