remfx/utils.py

import logging
from typing import List, Tuple
import pytorch_lightning as pl
from omegaconf import DictConfig
from pytorch_lightning.utilities import rank_zero_only
import torch
import torchaudio
from torch import nn
import collections.abc


def get_logger(name=__name__) -> logging.Logger:
    """Initializes multi-GPU-friendly python command line logger."""

    logger = logging.getLogger(name)

    # this ensures all logging levels get marked with the rank zero decorator
    # otherwise logs would get multiplied for each GPU process in multi-GPU setup
    for level in (
        "debug",
        "info",
        "warning",
        "error",
        "exception",
        "fatal",
        "critical",
    ):
        setattr(logger, level, rank_zero_only(getattr(logger, level)))

    return logger


log = get_logger(__name__)


@rank_zero_only
def log_hyperparameters(
    config: DictConfig,
    model: pl.LightningModule,
    datamodule: pl.LightningDataModule,
    trainer: pl.Trainer,
    callbacks: List[pl.Callback],
    logger: pl.loggers.logger.Logger,
) -> None:
    """Controls which config parts are saved by Lightning loggers.
    Additionaly saves:
    - number of model parameters
    """

    if not trainer.logger:
        return

    hparams = {}

    # choose which parts of hydra config will be saved to loggers
    hparams["model"] = config["model"]

    # save number of model parameters
    hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
    hparams["model/params/trainable"] = sum(
        p.numel() for p in model.parameters() if p.requires_grad
    )
    hparams["model/params/non_trainable"] = sum(
        p.numel() for p in model.parameters() if not p.requires_grad
    )

    hparams["datamodule"] = config["datamodule"]
    hparams["trainer"] = config["trainer"]

    if "seed" in config:
        hparams["seed"] = config["seed"]
    if "callbacks" in config:
        hparams["callbacks"] = config["callbacks"]

    if isinstance(logger, pl.loggers.CSVLogger):
        logger.log_hyperparams(hparams)
    else:
        logger.experiment.config.update(hparams)


def create_random_chunks(
    audio_file: str, chunk_size: int, num_chunks: int
) -> Tuple[List[Tuple[int, int]], int]:
    """Create num_chunks random chunks of size chunk_size (seconds)
    from an audio file.
    Return sample_index of start of each chunk and original sr
    """
    audio, sr = torchaudio.load(audio_file)
    chunk_size_in_samples = chunk_size * sr
    if chunk_size_in_samples >= audio.shape[-1]:
        chunk_size_in_samples = audio.shape[-1] - 1
    chunks = []
    for i in range(num_chunks):
        start = torch.randint(0, audio.shape[-1] - chunk_size_in_samples, (1,)).item()
        chunks.append(start)
    return chunks, sr


def create_sequential_chunks(
    audio_file: str, chunk_size: int, sample_rate: int
) -> List[torch.Tensor]:
    """Create sequential chunks of size chunk_size from an audio file.
    Return each chunk
    """
    chunks = []
    audio, sr = torchaudio.load(audio_file)
    chunk_starts = torch.arange(0, audio.shape[-1], chunk_size)
    for start in chunk_starts:
        if start + chunk_size > audio.shape[-1]:
            break
        chunk = audio[:, start : start + chunk_size]
        resampled_chunk = torchaudio.functional.resample(chunk, sr, sample_rate)
        # Skip chunks that are too short
        if resampled_chunk.shape[-1] < chunk_size:
            continue
        chunks.append(chunk)
    return chunks


def select_random_chunk(
    audio_file: str, chunk_size: int, sample_rate: int
) -> List[torch.Tensor]:
    """Select random chunk of size chunk_size (samples) from an audio file."""
    audio, sr = torchaudio.load(audio_file)
    new_chunk_size = int(chunk_size * (sr / sample_rate))
    if new_chunk_size >= audio.shape[-1]:
        return None
    max_len = audio.shape[-1] - new_chunk_size
    random_start = torch.randint(0, max_len, (1,)).item()
    chunk = audio[:, random_start : random_start + new_chunk_size]
    # Skip if energy too low
    if torch.mean(torch.abs(chunk)) < 1e-4:
        return None
    resampled_chunk = torchaudio.functional.resample(chunk, sr, sample_rate)
    return resampled_chunk


def spectrogram(
    x: torch.Tensor,
    window: torch.Tensor,
    n_fft: int,
    hop_length: int,
    alpha: float,
) -> torch.Tensor:
    bs, chs, samp = x.size()
    x = x.view(bs * chs, -1)  # move channels onto batch dim

    X = torch.stft(
        x,
        n_fft=n_fft,
        hop_length=hop_length,
        window=window,
        return_complex=True,
    )

    # move channels back
    X = X.view(bs, chs, X.shape[-2], X.shape[-1])

    return torch.pow(X.abs() + 1e-8, alpha)


def init_layer(layer):
    """Initialize a Linear or Convolutional layer."""
    nn.init.xavier_uniform_(layer.weight)

    if hasattr(layer, "bias"):
        if layer.bias is not None:
            layer.bias.data.fill_(0.0)


def init_bn(bn):
    """Initialize a Batchnorm layer."""
    bn.bias.data.fill_(0.0)
    bn.weight.data.fill_(1.0)


def _ntuple(n: int):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple([x] * n)

    return parse


single = _ntuple(1)


def concat_complex(a: torch.tensor, b: torch.tensor, dim: int = 1) -> torch.tensor:
    """
    Concatenate two complex tensors in same dimension concept
    :param a: complex tensor
    :param b: another complex tensor
    :param dim: target dimension
    :return: concatenated tensor
    """
    a_real, a_img = a.chunk(2, dim)
    b_real, b_img = b.chunk(2, dim)
    return torch.cat([a_real, b_real, a_img, b_img], dim=dim)


def center_crop(x, length: int):
    start = (x.shape[-1] - length) // 2
    stop = start + length
    return x[..., start:stop]


def causal_crop(x, length: int):
    stop = x.shape[-1] - 1
    start = stop - length
    return x[..., start:stop]