def testNestedLists(self):
a = tracking.AutoTrackable()
a.l = []
b = tracking.AutoTrackable()
a.l.append([b])
c = tracking.AutoTrackable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.AutoTrackable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.AutoTrackable()
f = tracking.AutoTrackable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError,
"A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def testNestedLists(self):
a = tracking.AutoTrackable()
a.l = []
b = tracking.AutoTrackable()
a.l.append([b])
c = tracking.AutoTrackable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.AutoTrackable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.AutoTrackable()
f = tracking.AutoTrackable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError, "A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def testAddVariableOverwrite(self):
root = base.Trackable()
a = root._add_variable_with_custom_getter(
name="v", shape=[], getter=variable_scope.get_variable)
self.assertEqual([root, a], util.list_objects(root))
with ops.Graph().as_default():
b = root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=True,
getter=variable_scope.get_variable)
self.assertEqual([root, b], util.list_objects(root))
with ops.Graph().as_default():
with self.assertRaisesRegex(ValueError,
"already declared as a dependency"):
root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=False,
getter=variable_scope.get_variable)
def test_model(self):
num_classes = 5
model = build_deeplab_v3_plus(classes=num_classes,
bone_arch='resnet_50',
bone_init='imagenet',
bone_train=False,
aspp_filters=8,
aspp_stride=16,
low_filters=16,
decoder_filters=4)
model.compile(optimizer='sgd',
loss='sparse_categorical_crossentropy',
run_eagerly=test_utils.should_run_eagerly())
model.fit(np.random.random((2, 224, 224, 3)).astype(np.uint8),
np.random.randint(0, num_classes, (2, 224, 224)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present
# in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def testAddVariableOverwrite(self):
root = base.Trackable()
a = root._add_variable_with_custom_getter(
name="v", shape=[], getter=variable_scope.get_variable)
self.assertEqual([root, a], util.list_objects(root))
with ops.Graph().as_default():
b = root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=True,
getter=variable_scope.get_variable)
self.assertEqual([root, b], util.list_objects(root))
with ops.Graph().as_default():
with self.assertRaisesRegexp(
ValueError, "already declared as a dependency"):
root._add_variable_with_custom_getter(
name="v", shape=[], overwrite=False,
getter=variable_scope.get_variable)
def test_model(self):
model = build_fba_matting()
model.compile(
optimizer='sgd', loss=['mse', None, None, None],
run_eagerly=test_utils.should_run_eagerly())
model.fit(
[
np.random.random((2, 240, 240, 3)).astype(np.uint8),
np.random.random((2, 240, 240, 2)).astype(np.uint8),
np.random.random((2, 240, 240, 6)).astype(np.uint8),
],
[
np.random.random((2, 240, 240, 7)).astype(np.float32),
np.random.random((2, 240, 240, 1)).astype(np.float32),
np.random.random((2, 240, 240, 3)).astype(np.float32),
np.random.random((2, 240, 240, 3)).astype(np.float32)
],
epochs=1, batch_size=10)
# test config
model.get_config()
# check whether the model variables are present
# in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_timedistributed_dense(self):
model = keras.models.Sequential()
model.add(
keras.layers.TimeDistributed(
keras.layers.Dense(2), input_shape=(3, 4)
)
)
model.compile(optimizer="rmsprop", loss="mse")
model.fit(
np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10,
)
# test config
model.get_config()
# check whether the model variables are present in the
# trackable list of objects
checkpointed_object_ids = {
id(o) for o in trackable_util.list_objects(model)
}
for v in model.variables:
self.assertIn(id(v), checkpointed_object_ids)
def test_trackable_save_restore(self):
with self.test_session():
def _templated():
v = tf.compat.v1.get_variable(
"v",
shape=[1],
initializer=tf.compat.v1.zeros_initializer(),
use_resource=True)
