本文整理匯總了Python中dragnn.python.dragnn_ops.emit_annotations方法的典型用法代碼示例。如果您正苦於以下問題：Python dragnn_ops.emit_annotations方法的具體用法？Python dragnn_ops.emit_annotations怎麽用？Python dragnn_ops.emit_annotations使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類dragnn.python.dragnn_ops的用法示例。
在下文中一共展示了dragnn_ops.emit_annotations方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: add_annotation
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import emit_annotations [as 別名]
def add_annotation(self, name_scope='annotation', enable_tracing=False):
"""Adds an annotation pipeline to the graph.
This will create the following additional named targets by default, for use
in C++ annotation code (as well as regular ComputeSession targets):
annotation/ComputeSession/session_id (placeholder for giving unique id)
annotation/EmitAnnotations (get annotated data)
annotation/GetComponentTrace (get trace data)
annotation/SetTracing (sets tracing based on annotation/tracing_on)
Args:
name_scope: Scope for the annotation pipeline.
enable_tracing: Enabling this will result in two things:
1. Tracing will be enabled during inference.
2. A 'traces' node will be added to the outputs.
Returns:
A dictionary of input and output nodes.
"""
with tf.name_scope(name_scope):
handle, input_batch = self._get_session_with_reader(enable_tracing)
handle = self.build_inference(handle, use_moving_average=True)
annotations = dragnn_ops.emit_annotations(
handle, component=self.spec.component[-1].name)
outputs = {'annotations': annotations}
if enable_tracing:
outputs['traces'] = dragnn_ops.get_component_trace(
handle, component=self.spec.component[-1].name)
return self._outputs_with_release(handle, {'input_batch': input_batch},
outputs) 示例2: add_annotation
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import emit_annotations [as 別名]
def add_annotation(self,
name_scope='annotation',
enable_tracing=False,
build_runtime_graph=False):
"""Adds an annotation pipeline to the graph.
This will create the following additional named targets by default, for use
in C++ annotation code (as well as regular ComputeSession targets):
annotation/ComputeSession/session_id (placeholder for giving unique id)
annotation/EmitAnnotations (get annotated data)
annotation/GetComponentTrace (get trace data)
annotation/SetTracing (sets tracing based on annotation/tracing_on)
Args:
name_scope: Scope for the annotation pipeline.
enable_tracing: Enabling this will result in two things:
1. Tracing will be enabled during inference.
2. A 'traces' node will be added to the outputs.
build_runtime_graph: Whether to build a graph for use by the runtime.
Returns:
A dictionary of input and output nodes.
"""
with tf.name_scope(name_scope):
handle, input_batch = self._get_session_with_reader(enable_tracing)
handle = self.build_inference(
handle,
use_moving_average=True,
build_runtime_graph=build_runtime_graph)
annotations = dragnn_ops.emit_annotations(
handle, component=self.spec.component[-1].name)
outputs = {'annotations': annotations}
if enable_tracing:
outputs['traces'] = dragnn_ops.get_component_trace(
handle, component=self.spec.component[-1].name)
return self._outputs_with_release(handle, {'input_batch': input_batch},
outputs)