def __init__(self, label_count, apply_quantization, **kwargs):
super(ConvModel, self).__init__(**kwargs)
# create layers
self.input_quant = quantize_layer.QuantizeLayer(
AllValuesQuantizer(num_bits=8,
per_axis=False,
symmetric=False,
narrow_range=False))
self.conv1 = quantize.quantize_layer(
tf.keras.layers.Conv2D(filters=2,
kernel_size=[1, 3],
padding='SAME'))
self.bn1 = quantize.quantize_layer(
tf.keras.layers.BatchNormalization())
self.relu1 = quantize.quantize_layer(tf.keras.layers.ReLU())
self.conv2 = ring_buffer.RingBuffer(
quantize.quantize_layer(
tf.keras.layers.Conv2D(filters=2,
kernel_size=(3, 1),
dilation_rate=1,
strides=2,
use_bias=False), apply_quantization,
quantize.NoOpActivationConfig(['kernel'], ['activation'],
False)),
use_one_step=False,
inference_batch_size=self.inference_batch_size,
pad_time_dim='causal')
self.bn2 = quantize.quantize_layer(
tf.keras.layers.BatchNormalization(),
default_8bit_quantize_configs.NoOpQuantizeConfig())
self.relu2 = quantize.quantize_layer(tf.keras.layers.ReLU())
self.flatten = ring_buffer.RingBuffer(
quantize.quantize_layer(tf.keras.layers.Flatten(),
apply_quantization),
use_one_step=True,
inference_batch_size=self.inference_batch_size)
self.dense = quantize.quantize_layer(
tf.keras.layers.Dense(label_count,
activation='softmax',
use_bias=False), apply_quantization)
def model(flags):
"""CNN model.
It is based on paper:
Convolutional Neural Networks for Small-footprint Keyword Spotting
http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf
Model topology is similar with "Hello Edge: Keyword Spotting on
Microcontrollers" https://arxiv.org/pdf/1711.07128.pdf
Args:
flags: data/model parameters
Returns:
Keras model for training
"""
input_audio = tf.keras.layers.Input(shape=modes.get_input_data_shape(
flags, modes.Modes.TRAINING),
batch_size=flags.batch_size)
net = input_audio
if flags.preprocess == 'raw':
# it is a self contained model, user need to feed raw audio only
net = speech_features.SpeechFeatures(
speech_features.SpeechFeatures.get_params(flags))(net)
if flags.quantize:
net = quantize_layer.QuantizeLayer(
AllValuesQuantizer(num_bits=8,
per_axis=False,
symmetric=False,
narrow_range=False))(net)
net = tf.keras.backend.expand_dims(net)
for filters, kernel_size, activation, dilation_rate, strides in zip(
utils.parse(flags.cnn_filters), utils.parse(flags.cnn_kernel_size),
utils.parse(flags.cnn_act), utils.parse(flags.cnn_dilation_rate),
utils.parse(flags.cnn_strides)):
net = stream.Stream(cell=quantize.quantize_layer(
tf.keras.layers.Conv2D(filters=filters,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
activation='linear',
strides=strides), flags.quantize,
quantize.NoOpActivationConfig(['kernel'], ['activation'], False)),
pad_time_dim='causal',
use_one_step=False)(net)
net = quantize.quantize_layer(
tf.keras.layers.BatchNormalization(),
default_8bit_quantize_configs.NoOpQuantizeConfig())(net)
net = quantize.quantize_layer(
tf.keras.layers.Activation(activation))(net)
net = stream.Stream(cell=quantize.quantize_layer(
tf.keras.layers.Flatten(), apply_quantization=flags.quantize))(net)
net = tf.keras.layers.Dropout(rate=flags.dropout1)(net)
for units, activation in zip(utils.parse(flags.units2),
utils.parse(flags.act2)):
net = quantize.quantize_layer(tf.keras.layers.Dense(
units=units, activation=activation),
apply_quantization=flags.quantize)(net)
net = quantize.quantize_layer(
tf.keras.layers.Dense(units=flags.label_count),
apply_quantization=flags.quantize)(net)
if flags.return_softmax:
net = quantize.quantize_layer(tf.keras.layers.Activation('softmax'),
apply_quantization=flags.quantize)(net)
return tf.keras.Model(input_audio, net)