v2 = tf.compat.v1.get_variable(
"v2",
shape=[1],
initializer=tf.compat.v1.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = tf.compat.v1.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
self.assertEqual(
set([
id(v1_save),
id(v2_save),
id(manual_scope),
id(manual_scope_v),
id(save_template)
]), set(map(id, trackable_utils.list_objects(save_template))))
self.assertDictEqual({"in_manual_scope": manual_scope_v},
manual_scope._trackable_children())
optimizer = adam.Adam(0.0)
save_root = tf.train.Checkpoint(my_template=save_template,
optimizer=optimizer)
optimizer.minimize(v1_save.read_value, var_list=[v1_save])
self.evaluate([v.initializer for v in save_template.variables])
optimizer_variables = optimizer.variables() + list(
optimizer._hyper.values())
self.evaluate([v.initializer for v in optimizer_variables])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = tf.compat.v1.make_template("s2", _templated)
load_optimizer = adam.Adam(0.0)
load_root = tf.train.Checkpoint(my_template=load_template,
optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value, var_list=[var])
children = load_template._trackable_children()
self.assertEqual({"v", "v2", "ManualScope"}, children.keys())
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testDictWrapperNoDependency(self):
a = tracking.AutoTrackable()
a.d = data_structures.NoDependency({})
a.d[1] = [3]
self.assertEqual([a], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testDictWrapperNoDependency(self):
a = tracking.AutoTrackable()
a.d = data_structures.NoDependency({})
a.d[1] = [3]
self.assertEqual([a], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonStringKeyNotTrackableValue(self):
a = tracking.AutoTrackable()
a.d = {}
a.d["a"] = [3]
a.d[1] = data_structures.NoDependency([3])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonStringKeyNotTrackableValue(self):
a = tracking.AutoTrackable()
a.d = {}
a.d["a"] = [3]
a.d[1] = data_structures.NoDependency([3])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def test_trackable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
v2 = variable_scope.get_variable(
"v2", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = template.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
six.assertCountEqual(
self,
[id(v1_save), id(v2_save), id(manual_scope),
id(manual_scope_v), id(save_template)],
map(id, trackable_utils.list_objects(save_template)))
manual_dep, = manual_scope._checkpoint_dependencies
self.assertEqual("in_manual_scope", manual_dep.name)
self.assertIs(manual_scope_v, manual_dep.ref)
optimizer = adam.Adam(0.0)
save_root = trackable_utils.Checkpoint(
my_template=save_template, optimizer=optimizer)
optimizer.minimize(v1_save.read_value,
var_list=[v1_save])
self.evaluate([v.initializer for v in save_template.variables])
optimizer_variables = optimizer.variables() + list(
optimizer._hyper.values())
self.evaluate([v.initializer for v in optimizer_variables])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = template.make_template("s2", _templated)
load_optimizer = adam.Adam(0.0)
load_root = trackable_utils.Checkpoint(
my_template=load_template, optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value, var_list=[var])
self.assertLen(load_template._checkpoint_dependencies, 3)
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
self.assertEqual("ManualScope",
load_template._checkpoint_dependencies[2].name)
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testShallowCopyTrackable(self):
original = tracking.AutoTrackable()
original_sub = tracking.AutoTrackable()
original.a = [[1.]]
original.b = {"a": original_sub}
shallow_copied = copy.copy(original)
self.assertIs(original_sub, shallow_copied.b["a"])
self.assertIsNot(original, shallow_copied)
self.assertEqual([[1.]], shallow_copied.a)
shallow_deps = util.list_objects(shallow_copied)
self.assertIn(shallow_copied.a, shallow_deps)
self.assertIn(shallow_copied.b, shallow_deps)
self.assertIn(shallow_copied.b["a"], shallow_deps)
def testShallowCopyTrackable(self):
original = tracking.AutoTrackable()
original_sub = tracking.AutoTrackable()
original.a = [[1.]]
