def test_box_size_regression_loss_on_voxel_tensors_relative(self):
(inputs, outputs1,
outputs2) = self._get_dictionaries_for_distance_loss_relative()
inputs[standard_fields.InputDataFields.num_valid_voxels] = tf.constant(
[1], dtype=tf.int32)
loss1 = box_prediction_losses.box_size_regression_loss_on_voxel_tensors(
inputs=inputs, outputs=outputs1, loss_type='huber')
loss2 = box_prediction_losses.box_size_regression_loss_on_voxel_tensors(
inputs=inputs, outputs=outputs2, loss_type='huber')
self.assertGreater(loss2.numpy(), loss1.numpy())
def test_box_size_regression_loss_on_voxel_tensors_empty_inputs(self):
inputs = self._get_empty_inputs()
outputs = {
standard_fields.DetectionResultFields.object_length_voxels:
tf.zeros([1, 0, 3], dtype=tf.float32),
standard_fields.DetectionResultFields.object_height_voxels:
tf.zeros([1, 0, 3], dtype=tf.float32),
standard_fields.DetectionResultFields.object_width_voxels:
tf.zeros([1, 0, 3], dtype=tf.float32),
}
loss = box_prediction_losses.box_size_regression_loss_on_voxel_tensors(
inputs=inputs, outputs=outputs, loss_type='huber')
self.assertAllClose(loss.numpy(), 0.0)
def test_box_size_regression_loss_on_voxel_tensors_correct_prediction(
self):
inputs = self._get_random_inputs()
inputs[standard_fields.InputDataFields.num_valid_voxels] = tf.constant(
[100], dtype=tf.int32)
outputs = {
standard_fields.DetectionResultFields.object_length_voxels:
inputs[standard_fields.InputDataFields.object_length_voxels],
standard_fields.DetectionResultFields.object_height_voxels:
inputs[standard_fields.InputDataFields.object_height_voxels],
standard_fields.DetectionResultFields.object_width_voxels:
inputs[standard_fields.InputDataFields.object_width_voxels],
}
loss = box_prediction_losses.box_size_regression_loss_on_voxel_tensors(
inputs=inputs, outputs=outputs, loss_type='huber')
self.assertAllClose(loss.numpy(), 0.0)