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import torch
from pipeline_tacotron2.loss import Tacotron2Loss
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class Tacotron2LossInputMixin(TestBaseMixin):
def _get_inputs(self, n_mel=80, n_batch=16, max_mel_specgram_length=300):
mel_specgram = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
mel_specgram_postnet = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
gate_out = torch.rand(n_batch, dtype=self.dtype, device=self.device)
truth_mel_specgram = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
truth_gate_out = torch.rand(n_batch, dtype=self.dtype, device=self.device)
truth_mel_specgram.requires_grad = False
truth_gate_out.requires_grad = False
return (
mel_specgram,
mel_specgram_postnet,
gate_out,
truth_mel_specgram,
truth_gate_out,
)
class Tacotron2LossShapeTests(Tacotron2LossInputMixin):
def test_tacotron2_loss_shape(self):
"""Validate the output shape of Tacotron2Loss."""
n_batch = 16
(
mel_specgram,
mel_specgram_postnet,
gate_out,
truth_mel_specgram,
truth_gate_out,
) = self._get_inputs(n_batch=n_batch)
mel_loss, mel_postnet_loss, gate_loss = Tacotron2Loss()(
(mel_specgram, mel_specgram_postnet, gate_out), (truth_mel_specgram, truth_gate_out)
)
self.assertEqual(mel_loss.size(), torch.Size([]))
self.assertEqual(mel_postnet_loss.size(), torch.Size([]))
self.assertEqual(gate_loss.size(), torch.Size([]))
class Tacotron2LossTorchscriptTests(Tacotron2LossInputMixin):
def _assert_torchscript_consistency(self, fn, tensors):
ts_func = torch_script(fn)
output = fn(tensors[:3], tensors[3:])
ts_output = ts_func(tensors[:3], tensors[3:])
self.assertEqual(ts_output, output)
def test_tacotron2_loss_torchscript_consistency(self):
"""Validate the torchscript consistency of Tacotron2Loss."""
loss_fn = Tacotron2Loss()
self._assert_torchscript_consistency(loss_fn, self._get_inputs())
class Tacotron2LossGradcheckTests(Tacotron2LossInputMixin):
def test_tacotron2_loss_gradcheck(self):
"""Performing gradient check on Tacotron2Loss."""
(
mel_specgram,
mel_specgram_postnet,
gate_out,
truth_mel_specgram,
truth_gate_out,
) = self._get_inputs()
mel_specgram.requires_grad_(True)
mel_specgram_postnet.requires_grad_(True)
gate_out.requires_grad_(True)
def _fn(mel_specgram, mel_specgram_postnet, gate_out, truth_mel_specgram, truth_gate_out):
loss_fn = Tacotron2Loss()
return loss_fn(
(mel_specgram, mel_specgram_postnet, gate_out),
(truth_mel_specgram, truth_gate_out),
)
gradcheck(
_fn,
(mel_specgram, mel_specgram_postnet, gate_out, truth_mel_specgram, truth_gate_out),
fast_mode=True,
)
gradgradcheck(
_fn,
(mel_specgram, mel_specgram_postnet, gate_out, truth_mel_specgram, truth_gate_out),
fast_mode=True,
)
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