original.b = {"a": original_sub}
shallow_copied = copy.copy(original)
self.assertIs(original_sub, shallow_copied.b["a"])
self.assertIsNot(original, shallow_copied)
self.assertEqual([[1.]], shallow_copied.a)
shallow_deps = util.list_objects(shallow_copied)
self.assertIn(shallow_copied.a, shallow_deps)
self.assertIn(shallow_copied.b, shallow_deps)
self.assertIn(shallow_copied.b["a"], shallow_deps)
def testListBasic(self):
a = autotrackable.AutoTrackable()
b = autotrackable.AutoTrackable()
a.l = [b]
c = autotrackable.AutoTrackable()
a.l.append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertIn("l", a._trackable_children())
direct_a_dep = a._trackable_children()["l"]
self.assertIn(b, direct_a_dep)
self.assertIn(c, direct_a_dep)
def testListBasic(self):
a = tracking.AutoTrackable()
b = tracking.AutoTrackable()
a.l = [b]
c = tracking.AutoTrackable()
a.l.append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
direct_a_dep, = a._checkpoint_dependencies
self.assertEqual("l", direct_a_dep.name)
self.assertIn(b, direct_a_dep.ref)
self.assertIn(c, direct_a_dep.ref)
def testListBasic(self):
a = tracking.AutoTrackable()
b = tracking.AutoTrackable()
a.l = [b]
c = tracking.AutoTrackable()
a.l.append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
direct_a_dep, = a._checkpoint_dependencies
self.assertEqual("l", direct_a_dep.name)
self.assertIn(b, direct_a_dep.ref)
self.assertIn(c, direct_a_dep.ref)
def test_trackable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
v2 = variable_scope.get_variable(
"v2", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = template.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
six.assertCountEqual(
self,
[v1_save, v2_save, manual_scope, manual_scope_v, save_template],
trackable_utils.list_objects(save_template))
manual_dep, = manual_scope._checkpoint_dependencies
self.assertEqual("in_manual_scope", manual_dep.name)
self.assertIs(manual_scope_v, manual_dep.ref)
optimizer = adam.AdamOptimizer(0.0)
save_root = trackable_utils.Checkpoint(
my_template=save_template, optimizer=optimizer)
optimizer.minimize(v1_save.read_value)
self.evaluate([v.initializer for v in save_template.variables])
self.evaluate([v.initializer for v in optimizer.variables()])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = template.make_template("s2", _templated)
load_optimizer = adam.AdamOptimizer(0.0)
load_root = trackable_utils.Checkpoint(
my_template=load_template, optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value)
self.assertEqual(3, len(load_template._checkpoint_dependencies))
self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
self.assertEqual("ManualScope",
load_template._checkpoint_dependencies[2].name)
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testDeepCopyTrackable(self):
original = tracking.AutoTrackable()
original_sub = tracking.AutoTrackable()
original.a = [[1.]]
original.b = {"a": original_sub}
deep_copied = copy.deepcopy(original)
self.assertIsNot(original, deep_copied)
self.assertIsNot(original_sub, deep_copied.b["a"])
self.assertEqual([[1.]], deep_copied.a)
self.assertIsInstance(deep_copied.b["a"], tracking.AutoTrackable)
deps = util.list_objects(deep_copied)
self.assertIn(deep_copied.a, deps)
self.assertIn(deep_copied.b, deps)
self.assertIn(deep_copied.b["a"], deps)
self.assertNotIn(original_sub, deps)
def test_model(self):
model = build_uper_net(classes=2, bone_arch='swin_tiny_224', bone_init='imagenet', bone_train=False)
model.compile(optimizer='sgd', loss='mse', run_eagerly=test_utils.should_run_eagerly())
model.fit(
np.random.random((2, 240, 240, 3)).astype(np.uint8),
np.random.random((2, 240, 240, 2)).astype(np.float32),
epochs=1, batch_size=10)
# test config
model.get_config()
# check whether the model variables are present
# in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_trackable_save_restore(self):
def _templated():
v = variable_scope.get_variable(
"v", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
v2 = variable_scope.get_variable(
"v2", shape=[1], initializer=init_ops.zeros_initializer(),
use_resource=True)
manual = _ManualScope()
return v, v + 1., v2, manual, manual()
save_template = template.make_template("s1", _templated)
v1_save, _, v2_save, manual_scope, manual_scope_v = save_template()
six.assertCountEqual(self, [
id(obj) for obj in
[v1_save, v2_save, manual_scope, manual_scope_v, save_template]
], [id(obj) for obj in trackable_utils.list_objects(save_template)])
self.assertDictEqual({"in_manual_scope": manual_scope_v},
manual_scope._trackable_children())
optimizer = adam.AdamOptimizer(0.0)
save_root = trackable_utils.Checkpoint(
my_template=save_template, optimizer=optimizer)
optimizer.minimize(v1_save.read_value)
self.evaluate([v.initializer for v in save_template.variables])
self.evaluate([v.initializer for v in optimizer.variables()])
self.evaluate(v1_save.assign([12.]))
