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loss.py
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loss.py
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# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import torch
import torch.nn as nn
from torch.nn import functional as F
from common import get_mask_from_lengths
def compute_flow_loss(z, log_det_W_list, log_s_list, n_elements, n_dims, mask,
sigma=1.0):
log_det_W_total = 0.0
for i, log_s in enumerate(log_s_list):
if i == 0:
log_s_total = torch.sum(log_s * mask)
if len(log_det_W_list):
log_det_W_total = log_det_W_list[i]
else:
log_s_total = log_s_total + torch.sum(log_s * mask)
if len(log_det_W_list):
log_det_W_total += log_det_W_list[i]
if len(log_det_W_list):
log_det_W_total *= n_elements
z = z * mask
prior_NLL = torch.sum(z*z)/(2*sigma*sigma)
loss = prior_NLL - log_s_total - log_det_W_total
denom = n_elements * n_dims
loss = loss / denom
loss_prior = prior_NLL / denom
return loss, loss_prior
def compute_regression_loss(x_hat, x, mask, name=False):
x = x[:, None] if len(x.shape) == 2 else x # add channel dim
mask = mask[:, None] if len(mask.shape) == 2 else mask # add channel dim
assert len(x.shape) == len(mask.shape)
x = x * mask
x_hat = x_hat * mask
if name == 'vpred':
loss = F.binary_cross_entropy_with_logits(x_hat, x, reduction='sum')
else:
loss = F.mse_loss(x_hat, x, reduction='sum')
loss = loss / mask.sum()
loss_dict = {"loss_{}".format(name): loss}
return loss_dict
class AttributePredictionLoss(torch.nn.Module):
def __init__(self, name, model_config, loss_weight, sigma=1.0):
super(AttributePredictionLoss, self).__init__()
self.name = name
self.sigma = sigma
self.model_name = model_config['name']
self.loss_weight = loss_weight
self.n_group_size = 1
if 'n_group_size' in model_config['hparams']:
self.n_group_size = model_config['hparams']['n_group_size']
def forward(self, model_output, lens):
mask = get_mask_from_lengths(lens // self.n_group_size)
mask = mask[:, None].float()
loss_dict = {}
if 'z' in model_output:
n_elements = lens.sum() // self.n_group_size
n_dims = model_output['z'].size(1)
loss, loss_prior = compute_flow_loss(
model_output['z'], model_output['log_det_W_list'],
model_output['log_s_list'], n_elements, n_dims, mask,
self.sigma)
loss_dict = {"loss_{}".format(self.name): (loss, self.loss_weight),
"loss_prior_{}".format(self.name): (loss_prior, 0.0)}
elif 'x_hat' in model_output:
loss_dict = compute_regression_loss(
model_output['x_hat'], model_output['x'], mask, self.name)
for k, v in loss_dict.items():
loss_dict[k] = (v, self.loss_weight)
if len(loss_dict) == 0:
raise Exception("loss not supported")
return loss_dict
class AttentionCTCLoss(torch.nn.Module):
def __init__(self, blank_logprob=-1):
super(AttentionCTCLoss, self).__init__()
self.log_softmax = torch.nn.LogSoftmax(dim=3)
self.blank_logprob = blank_logprob
self.CTCLoss = nn.CTCLoss(zero_infinity=True)
def forward(self, attn_logprob, in_lens, out_lens):
key_lens = in_lens
query_lens = out_lens
attn_logprob_padded = F.pad(
input=attn_logprob, pad=(1, 0, 0, 0, 0, 0, 0, 0),
value=self.blank_logprob)
cost_total = 0.0
for bid in range(attn_logprob.shape[0]):
target_seq = torch.arange(1, key_lens[bid]+1).unsqueeze(0)
curr_logprob = attn_logprob_padded[bid].permute(1, 0, 2)[
:query_lens[bid], :, :key_lens[bid]+1]
curr_logprob = self.log_softmax(curr_logprob[None])[0]
ctc_cost = self.CTCLoss(curr_logprob, target_seq,
input_lengths=query_lens[bid:bid+1],
target_lengths=key_lens[bid:bid+1])
cost_total += ctc_cost
cost = cost_total/attn_logprob.shape[0]
return cost
class AttentionBinarizationLoss(torch.nn.Module):
def __init__(self):
super(AttentionBinarizationLoss, self).__init__()
def forward(self, hard_attention, soft_attention):
log_sum = torch.log(soft_attention[hard_attention == 1]).sum()
return -log_sum / hard_attention.sum()
class RADTTSLoss(torch.nn.Module):
def __init__(self, sigma=1.0, n_group_size=1, dur_model_config=None,
f0_model_config=None, energy_model_config=None,
vpred_model_config=None, loss_weights=None):
super(RADTTSLoss, self).__init__()
self.sigma = sigma
self.n_group_size = n_group_size
self.loss_weights = loss_weights
self.attn_ctc_loss = AttentionCTCLoss(
blank_logprob=loss_weights.get('blank_logprob', -1))
self.loss_fns = {}
if dur_model_config is not None:
self.loss_fns['duration_model_outputs'] = AttributePredictionLoss(
'duration', dur_model_config, loss_weights['dur_loss_weight'])
if f0_model_config is not None:
self.loss_fns['f0_model_outputs'] = AttributePredictionLoss(
'f0', f0_model_config, loss_weights['f0_loss_weight'],
sigma=1.0)
if energy_model_config is not None:
self.loss_fns['energy_model_outputs'] = AttributePredictionLoss(
'energy',
energy_model_config, loss_weights['energy_loss_weight'])
if vpred_model_config is not None:
self.loss_fns['vpred_model_outputs'] = AttributePredictionLoss(
'vpred', vpred_model_config, loss_weights['vpred_loss_weight'])
def forward(self, model_output, in_lens, out_lens):
loss_dict = {}
if len(model_output['z_mel']):
n_elements = out_lens.sum() // self.n_group_size
mask = get_mask_from_lengths(out_lens // self.n_group_size)
mask = mask[:, None].float()
n_dims = model_output['z_mel'].size(1)
loss_mel, loss_prior_mel = compute_flow_loss(
model_output['z_mel'], model_output['log_det_W_list'],
model_output['log_s_list'], n_elements, n_dims, mask,
self.sigma)
loss_dict['loss_mel'] = (loss_mel, 1.0) # loss, weight
loss_dict['loss_prior_mel'] = (loss_prior_mel, 0.0)
ctc_cost = self.attn_ctc_loss(
model_output['attn_logprob'], in_lens, out_lens)
loss_dict['loss_ctc'] = (
ctc_cost, self.loss_weights['ctc_loss_weight'])
for k in model_output:
if k in self.loss_fns:
if model_output[k] is not None and len(model_output[k]) > 0:
t_lens = in_lens if 'dur' in k else out_lens
mout = model_output[k]
for loss_name, v in self.loss_fns[k](mout, t_lens).items():
loss_dict[loss_name] = v
return loss_dict