def __init__(self,
filters,
kernel_size,
activation=None,
use_bias=True,
kernel_initializer='uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
align_corners=True,
coordinate_mapping='ball_to_cube_radial',
interpolation='linear',
normalize=True,
radius_search_ignore_query_points=False,
radius_search_metric='L2',
offset=None,
window_function=None,
use_dense_layer_for_center=False,
in_channels=None,
**kwargs):
from tensorflow.keras import activations, initializers, regularizers
self.filters = filters
self.kernel_size = kernel_size
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.align_corners = align_corners
self.coordinate_mapping = coordinate_mapping
self.interpolation = interpolation
self.normalize = normalize
self.radius_search_ignore_query_points = radius_search_ignore_query_points
self.radius_search_metric = radius_search_metric
if offset is None:
self.offset = tf.zeros(shape=(3, ))
else:
self.offset = offset
self.window_function = window_function
self.fixed_radius_search = layers.FixedRadiusSearch(
metric=self.radius_search_metric,
ignore_query_point=self.radius_search_ignore_query_points,
return_distances=not self.window_function is None)
self.radius_search = layers.RadiusSearch(
metric=self.radius_search_metric,
ignore_query_point=self.radius_search_ignore_query_points,
return_distances=not self.window_function is None,
normalize_distances=not self.window_function is None)
self.use_dense_layer_for_center = use_dense_layer_for_center
if self.use_dense_layer_for_center:
self.dense = tf.keras.layers.Dense(self.filters, use_bias=False)
super().__init__(**kwargs)
def __init__(self,
filters,
kernel_size,
activation=None,
use_bias=True,
kernel_initializer='uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
normalize=False,
offset=None,
in_channels=None,
**kwargs):
from tensorflow.keras import activations, initializers, regularizers
self.filters = filters
self.kernel_size = kernel_size
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.normalize = normalize
if not (np.asarray(kernel_size) == kernel_size[0]).all():
raise Exception("Only cubic kernel sizes are supported.")
if offset is None:
if kernel_size[0] % 2:
self.offset = tf.zeros(shape=(3, ))
else:
self.offset = tf.fill([3], -0.5)
else:
self.offset = offset
self.fixed_radius_search = layers.FixedRadiusSearch(
metric='Linf', ignore_query_point=False, return_distances=False)
super().__init__(**kwargs)