def setUp(self):
super(UtilsTest, self).setUp()
tf1.reset_default_graph()
self.sess = tf1.Session()
tf1.keras.backend.set_session(self.sess)
self.flags = Flags()
self.flags.desired_samples = 16000
self.flags.window_size_ms = 30.0
self.flags.window_stride_ms = 20.0
self.flags.sample_rate = 16000.0
self.flags.window_stride_samples = 320
self.flags.window_size_samples = 480
self.flags.label_count = 3
self.flags.preemph = 0.0
self.flags.window_type = 'hann'
self.flags.mel_num_bins = 40
self.flags.mel_lower_edge_hertz = 20
self.flags.mel_upper_edge_hertz = 4000
self.flags.fft_magnitude_squared = False
self.flags.dct_num_features = 10
self.flags.use_tf_fft = False
self.flags.units1 = '32'
self.flags.act1 = "'relu'"
self.flags.pool_size = 2
self.flags.strides = 2
self.flags.dropout1 = 0.1
self.flags.units2 = '256,256'
self.flags.act2 = "'relu','relu'"
self.flags.train_dir = FLAGS.test_tmpdir
self.flags.mel_non_zero_only = 1
self.flags.batch_size = 1
self.model = dnn.model(self.flags)
self.model.summary()
def setUp(self): super(UtilsTest, self).setUp() tf1.reset_default_graph() config = tf1.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf1.Session(config=config) tf1.keras.backend.set_session(self.sess)
def test_streaming_on_2d_data_strides(self, stride):
"""Tests Conv2DTranspose on 2d in streaming mode with different strides.
Args:
stride: controls the upscaling factor
"""
tf1.reset_default_graph()
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf1.Session(config=config)
tf1.keras.backend.set_session(sess)
# model and data parameters
step = 1 # amount of data fed into streaming model on every iteration
params = test_utils.Params([step], clip_duration_ms=0.25)
input_features = 3
# prepare input data: [batch, time, features, channels]
x = np.random.rand(1, params.desired_samples, input_features,
self.input_channels)
inp_audio = x
# prepare non-streaming model
model = conv2d_transpose_model(
params,
filters=1,
kernel_size=(3, 3),
strides=(stride, stride),
features=input_features,
channels=self.input_channels)
model.summary()
# set weights with bias
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Conv2DTranspose):
layer.set_weights([
np.ones(layer.weights[0].shape),
np.zeros(layer.weights[1].shape) + 0.5
])
params.data_shape = (1, input_features, self.input_channels)
# prepare streaming model
model_stream = utils.to_streaming_inference(
model, params, modes.Modes.STREAM_INTERNAL_STATE_INFERENCE)
model_stream.summary()
# run inference
non_stream_out = model.predict(inp_audio)
stream_out = inference.run_stream_inference(params, model_stream, inp_audio)
self.assertAllClose(stream_out, non_stream_out)
def test_streaming_on_1d_data_strides(self, stride):
"""Tests Conv2DTranspose on 1d in streaming mode with different strides.
Args:
stride: controls the upscaling factor
"""
tf1.reset_default_graph()
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf1.Session(config=config)
tf1.keras.backend.set_session(sess)
# model and data parameters
step = 1 # amount of data fed into streaming model on every iteration
params = test_utils.Params([step], clip_duration_ms=0.25)
# prepare input data: [batch, time, 1, channels]
x = np.random.rand(1, params.desired_samples, 1, self.input_channels)
inp_audio = x
# prepare non-streaming model
model = conv2d_transpose_model(
params,
filters=1,
kernel_size=(3, 1),
strides=(stride, 1),
channels=self.input_channels)
model.summary()
# set weights with bias
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Conv2DTranspose):
layer.set_weights([
np.ones(layer.weights[0].shape),
np.zeros(layer.weights[1].shape) + 0.5
])
params.data_shape = (1, 1, self.input_channels)
# prepare streaming model
model_stream = utils.to_streaming_inference(
model, params, modes.Modes.STREAM_INTERNAL_STATE_INFERENCE)
model_stream.summary()
# run inference
non_stream_out = model.predict(inp_audio)
stream_out = inference.run_stream_inference(params, model_stream, inp_audio)
self.assertAllClose(stream_out, non_stream_out)
# Convert TF non-streaming model to TFLite external-state streaming model.
tflite_streaming_model = utils.model_to_tflite(
sess, model, params, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE)
self.assertTrue(tflite_streaming_model)
# Run TFLite external-state streaming inference.
interpreter = tf.lite.Interpreter(model_content=tflite_streaming_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
input_states = []
# before processing test sequence we create model state
for s in range(len(input_details)):
input_states.append(np.zeros(input_details[s]['shape'], dtype=np.float32))
stream_out_tflite_external_st = inference.run_stream_inference_tflite(
params, interpreter, inp_audio, input_states, concat=True)
# compare streaming TFLite with external-state vs TF non-streaming
self.assertAllClose(stream_out_tflite_external_st, non_stream_out)
