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_ds_tc_resnet_stream_internal_tflite(self):
"""Test tflite streaming with internal state."""
test_utils.set_seed(123)
tf.keras.backend.set_learning_phase(0)
params = utils.ds_tc_resnet_model_params(True)
model = ds_tc_resnet.model(params)
model.summary()
input_data = np.random.rand(params.batch_size, params.desired_samples)
# run non streaming inference
non_stream_out = model.predict(input_data)
tflite_streaming_model = utils.model_to_tflite(
None, model, params, Modes.STREAM_INTERNAL_STATE_INFERENCE)
interpreter = tf.lite.Interpreter(model_content=tflite_streaming_model)
interpreter.allocate_tensors()
stream_out = inference.run_stream_inference_classification_tflite(
params, interpreter, input_data, input_states=None)
self.assertAllClose(stream_out, non_stream_out, atol=1e-5)
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)
