def get_inputs(
self,
output_index: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Returns labels, predictions, and weights for output at given offset."""
labels, preds, example_weights = super(TFCompilableMetricsAccumulator,
self).get_inputs(output_index)
if self._pad:
def pad_value(
name: Text, a: np.ndarray,
configured_value: Optional[Union[float, int]]
) -> Union[int, float]:
if configured_value is None:
return 0 if a.dtype.kind == 'i' else .0
if isinstance(configured_value, int) and a.dtype.kind == 'i':
return configured_value
if isinstance(configured_value, float) and a.dtype.kind == 'f':
return configured_value
raise ValueError(
'%s padding is configured to be %s but data is %s' %
(name, type(configured_value), a.dtype))
labels = [
metric_util.pad(l, self._pad_to_dim,
pad_value('label', l, self._label_padding))
for l in labels
]
preds = [
metric_util.pad(
p, self._pad_to_dim,
pad_value('prediction', p, self._prediction_padding))
for p in preds
]
return (np.array(labels), np.array(preds), np.array(example_weights))
def get_inputs(
self,
output_index: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Returns labels, predictions, and weights for output at given offset."""
labels, preds, example_weights = self._inputs[output_index]
if self._pad:
def pad_value(a: np.array) -> Union[int, float]:
return -1 if a.dtype.kind == 'i' else -1.0
labels = [
metric_util.pad(l, self._last_dim, pad_value(l))
for l in labels
]
preds = [
metric_util.pad(p, self._last_dim, pad_value(p)) for p in preds
]
return (np.array(labels), np.array(preds), np.array(example_weights))
def testPad2D(self):
self.assertAllClose(
np.array([[1.0, 2.0, 0.0, 0.0, 0.0], [3.0, 4.0, 0.0, 0.0, 0.0]]),
metric_util.pad(np.array([[1.0, 2.0], [3.0, 4.0]]), 5, 0.0))
def testPad1DMultipleValues(self):
self.assertAllClose(np.array([1.0, 2.0, -1.0, -1.0]),
metric_util.pad(np.array([1.0, 2.0]), 4, -1.0))
def testPad1DSingleValue(self):
self.assertAllClose(np.array([1.0, -1.0]),
metric_util.pad(np.array([1.0]), 2, -1.0))
def testPadNoChange(self):
self.assertAllClose(np.array([1.0, 2.0]),
metric_util.pad(np.array([1.0, 2.0]), 2, -1.0))