def _monkey_patch_context(iteration_step_scope, scoped_summary,
trainable_vars):
"""Monkey-patches global attributes with subnetwork-specifics ones."""
old_get_global_step_fn = tf_compat.v1.train.get_global_step
old_get_or_create_global_step_fn = tf_compat.v1.train.get_or_create_global_step
old_trainable_vars = tf_compat.v1.trainable_variables()
def iteration_step(graph=None):
graph = graph or tf_compat.v1.get_default_graph()
with graph.as_default() as g, g.name_scope(None):
with tf_compat.v1.variable_scope(iteration_step_scope,
reuse=tf_compat.v1.AUTO_REUSE):
return tf_compat.v1.get_variable(
"iteration_step",
shape=[],
initializer=tf_compat.v1.zeros_initializer(),
trainable=False,
dtype=tf.int64)
# monkey-patch global attributes.
setattr(tf_compat.v1.train, "get_global_step", iteration_step)
setattr(tf_compat.v1.train, "get_or_create_global_step", iteration_step)
setattr(tf_v1.train, "get_global_step", iteration_step)
setattr(tf_v1.train, "get_or_create_global_step", iteration_step)
setattr(tf.train, "get_global_step", iteration_step)
setattr(tf.train, "get_or_create_global_step", iteration_step)
setattr(train, "get_global_step", iteration_step)
setattr(training_util, "get_global_step", iteration_step)
setattr(train, "get_or_create_global_step", iteration_step)
setattr(training_util, "get_or_create_global_step", iteration_step)
# The TPUEmbedding uses dummy variables to coordinate sending and receiving
# gradients. If no gradients are computed on these dummy variables, the
# TPUEmbedding will throw an error.
embedding_variables = tf_compat.v1.get_collection(
"tpu_embedding_dummy_table_variables")
_set_trainable_variables(trainable_vars + embedding_variables)
try:
with monkey_patched_summaries(scoped_summary):
yield
finally:
# Revert monkey-patches.
new_trainable_vars = _new_trainable_variables(trainable_vars)
_set_trainable_variables(old_trainable_vars + new_trainable_vars)
setattr(training_util, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(training_util, "get_global_step", old_get_global_step_fn)
setattr(train, "get_global_step", old_get_global_step_fn)
setattr(tf.train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(tf.train, "get_global_step", old_get_global_step_fn)
setattr(tf_v1.train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(tf_v1.train, "get_global_step", old_get_global_step_fn)
setattr(tf_compat.v1.train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(tf_compat.v1.train, "get_global_step", old_get_global_step_fn)
def _subnetwork_context(iteration_step_scope, scoped_summary):
"""Monkey-patches global attributes with subnetwork-specifics ones."""
old_get_global_step_fn = tf.train.get_global_step
old_get_or_create_global_step_fn = tf.train.get_or_create_global_step
def iteration_step(graph=None):
del graph
with tf.variable_scope(iteration_step_scope, reuse=tf.AUTO_REUSE):
return tf.get_variable("iteration_step",
shape=[],
initializer=tf.zeros_initializer(),
trainable=False,
dtype=tf.int64)
# monkey-patch global attributes.
tf.train.get_global_step = iteration_step
tf.train.get_or_create_global_step = iteration_step
training_util.get_global_step = iteration_step
training_util.get_or_create_global_step = iteration_step
try:
with monkey_patched_summaries(scoped_summary):
yield
finally:
# Revert monkey-patches.
training_util.get_or_create_global_step = old_get_or_create_global_step_fn
training_util.get_global_step = old_get_global_step_fn
tf.train.get_or_create_global_step = old_get_or_create_global_step_fn
tf.train.get_global_step = old_get_global_step_fn
def test_monkey_patched_summaries_kwargs(self, summary_maker):
summary = summary_maker()
before = _summaries()
with monkey_patched_summaries(summary):
for want, got in zip(before, _summaries()):
self.assertNotEqual(want, got)
tf.summary.scalar(name="scalar",
tensor=1,
collections=["collection"],
family="family")
tf.summary.image(name="image",
tensor=1,
max_outputs=3,
collections=["collection"],
family="family")
tf.summary.histogram(name="histogram",
values=1,
collections=["collection"],
family="family")
tf.summary.audio(name="audio",
tensor=1,
sample_rate=3,
max_outputs=3,
collections=["collection"],
family="family")
want_summary_fn_count = 4
try:
tf.contrib.summary.scalar(name="scalar_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.image(name="image_v2",
tensor=1,
bad_color=True,
max_images=3,
family="family",
step=10)
tf.contrib.summary.histogram(name="histogram_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.audio(name="audio_v2",
tensor=1,
sample_rate=3,
max_outputs=3,
family="family",
step=10)
want_summary_fn_count += 4
except (AttributeError, ImportError):
