def testPreprocessStreamInferenceModeTFandTFLite(self,
preprocess,
feature_type,
model_name='gru'):
# Validate that model with different preprocessing
# can be converted to stream inference mode with TF and TFLite.
params = model_params.HOTWORD_MODEL_PARAMS[model_name]
# set parameters to test
params.preprocess = preprocess
params.feature_type = feature_type
params = model_flags.update_flags(params)
# create model
model = models.MODELS[params.model_name](params)
# convert TF non streaming model to TFLite streaming inference
# with external states
self.assertTrue(utils.model_to_tflite(
self.sess, model, params, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE))
# convert TF non streaming model to TF streaming with external states
self.assertTrue(utils.to_streaming_inference(
model, params, modes.Modes.STREAM_EXTERNAL_STATE_INFERENCE))
# convert TF non streaming model to TF streaming with internal states
self.assertTrue(utils.to_streaming_inference(
model, params, modes.Modes.STREAM_INTERNAL_STATE_INFERENCE))
def testToNonStreamInferenceTFandTFLite(self, model_name='svdf'):
"""Validate that model can be converted to non stream inference mode."""
params = _HOTWORD_MODEL_PARAMS[model_name]
params = model_flags.update_flags(params)
# create model
model = models.MODELS[params.model_name](params)
# convert TF non streaming model to TF non streaming inference model
# it will disable dropouts
self.assertTrue(
utils.to_streaming_inference(model, params,
Modes.NON_STREAM_INFERENCE))
# convert TF non streaming model to TFLite non streaming inference
self.assertTrue(
utils.model_to_tflite(self.sess, model, params,
Modes.NON_STREAM_INFERENCE))
def test_ds_tc_resnet_stream_tflite(self):
tflite_streaming_model = utils.model_to_tflite(
self.sess, self.model, self.params,
Modes.STREAM_EXTERNAL_STATE_INFERENCE)
interpreter = tf.lite.Interpreter(model_content=tflite_streaming_model)
interpreter.allocate_tensors()
# before processing new test sequence we reset model state
inputs = []
for detail in interpreter.get_input_details():
inputs.append(np.zeros(detail['shape'], dtype=np.float32))
stream_out = test.run_stream_inference_classification_tflite(
self.params, interpreter, self.input_data, inputs)
self.assertAllClose(stream_out, self.non_stream_out, atol=1e-5)
def _testTFLite(self,
preprocess='raw',
feature_type='mfcc_op',
model_name='svdf'):
params = model_params.HOTWORD_MODEL_PARAMS[model_name]
# set parameters to test
params.preprocess = preprocess
params.feature_type = feature_type
params = model_flags.update_flags(params)
# create model
model = models.MODELS[params.model_name](params)
# convert TF non streaming model to TFLite non streaming inference
self.assertTrue(
utils.model_to_tflite(self.sess, model, params,
Modes.NON_STREAM_INFERENCE))
def convert_model_tflite(flags,
folder,
mode,
fname,
weights_name='best_weights',
optimizations=None):
"""Convert model to streaming and non streaming TFLite.
Args:
flags: model and data settings
folder: folder where converted model will be saved
mode: inference mode
fname: file name of converted model
weights_name: file name with model weights
optimizations: list of optimization options
"""
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
tf.keras.backend.set_learning_phase(0)
flags.batch_size = 1 # set batch size for inference
flags.approximate_gelu = True # use approximate GELU in TFLite
model = models.MODELS[flags.model_name](flags)
weights_path = os.path.join(flags.train_dir, weights_name)
model.load_weights(weights_path).expect_partial()
# convert trained model to non streaming TFLite stateless
# to finish other tests we do not stop program if exception happen here
path_model = os.path.join(flags.train_dir, folder)
if not os.path.exists(path_model):
os.makedirs(path_model)
try:
with open(os.path.join(path_model, fname), 'wb') as fd:
fd.write(
utils.model_to_tflite(sess, model, flags, mode, path_model,
optimizations))
except IOError as e:
logging.warning('FAILED to write file: %s', e)
except (ValueError, AttributeError, RuntimeError, TypeError) as e:
logging.warning('FAILED to convert to mode %s, tflite: %s', mode, e)
def _testTFLite(self,
preprocess='raw',
feature_type='mfcc_tf',
use_tf_fft=False,
model_name='svdf'):
params = model_params.HOTWORD_MODEL_PARAMS[model_name]
params.clip_duration_ms = 100 # make it shorter for testing
# set parameters to test
params.preprocess = preprocess
params.feature_type = feature_type
params.use_tf_fft = use_tf_fft
params = model_flags.update_flags(params)
# create model
model = models.MODELS[params.model_name](params)
# convert TF non streaming model to TFLite non streaming inference
self.assertTrue(
utils.model_to_tflite(self.sess, model, params,
modes.Modes.NON_STREAM_INFERENCE))
def testToStreamInferenceModeTFandTFLite(self, model_name='gru'):
"""Validate that model can be converted to any streaming inference mode."""
params = _HOTWORD_MODEL_PARAMS[model_name]
params = model_flags.update_flags(params)
# create model
model = models.MODELS[params.model_name](params)
# convert TF non streaming model to TFLite streaming inference
# with external states
self.assertTrue(
utils.model_to_tflite(self.sess, model, params,
Modes.STREAM_EXTERNAL_STATE_INFERENCE))
# convert TF non streaming model to TF streaming with external states
self.assertTrue(
utils.to_streaming_inference(
model, params, Modes.STREAM_EXTERNAL_STATE_INFERENCE))
# convert TF non streaming model to TF streaming with internal states
self.assertTrue(
utils.to_streaming_inference(
model, params, Modes.STREAM_INTERNAL_STATE_INFERENCE))
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 test_model_to_tflite(self):
"""TFLite supports stateless graphs."""
self.assertTrue(
utils.model_to_tflite(self.sess, self.model, self.flags))
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 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)
