def apply_unbatched_loss_on_object_tensors(inputs, outputs, unbatched_loss_fn):
"""Applies the `unbatched_loss_fn` to each example in the batch."""
batch_size = len(inputs[standard_fields.InputDataFields.objects_length])
losses = []
for b in range(batch_size):
inputs_1 = batch_utils.get_batch_size_1_input_objects(inputs=inputs,
b=b)
outputs_1 = batch_utils.get_batch_size_1_output_objects(
outputs=outputs, b=b)
cond_input = tf.greater(
tf.shape(
inputs_1[standard_fields.InputDataFields.objects_length])[0],
0)
cond_output = tf.greater(
tf.shape(outputs_1[
standard_fields.DetectionResultFields.objects_length])[0], 0)
cond = tf.logical_and(cond_input, cond_output)
# pylint: disable=cell-var-from-loop
loss = tf.cond(
cond,
lambda: unbatched_loss_fn(inputs_1=inputs_1, outputs_1=outputs_1),
lambda: tf.constant(0.0, dtype=tf.float32))
# pylint: enable=cell-var-from-loop
losses.append(loss)
return tf.reduce_mean(tf.stack(losses))
def box_corner_distance_loss_on_object_tensors(inputs,
outputs,
loss_type,
delta=1.0,
is_balanced=False):
"""Computes regression loss on object corner locations using object tensors.
Args:
inputs: A dictionary of tf.Tensors with our input data.
outputs: A dictionary of tf.Tensors with the network output.
loss_type: Loss type.
delta: float, the voxel where the huber loss function changes from a
quadratic to linear.
is_balanced: If True, the per-voxel losses are re-weighted to have equal
total weight for each object instance.
Returns:
localization_loss: A tf.float32 scalar corresponding to localization loss.
"""
def fn(inputs_1, outputs_1):
return _box_corner_distance_loss_on_object_tensors(
inputs=inputs_1,
outputs=outputs_1,
loss_type=loss_type,
delta=delta,
is_balanced=is_balanced)
batch_size = len(inputs[standard_fields.InputDataFields.objects_length])
losses = []
for b in range(batch_size):
inputs_1 = batch_utils.get_batch_size_1_input_objects(inputs=inputs,
b=b)
outputs_1 = batch_utils.get_batch_size_1_output_objects(
outputs=outputs, b=b)
cond_input = tf.greater(
tf.shape(
inputs_1[standard_fields.InputDataFields.objects_length])[0],
0)
cond_output = tf.greater(
tf.shape(outputs_1[
standard_fields.DetectionResultFields.objects_length])[0], 0)
cond = tf.logical_and(cond_input, cond_output)
# pylint: disable=cell-var-from-loop
loss = tf.cond(cond,
lambda: fn(inputs_1=inputs_1, outputs_1=outputs_1),
lambda: tf.constant(0.0, dtype=tf.float32))
# pylint: enable=cell-var-from-loop
losses.append(loss)
return tf.reduce_mean(tf.stack(losses))