demucs/utils.py

# Copyright (c) Meta Platforms, Inc. and 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.

from collections import defaultdict
from concurrent.futures import CancelledError
from contextlib import contextmanager
import math
import os
import tempfile
import typing as tp

import torch
from torch.nn import functional as F
from torch.utils.data import Subset


def unfold(a, kernel_size, stride):
    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
    with K the kernel size, by extracting frames with the given stride.

    This will pad the input so that `F = ceil(T / K)`.

    see https://github.com/pytorch/pytorch/issues/60466
    """
    *shape, length = a.shape
    n_frames = math.ceil(length / stride)
    tgt_length = (n_frames - 1) * stride + kernel_size
    a = F.pad(a, (0, tgt_length - length))
    strides = list(a.stride())
    assert strides[-1] == 1, 'data should be contiguous'
    strides = strides[:-1] + [stride, 1]
    return a.as_strided([*shape, n_frames, kernel_size], strides)


def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):
    """
    Center trim `tensor` with respect to `reference`, along the last dimension.
    `reference` can also be a number, representing the length to trim to.
    If the size difference != 0 mod 2, the extra sample is removed on the right side.
    """
    ref_size: int
    if isinstance(reference, torch.Tensor):
        ref_size = reference.size(-1)
    else:
        ref_size = reference
    delta = tensor.size(-1) - ref_size
    if delta < 0:
        raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
    if delta:
        tensor = tensor[..., delta // 2:-(delta - delta // 2)]
    return tensor


def pull_metric(history: tp.List[dict], name: str):
    out = []
    for metrics in history:
        metric = metrics
        for part in name.split("."):
            metric = metric[part]
        out.append(metric)
    return out


def EMA(beta: float = 1):
    """
    Exponential Moving Average callback.
    Returns a single function that can be called to repeatidly update the EMA
    with a dict of metrics. The callback will return
    the new averaged dict of metrics.

    Note that for `beta=1`, this is just plain averaging.
    """
    fix: tp.Dict[str, float] = defaultdict(float)
    total: tp.Dict[str, float] = defaultdict(float)

    def _update(metrics: dict, weight: float = 1) -> dict:
        nonlocal total, fix
        for key, value in metrics.items():
            total[key] = total[key] * beta + weight * float(value)
            fix[key] = fix[key] * beta + weight
        return {key: tot / fix[key] for key, tot in total.items()}
    return _update


def sizeof_fmt(num: float, suffix: str = 'B'):
    """
    Given `num` bytes, return human readable size.
    Taken from https://stackoverflow.com/a/1094933
    """
    for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
        if abs(num) < 1024.0:
            return "%3.1f%s%s" % (num, unit, suffix)
        num /= 1024.0
    return "%.1f%s%s" % (num, 'Yi', suffix)


@contextmanager
def temp_filenames(count: int, delete=True):
    names = []
    try:
        for _ in range(count):
            names.append(tempfile.NamedTemporaryFile(delete=False).name)
        yield names
    finally:
        if delete:
            for name in names:
                os.unlink(name)


def random_subset(dataset, max_samples: int, seed: int = 42):
    if max_samples >= len(dataset):
        return dataset

    generator = torch.Generator().manual_seed(seed)
    perm = torch.randperm(len(dataset), generator=generator)
    return Subset(dataset, perm[:max_samples].tolist())


class DummyPoolExecutor:
    class DummyResult:
        def __init__(self, func, _dict, *args, **kwargs):
            self.func = func
            self._dict = _dict
            self.args = args
            self.kwargs = kwargs

        def result(self):
            if self._dict["run"]:
                return self.func(*self.args, **self.kwargs)
            else:
                raise CancelledError()

    def __init__(self, workers=0):
        self._dict = {"run": True}

    def submit(self, func, *args, **kwargs):
        return DummyPoolExecutor.DummyResult(func, self._dict, *args, **kwargs)

    def shutdown(self, *_, **__):
        self._dict["run"] = False

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, exc_tb):
        return