mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-12-19 10:55:56 +01:00
478 lines
15 KiB
Python
478 lines
15 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from collections import defaultdict
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from contextlib import contextmanager
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import math
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import os
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import tempfile
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import typing as tp
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import errno
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import functools
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import hashlib
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import inspect
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import io
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import os
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import random
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import socket
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import tempfile
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import warnings
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import zlib
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from diffq import UniformQuantizer, DiffQuantizer
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import torch as th
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import tqdm
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from torch import distributed
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from torch.nn import functional as F
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import torch
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def unfold(a, kernel_size, stride):
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"""Given input of size [*OT, T], output Tensor of size [*OT, F, K]
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with K the kernel size, by extracting frames with the given stride.
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This will pad the input so that `F = ceil(T / K)`.
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see https://github.com/pytorch/pytorch/issues/60466
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"""
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*shape, length = a.shape
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n_frames = math.ceil(length / stride)
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tgt_length = (n_frames - 1) * stride + kernel_size
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a = F.pad(a, (0, tgt_length - length))
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strides = list(a.stride())
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assert strides[-1] == 1, 'data should be contiguous'
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strides = strides[:-1] + [stride, 1]
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return a.as_strided([*shape, n_frames, kernel_size], strides)
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def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):
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"""
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Center trim `tensor` with respect to `reference`, along the last dimension.
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`reference` can also be a number, representing the length to trim to.
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If the size difference != 0 mod 2, the extra sample is removed on the right side.
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"""
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ref_size: int
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if isinstance(reference, torch.Tensor):
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ref_size = reference.size(-1)
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else:
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ref_size = reference
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delta = tensor.size(-1) - ref_size
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if delta < 0:
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raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
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if delta:
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tensor = tensor[..., delta // 2:-(delta - delta // 2)]
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return tensor
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def pull_metric(history: tp.List[dict], name: str):
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out = []
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for metrics in history:
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metric = metrics
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for part in name.split("."):
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metric = metric[part]
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out.append(metric)
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return out
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def EMA(beta: float = 1):
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"""
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Exponential Moving Average callback.
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Returns a single function that can be called to repeatidly update the EMA
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with a dict of metrics. The callback will return
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the new averaged dict of metrics.
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Note that for `beta=1`, this is just plain averaging.
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"""
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fix: tp.Dict[str, float] = defaultdict(float)
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total: tp.Dict[str, float] = defaultdict(float)
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def _update(metrics: dict, weight: float = 1) -> dict:
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nonlocal total, fix
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for key, value in metrics.items():
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total[key] = total[key] * beta + weight * float(value)
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fix[key] = fix[key] * beta + weight
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return {key: tot / fix[key] for key, tot in total.items()}
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return _update
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def sizeof_fmt(num: float, suffix: str = 'B'):
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"""
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Given `num` bytes, return human readable size.
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Taken from https://stackoverflow.com/a/1094933
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"""
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for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
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if abs(num) < 1024.0:
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return "%3.1f%s%s" % (num, unit, suffix)
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num /= 1024.0
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return "%.1f%s%s" % (num, 'Yi', suffix)
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@contextmanager
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def temp_filenames(count: int, delete=True):
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names = []
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try:
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for _ in range(count):
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names.append(tempfile.NamedTemporaryFile(delete=False).name)
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yield names
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finally:
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if delete:
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for name in names:
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os.unlink(name)
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def average_metric(metric, count=1.):
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"""
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Average `metric` which should be a float across all hosts. `count` should be
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the weight for this particular host (i.e. number of examples).
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"""
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metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
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distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
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return metric[1].item() / metric[0].item()
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def free_port(host='', low=20000, high=40000):
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"""
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Return a port number that is most likely free.
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This could suffer from a race condition although
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it should be quite rare.
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"""
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sock = socket.socket()
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while True:
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port = random.randint(low, high)
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try:
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sock.bind((host, port))
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except OSError as error:
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if error.errno == errno.EADDRINUSE:
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continue
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raise
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return port
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def sizeof_fmt(num, suffix='B'):
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"""
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Given `num` bytes, return human readable size.
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Taken from https://stackoverflow.com/a/1094933
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"""
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for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
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if abs(num) < 1024.0:
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return "%3.1f%s%s" % (num, unit, suffix)
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num /= 1024.0
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return "%.1f%s%s" % (num, 'Yi', suffix)
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def human_seconds(seconds, display='.2f'):
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"""
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Given `seconds` seconds, return human readable duration.
