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 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()))
