Initial commit and v1.0

demucs/utils.py

@@ -0,0 +1,141 @@
+# 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 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, *args, **kwargs):
+            self.func = func
+            self.args = args
+            self.kwargs = kwargs
+
+        def result(self):
+            return self.func(*self.args, **self.kwargs)
+
+    def __init__(self, workers=0):
+        pass
+
+    def submit(self, func, *args, **kwargs):
+        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        return