62 lines
1.3 KiB
Python
62 lines
1.3 KiB
Python
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import torch
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from torch.nn import functional as F
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import modules.commons as commons
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def feature_loss(fmap_r, fmap_g):
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loss = 0
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for dr, dg in zip(fmap_r, fmap_g):
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for rl, gl in zip(dr, dg):
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rl = rl.float().detach()
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gl = gl.float()
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loss += torch.mean(torch.abs(rl - gl))
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return loss * 2
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def discriminator_loss(disc_real_outputs, disc_generated_outputs):
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loss = 0
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r_losses = []
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g_losses = []
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for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
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dr = dr.float()
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dg = dg.float()
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r_loss = torch.mean((1-dr)**2)
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g_loss = torch.mean(dg**2)
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loss += (r_loss + g_loss)
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r_losses.append(r_loss.item())
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g_losses.append(g_loss.item())
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return loss, r_losses, g_losses
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def generator_loss(disc_outputs):
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loss = 0
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gen_losses = []
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for dg in disc_outputs:
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dg = dg.float()
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l = torch.mean((1-dg)**2)
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gen_losses.append(l)
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loss += l
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return loss, gen_losses
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def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
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"""
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z_p, logs_q: [b, h, t_t]
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m_p, logs_p: [b, h, t_t]
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"""
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z_p = z_p.float()
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logs_q = logs_q.float()
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m_p = m_p.float()
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logs_p = logs_p.float()
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z_mask = z_mask.float()
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#print(logs_p)
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kl = logs_p - logs_q - 0.5
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kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
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kl = torch.sum(kl * z_mask)
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l = kl / torch.sum(z_mask)
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return l
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