mono2binaural/src/utils.py

"""
Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.

This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""

import numpy as np
import torch as th
#import torchaudio as ta


class Net(th.nn.Module):

    def __init__(self, model_name="network", use_cuda=True):
        super().__init__()
        self.use_cuda = use_cuda
        self.model_name = model_name

    def save(self, model_dir, suffix=''):
        '''
        save the network to model_dir/model_name.suffix.net
        :param model_dir: directory to save the model to
        :param suffix: suffix to append after model name
        '''
        if self.use_cuda:
            self.cpu()

        if suffix == "":
            fname = f"{model_dir}/{self.model_name}.net"
        else:
            fname = f"{model_dir}/{self.model_name}.{suffix}.net"

        th.save(self.state_dict(), fname)
        if self.use_cuda:
            self.cuda()

    def load_from_file(self, model_file):
        '''
        load network parameters from model_file
        :param model_file: file containing the model parameters
        '''
        if self.use_cuda:
            self.cpu()

        states = th.load(model_file)
        self.load_state_dict(states)

        if self.use_cuda:
            self.cuda()
        print(f"Loaded: {model_file}")

    def load(self, model_dir, suffix=''):
        '''
        load network parameters from model_dir/model_name.suffix.net
        :param model_dir: directory to load the model from
        :param suffix: suffix to append after model name
        '''
        if suffix == "":
            fname = f"{model_dir}/{self.model_name}.net"
        else:
            fname = f"{model_dir}/{self.model_name}.{suffix}.net"
        self.load_from_file(fname)

    def num_trainable_parameters(self):
        '''
        :return: the number of trainable parameters in the model
        '''
        return sum(p.numel() for p in self.parameters() if p.requires_grad)


# class NewbobAdam(th.optim.Adam):

#     def __init__(self,
#                  weights,
#                  net,
#                  artifacts_dir,
#                  initial_learning_rate=0.001,
#                  decay=0.5,
#                  max_decay=0.01
#                  ):
#         '''
#         Newbob learning rate scheduler
#         :param weights: weights to optimize
#         :param net: the network, must be an instance of type src.utils.Net
#         :param artifacts_dir: (str) directory to save/restore models to/from
#         :param initial_learning_rate: (float) initial learning rate
#         :param decay: (float) value to decrease learning rate by when loss doesn't improve further
#         :param max_decay: (float) maximum decay of learning rate
#         '''
#         super().__init__(weights, lr=initial_learning_rate)
#         self.last_epoch_loss = np.inf
#         self.total_decay = 1
#         self.net = net
#         self.decay = decay
#         self.max_decay = max_decay
#         self.artifacts_dir = artifacts_dir
#         # store initial state as backup
#         if decay < 1.0:
#             net.save(artifacts_dir, suffix="newbob")

#     def update_lr(self, loss):
#         '''
#         update the learning rate based on the current loss value and historic loss values
#         :param loss: the loss after the current iteration
#         '''
#         if loss > self.last_epoch_loss and self.decay < 1.0 and self.total_decay > self.max_decay:
#             self.total_decay = self.total_decay * self.decay
#             print(f"NewbobAdam: Decay learning rate (loss degraded from {self.last_epoch_loss} to {loss})."
#                   f"Total decay: {self.total_decay}")
#             # restore previous network state
#             self.net.load(self.artifacts_dir, suffix="newbob")
#             # decrease learning rate
#             for param_group in self.param_groups:
#                 param_group['lr'] = param_group['lr'] * self.decay
#         else:
#             self.last_epoch_loss = loss
#         # save last snapshot to restore it in case of lr decrease
#         if self.decay < 1.0 and self.total_decay > self.max_decay:
#             self.net.save(self.artifacts_dir, suffix="newbob")


# class FourierTransform:
#     def __init__(self,
#                  fft_bins=2048,
#                  win_length_ms=40,
#                  frame_rate_hz=100,
#                  causal=False,
#                  preemphasis=0.0,
#                  sample_rate=48000,
#                  normalized=False):
#         self.sample_rate = sample_rate
#         self.frame_rate_hz = frame_rate_hz
#         self.preemphasis = preemphasis
#         self.fft_bins = fft_bins
#         self.win_length = int(sample_rate * win_length_ms / 1000)
#         self.hop_length = int(sample_rate / frame_rate_hz)
#         self.causal = causal
#         self.normalized = normalized
#         if self.win_length > self.fft_bins:
#             print('FourierTransform Warning: fft_bins should be larger than win_length')

