def _build_calibrated_score_converter(score_converter_fn, calibration_config):
"""Wraps a score_converter_fn, adding a calibration step.
Builds a score converter function witha calibration transformation according
to calibration_builder.py. Calibration applies positive monotonic
transformations to inputs (i.e. score ordering is strictly preserved or
adjacent scores are mapped to the same score). When calibration is
class-agnostic, the highest-scoring class remains unchanged, unless two
adjacent scores are mapped to the same value and one class arbitrarily
selected to break the tie. In per-class calibration, it's possible (though
rare in practice) that the highest-scoring class will change, since positive
monotonicity is only required to hold within each class.
Args:
score_converter_fn: callable that takes logit scores as input.
calibration_config: post_processing_pb2.PostProcessing.calibration_config.
Returns:
Callable calibrated score coverter op.
"""
calibration_fn = calibration_builder.build(calibration_config)
def calibrated_score_converter_fn(logits):
converted_logits = score_converter_fn(logits)
return calibration_fn(converted_logits)
calibrated_score_converter_fn.__name__ = (
'calibrate_with_%s' % calibration_config.WhichOneof('calibrator'))
return calibrated_score_converter_fn
def _build_calibrated_score_converter(score_converter_fn, calibration_config):
"""Wraps a score_converter_fn, adding a calibration step.
Builds a score converter function witha calibration transformation according
to calibration_builder.py. Calibration applies positive monotonic
transformations to inputs (i.e. score ordering is strictly preserved or
adjacent scores are mapped to the same score). When calibration is
class-agnostic, the highest-scoring class remains unchanged, unless two
adjacent scores are mapped to the same value and one class arbitrarily
selected to break the tie. In per-class calibration, it's possible (though
rare in practice) that the highest-scoring class will change, since positive
monotonicity is only required to hold within each class.
Args:
score_converter_fn: callable that takes logit scores as input.
calibration_config: post_processing_pb2.PostProcessing.calibration_config.
Returns:
Callable calibrated score coverter op.
"""
calibration_fn = calibration_builder.build(calibration_config)
def calibrated_score_converter_fn(logits):
converted_logits = score_converter_fn(logits)
return calibration_fn(converted_logits)
calibrated_score_converter_fn.__name__ = (
'calibrate_with_%s' % calibration_config.WhichOneof('calibrator'))
return calibrated_score_converter_fn
def test_multiclass_function_approximations(self):
"""Tests that calibration produces correct multiclass values."""
# Background class (0-index) maps all predictions to 0.5.
class_0_x = np.asarray([0.0, 0.5, 1.0])
class_0_y = np.asarray([0.5, 0.5, 0.5])
calibration_config = calibration_pb2.CalibrationConfig()
self._add_function_approximation_to_calibration_proto(
calibration_config, class_0_x, class_0_y, class_id=0)
# Class id 1 will interpolate using these values.
class_1_x = np.asarray([0.0, 0.2, 1.0])
class_1_y = np.asarray([0.0, 0.6, 1.0])
self._add_function_approximation_to_calibration_proto(
calibration_config, class_1_x, class_1_y, class_id=1)
od_graph = tf.Graph()
with self.test_session(graph=od_graph) as sess:
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]],
dtype=tf.float32)
calibrated_scores = calibration_fn(
class_predictions_with_background)
calibrated_scores_np = sess.run(calibrated_scores)
self.assertAllClose(
calibrated_scores_np,
[[[0.5, 0.6], [0.5, 0.3]], [[0.5, 0.7], [0.5, 0.96]]])
def test_class_agnostic_function_approximation(self):
"""Tests that calibration produces correct class-agnostic values."""
