215 lines
7.5 KiB
Python
215 lines
7.5 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import io
|
|
import os
|
|
|
|
import numpy
|
|
|
|
from tensorflow.python.client import session
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.module import module
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.training.tracking import python_state
|
|
from tensorflow.python.training.tracking import util
|
|
|
|
|
|
class _NumpyState(module.Module):
|
|
"""A checkpointable object whose NumPy array attributes are saved/restored.
|
|
|
|
Example usage:
|
|
|
|
```python
|
|
arrays = _NumpyState()
|
|
checkpoint = tf.train.Checkpoint(numpy_arrays=arrays)
|
|
arrays.x = numpy.zeros([3, 4])
|
|
save_path = checkpoint.save("/tmp/ckpt")
|
|
arrays.x[1, 1] = 4.
|
|
checkpoint.restore(save_path)
|
|
assert (arrays.x == numpy.zeros([3, 4])).all()
|
|
|
|
second_checkpoint = tf.train.Checkpoint(
|
|
numpy_arrays=_NumpyState())
|
|
# Attributes of NumpyState objects are created automatically by restore()
|
|
second_checkpoint.restore(save_path)
|
|
assert (second_checkpoint.numpy_arrays.x == numpy.zeros([3, 4])).all()
|
|
```
|
|
|
|
Note that `NumpyState` objects re-create the attributes of the previously
|
|
saved object on `restore()`. This is in contrast to TensorFlow variables, for
|
|
which a `Variable` object must be created and assigned to an attribute.
|
|
|
|
This snippet works both when graph building and when executing eagerly. On
|
|
save, the NumPy array(s) are fed as strings to be saved in the checkpoint (via
|
|
a placeholder when graph building, or as a string constant when executing
|
|
eagerly). When restoring they skip the TensorFlow graph entirely, and so no
|
|
restore ops need be run. This means that restoration always happens eagerly,
|
|
rather than waiting for `checkpoint.restore(...).run_restore_ops()` like
|
|
TensorFlow variables when graph building.
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(_NumpyState, self).__setattr__("_arrays", module.Module())
|
|
|
|
def __getattribute__(self, name):
|
|
"""Un-wrap `_NumpyWrapper` objects when accessing attributes."""
|
|
try:
|
|
arrays = super(_NumpyState, self).__getattribute__("_arrays")
|
|
except AttributeError:
|
|
# _arrays hasn't been assigned yet
|
|
return super(_NumpyState, self).__getattribute__(name)
|
|
try:
|
|
value = getattr(arrays, name)
|
|
except AttributeError:
|
|
dummy_array = numpy.array([])
|
|
setattr(arrays, name, _NumpyWrapper(dummy_array))
|
|
value = getattr(arrays, name)
|
|
if value.array is dummy_array:
|
|
# No set or restored attribute with this name
|
|
delattr(arrays, name)
|
|
return super(_NumpyState, self).__getattribute__(name)
|
|
|
|
if isinstance(value, _NumpyWrapper):
|
|
return value.array
|
|
return super(_NumpyState, self).__getattribute__(name)
|
|
|
|
def __setattr__(self, name, value):
|
|
"""Automatically wrap NumPy arrays assigned to attributes."""
|
|
if isinstance(value, (numpy.ndarray, numpy.generic)):
|
|
try:
|
|
existing = getattr(self._arrays, name)
|
|
existing.array = value
|
|
return
|
|
except AttributeError:
|
|
value = _NumpyWrapper(value)
|
|
setattr(self._arrays, name, value)
|
|
return
|
|
super(_NumpyState, self).__setattr__(name, value)
|
|
|
|
|
|
class _NumpyWrapper(python_state.PythonState):
|
|
"""Wraps a NumPy array for storage in an object-based checkpoint."""
|
|
|
|
def __init__(self, array):
|
|
"""Specify a NumPy array to wrap.
|
|
|
|
Args:
|
|
array: The NumPy array to save and restore (may be overwritten).
|
|
"""
|
|
self.array = array
|
|
|
|
def serialize(self):
|
|
"""Callback to serialize the array."""
|
|
string_file = io.BytesIO()
|
|
try:
|
|
numpy.save(string_file, self.array, allow_pickle=False)
|
|
serialized = string_file.getvalue()
|
|
finally:
|
|
string_file.close()
|
|
return serialized
|
|
|
|
def deserialize(self, string_value):
|
|
"""Callback to deserialize the array."""
