def test_box_corner_distance_loss_on_object_tensors_relative(self):
(voxel_inputs, voxel_outputs1,
voxel_outputs2) = self._get_dictionaries_for_distance_loss_relative()
inputs = {}
outputs1 = {}
outputs2 = {}
for key, value in standard_fields.get_input_voxel_to_object_field_mapping(
).items():
if key in voxel_inputs:
inputs[value] = [voxel_inputs[key][0, Ellipsis]]
for key, value in standard_fields.get_output_voxel_to_object_field_mapping(
).items():
if key in voxel_outputs1:
outputs1[value] = [voxel_outputs1[key][0, Ellipsis]]
for key, value in standard_fields.get_output_voxel_to_object_field_mapping(
).items():
if key in voxel_outputs2:
outputs2[value] = [voxel_outputs2[key][0, Ellipsis]]
loss1 = box_prediction_losses.box_corner_distance_loss_on_object_tensors(
inputs=inputs,
outputs=outputs1,
loss_type='normalized_huber')
loss2 = box_prediction_losses.box_corner_distance_loss_on_object_tensors(
inputs=inputs,
outputs=outputs2,
loss_type='normalized_huber')
self.assertGreater(loss2.numpy(), loss1.numpy())
def mask_valid_voxels(inputs, outputs):
"""Mask the voxels that are valid."""
if standard_fields.DetectionResultFields.objects_center in outputs:
return outputs
valid_mask = mask_utils.num_voxels_mask(inputs=inputs)
mask_utils.apply_mask_to_output_voxel_tensors(outputs=outputs,
valid_mask=valid_mask)
for key, value in standard_fields.get_output_voxel_to_object_field_mapping(
).items():
if key in outputs:
outputs[value] = outputs[key]