nn.py

#!/usr/bin/env python
# encoding: utf-8
#
# Copyright 2022 Spotify AB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, List

import tensorflow as tf
import tensorflow.keras.backend as K

from basic_pitch.layers.math import log_base_b


class HarmonicStacking(tf.keras.layers.Layer):
    """Harmonic stacking layer

    Input shape: (n_batch, n_times, n_freqs, 1)
    Output shape: (n_batch, n_times, n_output_freqs, len(harmonics))

    n_freqs should be much larger than n_output_freqs so that information from the upper
    harmonics is captured.

    Attributes:
        bins_per_semitone: The number of bins per semitone of the input CQT
        harmonics: List of harmonics to use. Should be positive numbers.
        shifts: A list containing the number of bins to shift in frequency for each harmonic
        n_output_freqs: The number of frequency bins in each harmonic layer.
    """

    def __init__(
        self, bins_per_semitone: int, harmonics: List[float], n_output_freqs: int, name: str = "harmonic_stacking"
    ):
        """Downsample frequency by stride, upsample channels by 4."""
        super().__init__(trainable=False, name=name)
        self.bins_per_semitone = bins_per_semitone
        self.harmonics = harmonics
        self.shifts = [
            int(tf.math.round(12.0 * self.bins_per_semitone * log_base_b(float(h), 2))) for h in self.harmonics
        ]
        self.n_output_freqs = n_output_freqs

    def get_config(self) -> Any:
        config = super().get_config().copy()
        config.update(
            {
                "bins_per_semitone": self.bins_per_semitone,
                "harmonics": self.harmonics,
                "n_output_freqs": self.n_output_freqs,
                "name": self.name,
            }
        )
        return config

    def call(self, x: tf.Tensor) -> tf.Tensor:
        # (n_batch, n_times, n_freqs, 1)
        tf.debugging.assert_equal(tf.shape(x).shape, 4)
        channels = []
        for shift in self.shifts:
            if shift == 0:
                padded = x
            elif shift > 0:
                paddings = tf.constant([[0, 0], [0, 0], [0, shift], [0, 0]])
                padded = tf.pad(x[:, :, shift:, :], paddings)
            elif shift < 0:
                paddings = tf.constant([[0, 0], [0, 0], [-shift, 0], [0, 0]])
                padded = tf.pad(x[:, :, :shift, :], paddings)
            else:
                raise ValueError

            channels.append(padded)
        x = tf.concat(channels, axis=-1)
        x = x[:, :, : self.n_output_freqs, :]  # return only the first n_output_freqs frequency channels
        return x


class FlattenAudioCh(tf.keras.layers.Layer):
    """Layer which removes a "channels" dimension of size 1.

    Input shape: (batch, time, 1)
    Output shape: (batch, time)
    """

    def call(self, x: tf.Tensor) -> tf.Tensor:
        """x: (batch, time, ch)"""
        shapes = K.int_shape(x)
        tf.assert_equal(shapes[2], 1)
        return tf.keras.layers.Reshape([shapes[1]])(x)  # ignore batch size


class FlattenFreqCh(tf.keras.layers.Layer):
    """Layer to flatten the frequency channel and make each channel
    part of the frequency dimension.

    Input shape: (batch, time, freq, ch)
    Output shape: (batch, time, freq*ch)
    """

    def call(self, x: tf.Tensor) -> tf.Tensor:
        shapes = K.int_shape(x)
        return tf.keras.layers.Reshape([shapes[1], shapes[2] * shapes[3]])(x)  # ignore batch size