Spaces:
Running
Running
File size: 7,476 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# Copyright 2019 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 span_labeling network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import
from official.nlp.modeling.networks import span_labeling
# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
# guarantees forward compatibility of this code for the V2 switchover.
@keras_parameterized.run_all_keras_modes
class SpanLabelingTest(keras_parameterized.TestCase):
def test_network_creation(self):
"""Validate that the Keras object can be created."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
start_outputs, end_outputs = test_network(sequence_data)
# Validate that the outputs are of the expected shape.
expected_output_shape = [None, sequence_length]
self.assertEqual(expected_output_shape, start_outputs.shape.as_list())
self.assertEqual(expected_output_shape, end_outputs.shape.as_list())
def test_network_invocation(self):
"""Validate that the Keras object can be invoked."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(input_width=input_width)
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
outputs = test_network(sequence_data)
model = tf.keras.Model(sequence_data, outputs)
# Invoke the network as part of a Model.
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
# Validate that the outputs are of the expected shape.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
def test_network_invocation_with_internal_logit_output(self):
"""Validate that the logit outputs are correct."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
output = test_network(sequence_data)
model = tf.keras.Model(sequence_data, output)
logit_model = tf.keras.Model(
test_network.inputs,
[test_network.start_logits, test_network.end_logits])
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
start_logits, end_logits = logit_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
self.assertEqual(expected_output_shape, start_logits.shape)
self.assertEqual(expected_output_shape, end_logits.shape)
# Ensure that the logits, when softmaxed, create the outputs.
input_tensor = tf.keras.Input(expected_output_shape[1:])
output_tensor = tf.keras.layers.Activation(tf.nn.log_softmax)(input_tensor)
softmax_model = tf.keras.Model(input_tensor, output_tensor)
start_softmax = softmax_model.predict(start_logits)
self.assertAllClose(start_outputs, start_softmax)
end_softmax = softmax_model.predict(end_logits)
self.assertAllClose(end_outputs, end_softmax)
def test_network_invocation_with_external_logit_output(self):
"""Validate that the logit outputs are correct."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
logit_network = span_labeling.SpanLabeling(
input_width=input_width, output='logits')
logit_network.set_weights(test_network.get_weights())
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
output = test_network(sequence_data)
logit_output = logit_network(sequence_data)
model = tf.keras.Model(sequence_data, output)
logit_model = tf.keras.Model(sequence_data, logit_output)
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
start_logits, end_logits = logit_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
self.assertEqual(expected_output_shape, start_logits.shape)
self.assertEqual(expected_output_shape, end_logits.shape)
# Ensure that the logits, when softmaxed, create the outputs.
input_tensor = tf.keras.Input(expected_output_shape[1:])
output_tensor = tf.keras.layers.Activation(tf.nn.log_softmax)(input_tensor)
softmax_model = tf.keras.Model(input_tensor, output_tensor)
start_softmax = softmax_model.predict(start_logits)
self.assertAllClose(start_outputs, start_softmax)
end_softmax = softmax_model.predict(end_logits)
self.assertAllClose(end_outputs, end_softmax)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
network = span_labeling.SpanLabeling(
input_width=128,
activation='relu',
initializer='zeros',
output='predictions')
# Create another network object from the first object's config.
new_network = span_labeling.SpanLabeling.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
def test_unknown_output_type_fails(self):
with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'):
_ = span_labeling.SpanLabeling(input_width=10, output='bad')
if __name__ == '__main__':
tf.test.main()
|