modules/losses.py

import paddle 
from paddle.nn import functional as F

import modules.commons as commons


def feature_loss(fmap_r, fmap_g):
  loss = 0
  for dr, dg in zip(fmap_r, fmap_g):
    for rl, gl in zip(dr, dg):
      rl = rl.astype('float32').detach()
      gl = gl.astype('float32')
      loss += paddle.mean(paddle.abs(rl - gl))

  return loss * 2 


def discriminator_loss(disc_real_outputs, disc_generated_outputs):
  loss = 0
  r_losses = []
  g_losses = []
  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
    dr = dr.astype('float32')
    dg = dg.astype('float32')
    r_loss = paddle.mean((1-dr)**2)
    g_loss = paddle.mean(dg**2)
    loss += (r_loss + g_loss)
    r_losses.append(r_loss.item())
    g_losses.append(g_loss.item())

  return loss, r_losses, g_losses


def generator_loss(disc_outputs):
  loss = 0
  gen_losses = []
  for dg in disc_outputs:
    dg = dg.astype('float32')
    l = paddle.mean((1-dg)**2)
    gen_losses.append(l)
    loss += l

  return loss, gen_losses


def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
  """
  z_p, logs_q: [b, h, t_t]
  m_p, logs_p: [b, h, t_t]
  """
  z_p = z_p.astype('float32')
  logs_q = logs_q.astype('float32')
  m_p = m_p.astype('float32')
  logs_p = logs_p.astype('float32')
  z_mask = z_mask.astype('float32')
  #print(logs_p)
  kl = logs_p - logs_q - 0.5
  kl += 0.5 * ((z_p - m_p)**2) * paddle.exp(-2. * logs_p)
  kl = paddle.sum(kl * z_mask)
  l = kl / paddle.sum(z_mask)
  return l