def _run_graph_optimizations(graph_def,
input_arrays,
output_arrays,
graph=None):
"""Apply standard TensorFlow optimizations to the graph_def.
Args:
graph_def: Frozen GraphDef to be optimized.
input_arrays: List of arrays that are considered inputs of the graph.
output_arrays: List of arrays that are considered outputs of the graph.
graph: TensorFlow Graph. Required when Eager mode is enabled. (default None)
Returns:
A new, optimized GraphDef.
"""
meta_graph = _export_meta_graph(graph_def=graph_def, graph=graph)
# We need to add a collection called 'train_op' so that grappler
# knows what the outputs are.
fetch_collection = _meta_graph_pb2.CollectionDef()
for array in input_arrays + output_arrays:
fetch_collection.node_list.value.append(array.name)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
config = _config_pb2.ConfigProto()
rewrite_options = config.graph_options.rewrite_options
rewrite_options.layout_optimizer = _rewriter_config_pb2.RewriterConfig.ON
# Avoid remapping as it creates ops like _FusedConv2D, which are not
# supported by TF Lite.
rewrite_options.remapping = _rewriter_config_pb2.RewriterConfig.OFF
return _tf_optimizer.OptimizeGraph(config, meta_graph)
def run_graph_optimizations(graph_def,
input_arrays,
output_arrays,
config,
graph=None):
"""Apply standard TensorFlow optimizations to the graph_def.
Args:
graph_def: Frozen GraphDef to be optimized.
input_arrays: List of arrays that are considered inputs of the graph.
output_arrays: List of arrays that are considered outputs of the graph.
config: tf.ConfigProto.
graph: TensorFlow Graph. Required when Eager mode is enabled. (default None)
Returns:
A new, optimized GraphDef.
"""
meta_graph = _export_meta_graph(graph_def=graph_def, graph=graph)
# We need to add a collection called 'train_op' so that grappler
# knows what the outputs are.
fetch_collection = _meta_graph_pb2.CollectionDef()
for array in input_arrays + output_arrays:
fetch_collection.node_list.value.append(array.name)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
return tf_optimizer.OptimizeGraph(config, meta_graph)
def run_graph_optimizations(graph_def,
input_arrays,
output_arrays,
config,
graph=None):
"""Apply standard TensorFlow optimizations to the graph_def.
Args:
graph_def: Frozen GraphDef to be optimized.
input_arrays: List of arrays that are considered inputs of the graph.
output_arrays: List of arrays that are considered outputs of the graph.
config: tf.ConfigProto.
graph: TensorFlow Graph. Required when Eager mode is enabled. (default None)
Returns:
A new, optimized GraphDef.
"""
meta_graph = _export_meta_graph(graph_def=graph_def, graph=graph)
# We need to add a collection called 'train_op' so that grappler
# knows what the outputs are.
fetch_collection = _meta_graph_pb2.CollectionDef()
for array in input_arrays + output_arrays:
fetch_collection.node_list.value.append(array.name)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
return tf_optimizer.OptimizeGraph(config, meta_graph)
def _run_graph_optimizations(graph_def, input_arrays, output_arrays):
"""Apply standard TensorFlow optimizations to the graph_def.
Args:
graph_def: Frozen GraphDef to be optimized.
input_arrays: List of arrays that are considered inputs of the graph.
output_arrays: List of arrays that are considered outputs of the graph.
Returns:
A new, optimized GraphDef.
"""
meta_graph = _export_meta_graph(graph_def=graph_def)
# We need to add a collection called 'train_op' so that grappler
# knows what the outputs are.
fetch_collection = _meta_graph_pb2.CollectionDef()
for array in input_arrays + output_arrays:
fetch_collection.node_list.value.append(array.name)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
config = _config_pb2.ConfigProto()
rewrite_options = config.graph_options.rewrite_options
rewrite_options.layout_optimizer = _rewriter_config_pb2.RewriterConfig.ON
# Avoid remapping as it creates ops like _FusedConv2D, which are not
# supported by TF Lite.
rewrite_options.remapping = _rewriter_config_pb2.RewriterConfig.OFF
return _tf_optimizer.OptimizeGraph(config, meta_graph)
def _run_graph_optimizations(graph_def, output_arrays):
"""Apply standard TensorFlow optimizations to the graph_def.
Args:
graph_def: Frozen GraphDef to be optimized.
output_arrays: List of arrays that are considered outputs of the graph.
Returns:
A new, optimized GraphDef.
"""
meta_graph = _export_meta_graph(graph_def=graph_def)
# We need to add a collection called 'train_op' so that grappler
# knows what the outputs are.
fetch_collection = _meta_graph_pb2.CollectionDef()
for output in output_arrays:
fetch_collection.node_list.value.append(output)
meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
config = _config_pb2.ConfigProto()
rewrite_options = config.graph_options.rewrite_options
rewrite_options.layout_optimizer = _rewriter_config_pb2.RewriterConfig.ON
return _tf_optimizer.OptimizeGraph(config, meta_graph)
