def _to_proto(self):
pb = dataset_options_pb2.OptimizationOptions()
if self.apply_default_optimizations is not None:
pb.apply_default_optimizations = self.apply_default_optimizations
if self.autotune is not None:
pb.autotune = self.autotune
if self.autotune_buffers is not None:
pb.autotune_buffers = self.autotune_buffers
if self.autotune_cpu_budget is not None:
pb.autotune_cpu_budget = self.autotune_cpu_budget
if self.autotune_ram_budget is not None:
pb.autotune_ram_budget = self.autotune_ram_budget
if self.filter_fusion is not None:
pb.filter_fusion = self.filter_fusion
if self.map_and_batch_fusion is not None:
pb.map_and_batch_fusion = self.map_and_batch_fusion
if self.map_and_filter_fusion is not None:
pb.map_and_filter_fusion = self.map_and_filter_fusion
if self.map_fusion is not None:
pb.map_fusion = self.map_fusion
if self.map_parallelization is not None:
pb.map_parallelization = self.map_parallelization
if self.noop_elimination is not None:
pb.noop_elimination = self.noop_elimination
if self.parallel_batch is not None:
pb.parallel_batch = self.parallel_batch
if self.shuffle_and_repeat_fusion is not None:
pb.shuffle_and_repeat_fusion = self.shuffle_and_repeat_fusion
return pb
def _to_proto(self):
pb = dataset_options_pb2.OptimizationOptions()
if self.apply_default_optimizations is not None:
pb.apply_default_optimizations = self.apply_default_optimizations
if self.filter_fusion is not None:
pb.filter_fusion = self.filter_fusion
if self.filter_parallelization is not None:
pb.filter_parallelization = self.filter_parallelization
if self.inject_prefetch is not None:
pb.inject_prefetch = self.inject_prefetch
if self.map_and_batch_fusion is not None:
pb.map_and_batch_fusion = self.map_and_batch_fusion
if self.map_and_filter_fusion is not None:
pb.map_and_filter_fusion = self.map_and_filter_fusion
if self.map_fusion is not None:
pb.map_fusion = self.map_fusion
if self.map_parallelization is not None:
pb.map_parallelization = self.map_parallelization
if self.noop_elimination is not None:
pb.noop_elimination = self.noop_elimination
if self.parallel_batch is not None:
pb.parallel_batch = self.parallel_batch
if self.shuffle_and_repeat_fusion is not None:
pb.shuffle_and_repeat_fusion = self.shuffle_and_repeat_fusion
return pb
def testProtoOptionsDefaultValuesRoundTrip(self):
pb = dataset_options_pb2.Options()
options = dataset_ops.Options()
options._from_proto(pb)
result = options._to_proto()
expected_pb = dataset_options_pb2.Options()
expected_pb.distribute_options.CopyFrom(
dataset_options_pb2.DistributeOptions())
expected_pb.optimization_options.CopyFrom(
dataset_options_pb2.OptimizationOptions())
expected_pb.threading_options.CopyFrom(
dataset_options_pb2.ThreadingOptions())
self.assertProtoEquals(expected_pb, result)
def _to_proto(self):
pb = dataset_options_pb2.OptimizationOptions()
if self.apply_default_optimizations is not None:
pb.apply_default_optimizations = self.apply_default_optimizations
if self.autotune is not None:
pb.autotune = self.autotune
if self.autotune_buffers is not None:
pb.autotune_buffers = self.autotune_buffers
if self.autotune_cpu_budget is not None:
pb.autotune_cpu_budget = self.autotune_cpu_budget
if self.autotune_ram_budget is not None:
pb.autotune_ram_budget = self.autotune_ram_budget
if self.filter_fusion is not None:
pb.filter_fusion = self.filter_fusion
if self.filter_with_random_uniform_fusion is not None:
pb.filter_with_random_uniform_fusion = (
self.filter_with_random_uniform_fusion)
if self.hoist_random_uniform is not None:
pb.hoist_random_uniform = self.hoist_random_uniform
if self.map_and_batch_fusion is not None:
pb.map_and_batch_fusion = self.map_and_batch_fusion
if self.map_and_filter_fusion is not None:
pb.map_and_filter_fusion = self.map_and_filter_fusion
if self.map_fusion is not None:
pb.map_fusion = self.map_fusion
if self.map_parallelization is not None:
pb.map_parallelization = self.map_parallelization
pb.map_vectorization.CopyFrom(self.map_vectorization._to_proto()) # pylint: disable=protected-access
if self.noop_elimination is not None:
pb.noop_elimination = self.noop_elimination
if self.parallel_batch is not None:
pb.parallel_batch = self.parallel_batch
if self.reorder_data_discarding_ops is not None:
pb.reorder_data_discarding_ops = self.reorder_data_discarding_ops
if self.shuffle_and_repeat_fusion is not None:
pb.shuffle_and_repeat_fusion = self.shuffle_and_repeat_fusion
return pb
