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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 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 slice_segments2(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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