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"""
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value = seconds * 1e6
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ratios = [1e3, 1e3, 60, 60, 24]
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names = ['us', 'ms', 's', 'min', 'hrs', 'days']
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last = names.pop(0)
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for name, ratio in zip(names, ratios):
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if value / ratio < 0.3:
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break
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value /= ratio
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last = name
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return f"{format(value, display)} {last}"
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class TensorChunk:
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def __init__(self, tensor, offset=0, length=None):
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total_length = tensor.shape[-1]
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assert offset >= 0
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assert offset < total_length
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if length is None:
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length = total_length - offset
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else:
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length = min(total_length - offset, length)
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self.tensor = tensor
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self.offset = offset
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self.length = length
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self.device = tensor.device
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@property
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def shape(self):
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shape = list(self.tensor.shape)
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shape[-1] = self.length
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return shape
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def padded(self, target_length):
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delta = target_length - self.length
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total_length = self.tensor.shape[-1]
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assert delta >= 0
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start = self.offset - delta // 2
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end = start + target_length
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correct_start = max(0, start)
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correct_end = min(total_length, end)
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pad_left = correct_start - start
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pad_right = end - correct_end
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out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
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assert out.shape[-1] == target_length
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return out
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def tensor_chunk(tensor_or_chunk):
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if isinstance(tensor_or_chunk, TensorChunk):
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return tensor_or_chunk
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else:
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assert isinstance(tensor_or_chunk, th.Tensor)
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return TensorChunk(tensor_or_chunk)
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def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
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"""
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Apply model to a given mixture.
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Args:
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shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
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and apply the oppositve shift to the output. This is repeated `shifts` time and
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all predictions are averaged. This effectively makes the model time equivariant
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and improves SDR by up to 0.2 points.
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split (bool): if True, the input will be broken down in 8 seconds extracts
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and predictions will be performed individually on each and concatenated.
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Useful for model with large memory footprint like Tasnet.
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progress (bool): if True, show a progress bar (requires split=True)
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"""
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channels, length = mix.size()
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device = mix.device
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if split:
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out = th.zeros(4, channels, length, device=device)
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shift = model.samplerate * 10
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offsets = range(0, length, shift)
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scale = 10
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if progress:
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offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
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for offset in offsets:
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chunk = mix[..., offset:offset + shift]
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chunk_out = apply_model_v1(model, chunk, shifts=shifts)
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out[..., offset:offset + shift] = chunk_out
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offset += shift
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return out
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elif shifts:
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max_shift = int(model.samplerate / 2)
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mix = F.pad(mix, (max_shift, max_shift))
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offsets = list(range(max_shift))
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random.shuffle(offsets)
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out = 0
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for offset in offsets[:shifts]:
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shifted = mix[..., offset:offset + length + max_shift]
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shifted_out = apply_model_v1(model, shifted)
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out += shifted_out[..., max_shift - offset:max_shift - offset + length]
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out /= shifts
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return out
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else:
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valid_length = model.valid_length(length)
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delta = valid_length - length
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padded = F.pad(mix, (delta // 2, delta - delta // 2))
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with th.no_grad():
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out = model(padded.unsqueeze(0))[0]
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return center_trim(out, mix)
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def apply_model_v2(model, mix, shifts=None, split=False,
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overlap=0.25, transition_power=1., progress=False):
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"""
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Apply model to a given mixture.
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Args:
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shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
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and apply the oppositve shift to the output. This is repeated `shifts` time and
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all predictions are averaged. This effectively makes the model time equivariant
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and improves SDR by up to 0.2 points.
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split (bool): if True, the input will be broken down in 8 seconds extracts
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and predictions will be performed individually on each and concatenated.
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Useful for model with large memory footprint like Tasnet.
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progress (bool): if True, show a progress bar (requires split=True)
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"""
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assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
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device = mix.device
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channels, length = mix.shape
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if split:
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out = th.zeros(len(model.sources), channels, length, device=device)
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sum_weight = th.zeros(length, device=device)
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segment = model.segment_length
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stride = int((1 - overlap) * segment)
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offsets = range(0, length, stride)
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scale = stride / model.samplerate
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if progress:
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offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
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# We start from a triangle shaped weight, with maximal weight in the middle
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# of the segment. Then we normalize and take to the power `transition_power`.
