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import tqdm
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import torch
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import random
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from torch import nn
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from torch.nn import functional as F
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from concurrent.futures import ThreadPoolExecutor
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from .utils import center_trim
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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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class BagOfModels(nn.Module):
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def __init__(self, models, weights = None, segment = None):
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super().__init__()
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assert len(models) > 0
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first = models[0]
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for other in models:
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assert other.sources == first.sources
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assert other.samplerate == first.samplerate
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assert other.audio_channels == first.audio_channels
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if segment is not None: other.segment = segment
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self.audio_channels = first.audio_channels
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self.samplerate = first.samplerate
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self.sources = first.sources
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self.models = nn.ModuleList(models)
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if weights is None: weights = [[1.0 for _ in first.sources] for _ in models]
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else:
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assert len(weights) == len(models)
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for weight in weights:
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assert len(weight) == len(first.sources)
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self.weights = weights
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def forward(self, x):
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pass
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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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length = total_length - offset if length is None else min(total_length - offset, length)
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if isinstance(tensor, TensorChunk):
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self.tensor = tensor.tensor
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self.offset = offset + tensor.offset
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else:
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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): return tensor_or_chunk
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else:
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assert isinstance(tensor_or_chunk, torch.Tensor)
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return TensorChunk(tensor_or_chunk)
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def apply_model(model, mix, shifts=1, split=True, overlap=0.25, transition_power=1.0, static_shifts=1, set_progress_bar=None, device=None, progress=False, num_workers=0, pool=None):
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global fut_length, bag_num, prog_bar
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device = mix.device if device is None else torch.device(device)
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if pool is None: pool = ThreadPoolExecutor(num_workers) if num_workers > 0 and device.type == "cpu" else DummyPoolExecutor()
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kwargs = {
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"shifts": shifts,
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"split": split,
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"overlap": overlap,
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"transition_power": transition_power,
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"progress": progress,
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"device": device,
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"pool": pool,
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"set_progress_bar": set_progress_bar,
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"static_shifts": static_shifts,
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}
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if isinstance(model, BagOfModels):
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estimates, fut_length, prog_bar, current_model = 0, 0, 0, 0
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totals = [0] * len(model.sources)
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bag_num = len(model.models)
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for sub_model, weight in zip(model.models, model.weights):
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original_model_device = next(iter(sub_model.parameters())).device
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sub_model.to(device)
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fut_length += fut_length
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current_model += 1
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out = apply_model(sub_model, mix, **kwargs)
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sub_model.to(original_model_device)
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for k, inst_weight in enumerate(weight):
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out[:, k, :, :] *= inst_weight
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totals[k] += inst_weight
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estimates += out
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del out
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for k in range(estimates.shape[1]):
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estimates[:, k, :, :] /= totals[k]
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return estimates
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model.to(device)
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model.eval()
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assert transition_power >= 1
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batch, channels, length = mix.shape
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if shifts:
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kwargs["shifts"] = 0
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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(model, shifted, **kwargs)
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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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elif split:
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kwargs["split"] = False
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out = torch.zeros(batch, len(model.sources), channels, length, device=mix.device)
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sum_weight = torch.zeros(length, device=mix.device)
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segment = int(model.samplerate * model.segment)
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stride = int((1 - overlap) * segment)
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offsets = range(0, length, stride)
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weight = torch.cat([torch.arange(1, segment // 2 + 1, device=device), torch.arange(segment - segment // 2, 0, -1, device=device)])
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assert len(weight) == segment
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weight = (weight / weight.max()) ** transition_power
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futures = []
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for offset in offsets:
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chunk = TensorChunk(mix, offset, segment)
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future = pool.submit(apply_model, model, chunk, **kwargs)
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futures.append((future, offset))
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offset += segment
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if progress: futures = tqdm.tqdm(futures)
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for future, offset in futures:
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if set_progress_bar:
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fut_length = len(futures) * bag_num * static_shifts
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prog_bar += 1
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set_progress_bar(0.1, (0.8 / fut_length * prog_bar))
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chunk_out = future.result()
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chunk_length = chunk_out.shape[-1]
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out[..., offset : offset + segment] += (weight[:chunk_length] * chunk_out).to(mix.device)
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sum_weight[offset : offset + segment] += weight[:chunk_length].to(mix.device)
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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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else:
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valid_length = model.valid_length(length) if hasattr(model, "valid_length") else length
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mix = tensor_chunk(mix)
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padded_mix = mix.padded(valid_length).to(device)
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with torch.no_grad():
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out = model(padded_mix)
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return center_trim(out, length)
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def demucs_segments(demucs_segment, demucs_model):
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if demucs_segment == "Default":
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segment = None
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if isinstance(demucs_model, BagOfModels):
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if segment is not None:
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for sub in demucs_model.models:
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sub.segment = segment
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else:
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if segment is not None: sub.segment = segment
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else:
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try:
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segment = int(demucs_segment)
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if isinstance(demucs_model, BagOfModels):
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if segment is not None:
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for sub in demucs_model.models:
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sub.segment = segment
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else:
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if segment is not None: sub.segment = segment
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except:
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segment = None
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if isinstance(demucs_model, BagOfModels):
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if segment is not None:
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for sub in demucs_model.models:
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sub.segment = segment
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else:
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if segment is not None: sub.segment = segment
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return demucs_model |