|
|
string_file = io.BytesIO(string_value)
|
|
try:
|
|
self.array = numpy.load(string_file, allow_pickle=False)
|
|
finally:
|
|
string_file.close()
|
|
|
|
|
|
class NumpyStateTests(test.TestCase):
|
|
|
|
def testWrapper(self):
|
|
directory = self.get_temp_dir()
|
|
prefix = os.path.join(directory, "ckpt")
|
|
root = util.Checkpoint(numpy=_NumpyWrapper(numpy.array([1.])))
|
|
save_path = root.save(prefix)
|
|
root.numpy.array *= 2.
|
|
self.assertEqual([2.], root.numpy.array)
|
|
root.restore(save_path)
|
|
self.assertEqual([1.], root.numpy.array)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testSaveRestoreNumpyState(self):
|
|
directory = self.get_temp_dir()
|
|
prefix = os.path.join(directory, "ckpt")
|
|
save_state = _NumpyState()
|
|
saver = util.Checkpoint(numpy=save_state)
|
|
save_state.a = numpy.ones([2, 2])
|
|
save_state.b = numpy.ones([2, 2])
|
|
save_state.b = numpy.zeros([2, 2])
|
|
save_state.c = numpy.int64(3)
|
|
self.assertAllEqual(numpy.ones([2, 2]), save_state.a)
|
|
self.assertAllEqual(numpy.zeros([2, 2]), save_state.b)
|
|
self.assertEqual(3, save_state.c)
|
|
first_save_path = saver.save(prefix)
|
|
save_state.a[1, 1] = 2.
|
|
save_state.c = numpy.int64(4)
|
|
second_save_path = saver.save(prefix)
|
|
|
|
load_state = _NumpyState()
|
|
loader = util.Checkpoint(numpy=load_state)
|
|
loader.restore(first_save_path).initialize_or_restore()
|
|
self.assertAllEqual(numpy.ones([2, 2]), load_state.a)
|
|
self.assertAllEqual(numpy.zeros([2, 2]), load_state.b)
|
|
self.assertEqual(3, load_state.c)
|
|
load_state.a[0, 0] = 42.
|
|
self.assertAllEqual([[42., 1.], [1., 1.]], load_state.a)
|
|
loader.restore(first_save_path).run_restore_ops()
|
|
self.assertAllEqual(numpy.ones([2, 2]), load_state.a)
|
|
loader.restore(second_save_path).run_restore_ops()
|
|
self.assertAllEqual([[1., 1.], [1., 2.]], load_state.a)
|
|
self.assertAllEqual(numpy.zeros([2, 2]), load_state.b)
|
|
self.assertEqual(4, load_state.c)
|
|
|
|
def testNoGraphPollution(self):
|
|
graph = ops.Graph()
|
|
with graph.as_default(), session.Session():
|
|
directory = self.get_temp_dir()
|
|
prefix = os.path.join(directory, "ckpt")
|
|
save_state = _NumpyState()
|
|
saver = util.Checkpoint(numpy=save_state)
|
|
save_state.a = numpy.ones([2, 2])
|
|
save_path = saver.save(prefix)
|
|
saver.restore(save_path)
|
|
graph.finalize()
|
|
saver.save(prefix)
|
|
save_state.a = numpy.zeros([2, 2])
|
|
saver.save(prefix)
|
|
saver.restore(save_path)
|
|
|
|
@test_util.run_in_graph_and_eager_modes
|
|
def testDocstringExample(self):
|
|
arrays = _NumpyState()
|
|
checkpoint = util.Checkpoint(numpy_arrays=arrays)
|
|
arrays.x = numpy.zeros([3, 4])
|
|
save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
|
|
arrays.x[1, 1] = 4.
|
|
checkpoint.restore(save_path)
|
|
self.assertAllEqual(numpy.zeros([3, 4]), arrays.x)
|
|
|
|
second_checkpoint = util.Checkpoint(numpy_arrays=_NumpyState())
|
|
second_checkpoint.restore(save_path)
|
|
self.assertAllEqual(numpy.zeros([3, 4]), second_checkpoint.numpy_arrays.x)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
ops.enable_eager_execution()
|
|
test.main()
|