#     def _convert_format(self, data, expected_dims):
#         if not type(data) == th.Tensor:
#             data = th.Tensor(data)
#         if len(data.shape) < expected_dims:
#             data = data.unsqueeze(0)
#         if not len(data.shape) == expected_dims:
#             raise Exception(f"FourierTransform: data needs to be a Tensor with {expected_dims} dimensions but got shape {data.shape}")
#         return data

#     def _preemphasis(self, audio):
#         if self.preemphasis > 0:
#             return th.cat((audio[:, 0:1], audio[:, 1:] - self.preemphasis * audio[:, :-1]), dim=1)
#         return audio

#     def _revert_preemphasis(self, audio):
#         if self.preemphasis > 0:
#             for i in range(1, audio.shape[1]):
#                 audio[:, i] = audio[:, i] + self.preemphasis * audio[:, i-1]
#         return audio

#     def _magphase(self, complex_stft):
#         mag, phase = ta.functional.magphase(complex_stft, 1.0)
#         return mag, phase

#     def stft(self, audio):
#         '''
#         wrapper around th.stft
#         audio: wave signal as th.Tensor
#         '''
#         hann = th.hann_window(self.win_length)
#         hann = hann.cuda() if audio.is_cuda else hann
#         spec = th.stft(audio, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length,
#                        window=hann, center=not self.causal, normalized=self.normalized)
#         return spec.contiguous()

#     def complex_spectrogram(self, audio):
#         '''
#         audio: wave signal as th.Tensor
#         return: th.Tensor of size channels x frequencies x time_steps (channels x y_axis x x_axis)
#         '''
#         self._convert_format(audio, expected_dims=2)
#         audio = self._preemphasis(audio)
#         return self.stft(audio)

#     def magnitude_phase(self, audio):
#         '''
#         audio: wave signal as th.Tensor
#         return: tuple containing two th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
#         '''
#         stft = self.complex_spectrogram(audio)
#         return self._magphase(stft)

#     def mag_spectrogram(self, audio):
#         '''
#         audio: wave signal as th.Tensor
#         return: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
#         '''
#         return self.magnitude_phase(audio)[0]

#     def power_spectrogram(self, audio):
#         '''
#         audio: wave signal as th.Tensor
#         return: power spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
#         '''
#         return th.pow(self.mag_spectrogram(audio), 2.0)

#     def phase_spectrogram(self, audio):
#         '''
#         audio: wave signal as th.Tensor
#         return: phase spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
#         '''
#         return self.magnitude_phase(audio)[1]

#     def mel_spectrogram(self, audio, n_mels):
#         '''
#         audio: wave signal as th.Tensor
#         n_mels: number of bins used for mel scale warping
#         return: mel spectrogram as th.Tensor of size channels x n_mels x time_steps for magnitude and phase spectrum
#         '''
#         spec = self.power_spectrogram(audio)
#         mel_warping = ta.transforms.MelScale(n_mels, self.sample_rate)
#         return mel_warping(spec)

#     def complex_spec2wav(self, complex_spec, length):
#         '''
#         inverse stft
#         complex_spec: complex spectrum as th.Tensor of size channels x frequencies x time_steps x 2 (real part/imaginary part)
#         length: length of the audio to be reconstructed (in frames)
#         '''
#         complex_spec = self._convert_format(complex_spec, expected_dims=4)
#         hann = th.hann_window(self.win_length)
#         hann = hann.cuda() if complex_spec.is_cuda else hann
#         wav = ta.functional.istft(complex_spec, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, window=hann, length=length, center=not self.causal)
#         wav = self._revert_preemphasis(wav)
#         return wav

#     def magphase2wav(self, mag_spec, phase_spec, length):
#         '''
#         reconstruction of wav signal from magnitude and phase spectrum
#         mag_spec: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps
#         phase_spec: phase spectrum as th.Tensor of size channels x frequencies x time_steps
#         length: length of the audio to be reconstructed (in frames)
#         '''
#         mag_spec = self._convert_format(mag_spec, expected_dims=3)
#         phase_spec = self._convert_format(phase_spec, expected_dims=3)
#         complex_spec = th.stack([mag_spec * th.cos(phase_spec), mag_spec * th.sin(phase_spec)], dim=-1)
#         return self.complex_spec2wav(complex_spec, length)