self.evaluate(v2_save.assign([14.]))
checkpoint_directory = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
save_path = save_root.save(checkpoint_prefix)
load_template = template.make_template("s2", _templated)
load_optimizer = adam.AdamOptimizer(0.0)
load_root = trackable_utils.Checkpoint(
my_template=load_template, optimizer=load_optimizer)
status = load_root.restore(save_path)
var, var_plus_one, var2, _, _ = load_template()
load_optimizer.minimize(var.read_value)
self.assertEqual(3, len(load_template._trackable_children()))
self.assertEqual(set(["v", "v2", "ManualScope"]),
load_template._trackable_children().keys())
status.assert_consumed().run_restore_ops()
self.assertAllEqual([12.], self.evaluate(var))
self.assertAllEqual([13.], self.evaluate(var_plus_one))
self.assertAllEqual([14.], self.evaluate(var2))
def testDeepCopyTrackable(self):
original = tracking.AutoTrackable()
original_sub = tracking.AutoTrackable()
original.a = [[1.]]
original.b = {"a": original_sub}
self.assertIsInstance(original.b, dict)
deep_copied = copy.deepcopy(original)
self.assertIsInstance(deep_copied.b, dict)
self.assertIsNot(original, deep_copied)
self.assertIsNot(original_sub, deep_copied.b["a"])
self.assertEqual([[1.]], deep_copied.a)
self.assertIsInstance(deep_copied.b["a"], tracking.AutoTrackable)
deps = util.list_objects(deep_copied)
self.assertIn(deep_copied.a, deps)
self.assertIn(deep_copied.b, deps)
self.assertIn(deep_copied.b["a"], deps)
self.assertNotIn(original_sub, deps)
def test_trackable_dependencies(self):
rnn = keras.layers.SimpleRNN
x = np.random.random((2, 2, 2))
y = np.random.random((2, 2))
model = keras.models.Sequential()
model.add(rnn(2))
model.compile(
optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# trackable list of objects
checkpointed_objects = set(trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_trackable_dependencies(self):
rnn = keras.layers.SimpleRNN
x = np.random.random((2, 2, 2))
y = np.random.random((2, 2))
model = keras.models.Sequential()
model.add(rnn(2))
model.compile(
optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_weight_norm_dense(self):
model = tf.keras.models.Sequential()
model.add(WeightNorm(tf.keras.layers.Dense(2), input_shape=(3, 4)))
model.compile(optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_custom_backward_layer(self, mode):
rnn = keras.layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == "concat" else output_dim
y = np.random.random((samples, target_dim))
forward_layer = rnn(output_dim)
backward_layer = rnn(output_dim, go_backwards=True)
# test with Sequential model
model = keras.models.Sequential()
model.add(
keras.layers.Bidirectional(
forward_layer,
merge_mode=mode,
backward_layer=backward_layer,
input_shape=(timesteps, dim),
))
model.compile(optimizer="rmsprop", loss="mse")
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# trackable list of objects
checkpointed_object_ids = {
id(o)
for o in trackable_util.list_objects(model)
}
for v in model.variables:
self.assertIn(id(v), checkpointed_object_ids)
# test compute output shape
ref_shape = model.layers[-1].output.shape
shape = model.layers[-1].compute_output_shape((None, timesteps, dim))
self.assertListEqual(shape.as_list(), ref_shape.as_list())
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
def testDictionariesBasic(self):
a = training.Model()
b = training.Model()
a.attribute = {"b": b}
c = training.Model()
a.attribute["c"] = []
a.attribute["c"].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertIs(b, a.attribute["b"])
self.assertEqual({"b", "c"}, a.attribute._trackable_children().keys())
self.assertEqual([b, c], a.layers)
self.assertEqual([b, c], a.attribute.layers)
self.assertEqual([c], a.attribute["c"].layers)
checkpoint = tf.train.Checkpoint(a=a)
save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
with self.cached_session():
checkpoint.restore(
save_path).assert_consumed().initialize_or_restore()
def test_model(self):
model = tf.keras.models.Sequential()
model.add(
TemporalConvNet(filters=[5, 4, 3], kernel_size=3, dropout=0.2))
model.compile(optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 3)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_model(self):
num_classes = 1
model = build_dexi_ned(num_classes)
model.compile(optimizer='sgd',
loss='binary_crossentropy',
run_eagerly=test_utils.should_run_eagerly())
model.fit(np.random.random((2, 224, 224, 3)).astype(np.uint8),
np.random.randint(0, num_classes, (2, 224, 224)),
epochs=1,
batch_size=1)
# test config
model.get_config()
