def __init__(self,
names=('vector', ),
vector_size=(100, ),
batch_size=10,
n_interpolations=10,
mean=0.0,
stddev=1.0,
repeat=1,
queue_length=10,
name='random_vector_sampler'):
# repeat=None for infinite loops
Layer.__init__(self, name=name)
self.n_interpolations = max(n_interpolations, 1)
self.mean = mean
self.stddev = stddev
self.repeat = repeat
InputBatchQueueRunner.__init__(self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed images')
vector_shapes = {names[0]: vector_size}
vector_dtypes = {names[0]: tf.float32}
self.window = ImageWindow(shapes=vector_shapes, dtypes=vector_dtypes)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=self.n_interpolations,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
def __init__(self,
reader,
data_param,
batch_size,
spatial_window_size=(),
window_border=(),
queue_length=10):
self.batch_size = batch_size
self.reader = reader
Layer.__init__(self, name='input_buffer')
InputBatchQueueRunner.__init__(
self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed inputs')
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources,
self.reader.shapes,
self.reader.tf_dtypes,
data_param)
if spatial_window_size:
# override all spatial window defined in input
# modalities sections
# this is useful when do inference with a spatial window
# which is different from the training specifications
self.window.set_spatial_shape(spatial_window_size)
self.border_size = window_border
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=1,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s", self.window.shapes)
def __init__(self,
reader,
data_param,
batch_size,
spatial_window_size=(),
windows_per_image=1,
shuffle_buffer=True,
queue_length=10,
name='resize_sampler'):
self.reader = reader
self.windows_per_image = windows_per_image
self.shuffle = bool(shuffle_buffer)
Layer.__init__(self, name=name)
InputBatchQueueRunner.__init__(self,
capacity=queue_length,
shuffle=self.shuffle)
tf.logging.info('reading size of preprocessed images')
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources, self.reader.shapes,
self.reader.tf_dtypes, data_param)
if spatial_window_size:
# override all spatial window defined in input
# modalities sections
# this is useful when do inference with a spatial window
# which is different from the training specifications
self.window.set_spatial_shape(spatial_window_size)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=1,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
def __init__(self,
reader,
data_param,
batch_size=10,
n_interpolations=10,
queue_length=10,
name='linear_interpolation_sampler'):
self.n_interpolations = n_interpolations
self.reader = reader
Layer.__init__(self, name=name)
InputBatchQueueRunner.__init__(self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed images')
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources, self.reader.shapes,
self.reader.tf_dtypes, data_param)
# only try to use the first spatial shape available
image_spatial_shape = list(self.reader.shapes.values())[0][:3]
self.window.set_spatial_shape(image_spatial_shape)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=self.n_interpolations,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
assert not self.window.has_dynamic_shapes, \
"dynamic shapes not supported, please specify " \
"spatial_window_size = (1, 1, 1)"
def __init__(self,
reader,
data_param,
batch_size,
windows_per_image,
queue_length=10):
self.reader = reader
Layer.__init__(self, name='input_buffer')
InputBatchQueueRunner.__init__(self,
capacity=queue_length,
shuffle=True)
tf.logging.info('reading size of preprocessed images')
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources, self.reader.shapes,
self.reader.tf_dtypes, data_param)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=windows_per_image,
dequeue_size=batch_size)
tf.logging.info(
"initialised sampler output %s "
" [-1 for dynamic size]", self.window.shapes)
self.spatial_coordinates_generator = rand_spatial_coordinates
def __init__(self,
names=('vector',),
vector_size=(100,),
batch_size=10,
n_interpolations=10,
mean=0.0,
stddev=1.0,
repeat=1,
queue_length=10):
self.n_interpolations = max(n_interpolations, 1)
self.mean = mean
self.stddev = stddev
self.repeat = repeat
Layer.__init__(self, name='input_buffer')
InputBatchQueueRunner.__init__(
self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed images')
self.names = names
vector_shapes = {names[0]: vector_size}
vector_dtypes = {names[0]: tf.float32}
self.window = ImageWindow(names=tuple(vector_shapes),
shapes=vector_shapes,
dtypes=vector_dtypes)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=self.n_interpolations,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
def __init__(self,
reader,
data_param,
batch_size,
spatial_window_size=None,
window_border=None,
queue_length=10,
name='grid_sampler'):
self.batch_size = batch_size
self.border_size = window_border or (0, 0, 0)
self.reader = reader
Layer.__init__(self, name=name)
InputBatchQueueRunner.__init__(self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed inputs')
# override all spatial window defined in input
# modalities sections
# this is useful when do inference with a spatial window
# which is different from the training specifications
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources, self.reader.shapes,
self.reader.tf_dtypes, spatial_window_size or data_param)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=1,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s", self.window.shapes)
def __init__(self,
reader,
data_param,
batch_size=10,
n_interpolations=10,
queue_length=10):
self.n_interpolations = n_interpolations
self.reader = reader
Layer.__init__(self, name='input_buffer')
InputBatchQueueRunner.__init__(
self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed images')
self.window = ImageWindow.from_data_reader_properties(
self.reader.input_sources,
self.reader.shapes,
self.reader.tf_dtypes,
data_param)
# only try to use the first spatial shape available
image_spatial_shape = list(self.reader.shapes.values())[0][:3]
self.window.set_spatial_shape(image_spatial_shape)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=self.n_interpolations,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
assert not self.window.has_dynamic_shapes, \
"dynamic shapes not supported, please specify " \
"spatial_window_size = (1, 1, 1)"
