def merge_default_with_oplog(graph,
op_log=None,
run_meta=None,
add_trace=True,
add_trainable_var=True):
"""Merge the tfprof default extra info with caller's op_log.
Args:
graph: tf.Graph. If None and eager execution is not enabled, use
default graph.
op_log: OpLogProto proto.
run_meta: RunMetadata proto used to complete shape information.
add_trace: Whether to add op trace information.
add_trainable_var: Whether to assign tf.compat.v1.trainable_variables() op
type '_trainable_variables'.
Returns:
tmp_op_log: Merged OpLogProto proto.
"""
if not graph and not context.executing_eagerly():
graph = ops.get_default_graph()
tmp_op_log = tfprof_log_pb2.OpLogProto()
if not graph:
return tmp_op_log
logged_ops, string_to_id = _get_logged_ops(
graph,
run_meta,
add_trace=add_trace,
add_trainable_var=add_trainable_var)
if not op_log:
tmp_op_log.log_entries.extend(logged_ops.values())
else:
all_ops = {}
for entry in op_log.log_entries:
all_ops[entry.name] = entry
for op_name, entry in six.iteritems(logged_ops):
if op_name in all_ops:
all_ops[op_name].types.extend(entry.types)
if entry.float_ops > 0 and all_ops[op_name].float_ops == 0:
all_ops[op_name].float_ops = entry.float_ops
if entry.code_def.traces and not all_ops[
op_name].code_def.traces:
all_ops[op_name].code_def.MergeFrom(entry.code_def)
else:
all_ops[op_name] = entry
tmp_op_log.log_entries.extend(all_ops.values())
for s, i in six.iteritems(string_to_id):
tmp_op_log.id_to_string[i] = s
return tmp_op_log
def _merge_default_with_oplog(graph,
op_log=None,
run_meta=None,
add_trace=True,
add_trainable_var=True):
"""Merge the tfprof default extra info with caller's op_log.
Args:
graph: tf.Graph.
op_log: OpLogProto proto.
run_meta: RunMetadata proto used to complete shape information.
add_trace: Whether to add op trace information.
add_trainable_var: Whether to assign tf.trainable_variables() op type
'_trainable_variables'.
Returns:
tmp_op_log: Merged OpLogProto proto.
"""
tmp_op_log = tfprof_log_pb2.OpLogProto()
logged_ops = _get_logged_ops(graph,
run_meta,
add_trace=add_trace,
add_trainable_var=add_trainable_var)
if not op_log:
tmp_op_log.log_entries.extend(logged_ops.values())
else:
all_ops = dict()
for entry in op_log.log_entries:
all_ops[entry.name] = entry
for op_name, entry in six.iteritems(logged_ops):
if op_name in all_ops:
all_ops[op_name].types.extend(entry.types)
if entry.float_ops > 0 and all_ops[op_name].float_ops == 0:
all_ops[op_name].float_ops = entry.float_ops
if entry.code_def.traces and not all_ops[
op_name].code_def.traces:
all_ops[op_name].code_def.MergeFrom(entry.code_def)
else:
all_ops[op_name] = entry
tmp_op_log.log_entries.extend(all_ops.values())
return tmp_op_log
