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| import math | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| def init_weights(m, mean=0.0, std=0.01): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(mean, std) | |
| def get_padding(kernel_size, dilation=1): | |
| return int((kernel_size * dilation - dilation) / 2) | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def kl_divergence(m_p, logs_p, m_q, logs_q): | |
| """KL(P||Q)""" | |
| kl = (logs_q - logs_p) - 0.5 | |
| kl += ( | |
| 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) | |
| ) | |
| return kl | |
| def rand_gumbel(shape): | |
| """Sample from the Gumbel distribution, protect from overflows.""" | |
| uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 | |
| return -torch.log(-torch.log(uniform_samples)) | |
| def rand_gumbel_like(x): | |
| g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) | |
| return g | |
| def slice_segments(x, ids_str, segment_size=4): | |
| ret = torch.zeros_like(x[:, :, :segment_size]) | |
| for i in range(x.size(0)): | |
| idx_str = ids_str[i] | |
| idx_end = idx_str + segment_size | |
| ret[i] = x[i, :, idx_str:idx_end] | |
| return ret | |
| def slice_segments2(x, ids_str, segment_size=4): | |
| ret = torch.zeros_like(x[:, :segment_size]) | |
| for i in range(x.size(0)): | |
| idx_str = ids_str[i] | |
| idx_end = idx_str + segment_size | |
| ret[i] = x[i, idx_str:idx_end] | |
| return ret | |
| def rand_slice_segments(x, x_lengths=None, segment_size=4): | |
| b, d, t = x.size() | |
| if x_lengths is None: | |
| x_lengths = t | |
| ids_str_max = x_lengths - segment_size + 1 | |
| ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) | |
| ret = slice_segments(x, ids_str, segment_size) | |
| return ret, ids_str | |
| def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): | |
| position = torch.arange(length, dtype=torch.float) | |
| num_timescales = channels // 2 | |
| log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( | |
| num_timescales - 1 | |
| ) | |
| inv_timescales = min_timescale * torch.exp( | |
| torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment | |
| ) | |
| scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) | |
| signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) | |
| signal = F.pad(signal, [0, 0, 0, channels % 2]) | |
| signal = signal.view(1, channels, length) | |
| return signal | |
| def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): | |
| b, channels, length = x.size() | |
| signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) | |
| return x + signal.to(dtype=x.dtype, device=x.device) | |
| def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): | |
| b, channels, length = x.size() | |
| signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) | |
| return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) | |
| def subsequent_mask(length): | |
| mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) | |
| return mask | |
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): | |
| n_channels_int = n_channels[0] | |
| in_act = input_a + input_b | |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def shift_1d(x): | |
| x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] | |
| return x | |
| def sequence_mask(length, max_length=None): | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(max_length, dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| def generate_path(duration, mask): | |
| """ | |
| duration: [b, 1, t_x] | |
| mask: [b, 1, t_y, t_x] | |
| """ | |
| device = duration.device | |
| b, _, t_y, t_x = mask.shape | |
| cum_duration = torch.cumsum(duration, -1) | |
| cum_duration_flat = cum_duration.view(b * t_x) | |
| path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) | |
| path = path.view(b, t_x, t_y) | |
| path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] | |
| path = path.unsqueeze(1).transpose(2, 3) * mask | |
| return path | |
| def clip_grad_value_(parameters, clip_value, norm_type=2): | |
| if isinstance(parameters, torch.Tensor): | |
| parameters = [parameters] | |
| parameters = list(filter(lambda p: p.grad is not None, parameters)) | |
| norm_type = float(norm_type) | |
| if clip_value is not None: | |
| clip_value = float(clip_value) | |
| total_norm = 0 | |
| for p in parameters: | |
| param_norm = p.grad.data.norm(norm_type) | |
| total_norm += param_norm.item() ** norm_type | |
| if clip_value is not None: | |
| p.grad.data.clamp_(min=-clip_value, max=clip_value) | |
| total_norm = total_norm ** (1.0 / norm_type) | |
| return total_norm | |