def testProperties(self): input_context = distribute_lib.InputContext( num_input_pipelines=2, input_pipeline_id=1, num_replicas_in_sync=6) self.assertEqual(6, input_context.num_replicas_in_sync) self.assertEqual(1, input_context.input_pipeline_id) self.assertEqual(2, input_context.num_input_pipelines)
def testPerReplicaBatchSize(self): input_context = distribute_lib.InputContext( num_input_pipelines=2, input_pipeline_id=1, num_replicas_in_sync=6) self.assertEqual(2, input_context.get_per_replica_batch_size(12)) with self.assertRaises(ValueError): input_context.get_per_replica_batch_size(13)
def _experimental_distribute_datasets_from_function(self, dataset_fn): return input_lib.get_distributed_datasets_from_function( dataset_fn, self._input_workers, [distribute_lib.InputContext()], self._container_strategy())
def _make_input_context(self): input_context = distribute_lib.InputContext( num_input_pipelines=self._num_workers, input_pipeline_id=self._id_in_cluster, num_replicas_in_sync=self._num_replicas_in_sync) return input_context
def _create_dataset_or_input_fn(self, input_type, input_fn): if input_type == "input_fn": return input_fn else: return input_fn(distribute_lib.InputContext())
def _make_input_fn_iterator( self, input_fn, replication_mode=distribute_lib.InputReplicationMode.PER_WORKER): return input_lib.InputFunctionIterator(input_fn, self._input_workers, [distribute_lib.InputContext()])
def _distribute_datasets_from_function(self, dataset_fn, options): return input_lib.get_distributed_datasets_from_function( dataset_fn, self._input_workers_with_options(options), [distribute_lib.InputContext()], self._container_strategy())
def _distribute_datasets_from_function(self, dataset_fn, options): return dataset_fn(distribute_lib.InputContext())
def _experimental_distribute_datasets_from_function(self, dataset_fn): return dataset_fn(distribute_lib.InputContext())