def testExample(self):
class SlotManager(tracking.AutoTrackable):
def __init__(self):
self.slotdeps = containers.UniqueNameTracker()
slotdeps = self.slotdeps
slots = []
slots.append(
slotdeps.track(resource_variable_ops.ResourceVariable(3.),
"x"))
slots.append(
slotdeps.track(resource_variable_ops.ResourceVariable(4.),
"y"))
slots.append(
slotdeps.track(resource_variable_ops.ResourceVariable(5.),
"x"))
self.slots = data_structures.NoDependency(slots)
manager = SlotManager()
self.evaluate([v.initializer for v in manager.slots])
checkpoint = util.Checkpoint(slot_manager=manager)
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = checkpoint.save(checkpoint_prefix)
metadata = util.object_metadata(save_path)
dependency_names = []
for node in metadata.nodes:
for child in node.children:
dependency_names.append(child.local_name)
six.assertCountEqual(
self, dependency_names,
["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"])
def testObjectMetadata(self):
with context.eager_mode():
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
dense = core.Dense(1)
checkpoint = trackable_utils.Checkpoint(dense=dense)
dense(constant_op.constant([[1.]]))
save_path = checkpoint.save(checkpoint_prefix)
objects = trackable_utils.object_metadata(save_path)
all_variable_names = []
for obj in objects.nodes:
for attribute in obj.attributes:
all_variable_names.append(attribute.full_name)
self.assertIn("dense/kernel", all_variable_names)
def testTrainSpinn(self):
"""Test with fake toy SNLI data and GloVe vectors."""
# 1. Create and load a fake SNLI data file and a fake GloVe embedding file.
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
dev_data = data.SnliData(fake_train_file, word2index)
test_data = data.SnliData(fake_train_file, word2index)
# 2. Create a fake config.
config = _test_spinn_config(data.WORD_VECTOR_LEN,
4,
logdir=os.path.join(
self._temp_data_dir, "logdir"))
# 3. Test training of a SPINN model.
trainer = spinn.train_or_infer_spinn(embed, word2index, train_data,
dev_data, test_data, config)
# 4. Load train loss values from the summary files and verify that they
# decrease with training.
summary_file = glob.glob(os.path.join(config.logdir,
"events.out.*"))[0]
events = summary_test_util.events_from_file(summary_file)
train_losses = [
event.summary.value[0].simple_value for event in events if
event.summary.value and event.summary.value[0].tag == "train/loss"
]
self.assertEqual(config.epochs, len(train_losses))
# 5. Verify that checkpoints exist and contains all the expected variables.
self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
object_graph = trackable_utils.object_metadata(
checkpoint_management.latest_checkpoint(config.logdir))
ckpt_variable_names = set()
for node in object_graph.nodes:
for attribute in node.attributes:
ckpt_variable_names.add(attribute.full_name)
self.assertIn("global_step", ckpt_variable_names)
for v in trainer.variables:
variable_name = v.name[:v.name.
index(":")] if ":" in v.name else v.name
self.assertIn(variable_name, ckpt_variable_names)
def testObjectMetadata(self):
if not tf.executing_eagerly():
self.skipTest("Run in eager mode only.")
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
dense = core.Dense(1)
checkpoint = tf.train.Checkpoint(dense=dense)
dense(tf.constant([[1.]]))
save_path = checkpoint.save(checkpoint_prefix)
objects = trackable_utils.object_metadata(save_path)
all_variable_names = []
for obj in objects.nodes:
for attribute in obj.attributes:
all_variable_names.append(attribute.full_name)
self.assertIn("dense/kernel", all_variable_names)
def testTrainSpinn(self):
"""Test with fake toy SNLI data and GloVe vectors."""
# 1. Create and load a fake SNLI data file and a fake GloVe embedding file.
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
dev_data = data.SnliData(fake_train_file, word2index)
test_data = data.SnliData(fake_train_file, word2index)
# 2. Create a fake config.
config = _test_spinn_config(
data.WORD_VECTOR_LEN, 4,
logdir=os.path.join(self._temp_data_dir, "logdir"))
# 3. Test training of a SPINN model.
trainer = spinn.train_or_infer_spinn(
embed, word2index, train_data, dev_data, test_data, config)
# 4. Load train loss values from the summary files and verify that they
# decrease with training.
summary_file = glob.glob(os.path.join(config.logdir, "events.out.*"))[0]
events = summary_test_util.events_from_file(summary_file)
train_losses = [event.summary.value[0].simple_value for event in events
if event.summary.value
and event.summary.value[0].tag == "train/loss"]
self.assertEqual(config.epochs, len(train_losses))
# 5. Verify that checkpoints exist and contains all the expected variables.
self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
object_graph = trackable_utils.object_metadata(
checkpoint_management.latest_checkpoint(config.logdir))
ckpt_variable_names = set()
for node in object_graph.nodes:
for attribute in node.attributes:
ckpt_variable_names.add(attribute.full_name)
self.assertIn("global_step", ckpt_variable_names)
for v in trainer.variables:
variable_name = v.name[:v.name.index(":")] if ":" in v.name else v.name
self.assertIn(variable_name, ckpt_variable_names)
def dot_graph_from_checkpoint(save_path):
r"""Visualizes an object-based checkpoint (from `tf.train.Checkpoint`).
Useful for inspecting checkpoints and debugging loading issues.
