import numpy as np
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import torch
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import torch.nn.functional as F
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from itertools import permutations
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from torch import nn
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def standard_loss(ys, ts, label_delay=0):
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losses = [F.binary_cross_entropy(torch.sigmoid(y), t) * len(y) for y, t in zip(ys, ts)]
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loss = torch.sum(torch.stack(losses))
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n_frames = torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))).to(torch.float32).to(ys[0].device) # 计算总的帧数
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loss = loss / n_frames
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return loss
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def batch_pit_loss(ys, ts, label_delay=0):
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loss_w_labels = [pit_loss(y, t)
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for (y, t) in zip(ys, ts)]
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losses, labels = zip(*loss_w_labels)
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loss = torch.sum(torch.stack(losses))
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n_frames = torch.sum(torch.stack([t.shape[0] for t in ts]))
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loss = loss / n_frames
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return loss, labels
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def batch_pit_n_speaker_loss(ys, ts, n_speakers_list):
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max_n_speakers = ts[0].shape[1]
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olens = [y.shape[0] for y in ys]
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ys = nn.utils.rnn.pad_sequence(ys, batch_first=True, padding_value=-1)
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ys_mask = [torch.ones(olen).to(ys.device) for olen in olens]
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ys_mask = torch.nn.utils.rnn.pad_sequence(ys_mask, batch_first=True, padding_value=0).unsqueeze(-1)
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losses = []
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for shift in range(max_n_speakers):
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ts_roll = [torch.roll(t, -shift, dims=1) for t in ts]
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ts_roll = nn.utils.rnn.pad_sequence(ts_roll, batch_first=True, padding_value=-1)
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loss = F.binary_cross_entropy(torch.sigmoid(ys), ts_roll, reduction='none')
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if ys_mask is not None:
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loss = loss * ys_mask
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loss = torch.sum(loss, dim=1)
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losses.append(loss)
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losses = torch.stack(losses, dim=2)
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perms = np.array(list(permutations(range(max_n_speakers)))).astype(np.float32)
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perms = torch.from_numpy(perms).to(losses.device)
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y_ind = torch.arange(max_n_speakers, dtype=torch.float32, device=losses.device)
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t_inds = torch.fmod(perms - y_ind, max_n_speakers).to(torch.long)
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losses_perm = []
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for t_ind in t_inds:
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losses_perm.append(
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torch.mean(losses[:, y_ind.to(torch.long), t_ind], dim=1))
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losses_perm = torch.stack(losses_perm, dim=1)
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def select_perm_indices(num, max_num):
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perms = list(permutations(range(max_num)))
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sub_perms = list(permutations(range(num)))
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return [
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[x[:num] for x in perms].index(perm)
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for perm in sub_perms]
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masks = torch.full_like(losses_perm, device=losses.device, fill_value=float('inf'))
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for i, t in enumerate(ts):
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n_speakers = n_speakers_list[i]
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indices = select_perm_indices(n_speakers, max_n_speakers)
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masks[i, indices] = 0
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losses_perm += masks
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min_loss = torch.sum(torch.min(losses_perm, dim=1)[0])
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n_frames = torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))).to(losses.device)
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min_loss = min_loss / n_frames
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min_indices = torch.argmin(losses_perm, dim=1)
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labels_perm = [t[:, perms[idx].to(torch.long)] for t, idx in zip(ts, min_indices)]
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labels_perm = [t[:, :n_speakers] for t, n_speakers in zip(labels_perm, n_speakers_list)]
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return min_loss, labels_perm
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