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 setUp(self):
super(DsTcResnetTest, self).setUp()
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf1.Session(config=config)
tf1.keras.backend.set_session(self.sess)
tf.keras.backend.set_learning_phase(0)
test_utils.set_seed(123)
self.params = utils.ds_tc_resnet_model_params(True)
self.model = ds_tc_resnet.model(self.params)
self.model.summary()
self.input_data = np.random.rand(self.params.batch_size,
self.params.desired_samples)
# run non streaming inference
self.non_stream_out = self.model.predict(self.input_data)
def test_cnn_model_end_to_end(self):
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf1.Session(config=config)
tf1.keras.backend.set_session(sess)
test_utils.set_seed(123)
# data parameters
num_time_bins = 12
feature_size = 12
# model params.
total_stride = 2
params = test_utils.Params([total_stride], 0)
params.model_name = 'cnn'
params.cnn_filters = '2'
params.cnn_kernel_size = '(3,3)'
params.cnn_act = "'relu'"
params.cnn_dilation_rate = '(1,1)'
params.cnn_strides = '(2,2)'
params.dropout1 = 0.5
params.units2 = ''
params.act2 = ''
params.label_count = 2
params.return_softmax = True
params.quantize = 1 # apply quantization aware training
params.data_shape = (num_time_bins, feature_size)
params.preprocess = 'custom'
model = cnn.model(params)
model.summary()
# prepare training and testing data
train_images, train_labels = test_utils.generate_data(
img_size_y=num_time_bins, img_size_x=feature_size, n_samples=32)
test_images = train_images
test_labels = train_labels
# create and train quantization aware model in non streaming mode
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
model.fit(
train_images,
train_labels,
epochs=1,
validation_data=(test_images, test_labels))
model.summary()
# one test image
train_image = train_images[:1,]
# run tf non streaming inference
non_stream_output_tf = model.predict(train_image)
# specify input data shape for streaming mode
params.data_shape = (total_stride, feature_size)
# TODO(rybakov) add params structure for model with no feature extractor
# prepare tf streaming model and use it to generate representative_dataset
with quantize.quantize_scope():
stream_quantized_model = utils.to_streaming_inference(
model, params, Modes.STREAM_EXTERNAL_STATE_INFERENCE)
calibration_data = prepare_calibration_data(stream_quantized_model,
total_stride, train_image)
def representative_dataset(dtype):
def _representative_dataset_gen():
for i in range(len(calibration_data)):
yield [
calibration_data[i][0].astype(dtype), # input audio packet
calibration_data[i][1].astype(dtype), # conv state
calibration_data[i][2].astype(dtype) # flatten state
]
return _representative_dataset_gen
# convert streaming quantization aware model to tflite
# and apply post training quantization
with quantize.quantize_scope():
tflite_streaming_model = utils.model_to_tflite(
sess, model, params,
Modes.STREAM_EXTERNAL_STATE_INFERENCE,
optimizations=[tf.lite.Optimize.DEFAULT],
inference_type=tf.int8,
experimental_new_quantizer=True,
representative_dataset=representative_dataset(np.float32))
# run tflite in streaming mode and compare output logits with tf
interpreter = tf.lite.Interpreter(model_content=tflite_streaming_model)
interpreter.allocate_tensors()
input_states = []
for detail in interpreter.get_input_details():
input_states.append(np.zeros(detail['shape'], dtype=np.float32))
stream_out_tflite = inference.run_stream_inference_classification_tflite(
params, interpreter, train_image, input_states)
self.assertAllClose(stream_out_tflite, non_stream_output_tf, atol=0.001)
def setUp(self):
super(DsTcResnetTest, self).setUp()
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf1.Session(config=config)
tf1.keras.backend.set_session(self.sess)
test_utils.set_seed(123)
tf.keras.backend.set_learning_phase(0)
# model parameters
model_name = 'ds_tc_resnet'
self.params = model_params.HOTWORD_MODEL_PARAMS[model_name]
self.params.clip_duration_ms = 160
self.params.window_size_ms = 4.0
self.params.window_stride_ms = 2.0
self.params.wanted_words = 'a,b,c'
self.params.ds_padding = "'causal','causal','causal'"
self.params.ds_filters = '8,8,4'
self.params.ds_repeat = '1,1,1'
self.params.ds_residual = '0,1,1' # residual can not be applied with stride
self.params.ds_kernel_size = '3,3,3'
self.params.ds_stride = '2,1,1' # streaming conv with stride
self.params.ds_dilation = '1,1,1'
self.params.ds_pool = '1,2,1' # streaming conv with pool
self.params.ds_filter_separable = '1,1,1'
# convert ms to samples and compute labels count
self.params = model_flags.update_flags(self.params)
# compute total stride
pools = utils.parse(self.params.ds_pool)
strides = utils.parse(self.params.ds_stride)
time_stride = [1]
for pool in pools:
if pool > 1:
time_stride.append(pool)
for stride in strides:
if stride > 1:
time_stride.append(stride)
total_stride = np.prod(time_stride)
# overide input data shape for streaming model with stride/pool
self.params.data_stride = total_stride
self.params.data_frame_padding = 'causal'
# set desired number of frames in model
frames_number = 16
frames_per_call = total_stride
frames_number = (frames_number // frames_per_call) * frames_per_call
# number of input audio samples required to produce one output frame
framing_stride = max(
self.params.window_stride_samples,
max(0, self.params.window_size_samples -
self.params.window_stride_samples))
signal_size = framing_stride * frames_number
# desired number of samples in the input data to train non streaming model
self.params.desired_samples = signal_size
self.params.batch_size = 1
self.model = ds_tc_resnet.model(self.params)
self.model.summary()
self.input_data = np.random.rand(self.params.batch_size,
self.params.desired_samples)
# run non streaming inference
self.non_stream_out = self.model.predict(self.input_data)
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)