Example usage from Python (requires pydot):
```python
import tensorflow as tf
import pydot
dot_string = tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt')
parsed, = pydot.graph_from_dot_data(dot_string)
parsed.write_svg('/tmp/tensorflow/visualized_checkpoint.svg')
```
Example command line usage:
```sh
python -c "import tensorflow as tf;\
print(tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt'))"\
| dot -Tsvg > /tmp/tensorflow/checkpoint_viz.svg
```
Args:
save_path: The checkpoint prefix, as returned by `tf.train.Checkpoint.save`
or `tf.train.latest_checkpoint`.
Returns:
A graph in DOT format as a string.
"""
reader = pywrap_tensorflow.NewCheckpointReader(save_path)
object_graph = trackable_utils.object_metadata(save_path)
shape_map = reader.get_variable_to_shape_map()
dtype_map = reader.get_variable_to_dtype_map()
graph = 'digraph {\n'
def _escape(name):
return name.replace('"', '\\"')
slot_ids = set()
for node in object_graph.nodes:
for slot_reference in node.slot_variables:
slot_ids.add(slot_reference.slot_variable_node_id)
for node_id, node in enumerate(object_graph.nodes):
if (len(node.attributes) == 1
and node.attributes[0].name == trackable.VARIABLE_VALUE_KEY):
if node_id in slot_ids:
color = 'orange'
tooltip_prefix = 'Slot variable'
else:
color = 'blue'
tooltip_prefix = 'Variable'
attribute = node.attributes[0]
graph += ('N_%d [shape=point label="" color=%s width=.25'
' tooltip="%s %s shape=%s %s"]\n') % (
node_id,
color,
tooltip_prefix,
_escape(attribute.full_name),
shape_map[attribute.checkpoint_key],
dtype_map[attribute.checkpoint_key].name)
elif node.slot_variables:
graph += ('N_%d [shape=point label="" width=.25 color=red,'
'tooltip="Optimizer"]\n') % node_id
else:
graph += 'N_%d [shape=point label="" width=.25]\n' % node_id
for reference in node.children:
graph += 'N_%d -> N_%d [label="%s"]\n' % (
node_id, reference.node_id, _escape(reference.local_name))
for slot_reference in node.slot_variables:
graph += 'N_%d -> N_%d [label="%s" style=dotted]\n' % (
node_id,
slot_reference.slot_variable_node_id,
_escape(slot_reference.slot_name))
graph += 'N_%d -> N_%d [style=dotted]\n' % (
slot_reference.original_variable_node_id,
slot_reference.slot_variable_node_id)
graph += '}\n'
return graph
def dot_graph_from_checkpoint(save_path):
r"""Visualizes an object-based checkpoint (from `tf.train.Checkpoint`).
Useful for inspecting checkpoints and debugging loading issues.
Example usage from Python (requires pydot):
```python
import tensorflow as tf
import pydot
dot_string = tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt')
parsed, = pydot.graph_from_dot_data(dot_string)
parsed.write_svg('/tmp/tensorflow/visualized_checkpoint.svg')
```
Example command line usage:
```sh
python -c "import tensorflow as tf;\
print(tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt'))"\
| dot -Tsvg > /tmp/tensorflow/checkpoint_viz.svg
```
Args:
save_path: The checkpoint prefix, as returned by `tf.train.Checkpoint.save`
or `tf.train.latest_checkpoint`.
Returns:
A graph in DOT format as a string.
"""
reader = pywrap_tensorflow.NewCheckpointReader(save_path)
object_graph = trackable_utils.object_metadata(save_path)
shape_map = reader.get_variable_to_shape_map()
dtype_map = reader.get_variable_to_dtype_map()
graph = 'digraph {\n'
def _escape(name):
return name.replace('"', '\\"')
slot_ids = set()
for node in object_graph.nodes:
for slot_reference in node.slot_variables:
slot_ids.add(slot_reference.slot_variable_node_id)
for node_id, node in enumerate(object_graph.nodes):
if (len(node.attributes) == 1
and node.attributes[0].name == trackable.VARIABLE_VALUE_KEY):
if node_id in slot_ids:
color = 'orange'
tooltip_prefix = 'Slot variable'
else:
color = 'blue'
tooltip_prefix = 'Variable'
attribute = node.attributes[0]
graph += ('N_%d [shape=point label="" color=%s width=.25'
' tooltip="%s %s shape=%s %s"]\n') % (
node_id, color, tooltip_prefix,
_escape(attribute.full_name),
shape_map[attribute.checkpoint_key],
dtype_map[attribute.checkpoint_key].name)
elif node.slot_variables:
graph += ('N_%d [shape=point label="" width=.25 color=red,'
'tooltip="Optimizer"]\n') % node_id
else:
graph += 'N_%d [shape=point label="" width=.25]\n' % node_id
for reference in node.children:
graph += 'N_%d -> N_%d [label="%s"]\n' % (
node_id, reference.node_id, _escape(reference.local_name))
for slot_reference in node.slot_variables:
graph += 'N_%d -> N_%d [label="%s" style=dotted]\n' % (
node_id, slot_reference.slot_variable_node_id,
_escape(slot_reference.slot_name))
graph += 'N_%d -> N_%d [style=dotted]\n' % (
slot_reference.original_variable_node_id,
slot_reference.slot_variable_node_id)
graph += '}\n'
return graph