def __init__(self,
reader,
window_sizes,
batch_size=10,
n_interpolations=10,
queue_length=10,
name='linear_interpolation_sampler'):
ImageWindowDataset.__init__(self,
reader,
window_sizes=window_sizes,
batch_size=batch_size,
queue_length=queue_length,
shuffle=False,
epoch=1,
smaller_final_batch_mode='drop',
name=name)
self.n_interpolations = n_interpolations
# 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 linear interpolation sampler %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,
window_sizes,
batch_size=1,
spatial_window_size=None,
window_border=None,
queue_length=10,
smaller_final_batch_mode='pad',
name='grid_sampler'):
# 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
ImageWindowDataset.__init__(
self,
reader=reader,
window_sizes=spatial_window_size or window_sizes,
batch_size=batch_size,
windows_per_image=1,
queue_length=queue_length,
shuffle=False,
epoch=1,
smaller_final_batch_mode=smaller_final_batch_mode,
name=name)
self.border_size = window_border or (0, 0, 0)
assert isinstance(self.border_size, (list, tuple)), \
"window_border should be a list or tuple"
while len(self.border_size) < N_SPATIAL:
self.border_size = tuple(self.border_size) + \
(self.border_size[-1],)
self.border_size = self.border_size[:N_SPATIAL]
tf.logging.info('initialised window instance')
tf.logging.info("initialised grid sampler %s", self.window.shapes)
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
self.n_interpolations = max(n_interpolations, 1)
self.mean = mean
self.stddev = stddev
self.repeat = repeat
self.names = names
ImageWindowDataset.__init__(
self,
reader=None,
window_sizes={names[0]: {
'spatial_window_size': vector_size
}},
batch_size=batch_size,
queue_length=queue_length,
shuffle=False,
epoch=1,
smaller_final_batch_mode='drop',
name=name)
self.window = ImageWindow(shapes={names[0]: vector_size},
dtypes={names[0]: tf.float32})
tf.logging.info("initialised sampler output %s ", self.window.shapes)
def __init__(self,
reader,
window_sizes,
batch_size=1,
spatial_window_size=None,
windows_per_image=1,
shuffle=True,
queue_length=10,
smaller_final_batch_mode='pad',
name='resize_sampler_v2'):
tf.logging.info('reading size of preprocessed images')
ImageWindowDataset.__init__(
self,
reader=reader,
window_sizes=window_sizes,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length,
shuffle=shuffle,
epoch=-1 if shuffle else 1,
smaller_final_batch_mode=smaller_final_batch_mode,
name=name)
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 resize sampler %s ", self.window.shapes)
def __init__(self,
reader,
csv_reader=None,
window_sizes=None,
batch_size=10,
windows_per_image=1,
shuffle=True,
queue_length=10,
num_threads=4,
epoch=-1,
smaller_final_batch_mode='pad',
name='random_vector_sampler'):
self.csv_reader = csv_reader
print("assigned csv_reader")
ImageWindowDataset.__init__(
self,
reader=reader,
window_sizes=window_sizes,
batch_size=batch_size,
windows_per_image=windows_per_image,
shuffle=shuffle,
queue_length=queue_length,
epoch=epoch,
smaller_final_batch_mode=smaller_final_batch_mode,
name=name)
print("initialised IWD")
self.set_num_threads(num_threads)
def __init__(self,
reader,
window_sizes,
batch_size=1,
windows_per_image=1,
queue_length=10,
name='uniform_sampler_v2'):
ImageWindowDataset.__init__(self,
reader=reader,
window_sizes=window_sizes,
batch_size=batch_size,
windows_per_image=windows_per_image,
queue_length=queue_length,
shuffle=True,
epoch=-1,
smaller_final_batch_mode='drop',
name=name)
tf.logging.info("initialised uniform sampler %s ", self.window.shapes)
self.window_centers_sampler = rand_spatial_coordinates
def __init__(self,
reader,
csv_reader=None,
window_sizes=None,
batch_size=10,
windows_per_image=1,
shuffle=True,
queue_length=10,
epoch=-1,
smaller_final_batch_mode='pad',
name='random_vector_sampler'):
self.csv_reader = csv_reader
ImageWindowDataset.__init__(
self,
reader=reader,
window_sizes=window_sizes,
batch_size=batch_size,
windows_per_image=windows_per_image,
shuffle=shuffle,
queue_length=queue_length,
epoch=epoch,
smaller_final_batch_mode=smaller_final_batch_mode,
name=name)
def __init__(self, *args, **kwargs):
ImageWindowDataset.__init__(self, *args, **kwargs)