Hi everyone! I woul like to run a Mobile Net on an embedded system (Coral USB Accelerator) to analyze audio recordings that I convert into spectrograms. I have implemented the model according to the Mobile Net paper 16 in Tensorflow.
def SeparableConv(x, num_filters, strides, alpha=1.0):
     x = tf.keras.layers.DepthwiseConv2D(kernel_size=3, padding='same')(x)
     x = tf.keras.layers.BatchNormalization()(x) 
     x = tf.keras.layers.ReLU()(x)
     x = tf.keras.layers.Conv2D(np.floor( num_filters * alpha), kernel_size=(1, 1), strides=strides, use_bias=False, padding='same')(x) 
     x = tf.keras.layers.BatchNormalization()(x)
     x = tf.keras.layers.ReLU()(x)
     return x

def Conv(x, num_filters, kernel_size, strides=1, alpha=1.0):
    x = tf.keras.layers.Conv2D((np.floor( num_filters * alpha)), kernel_size=kernel_size, strides=strides, use_bias=False , padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.ReLU()(x)
    return x

inputs = tf.keras.layers.Input(shape=(64, 64, 1))

x = Conv(inputs, num_filters=16, kernel_size=3 , strides=2)
x = SeparableConv(x, num_filters=32, strides=1)
x = SeparableConv(x, num_filters=64, strides=2)
x = SeparableConv(x, num_filters=64, strides=1)
x = SeparableConv(x, num_filters=128, strides=2)
x = SeparableConv(x, num_filters=128, strides=1)
x = SeparableConv(x, num_filters=256, strides=1)
x = SeparableConv(x, num_filters=256, strides=2)
x = SeparableConv(x, num_filters=256, strides=1)
x = SeparableConv(x, num_filters=256, strides=1)
x = SeparableConv(x, num_filters=256, strides=1)
x = SeparableConv(x, num_filters=256, strides=1)
x = SeparableConv(x, num_filters=512, strides=2)
x = SeparableConv(x, num_filters=512, strides=1)

x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(512)(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dropout(0.001)(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(2)(x)

outputs = tf.keras.layers.ReLU()(x)
model = tf.keras.models.Model(inputs, outputs)

To run it on the Coral USB Accelerator I need to quantize it and convert it to a TensorFlow Lite model (and then compile it again with the Edge TPU compiler). But the prediction is massively worse after quantizing from 32bit float to 8bit int. The problem seems to be the batch normalization keras.layers.BatchNormalization() 2. This is not an instruction that is allowed in TensorFlow lite. But since Mobile Net was designed specifically for embedded systems and Batch Normalization is a fundamental part of it I can’t imagine that this is not possible on the Edge TPU. So I wanted to ask if anyone knows a workaround to get Mobile Net, specifically Batch Normalization working on the Coral USB Accelerator?
Many thanks for your help and advice in advance!