A Keras Subpixel1D layer for upsampling audio and other time-series data in neural networks.

Subpixel1D.py

class Subpixel1D(tf.keras.layers.Layer):
    def __init__(self,
                 r,
                 **kwargs):
        super(Subpixel1D, self).__init__(**kwargs)
        self.r = r
        
    def build(self, input_shape):
        # check if channels are evenly divisible for subpixel1d to work!
        input_shape = tf.TensorShape(input_shape).as_list()
        if input_shape[2] % self.r != 0:
            raise ValueError(
                f'The number of input channels must be evenly divisible by the upsampling '
                f'factor r. Received r={self.r}, but the input has {input_shape[2]} channels '
                f'(full input shape is {input_shape}).'
            )
        
    def call(self, inputs):
        # (batch, samples, channels) -> (channels, samples, batch)
        outputs = tf.transpose(inputs, [2, 1, 0])
        # (channels, samples, batch) -> (channels/r, r*samples, batch)
        outputs = tf.batch_to_space(outputs, [self.r], [[0, 0]])
        # (channels, samples, batch) -> (batch, samples, channels)
        outputs = tf.transpose(outputs, [2, 1, 0])
        return outputs
    
    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        return (input_shape[0], input_shape[1] * self.r, input_shape[2] // self.r)

    def get_config(self):
        config = {
            'r': self.r,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))