# Generate fake calibration proto. For this interpolation, any input on
# [0.0, 0.5] should be divided by 2 and any input on (0.5, 1.0] should have
# 0.25 subtracted from it.
class_agnostic_x = np.asarray([0.0, 0.5, 1.0])
class_agnostic_y = np.asarray([0.0, 0.25, 0.75])
calibration_config = calibration_pb2.CalibrationConfig()
self._add_function_approximation_to_calibration_proto(
calibration_config,
class_agnostic_x,
class_agnostic_y,
class_id=None)
od_graph = tf.Graph()
with self.test_session(graph=od_graph) as sess:
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]],
[[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]],
dtype=tf.float32)
# Everything should map to 0.5 if classes are ignored.
calibrated_scores = calibration_fn(
class_predictions_with_background)
calibrated_scores_np = sess.run(calibrated_scores)
self.assertAllClose(calibrated_scores_np,
[[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]],
[[0.35, 0.45, 0.55], [0.65, 0.75, 0.75]]])
def _build_calibrated_score_converter(score_converter_fn, calibration_config):
calibration_fn = calibration_builder.build(calibration_config)
def calibrated_score_converter_fn(logits):
converted_logits = score_converter_fn(logits)
return calibration_fn(converted_logits)
calibrated_score_converter_fn.__name__ = (
'calibrate_with_%s' % calibration_config.WhichOneof('calibrator'))
return calibrated_score_converter_fn
def test_temperature_scaling_incorrect_value_error(self):
calibration_config = calibration_pb2.CalibrationConfig()
calibration_config.temperature_scaling_calibration.scaler = 0
calibration_fn = calibration_builder.build(calibration_config)
class_predictions_with_background = tf.constant(
[[[0.1, 0.2, 0.3]]], dtype=tf.float32)
with self.assertRaises(ValueError):
calibration_fn(class_predictions_with_background)
def graph_fn():
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2], [0.9, 0.1]], [[0.6, 0.4], [0.08, 0.92]]],
dtype=tf.float32)
calibrated_scores = calibration_fn(
class_predictions_with_background)
return calibrated_scores
def graph_fn():
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]],
[[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]],
dtype=tf.float32)
# Everything should map to 0.5 if classes are ignored.
calibrated_scores = calibration_fn(
class_predictions_with_background)
return calibrated_scores
def test_temperature_scaling(self):
"""Tests that calibration produces correct temperature scaling values."""
calibration_config = calibration_pb2.CalibrationConfig()
calibration_config.temperature_scaling_calibration.scaler = 2.0
od_graph = tf.Graph()
with self.test_session(graph=od_graph) as sess:
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.0]],
[[0.6, 0.7, 0.8], [0.9, 1.0, 1.0]]],
dtype=tf.float32)
calibrated_scores = calibration_fn(class_predictions_with_background)
calibrated_scores_np = sess.run(calibrated_scores)
self.assertAllClose(calibrated_scores_np,
[[[0.05, 0.1, 0.15], [0.2, 0.25, 0.0]],
[[0.3, 0.35, 0.4], [0.45, 0.5, 0.5]]])
def test_skips_class_when_calibration_parameters_not_present(self):
"""Tests that graph fails when parameters not present for all classes."""
# Only adding calibration parameters for class id = 0, even though class id
# 1 is present in the data.
class_0_x = np.asarray([0.0, 0.5, 1.0])
class_0_y = np.asarray([0.5, 0.5, 0.5])
calibration_config = calibration_pb2.CalibrationConfig()
self._add_function_approximation_to_calibration_proto(
calibration_config, class_0_x, class_0_y, class_id=0)
od_graph = tf.Graph()
with self.test_session(graph=od_graph) as sess:
calibration_fn = calibration_builder.build(calibration_config)
# batch_size = 2, num_classes = 2, num_anchors = 2.
class_predictions_with_background = tf.constant(
[[[0.1, 0.2], [0.9, 0.1]],
[[0.6, 0.4], [0.08, 0.92]]],
dtype=tf.float32)
calibrated_scores = calibration_fn(class_predictions_with_background)
calibrated_scores_np = sess.run(calibrated_scores)
self.assertAllClose(calibrated_scores_np, [[[0.5, 0.2], [0.5, 0.1]],
[[0.5, 0.4], [0.5, 0.92]]])
