def _pad_or_clip_point_properties(inputs, pad_or_clip_size):
"""Pads or clips the inputs point properties.
If pad_or_clip_size is None, it won't perform any action.
Args:
inputs: A dictionary containing input tensors.
pad_or_clip_size: Number of target points to pad or clip to. If None, it
will not perform the padding.
"""
inputs[standard_fields.InputDataFields.num_valid_points] = tf.shape(
inputs[standard_fields.InputDataFields.point_positions])[0]
if pad_or_clip_size is not None:
inputs[standard_fields.InputDataFields.num_valid_points] = tf.minimum(
inputs[standard_fields.InputDataFields.num_valid_points],
pad_or_clip_size)
for key in sorted(standard_fields.get_input_point_fields()):
if key == standard_fields.InputDataFields.num_valid_points:
continue
if key in inputs:
tensor_rank = len(inputs[key].get_shape().as_list())
padding_shape = [pad_or_clip_size]
for i in range(1, tensor_rank):
padding_shape.append(inputs[key].get_shape().as_list()[i])
inputs[key] = shape_utils.pad_or_clip_nd(
tensor=inputs[key], output_shape=padding_shape)
def apply_mask_to_input_point_tensors(inputs, valid_mask):
"""Applies mask to input point tensors."""
masked_tensors = {}
for field in standard_fields.get_input_point_fields():
if field in inputs:
if field != standard_fields.InputDataFields.num_valid_points:
masked_tensors[field] = tf.boolean_mask(
inputs[field], valid_mask)
return masked_tensors
def get_batch_size_1_input_points(inputs, b):
"""Returns input dictionary containing tensors with batch size of 1.
Note that this function only applies its example selection to the point
tensors.
Args:
inputs: A dictionary of tf.Tensors with our input data.
b: Example index in the batch.
Returns:
inputs_1: A dictionary of tf.Tensors with batch size of one.
"""
b_1_inputs = {}
for field in standard_fields.get_input_point_fields():
if field in inputs:
b_1_inputs[field] = inputs[field][b]
return b_1_inputs
def split_inputs(inputs,
input_field_mapping_fn,
image_preprocess_fn_dic,
images_points_correspondence_fn):
"""Splits inputs to view_image_inputs, view_indices_2d_inputs, mesh_inputs.
Args:
inputs: Input dictionary.
input_field_mapping_fn: A function that maps the input fields to the
fields expected by object detection pipeline.
image_preprocess_fn_dic: A dictionary of image preprocessing functions.
images_points_correspondence_fn: A function that returns image and points
correspondences.
Returns:
view_image_inputs: A dictionary containing image inputs.
view_indices_2d_inputs: A dictionary containing indices 2d inputs.
mesh_inputs: A dictionary containing mesh inputs.
object_inputs: A dictionary containing object inputs.
non_tensor_inputs: Other inputs.
"""
# Initializing empty dictionary for mesh, image, indices_2d and non tensor
# inputs.
non_tensor_inputs = {}
view_image_inputs = {}
view_indices_2d_inputs = {}
mesh_inputs = {}
object_inputs = {}
if image_preprocess_fn_dic is None:
image_preprocess_fn_dic = {}
# Acquire point / image correspondences.
if images_points_correspondence_fn is not None:
fn_outputs = images_points_correspondence_fn(inputs)
if 'points_position' in fn_outputs:
inputs[standard_fields.InputDataFields
.point_positions] = fn_outputs['points_position']
if 'points_intensity' in fn_outputs:
inputs[standard_fields.InputDataFields
.point_intensities] = fn_outputs['points_intensity']
if 'points_elongation' in fn_outputs:
inputs[standard_fields.InputDataFields
.point_elongations] = fn_outputs['points_elongation']
if 'points_normal' in fn_outputs:
inputs[standard_fields.InputDataFields
.point_normals] = fn_outputs['points_normal']
if 'points_color' in fn_outputs:
inputs[standard_fields.InputDataFields
.point_colors] = fn_outputs['points_color']
if 'view_images' in fn_outputs:
for key in sorted(fn_outputs['view_images']):
if len(fn_outputs['view_images'][key].shape) != 4:
raise ValueError(('%s image should have rank 4.' % key))
view_image_inputs = fn_outputs['view_images']
if 'view_indices_2d' in fn_outputs:
for key in sorted(fn_outputs['view_indices_2d']):
if len(fn_outputs['view_indices_2d'][key].shape) != 3:
raise ValueError(('%s indices_2d should have rank 3.' % key))
view_indices_2d_inputs = fn_outputs['view_indices_2d']
if input_field_mapping_fn is not None:
inputs = input_field_mapping_fn(inputs)
# Setting mesh inputs
mesh_keys = []
for key in standard_fields.get_input_point_fields():
if key in inputs:
mesh_keys.append(key)
object_keys = []
for key in standard_fields.get_input_object_fields():
if key in inputs:
object_keys.append(key)
for k, v in inputs.items():
if k in mesh_keys:
mesh_inputs[k] = v
elif k in object_keys:
object_inputs[k] = v
else:
non_tensor_inputs[k] = v
logging.info('view image inputs')
logging.info(view_image_inputs)
logging.info('view indices 2d inputs')
logging.info(view_indices_2d_inputs)
logging.info('mesh inputs')
logging.info(mesh_inputs)
logging.info('object inputs')
logging.info(object_inputs)
logging.info('non_tensor_inputs')
logging.info(non_tensor_inputs)
return (view_image_inputs, view_indices_2d_inputs, mesh_inputs, object_inputs,
non_tensor_inputs)