def _get_session_with_reader(self, enable_tracing):
"""Utility to create ComputeSession management ops.
Creates a new ComputeSession handle and provides the following
named nodes:
ComputeSession/InputBatch -- a placeholder for attaching a string
specification for AttachReader.
ComputeSession/AttachReader -- the AttachReader op.
Args:
enable_tracing: bool, whether to enable tracing before attaching the data.
Returns:
handle: handle to a new ComputeSession returned by the AttachReader op.
input_batch: InputBatch placeholder.
"""
with tf.name_scope('ComputeSession'):
input_batch = tf.placeholder(
dtype=tf.string, shape=[None], name='InputBatch')
# Get the ComputeSession and chain some essential ops.
handle = self._get_compute_session()
if enable_tracing:
handle = dragnn_ops.set_tracing(handle, True)
handle = dragnn_ops.attach_data_reader(
handle, input_batch, name='AttachReader')
return handle, input_batch
def _get_session_with_reader(self, enable_tracing):
"""Utility to create ComputeSession management ops.
Creates a new ComputeSession handle and provides the following
named nodes:
ComputeSession/InputBatch -- a placeholder for attaching a string
specification for AttachReader.
ComputeSession/AttachReader -- the AttachReader op.
Args:
enable_tracing: bool, whether to enable tracing before attaching the data.
Returns:
handle: handle to a new ComputeSession returned by the AttachReader op.
input_batch: InputBatch placeholder.
"""
with tf.name_scope('ComputeSession'):
input_batch = tf.placeholder(dtype=tf.string,
shape=[None],
name='InputBatch')
# Get the ComputeSession and chain some essential ops.
handle = self._get_compute_session()
if enable_tracing:
handle = dragnn_ops.set_tracing(handle, True)
handle = dragnn_ops.attach_data_reader(handle,
input_batch,
name='AttachReader')
return handle, input_batch
def testBulkFeatureIdExtractorExtractFocusWithOffset(self):
path = os.path.join(tf.test.get_temp_dir(), 'label-map')
with open(path, 'w') as label_map_file:
label_map_file.write('0\n')
master_spec = spec_pb2.MasterSpec()
text_format.Parse("""
component {
name: "test"
transition_system {
registered_name: "shift-only"
}
resource {
name: "label-map"
part {
file_pattern: "%s"
file_format: "text"
}
}
network_unit {
registered_name: "ExportFixedFeaturesNetwork"
}
backend {
registered_name: "SyntaxNetComponent"
}
fixed_feature {
name: "focus1" embedding_dim: -1 size: 1 fml: "input.focus"
predicate_map: "none"
}
fixed_feature {
name: "focus2" embedding_dim: -1 size: 1 fml: "input(1).focus"
predicate_map: "none"
}
fixed_feature {
name: "focus3" embedding_dim: -1 size: 1 fml: "input(2).focus"
predicate_map: "none"
}
}
""" % path, master_spec)
with tf.Graph().as_default():
corpus = _create_fake_corpus()
corpus = tf.constant(corpus, shape=[len(corpus)])
handle = dragnn_ops.get_session(
container='test',
master_spec=master_spec.SerializeToString(),
grid_point='')
handle = dragnn_ops.attach_data_reader(handle, corpus)
handle = dragnn_ops.init_component_data(
handle, beam_size=1, component='test')
batch_size = dragnn_ops.batch_size(handle, component='test')
master_state = component.MasterState(handle, batch_size)
extractor = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, master_spec.component[0])
network_state = component.NetworkState()
self.network_states['test'] = network_state
handle = extractor.build_greedy_inference(master_state,
self.network_states)
focus1 = network_state.activations['focus1'].bulk_tensor
focus2 = network_state.activations['focus2'].bulk_tensor
focus3 = network_state.activations['focus3'].bulk_tensor
with self.test_session() as sess:
focus1, focus2, focus3 = sess.run([focus1, focus2, focus3])
tf.logging.info('focus1=\n%s', focus1)
tf.logging.info('focus2=\n%s', focus2)
tf.logging.info('focus3=\n%s', focus3)
self.assertAllEqual(
focus1,
[[0], [-1], [-1], [-1],
[0], [1], [-1], [-1],
[0], [1], [2], [-1],
[0], [1], [2], [3]])
self.assertAllEqual(
focus2,
[[-1], [-1], [-1], [-1],
[1], [-1], [-1], [-1],
[1], [2], [-1], [-1],
[1], [2], [3], [-1]])
self.assertAllEqual(
focus3,
[[-1], [-1], [-1], [-1],
[-1], [-1], [-1], [-1],
[2], [-1], [-1], [-1],
[2], [3], [-1], [-1]])
def testBulkFeatureIdExtractorExtractFocusWithOffset(self):
path = os.path.join(tf.test.get_temp_dir(), 'label-map')
with open(path, 'w') as label_map_file:
label_map_file.write('0\n')
master_spec = spec_pb2.MasterSpec()
text_format.Parse("""
component {
name: "test"
transition_system {
registered_name: "shift-only"
}
resource {
name: "label-map"
part {
file_pattern: "%s"
file_format: "text"
}
}
network_unit {
registered_name: "ExportFixedFeaturesNetwork"
}
backend {
registered_name: "SyntaxNetComponent"
}
fixed_feature {
name: "focus1" embedding_dim: -1 size: 1 fml: "input.focus"
predicate_map: "none"
}
fixed_feature {
name: "focus2" embedding_dim: -1 size: 1 fml: "input(1).focus"
predicate_map: "none"
}
fixed_feature {
name: "focus3" embedding_dim: -1 size: 1 fml: "input(2).focus"
predicate_map: "none"
}
}
""" % path, master_spec)
with tf.Graph().as_default():
corpus = _create_fake_corpus()
corpus = tf.constant(corpus, shape=[len(corpus)])
handle = dragnn_ops.get_session(
container='test',
master_spec=master_spec.SerializeToString(),
grid_point='')
handle = dragnn_ops.attach_data_reader(handle, corpus)
handle = dragnn_ops.init_component_data(
handle, beam_size=1, component='test')
batch_size = dragnn_ops.batch_size(handle, component='test')
master_state = component.MasterState(handle, batch_size)
extractor = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, master_spec.component[0])
network_state = component.NetworkState()
self.network_states['test'] = network_state
handle = extractor.build_greedy_inference(master_state,
self.network_states)
focus1 = network_state.activations['focus1'].bulk_tensor
focus2 = network_state.activations['focus2'].bulk_tensor
focus3 = network_state.activations['focus3'].bulk_tensor
with self.test_session() as sess:
focus1, focus2, focus3 = sess.run([focus1, focus2, focus3])
tf.logging.info('focus1=\n%s', focus1)
tf.logging.info('focus2=\n%s', focus2)
tf.logging.info('focus3=\n%s', focus3)
self.assertAllEqual(
focus1,
[[0], [-1], [-1], [-1],
[0], [1], [-1], [-1],
[0], [1], [2], [-1],
[0], [1], [2], [3]])
self.assertAllEqual(
focus2,
[[-1], [-1], [-1], [-1],
[1], [-1], [-1], [-1],
[1], [2], [-1], [-1],
[1], [2], [3], [-1]])
self.assertAllEqual(
focus3,
[[-1], [-1], [-1], [-1],
[-1], [-1], [-1], [-1],
[2], [-1], [-1], [-1],
[2], [3], [-1], [-1]])