heimoshuiyu/ultimatevocalremovergui
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Delete demucs directory

Browse Source
This commit is contained in:
Anjok07
2022-06-13 02:10:01 -05:00
committed by GitHub
parent 50533ec73c
commit b16a422b39
13 changed files with 0 additions and 2580 deletions
Download Patch File Download Diff File Expand all files Collapse all files

323
demucs/utils.py
View File

@@ -1,323 +0,0 @@
# 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 errno
import functools
import hashlib
import inspect
import io
import os
import random
import socket
import tempfile
import warnings
import zlib
from contextlib import contextmanager
from diffq import UniformQuantizer, DiffQuantizer
import torch as th
import tqdm
from torch import distributed
from torch.nn import functional as F
def center_trim(tensor, reference):
"""
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.
"""
if hasattr(reference, "size"):
reference = reference.size(-1)
delta = tensor.size(-1) - reference
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 average_metric(metric, count=1.):
"""
Average `metric` which should be a float across all hosts. `count` should be
the weight for this particular host (i.e. number of examples).
"""
metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
return metric[1].item() / metric[0].item()
def free_port(host='', low=20000, high=40000):
"""
Return a port number that is most likely free.
This could suffer from a race condition although
it should be quite rare.
"""
sock = socket.socket()
while True:
port = random.randint(low, high)
try:
sock.bind((host, port))
except OSError as error:
if error.errno == errno.EADDRINUSE:
continue
raise
return port
def sizeof_fmt(num, suffix='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)
def human_seconds(seconds, display='.2f'):
"""
Given `seconds` seconds, return human readable duration.
"""
value = seconds * 1e6
ratios = [1e3, 1e3, 60, 60, 24]
names = ['us', 'ms', 's', 'min', 'hrs', 'days']
last = names.pop(0)
for name, ratio in zip(names, ratios):
if value / ratio < 0.3:
break
value /= ratio
last = name
return f"{format(value, display)} {last}"
class TensorChunk:
def __init__(self, tensor, offset=0, length=None):
total_length = tensor.shape[-1]
assert offset >= 0
assert offset < total_length
if length is None:
length = total_length - offset
else:
length = min(total_length - offset, length)
self.tensor = tensor
self.offset = offset
self.length = length
self.device = tensor.device
@property
def shape(self):
shape = list(self.tensor.shape)
shape[-1] = self.length
return shape
def padded(self, target_length):
delta = target_length - self.length
total_length = self.tensor.shape[-1]
assert delta >= 0
start = self.offset - delta // 2
end = start + target_length
correct_start = max(0, start)
correct_end = min(total_length, end)
pad_left = correct_start - start
pad_right = end - correct_end
out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
assert out.shape[-1] == target_length
return out
def tensor_chunk(tensor_or_chunk):
if isinstance(tensor_or_chunk, TensorChunk):
return tensor_or_chunk
else:
assert isinstance(tensor_or_chunk, th.Tensor)
return TensorChunk(tensor_or_chunk)
def apply_model(model, mix, shifts=None, split=False,
overlap=0.25, transition_power=1., progress=False):
"""
Apply model to a given mixture.
Args:
shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
and apply the oppositve shift to the output. This is repeated `shifts` time and
all predictions are averaged. This effectively makes the model time equivariant
and improves SDR by up to 0.2 points.
split (bool): if True, the input will be broken down in 8 seconds extracts
and predictions will be performed individually on each and concatenated.
Useful for model with large memory footprint like Tasnet.
progress (bool): if True, show a progress bar (requires split=True)
"""
assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
device = mix.device
channels, length = mix.shape
if split:
out = th.zeros(len(model.sources), channels, length, device=device)
sum_weight = th.zeros(length, device=device)
segment = model.segment_length
stride = int((1 - overlap) * segment)
offsets = range(0, length, stride)
scale = stride / model.samplerate
if progress:
offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
# We start from a triangle shaped weight, with maximal weight in the middle
# of the segment. Then we normalize and take to the power `transition_power`.
# Large values of transition power will lead to sharper transitions.
weight = th.cat([th.arange(1, segment // 2 + 1),
th.arange(segment - segment // 2, 0, -1)]).to(device)
assert len(weight) == segment
# If the overlap < 50%, this will translate to linear transition when
# transition_power is 1.
weight = (weight / weight.max())**transition_power
for offset in offsets:
chunk = TensorChunk(mix, offset, segment)
chunk_out = apply_model(model, chunk, shifts=shifts)
chunk_length = chunk_out.shape[-1]
out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out
sum_weight[offset:offset + segment] += weight[:chunk_length]
offset += segment
assert sum_weight.min() > 0
out /= sum_weight
return out
elif shifts:
max_shift = int(0.5 * model.samplerate)
mix = tensor_chunk(mix)
padded_mix = mix.padded(length + 2 * max_shift)
out = 0
for _ in range(shifts):
offset = random.randint(0, max_shift)
shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
shifted_out = apply_model(model, shifted)
out += shifted_out[..., max_shift - offset:]
out /= shifts
return out
else:
valid_length = model.valid_length(length)
mix = tensor_chunk(mix)
padded_mix = mix.padded(valid_length)
with th.no_grad():
out = model(padded_mix.unsqueeze(0))[0]
return center_trim(out, length)
@contextmanager
def temp_filenames(count, 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 get_quantizer(model, args, optimizer=None):
quantizer = None
if args.diffq:
quantizer = DiffQuantizer(
model, min_size=args.q_min_size, group_size=8)
if optimizer is not None:
quantizer.setup_optimizer(optimizer)
elif args.qat:
quantizer = UniformQuantizer(
model, bits=args.qat, min_size=args.q_min_size)
return quantizer
def load_model(path, strict=False):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
load_from = path
package = th.load(load_from, 'cpu')
klass = package["klass"]
args = package["args"]
kwargs = package["kwargs"]
if strict:
model = klass(*args, **kwargs)
else:
sig = inspect.signature(klass)
for key in list(kwargs):
if key not in sig.parameters:
warnings.warn("Dropping inexistant parameter " + key)
del kwargs[key]
model = klass(*args, **kwargs)
state = package["state"]
training_args = package["training_args"]
quantizer = get_quantizer(model, training_args)
set_state(model, quantizer, state)
return model
def get_state(model, quantizer):
if quantizer is None:
state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
else:
state = quantizer.get_quantized_state()
buf = io.BytesIO()
th.save(state, buf)
state = {'compressed': zlib.compress(buf.getvalue())}
return state
def set_state(model, quantizer, state):
if quantizer is None:
model.load_state_dict(state)
else:
buf = io.BytesIO(zlib.decompress(state["compressed"]))
state = th.load(buf, "cpu")
quantizer.restore_quantized_state(state)
return state
def save_state(state, path):
buf = io.BytesIO()
th.save(state, buf)
sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
path = path.parent / (path.stem + "-" + sig + path.suffix)
path.write_bytes(buf.getvalue())
def save_model(model, quantizer, training_args, path):
args, kwargs = model._init_args_kwargs
klass = model.__class__
state = get_state(model, quantizer)
save_to = path
package = {
'klass': klass,
'args': args,
'kwargs': kwargs,
'state': state,
'training_args': training_args,
}
th.save(package, save_to)
def capture_init(init):
@functools.wraps(init)
def __init__(self, *args, **kwargs):
self._init_args_kwargs = (args, kwargs)
init(self, *args, **kwargs)
return __init__