# TF 2.0 eliminates tf.contrib.
pass
self.assertEqual(before, _summaries())
self.assertLen(self._get_summary_ops(summary), want_summary_fn_count)
def test_monkey_patched_summaries_args(self, summary_maker):
summary = summary_maker()
with monkey_patched_summaries(summary):
tf.summary.scalar("scalar", 1, ["collection"], "family")
tf.summary.image("image", 1, 3, ["collection"], "family")
tf.summary.histogram("histogram", 1, ["collection"], "family")
tf.summary.audio("audio", 1, 3, 3, ["collection"], "family")
tf.contrib.summary.scalar("scalar_v2", 1, "family", 10)
tf.contrib.summary.image("image_v2", 1, True, 3, "family", 10)
tf.contrib.summary.histogram("histogram_v2", 1, "family", 10)
tf.contrib.summary.audio("audio_v2", 1, 3, 3, "family", 10)
self.assertLen(summary.merge_all(), 8)
def test_monkey_patched_summaries_args(self):
summary = _ScopedSummary(self.test_subdirectory, global_step=10)
with monkey_patched_summaries(summary):
tf.summary.scalar("scalar", 1, ["collection"], "family")
tf.summary.image("image", 1, 3, ["collection"], "family")
tf.summary.histogram("histogram", 1, ["collection"], "family")
tf.summary.audio("audio", 1, 3, 3, ["collection"], "family")
tf.contrib.summary.scalar("scalar_v2", 1, "family", 10)
tf.contrib.summary.image("image_v2", 1, True, 3, "family", 10)
tf.contrib.summary.histogram("histogram_v2", 1, "family", 10)
tf.contrib.summary.audio("audio_v2", 1, 3, 3, "family", 10)
self.assertLen(summary.merge_all(), 8)
def _monkey_patch_context(iteration_step_scope, scoped_summary,
trainable_vars):
"""Monkey-patches global attributes with subnetwork-specifics ones."""
old_get_global_step_fn = tf_compat.v1.train.get_global_step
old_get_or_create_global_step_fn = tf_compat.v1.train.get_or_create_global_step
old_trainable_vars = tf_compat.v1.trainable_variables()
def iteration_step(graph=None):
graph = graph or tf_compat.v1.get_default_graph()
with graph.as_default() as g, g.name_scope(None):
with tf_compat.v1.variable_scope(iteration_step_scope,
reuse=tf_compat.v1.AUTO_REUSE):
return tf_compat.v1.get_variable(
"iteration_step",
shape=[],
initializer=tf_compat.v1.zeros_initializer(),
trainable=False,
dtype=tf.int64)
# monkey-patch global attributes.
setattr(tf_compat.v1.train, "get_global_step", iteration_step)
setattr(tf_compat.v1.train, "get_or_create_global_step", iteration_step)
setattr(tf.train, "get_global_step", iteration_step)
setattr(tf.train, "get_or_create_global_step", iteration_step)
setattr(train, "get_global_step", iteration_step)
setattr(training_util, "get_global_step", iteration_step)
setattr(train, "get_or_create_global_step", iteration_step)
setattr(training_util, "get_or_create_global_step", iteration_step)
_set_trainable_variables(trainable_vars)
try:
with monkey_patched_summaries(scoped_summary):
yield
finally:
