def test_simple(self): with self.session(): TADDR_VALID = 'zrpull://127.0.0.1:5555' output = zmq_conn_handle(TADDR_VALID, ZMQ_HWM, 0) resources.initialize_resources(resources.local_resources()).run() # assertDTypeEqual not working for resource type. it trans tf.dtype to np.dtype and resource is incompatible with numpy #self.assertDtypeEqual(output, dtypes.resource.as_numpy_type) self.assertEqual(type(output.dtype), type(dtypes.resource))
def run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path=None): """Run `output_dict` tensors with each input in `feed_dicts`. If `restore_checkpoint_path` is supplied, restore from checkpoint. Otherwise, init all variables. Args: output_dict: A `dict` mapping string names to `Output` objects to run. Tensors must all be from the same graph. feed_dicts: Iterable of `dict` objects of input values to feed. restore_checkpoint_path: A string containing the path to a checkpoint to restore. Yields: A sequence of dicts of values read from `output_dict` tensors, one item yielded for each item in `feed_dicts`. Keys are the same as `output_dict`, values are the results read from the corresponding `Output` in `output_dict`. Raises: ValueError: if `output_dict` or `feed_dicts` is None or empty. """ if not output_dict: raise ValueError('output_dict is invalid: %s.' % output_dict) if not feed_dicts: raise ValueError('feed_dicts is invalid: %s.' % feed_dicts) graph = contrib_ops.get_graph_from_inputs(output_dict.values()) with graph.as_default() as g: with tf_session.Session('') as session: session.run( resources.initialize_resources(resources.shared_resources() + resources.local_resources())) if restore_checkpoint_path: _restore_from_checkpoint(session, g, restore_checkpoint_path) else: session.run(variables.global_variables_initializer()) session.run(variables.local_variables_initializer()) session.run(data_flow_ops.initialize_all_tables()) coord = coordinator.Coordinator() threads = None try: threads = queue_runner.start_queue_runners(session, coord=coord) for f in feed_dicts: yield session.run(output_dict, f) finally: coord.request_stop() if threads: coord.join(threads, stop_grace_period_secs=120)
def run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path=None): """Run `output_dict` tensors with each input in `feed_dicts`. If `restore_checkpoint_path` is supplied, restore from checkpoint. Otherwise, init all variables. Args: output_dict: A `dict` mapping string names to `Tensor` objects to run. Tensors must all be from the same graph. feed_dicts: Iterable of `dict` objects of input values to feed. restore_checkpoint_path: A string containing the path to a checkpoint to restore. Yields: A sequence of dicts of values read from `output_dict` tensors, one item yielded for each item in `feed_dicts`. Keys are the same as `output_dict`, values are the results read from the corresponding `Tensor` in `output_dict`. Raises: ValueError: if `output_dict` or `feed_dicts` is None or empty. """ if not output_dict: raise ValueError('output_dict is invalid: %s.' % output_dict) if not feed_dicts: raise ValueError('feed_dicts is invalid: %s.' % feed_dicts) graph = contrib_ops.get_graph_from_inputs(output_dict.values()) with graph.as_default() as g: with tf_session.Session('') as session: session.run( resources.initialize_resources(resources.shared_resources() + resources.local_resources())) if restore_checkpoint_path: _restore_from_checkpoint(session, g, restore_checkpoint_path) else: session.run(variables.global_variables_initializer()) session.run(variables.local_variables_initializer()) session.run(data_flow_ops.initialize_all_tables()) coord = coordinator.Coordinator() threads = None try: threads = queue_runner.start_queue_runners(session, coord=coord) for f in feed_dicts: yield session.run(output_dict, f) finally: coord.request_stop() if threads: coord.join(threads, stop_grace_period_secs=120)
def default_local_init_op(): """Returns an op that groups the default local init ops. This op is used during session initialization when a Scaffold is initialized without specifying the local_init_op arg. It includes `tf.local_variables_initializer`, `tf.tables_initializer`, and also initializes local session resources. Returns: The default Scaffold local init op. """ return control_flow_ops.group( variables.local_variables_initializer(), lookup_ops.tables_initializer(), resources.initialize_resources(resources.local_resources()))
def _default_local_init_op(): return control_flow_ops.group( variables.local_variables_initializer(), lookup_ops.tables_initializer(), resources.initialize_resources(resources.local_resources()))
def default_local_init_op(): return tf.group( tf.local_variables_initializer(), tf.tables_initializer(), resources.initialize_resources(resources.local_resources()))