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import math |
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import numpy as np |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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def init_weights(m, mean=0.0, std=0.01): |
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classname = m.__class__.__name__ |
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if classname.find("Conv") != -1: |
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m.weight.data.normal_(mean, std) |
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def get_padding(kernel_size, dilation=1): |
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return int((kernel_size * dilation - dilation) / 2) |
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def convert_pad_shape(pad_shape): |
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l = pad_shape[::-1] |
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pad_shape = [item for sublist in l for item in sublist] |
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return pad_shape |
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def intersperse(lst, item): |
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result = [item] * (len(lst) * 2 + 1) |
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result[1::2] = lst |
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return result |
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def kl_divergence(m_p, logs_p, m_q, logs_q): |
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"""KL(P||Q)""" |
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kl = (logs_q - logs_p) - 0.5 |
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kl += ( |
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0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) |
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) |
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return kl |
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def rand_gumbel(shape): |
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"""Sample from the Gumbel distribution, protect from overflows.""" |
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uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 |
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return -torch.log(-torch.log(uniform_samples)) |
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def rand_gumbel_like(x): |
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g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) |
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return g |
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def slice_segments(x, ids_str, segment_size=4): |
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ret = torch.zeros_like(x[:, :, :segment_size]) |
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for i in range(x.size(0)): |
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idx_str = ids_str[i] |
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idx_end = idx_str + segment_size |
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ret[i] = x[i, :, idx_str:idx_end] |
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return ret |
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def rand_slice_segments(x, x_lengths=None, segment_size=4): |
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b, d, t = x.size() |
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if x_lengths is None: |
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x_lengths = t |
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ids_str_max = x_lengths - segment_size + 1 |
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ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) |
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ret = slice_segments(x, ids_str, segment_size) |
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return ret, ids_str |
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def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): |
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position = torch.arange(length, dtype=torch.float) |
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num_timescales = channels // 2 |
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log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( |
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num_timescales - 1 |
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) |
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inv_timescales = min_timescale * torch.exp( |
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torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment |
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) |
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scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) |
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signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) |
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signal = F.pad(signal, [0, 0, 0, channels % 2]) |
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signal = signal.view(1, channels, length) |
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return signal |
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def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): |
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b, channels, length = x.size() |
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signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) |
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return x + signal.to(dtype=x.dtype, device=x.device) |
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def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): |
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b, channels, length = x.size() |
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signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) |
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return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) |
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def subsequent_mask(length): |
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mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) |
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return mask |
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@torch.jit.script |
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): |
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n_channels_int = n_channels[0] |
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in_act = input_a + input_b |
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t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
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acts = t_act * s_act |
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return acts |
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def convert_pad_shape(pad_shape): |
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l = pad_shape[::-1] |
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pad_shape = [item for sublist in l for item in sublist] |
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return pad_shape |
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def shift_1d(x): |
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x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] |
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return x |
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def sequence_mask(length, max_length=None): |
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if max_length is None: |
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max_length = length.max() |
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x = torch.arange(max_length, dtype=length.dtype, device=length.device) |
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return x.unsqueeze(0) < length.unsqueeze(1) |
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def generate_path(duration, mask): |
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""" |
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duration: [b, 1, t_x] |
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mask: [b, 1, t_y, t_x] |
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""" |
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device = duration.device |
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b, _, t_y, t_x = mask.shape |
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cum_duration = torch.cumsum(duration, -1) |
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cum_duration_flat = cum_duration.view(b * t_x) |
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path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) |
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path = path.view(b, t_x, t_y) |
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path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] |
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path = path.unsqueeze(1).transpose(2, 3) * mask |
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return path |
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def clip_grad_value_(parameters, clip_value, norm_type=2): |
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if isinstance(parameters, torch.Tensor): |
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parameters = [parameters] |
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parameters = list(filter(lambda p: p.grad is not None, parameters)) |
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norm_type = float(norm_type) |
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if clip_value is not None: |
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clip_value = float(clip_value) |
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total_norm = 0 |
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for p in parameters: |
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param_norm = p.grad.data.norm(norm_type) |
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total_norm += param_norm.item() ** norm_type |
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if clip_value is not None: |
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p.grad.data.clamp_(min=-clip_value, max=clip_value) |
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total_norm = total_norm ** (1.0 / norm_type) |
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return total_norm |
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