# check whether the model variables are present
# in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def testNonAppendNotTrackable(self):
# Non-append mutations (deleting or overwriting values) are OK when the
# values aren't tracked.
a = tracking.AutoTrackable()
a.d = {}
a.d["a"] = [3]
a.d[1] = 3
a.d[1] = 2
self.assertEqual(2, a.d[1])
del a.d[1]
a.d[2] = data_structures.NoDependency(tracking.AutoTrackable())
second = tracking.AutoTrackable()
a.d[2] = data_structures.NoDependency(second)
self.assertIs(second, a.d[2])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonAppendNotTrackable(self):
# Non-append mutations (deleting or overwriting values) are OK when the
# values aren't tracked.
a = tracking.AutoTrackable()
a.d = {}
a.d["a"] = [3]
a.d[1] = 3
a.d[1] = 2
self.assertEqual(2, a.d[1])
del a.d[1]
a.d[2] = data_structures.NoDependency(tracking.AutoTrackable())
second = tracking.AutoTrackable()
a.d[2] = data_structures.NoDependency(second)
self.assertIs(second, a.d[2])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNoDependency(self):
root = tf.Module()
hasdep = tf.Module()
root.hasdep = hasdep
nodep = tf.Module()
root.nodep = data_structures.NoDependency(nodep)
self.assertLen(root._trackable_children(), 1)
self.assertIs(root._trackable_children()["hasdep"], root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@tf.__internal__.tracking.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = tf.Module()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def testNoDependency(self):
root = module.Module()
hasdep = module.Module()
root.hasdep = hasdep
nodep = module.Module()
root.nodep = data_structures.NoDependency(nodep)
self.assertEqual(1, len(root._checkpoint_dependencies))
self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@base.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = module.Module()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def testNoDependency(self):
root = tracking.AutoTrackable()
hasdep = tracking.AutoTrackable()
root.hasdep = hasdep
nodep = tracking.AutoTrackable()
root.nodep = data_structures.NoDependency(nodep)
self.assertEqual(1, len(root._checkpoint_dependencies))
self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@base.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = tracking.AutoTrackable()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def test_timedistributed_dense(self):
model = keras.models.Sequential()
model.add(
keras.layers.TimeDistributed(
keras.layers.Dense(2), input_shape=(3, 4)))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(
np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present in the
# trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def test_custom_backward_layer(self, mode):
rnn = keras.layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
forward_layer = rnn(output_dim)
backward_layer = rnn(output_dim, go_backwards=True)
# test with Sequential model
model = keras.models.Sequential()
model.add(
keras.layers.Bidirectional(
forward_layer,
merge_mode=mode,
backward_layer=backward_layer,
input_shape=(timesteps, dim)))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
# test compute output shape
ref_shape = model.layers[-1].output.shape
shape = model.layers[-1].compute_output_shape((None, timesteps, dim))
self.assertListEqual(shape.as_list(), ref_shape.as_list())
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
def testListDeepCopy(self):
root = tracking.AutoTrackable()
orig_list = [[1.]]
root.a = orig_list
copied = copy.deepcopy(root.a)
self.assertAllEqual([[1.]], copied)
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a[0], copied[0])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_list.append(1.)
