Python源码示例：syntaxnet.util.check.Lt()
示例1
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例2
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例3
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例4
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例5
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例6
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例7
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例8
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例9
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例10
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例11
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例12
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例13
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例14
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例15
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例16
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例17
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例18
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例19
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例20
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例21
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例22
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例23
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例24
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例25
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例26
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
示例27
def Lt(lhs, rhs, message='', error=ValueError):
"""Raises an error if |lhs| is not less than |rhs|."""
if lhs >= rhs:
raise error('Expected (%s) < (%s): %s' % (lhs, rhs, message))
示例28
def testCheckLt(self):
check.Lt(1, 2, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.Lt(1, 1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.Lt(1, -1, 'baz', RuntimeError)
示例29
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
示例30
def __init__(self, master, component_spec):
"""Initializes the feature ID extractor component.
Args:
master: dragnn.MasterBuilder object.
component_spec: dragnn.ComponentSpec proto to be built.
"""
super(BulkFeatureIdExtractorComponentBuilder, self).__init__(
master, component_spec)
check.Eq(len(self.spec.linked_feature), 0, 'Linked features are forbidden')
for feature_spec in self.spec.fixed_feature:
check.Lt(feature_spec.embedding_dim, 0,
'Features must be non-embedded: %s' % feature_spec)
