src/sbv2/commons.py

import math
import torch
import torch.nn.functional as F
import json

def init_weights(m):
    if isinstance(m, torch.nn.Conv1d):
        torch.nn.init.xavier_uniform_(m.weight, gain=torch.nn.init.calculate_gain('relu'))
        if m.bias is not None:
            torch.nn.init.zeros_(m.bias)
    elif isinstance(m, torch.nn.ConvTranspose1d):
        torch.nn.init.xavier_uniform_(m.weight, gain=torch.nn.init.calculate_gain('relu'))
        if m.bias is not None:
            torch.nn.init.zeros_(m.bias)
    elif isinstance(m, torch.nn.Linear):
        torch.nn.init.xavier_uniform_(m.weight, gain=torch.nn.init.calculate_gain('relu'))
        if m.bias is not None:
            torch.nn.init.zeros_(m.bias)

def kl_divergence(m_p, logs_p, m_q, logs_q):
    """KLダイバージェンス（確率分布の違い）を計算"""
    return 0.5 * (logs_q - logs_p - 1 + (torch.exp(2 * logs_p) + (m_p - m_q) ** 2) / torch.exp(2 * logs_q))

def rand_gumbel(shape):
    """ガンベル分布から乱数をサンプリング"""
    return -math.log(-math.log(torch.rand(shape, device="cpu").clamp(1e-5, 1 - 1e-5)))

def rand_uniform(shape):
    """一様分布から乱数をサンプリング"""
    return torch.rand(shape, device="cpu")

def rand_logistic(shape):
    """ロジスティック分布から乱数をサンプリング"""
    return torch.distributions.RelaxedOneHotCategorical(1.0, logits=torch.zeros(shape)).sample()

def slice_segments(x, ids_str, segment_size=4):
    """入力テンソルxからids_strをもとにセグメントをスライス"""
    ret = []
    for i, ids in enumerate(ids_str):
        start = ids * segment_size
        ret.append(x[i, :, start: start + segment_size])
    return torch.stack(ret)

def rand_slice_segments(x, x_lengths=None, segment_size=4):
    """ランダムにセグメントをスライス"""
    b, d, t = x.size()
    if x_lengths is None:
        ids_str = torch.randint(0, t - segment_size, (b,), device=x.device)
    else:
        ids_str = (torch.rand(b, device=x.device) * (x_lengths - segment_size)).long()
    return slice_segments(x, ids_str, segment_size)

def get_hparams_from_file(config_path):
    """設定ファイル(config.json)を読み込んで辞書型に変換"""
    with open(config_path, "r", encoding="utf-8") as f:
        data = f.read()
    config = json.loads(data)
    return config