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def testDictDeepCopy(self):
root = tracking.AutoTrackable()
orig_dict = {"a": [1.]}
root.a = orig_dict
copied = copy.deepcopy(root.a)
self.assertAllEqual([1.], copied["a"])
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a["a"], copied["a"])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_dict["b"] = []
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def testListDeepCopy(self):
root = tracking.AutoTrackable()
orig_list = [[1.]]
root.a = orig_list
copied = copy.deepcopy(root.a)
self.assertAllEqual([[1.]], copied)
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a[0], copied[0])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_list.append(1.)
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def testDictDeepCopy(self):
root = tracking.AutoTrackable()
orig_dict = {"a": [1.]}
root.a = orig_dict
copied = copy.deepcopy(root.a)
self.assertAllEqual([1.], copied["a"])
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a["a"], copied["a"])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_dict["b"] = []
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def test_model(self):
num_classes = 5
model = build_deeplab_v3_plus_with_point_rend(classes=num_classes,
bone_arch='resnet_50',
bone_init='imagenet',
bone_train=False,
aspp_filters=8,
aspp_stride=16,
low_filters=16,
decoder_filters=4,
rend_strides=(2, 4),
rend_units=(256, ),
rend_points=(0.1697,
0.0005),
rend_oversample=3,
rend_importance=0.75,
rend_weights=True,
rend_corners=True)
model.compile(optimizer='sgd',
loss=['sparse_categorical_crossentropy', None],
run_eagerly=test_utils.should_run_eagerly())
model.fit(
{
'image': np.random.random((2, 224, 224, 3)).astype(np.uint8),
'label': np.random.randint(0, num_classes, (2, 224, 224)),
'weight': np.random.rand(2, 224, 224),
},
np.random.randint(0, num_classes, (2, 224, 224)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present
# in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def testDictionariesBasic(self):
a = training.Model()
b = training.Model()
a.attribute = {"b": b}
c = training.Model()
a.attribute["c"] = []
a.attribute["c"].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertIs(b, a.attribute["b"])
six.assertCountEqual(
self,
["b", "c"],
[dep.name for dep in a.attribute._checkpoint_dependencies])
self.assertEqual([b, c], a.layers)
self.assertEqual([b, c], a.attribute.layers)
self.assertEqual([c], a.attribute["c"].layers)
checkpoint = util.Checkpoint(a=a)
save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
with self.cached_session():
checkpoint.restore(save_path).assert_consumed().initialize_or_restore()
def test_model(self):
model = tf.keras.models.Sequential()
model.add(
tf.keras.layers.Bidirectional(
QRNN(units=12, window=2, zoneout=0.2, return_sequences=True)))
model.add(QRNN(units=2, window=1))
model.compile(optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# check whether the model variables are present in the trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
def testDictionariesBasic(self):
a = training.Model()
b = training.Model()
a.attribute = {"b": b}
c = training.Model()
a.attribute["c"] = []
a.attribute["c"].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertIs(b, a.attribute["b"])
six.assertCountEqual(
self, ["b", "c"],
[dep.name for dep in a.attribute._checkpoint_dependencies])
self.assertEqual([b, c], a.layers)
self.assertEqual([b, c], a.attribute.layers)
self.assertEqual([c], a.attribute["c"].layers)
checkpoint = util.Checkpoint(a=a)
save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
with self.cached_session():
checkpoint.restore(
save_path).assert_consumed().initialize_or_restore()
def test_bidirectional(self):
rnn = keras.layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
with self.cached_session():
for mode in ['sum', 'concat', 'ave', 'mul']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = keras.models.Sequential()
model.add(
keras.layers.Bidirectional(rnn(output_dim),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(x, y, epochs=1, batch_size=1)
# check whether the model variables are present in the
# trackable list of objects
checkpointed_objects = object_identity.ObjectIdentitySet(
trackable_util.list_objects(model))
for v in model.variables:
self.assertIn(v, checkpointed_objects)
# test compute output shape
ref_shape = model.layers[-1].output.shape
shape = model.layers[-1].compute_output_shape(
(None, timesteps, dim))
self.assertListEqual(shape.as_list(), ref_shape.as_list())
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
