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# Copyright 2017 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 speech commands models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.speech_commands import models
from tensorflow.python.platform import test
class ModelsTest(test.TestCase):
def _modelSettings(self):
return models.prepare_model_settings(
label_count=10,
sample_rate=16000,
clip_duration_ms=1000,
window_size_ms=20,
window_stride_ms=10,
feature_bin_count=40,
preprocess="mfcc")
def testPrepareModelSettings(self):
self.assertIsNotNone(
models.prepare_model_settings(
label_count=10,
sample_rate=16000,
clip_duration_ms=1000,
window_size_ms=20,
window_stride_ms=10,
feature_bin_count=40,
preprocess="mfcc"))
def testCreateModelConvTraining(self):
model_settings = self._modelSettings()
with self.test_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(fingerprint_input,
model_settings, "conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
def testCreateModelConvInference(self):
model_settings = self._modelSettings()
with self.test_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits = models.create_model(fingerprint_input, model_settings, "conv",
False)
self.assertIsNotNone(logits)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
def testCreateModelLowLatencyConvTraining(self):
model_settings = self._modelSettings()
with self.test_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "low_latency_conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
def testCreateModelFullyConnectedTraining(self):
model_settings = self._modelSettings()
with self.test_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "single_fc", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
def testCreateModelBadArchitecture(self):
model_settings = self._modelSettings()
with self.test_session():
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
with self.assertRaises(Exception) as e:
models.create_model(fingerprint_input, model_settings,
"bad_architecture", True)
self.assertTrue("not recognized" in str(e.exception))
def testCreateModelTinyConvTraining(self):
model_settings = self._modelSettings()
with self.test_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "tiny_conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
if __name__ == "__main__":
test.main()
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