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# Copyright 2023 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.
"""Tests for cls_head."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import cls_head
class ClassificationHeadTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(("no_pooler_layer", 0, 2),
("has_pooler_layer", 5, 4))
def test_pooler_layer(self, inner_dim, num_weights_expected):
test_layer = cls_head.ClassificationHead(inner_dim=inner_dim, num_classes=2)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
_ = test_layer(features)
num_weights_observed = len(test_layer.get_weights())
self.assertEqual(num_weights_observed, num_weights_expected)
def test_layer_invocation(self):
test_layer = cls_head.ClassificationHead(inner_dim=5, num_classes=2)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
output = test_layer(features)
self.assertAllClose(output, [[0., 0.], [0., 0.]])
self.assertSameElements(test_layer.checkpoint_items.keys(),
["pooler_dense"])
outputs = test_layer(features, only_project=True)
self.assertEqual(outputs.shape, (2, 5))
def test_layer_serialization(self):
layer = cls_head.ClassificationHead(10, 2)
new_layer = cls_head.ClassificationHead.from_config(layer.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(layer.get_config(), new_layer.get_config())
class MultiClsHeadsTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(("no_pooler_layer", 0, 4),
("has_pooler_layer", 5, 6))
def test_pooler_layer(self, inner_dim, num_weights_expected):
cls_list = [("foo", 2), ("bar", 3)]
test_layer = cls_head.MultiClsHeads(inner_dim=inner_dim, cls_list=cls_list)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
_ = test_layer(features)
num_weights_observed = len(test_layer.get_weights())
self.assertEqual(num_weights_observed, num_weights_expected)
def test_layer_invocation(self):
cls_list = [("foo", 2), ("bar", 3)]
test_layer = cls_head.MultiClsHeads(inner_dim=5, cls_list=cls_list)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
outputs = test_layer(features)
self.assertAllClose(outputs["foo"], [[0., 0.], [0., 0.]])
self.assertAllClose(outputs["bar"], [[0., 0., 0.], [0., 0., 0.]])
self.assertSameElements(test_layer.checkpoint_items.keys(),
["pooler_dense", "foo", "bar"])
outputs = test_layer(features, only_project=True)
self.assertEqual(outputs.shape, (2, 5))
def test_layer_serialization(self):
cls_list = [("foo", 2), ("bar", 3)]
test_layer = cls_head.MultiClsHeads(inner_dim=5, cls_list=cls_list)
new_layer = cls_head.MultiClsHeads.from_config(test_layer.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(test_layer.get_config(), new_layer.get_config())
class GaussianProcessClassificationHead(tf.test.TestCase,
parameterized.TestCase):
def setUp(self):
super().setUp()
self.spec_norm_kwargs = dict(norm_multiplier=1.,)
self.gp_layer_kwargs = dict(num_inducing=512)
@parameterized.named_parameters(("no_pooler_layer", 0, 7),
("has_pooler_layer", 5, 11))
def test_pooler_layer(self, inner_dim, num_weights_expected):
test_layer = cls_head.GaussianProcessClassificationHead(
inner_dim=inner_dim,
num_classes=2,
use_spec_norm=True,
use_gp_layer=True,
initializer="zeros",
**self.spec_norm_kwargs,
**self.gp_layer_kwargs)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
_ = test_layer(features)
num_weights_observed = len(test_layer.get_weights())
self.assertEqual(num_weights_observed, num_weights_expected)
def test_layer_invocation(self):
test_layer = cls_head.GaussianProcessClassificationHead(
inner_dim=5,
num_classes=2,
use_spec_norm=True,
use_gp_layer=True,
initializer="zeros",
**self.spec_norm_kwargs,
**self.gp_layer_kwargs)
features = tf.zeros(shape=(2, 10, 10), dtype=tf.float32)
output = test_layer(features)
self.assertAllClose(output, [[0., 0.], [0., 0.]])
self.assertSameElements(test_layer.checkpoint_items.keys(),
["pooler_dense"])
@parameterized.named_parameters(
("gp_layer_with_covmat", True, True),
("gp_layer_no_covmat", True, False),
("dense_layer_with_covmat", False, True),
("dense_layer_no_covmat", False, False))
def test_sngp_output_shape(self, use_gp_layer, return_covmat):
batch_size = 32
num_classes = 2
test_layer = cls_head.GaussianProcessClassificationHead(
inner_dim=5,
num_classes=num_classes,
use_spec_norm=True,
use_gp_layer=use_gp_layer,
**self.spec_norm_kwargs,
**self.gp_layer_kwargs)
features = tf.zeros(shape=(batch_size, 10, 10), dtype=tf.float32)
outputs = test_layer(features, return_covmat=return_covmat)
if use_gp_layer and return_covmat:
self.assertIsInstance(outputs, tuple)
self.assertEqual(outputs[0].shape, (batch_size, num_classes))
self.assertEqual(outputs[1].shape, (batch_size, batch_size))
else:
self.assertIsInstance(outputs, tf.Tensor)
self.assertEqual(outputs.shape, (batch_size, num_classes))
def test_sngp_train_logits(self):
"""Checks if temperature scaling is disabled during training."""
features = tf.zeros(shape=(5, 10, 10), dtype=tf.float32)
gp_layer = cls_head.GaussianProcessClassificationHead(
inner_dim=5, num_classes=2)
# Without temperature.
gp_layer.temperature = None
outputs_no_temp = gp_layer(features, training=True)
# With temperature.
gp_layer.temperature = 10.
outputs_with_temp = gp_layer(features, training=True)
self.assertAllEqual(outputs_no_temp, outputs_with_temp)
def test_layer_serialization(self):
layer = cls_head.GaussianProcessClassificationHead(
inner_dim=5,
num_classes=2,
use_spec_norm=True,
use_gp_layer=True,
**self.spec_norm_kwargs,
**self.gp_layer_kwargs)
new_layer = cls_head.GaussianProcessClassificationHead.from_config(
layer.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(layer.get_config(), new_layer.get_config())
def test_sngp_kwargs_serialization(self):
"""Tests if SNGP-specific kwargs are added during serialization."""
layer = cls_head.GaussianProcessClassificationHead(
inner_dim=5,
num_classes=2,
use_spec_norm=True,
use_gp_layer=True,
**self.spec_norm_kwargs,
**self.gp_layer_kwargs)
layer_config = layer.get_config()
# The config value should equal to those defined in setUp().
self.assertEqual(layer_config["norm_multiplier"], 1.)
self.assertEqual(layer_config["num_inducing"], 512)
class PerQueryDenseHeadTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(("single_query", 1, 3, False),
("multi_queries", 10, 2, False),
("with_bias", 10, 2, True))
def test_layer_invocation(self, num_queries, features, use_bias):
batch_size = 5
hidden_size = 10
layer = cls_head.PerQueryDenseHead(
num_queries=num_queries, features=features, use_bias=use_bias)
inputs = tf.zeros(
shape=(batch_size, num_queries, hidden_size), dtype=tf.float32)
outputs = layer(inputs)
self.assertEqual(outputs.shape, [batch_size, num_queries, features])
def test_layer_serialization(self):
layer = cls_head.PerQueryDenseHead(
num_queries=10, features=2, use_bias=True)
new_layer = cls_head.PerQueryDenseHead.from_config(layer.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(layer.get_config(), new_layer.get_config())
if __name__ == "__main__":
tf.test.main()
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