def build_inference(self, handle, use_moving_average=False,
clear_existing_annotations=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
clear_existing_annotations: Whether or not existing annotations
should be cleared when processing a new batch.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle, component=comp.name,
clear_existing_annotations=clear_existing_annotations)
master_state = component.MasterState(handle,
dragnn_ops.batch_size(
handle, component=comp.name))
with tf.control_dependencies([handle]):
handle = comp.build_inference(master_state, network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
return handle
def build_inference(self, handle, use_moving_average=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle,
beam_size=comp.inference_beam_size,
component=comp.name)
master_state = component.MasterState(
handle, dragnn_ops.batch_size(handle, component=comp.name))
with tf.control_dependencies([handle]):
handle = comp.build_greedy_inference(master_state,
network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
return handle
def build_inference(self,
handle,
use_moving_average=False,
build_runtime_graph=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
build_runtime_graph: Whether to build a graph for use by the runtime.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
self.build_runtime_graph = build_runtime_graph
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle, beam_size=comp.inference_beam_size, component=comp.name)
if build_runtime_graph:
batch_size = 1 # runtime uses singleton batches
else:
batch_size = dragnn_ops.batch_size(handle, component=comp.name)
master_state = component.MasterState(handle, batch_size)
with tf.control_dependencies([handle]):
handle = comp.build_greedy_inference(master_state, network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
self.build_runtime_graph = False
return handle