# Revert monkey-patches.
new_trainable_vars = _new_trainable_variables(trainable_vars)
_set_trainable_variables(old_trainable_vars + new_trainable_vars)
setattr(training_util, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(training_util, "get_global_step", old_get_global_step_fn)
setattr(train, "get_global_step", old_get_global_step_fn)
setattr(tf.train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(tf.train, "get_global_step", old_get_global_step_fn)
setattr(tf_compat.v1.train, "get_or_create_global_step",
old_get_or_create_global_step_fn)
setattr(tf_compat.v1.train, "get_global_step", old_get_global_step_fn)
def test_monkey_patched_summaries_args(self, summary_maker):
summary = summary_maker()
before = _summaries()
with monkey_patched_summaries(summary):
self.assertNotEqual(before, _summaries())
tf.summary.scalar("scalar", 1, ["collection"], "family")
tf.summary.image("image", 1, 3, ["collection"], "family")
tf.summary.histogram("histogram", 1, ["collection"], "family")
tf.summary.audio("audio", 1, 3, 3, ["collection"], "family")
tf.contrib.summary.scalar("scalar_v2", 1, "family", 10)
tf.contrib.summary.image("image_v2", 1, True, 3, "family", 10)
tf.contrib.summary.histogram("histogram_v2", 1, "family", 10)
tf.contrib.summary.audio("audio_v2", 1, 3, 3, "family", 10)
self.assertEqual(before, _summaries())
self.assertLen(self._get_summary_ops(summary), 8)
def test_monkey_patched_summaries_kwargs(self, summary_maker):
summary = summary_maker()
before = _summaries()
with monkey_patched_summaries(summary):
self.assertNotEqual(before, _summaries())
tf.summary.scalar(name="scalar",
tensor=1,
collections=["collection"],
family="family")
tf.summary.image(name="image",
tensor=1,
max_outputs=3,
collections=["collection"],
family="family")
tf.summary.histogram(name="histogram",
values=1,
collections=["collection"],
family="family")
tf.summary.audio(name="audio",
tensor=1,
sample_rate=3,
max_outputs=3,
collections=["collection"],
family="family")
tf.contrib.summary.scalar(name="scalar_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.image(name="image_v2",
tensor=1,
bad_color=True,
max_images=3,
family="family",
step=10)
tf.contrib.summary.histogram(name="histogram_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.audio(name="audio_v2",
tensor=1,
sample_rate=3,
max_outputs=3,
family="family",
step=10)
self.assertEqual(before, _summaries())
self.assertLen(self._get_summary_ops(summary), 8)
def test_monkey_patched_summaries_kwargs(self):
summary = _ScopedSummary(self.test_subdirectory, global_step=10)
with monkey_patched_summaries(summary):
tf.summary.scalar(name="scalar",
tensor=1,
collections=["collection"],
family="family")
tf.summary.image(name="image",
tensor=1,
max_outputs=3,
collections=["collection"],
family="family")
tf.summary.histogram(name="histogram",
values=1,
collections=["collection"],
family="family")
tf.summary.audio(name="audio",
tensor=1,
sample_rate=3,
max_outputs=3,
collections=["collection"],
family="family")
tf.contrib.summary.scalar(name="scalar_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.image(name="image_v2",
tensor=1,
bad_color=True,
max_images=3,
family="family",
step=10)
tf.contrib.summary.histogram(name="histogram_v2",
tensor=1,
family="family",
step=10)
tf.contrib.summary.audio(name="audio_v2",
tensor=1,
sample_rate=3,
max_outputs=3,
family="family",
step=10)
self.assertLen(summary.merge_all(), 8)
def test_monkey_patched_summaries_args(self, summary_maker):
summary = summary_maker()
before = _summaries()
with monkey_patched_summaries(summary):
for want, got in zip(before, _summaries()):
self.assertNotEqual(want, got)
tf.summary.scalar("scalar", 1, ["collection"], "family")
tf.summary.image("image", 1, 3, ["collection"], "family")
tf.summary.histogram("histogram", 1, ["collection"], "family")
tf.summary.audio("audio", 1, 3, 3, ["collection"], "family")
want_summary_fn_count = 4
try:
tf.contrib.summary.scalar("scalar_v2", 1, "family", 10)
tf.contrib.summary.image("image_v2", 1, True, 3, "family", 10)
tf.contrib.summary.histogram("histogram_v2", 1, "family", 10)
tf.contrib.summary.audio("audio_v2", 1, 3, 3, "family", 10)
want_summary_fn_count += 4
except (AttributeError, ImportError):
# TF 2.0 eliminates tf.contrib.
pass
self.assertEqual(before, _summaries())
self.assertLen(self._get_summary_ops(summary), want_summary_fn_count)
