def testGetGraphFromValidInputs(self):
g0 = ops.Graph()
with g0.as_default():
values = [constant_op.constant(0.0), constant_op.constant(1.0)]
self.assertIs(g0, ops_lib.get_graph_from_inputs(values))
self.assertIs(g0, ops_lib.get_graph_from_inputs(values, g0))
with ops.Graph().as_default():
self.assertIs(g0, ops_lib.get_graph_from_inputs(values))
self.assertIs(g0, ops_lib.get_graph_from_inputs(values, g0))
def testGetGraphFromValidInputs(self):
g0 = ops.Graph()
with g0.as_default():
values = [constant_op.constant(0.0), constant_op.constant(1.0)]
self.assertIs(g0, ops_lib.get_graph_from_inputs(values))
self.assertIs(g0, ops_lib.get_graph_from_inputs(values, g0))
with ops.Graph().as_default():
self.assertIs(g0, ops_lib.get_graph_from_inputs(values))
self.assertIs(g0, ops_lib.get_graph_from_inputs(values, g0))
def testGetGraphFromInvalidInputs(self):
g0 = ops.Graph()
with g0.as_default():
values = [constant_op.constant(0.0), constant_op.constant(1.0)]
g1 = ops.Graph()
with self.assertRaisesRegexp(ValueError, "not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g1)
with g1.as_default():
values.append(constant_op.constant(2.0))
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
ops_lib.get_graph_from_inputs(values)
with self.assertRaisesRegexp(ValueError, "not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g0)
with self.assertRaisesRegexp(ValueError, "not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g1)
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 testGetGraphFromInvalidInputs(self):
g0 = ops.Graph()
with g0.as_default():
values = [constant_op.constant(0.0), constant_op.constant(1.0)]
g1 = ops.Graph()
with self.assertRaisesRegexp(ValueError,
"not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g1)
with g1.as_default():
values.append(constant_op.constant(2.0))
with self.assertRaisesRegexp(ValueError,
"must be from the same graph"):
ops_lib.get_graph_from_inputs(values)
with self.assertRaisesRegexp(ValueError,
"not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g0)
with self.assertRaisesRegexp(ValueError,
"not from the passed-in graph"):
ops_lib.get_graph_from_inputs(values, g1)
def run_feeds(output_dict, feed_dicts, restore_checkpoint_path=None):
"""Run `output_dict` tensors with each input in `feed_dicts`.
If `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.
Returns:
A list of dicts of values read from `output_dict` tensors, one item in the
list 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:
if restore_checkpoint_path:
_restore_from_checkpoint(session, g, restore_checkpoint_path)
else:
session.run(variables.initialize_all_variables())
session.run(variables.initialize_local_variables())
session.run(data_flow_ops.initialize_all_tables())
coord = Coordinator()
try:
queue_runner.start_queue_runners(session, coord=coord)
return [_run_dict(session, output_dict, f) for f in feed_dicts]
finally:
coord.request_stop()
def run_feeds(output_dict, feed_dicts, restore_checkpoint_path=None):
"""Run `output_dict` tensors with each input in `feed_dicts`.
If `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.
Returns:
A list of dicts of values read from `output_dict` tensors, one item in the
list 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:
if restore_checkpoint_path:
_restore_from_checkpoint(session, g, restore_checkpoint_path)
else:
session.run(variables.initialize_all_variables())
session.run(variables.initialize_local_variables())
session.run(data_flow_ops.initialize_all_tables())
coord = Coordinator()
try:
queue_runner.start_queue_runners(session, coord=coord)
return [_run_dict(session, output_dict, f) for f in feed_dicts]
finally:
coord.request_stop()
def testGetGraphFromEmptyInputs(self):
with ops.Graph().as_default() as g0:
self.assertIs(g0, ops_lib.get_graph_from_inputs([]))
def testGetGraphFromEmptyInputs(self):
with ops.Graph().as_default() as g0:
self.assertIs(g0, ops_lib.get_graph_from_inputs([]))