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# Large values of transition power will lead to sharper transitions.
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weight = th.cat([th.arange(1, segment // 2 + 1),
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th.arange(segment - segment // 2, 0, -1)]).to(device)
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assert len(weight) == segment
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# If the overlap < 50%, this will translate to linear transition when
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# transition_power is 1.
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weight = (weight / weight.max())**transition_power
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for offset in offsets:
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chunk = TensorChunk(mix, offset, segment)
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chunk_out = apply_model_v2(model, chunk, shifts=shifts)
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chunk_length = chunk_out.shape[-1]
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out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out
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sum_weight[offset:offset + segment] += weight[:chunk_length]
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offset += segment
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assert sum_weight.min() > 0
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out /= sum_weight
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return out
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elif shifts:
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max_shift = int(0.5 * model.samplerate)
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mix = tensor_chunk(mix)
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padded_mix = mix.padded(length + 2 * max_shift)
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out = 0
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for _ in range(shifts):
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offset = random.randint(0, max_shift)
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shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
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shifted_out = apply_model_v2(model, shifted)
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out += shifted_out[..., max_shift - offset:]
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out /= shifts
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return out
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else:
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valid_length = model.valid_length(length)
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mix = tensor_chunk(mix)
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padded_mix = mix.padded(valid_length)
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with th.no_grad():
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out = model(padded_mix.unsqueeze(0))[0]
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return center_trim(out, length)
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@contextmanager
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def temp_filenames(count, delete=True):
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names = []
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try:
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for _ in range(count):
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names.append(tempfile.NamedTemporaryFile(delete=False).name)
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yield names
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finally:
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if delete:
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for name in names:
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os.unlink(name)
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def get_quantizer(model, args, optimizer=None):
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quantizer = None
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if args.diffq:
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quantizer = DiffQuantizer(
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model, min_size=args.q_min_size, group_size=8)
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if optimizer is not None:
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quantizer.setup_optimizer(optimizer)
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elif args.qat:
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quantizer = UniformQuantizer(
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model, bits=args.qat, min_size=args.q_min_size)
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return quantizer
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def load_model(path, strict=False):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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load_from = path
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package = th.load(load_from, 'cpu')
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klass = package["klass"]
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args = package["args"]
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kwargs = package["kwargs"]
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if strict:
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model = klass(*args, **kwargs)
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else:
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sig = inspect.signature(klass)
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for key in list(kwargs):
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if key not in sig.parameters:
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warnings.warn("Dropping inexistant parameter " + key)
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del kwargs[key]
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model = klass(*args, **kwargs)
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state = package["state"]
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training_args = package["training_args"]
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quantizer = get_quantizer(model, training_args)
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set_state(model, quantizer, state)
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return model
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def get_state(model, quantizer):
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if quantizer is None:
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state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
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else:
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state = quantizer.get_quantized_state()
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buf = io.BytesIO()
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th.save(state, buf)
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state = {'compressed': zlib.compress(buf.getvalue())}
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return state
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def set_state(model, quantizer, state):
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if quantizer is None:
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model.load_state_dict(state)
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else:
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buf = io.BytesIO(zlib.decompress(state["compressed"]))
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state = th.load(buf, "cpu")
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quantizer.restore_quantized_state(state)
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return state
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def save_state(state, path):
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buf = io.BytesIO()
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th.save(state, buf)
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sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
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path = path.parent / (path.stem + "-" + sig + path.suffix)
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path.write_bytes(buf.getvalue())
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def save_model(model, quantizer, training_args, path):
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args, kwargs = model._init_args_kwargs
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klass = model.__class__
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state = get_state(model, quantizer)
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save_to = path
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package = {
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'klass': klass,
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'args': args,
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'kwargs': kwargs,
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'state': state,
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'training_args': training_args,
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}
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th.save(package, save_to)
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def capture_init(init):
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@functools.wraps(init)
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def __init__(self, *args, **kwargs):
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self._init_args_kwargs = (args, kwargs)
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init(self, *args, **kwargs)
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return __init__
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class DummyPoolExecutor:
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class DummyResult:
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def __init__(self, func, *args, **kwargs):
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self.func = func
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self.args = args
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self.kwargs = kwargs
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def result(self):
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return self.func(*self.args, **self.kwargs)
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def __init__(self, workers=0):
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pass
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def submit(self, func, *args, **kwargs):
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return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, exc_tb):
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return
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