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