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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import random
import torch
from audiocraft.adversarial import (
AdversarialLoss,
get_adv_criterion,
get_real_criterion,
get_fake_criterion,
FeatureMatchingLoss,
MultiScaleDiscriminator,
)
class TestAdversarialLoss:
def test_adversarial_single_multidiscriminator(self):
adv = MultiScaleDiscriminator()
optimizer = torch.optim.Adam(
adv.parameters(),
lr=1e-4,
)
loss, loss_real, loss_fake = get_adv_criterion('mse'), get_real_criterion('mse'), get_fake_criterion('mse')
adv_loss = AdversarialLoss(adv, optimizer, loss, loss_real, loss_fake)
B, C, T = 4, 1, random.randint(1000, 5000)
real = torch.randn(B, C, T)
fake = torch.randn(B, C, T)
disc_loss = adv_loss.train_adv(fake, real)
assert isinstance(disc_loss, torch.Tensor) and isinstance(disc_loss.item(), float)
loss, loss_feat = adv_loss(fake, real)
assert isinstance(loss, torch.Tensor) and isinstance(loss.item(), float)
# we did not specify feature loss
assert loss_feat.item() == 0.
def test_adversarial_feat_loss(self):
adv = MultiScaleDiscriminator()
optimizer = torch.optim.Adam(
adv.parameters(),
lr=1e-4,
)
loss, loss_real, loss_fake = get_adv_criterion('mse'), get_real_criterion('mse'), get_fake_criterion('mse')
feat_loss = FeatureMatchingLoss()
adv_loss = AdversarialLoss(adv, optimizer, loss, loss_real, loss_fake, feat_loss)
B, C, T = 4, 1, random.randint(1000, 5000)
real = torch.randn(B, C, T)
fake = torch.randn(B, C, T)
loss, loss_feat = adv_loss(fake, real)
assert isinstance(loss, torch.Tensor) and isinstance(loss.item(), float)
assert isinstance(loss_feat, torch.Tensor) and isinstance(loss.item(), float)
class TestGeneratorAdversarialLoss:
def test_hinge_generator_adv_loss(self):
adv_loss = get_adv_criterion(loss_type='hinge')
t0 = torch.randn(1, 2, 0)
t1 = torch.FloatTensor([1.0, 2.0, 3.0])
assert adv_loss(t0).item() == 0.0
assert adv_loss(t1).item() == -2.0
def test_mse_generator_adv_loss(self):
adv_loss = get_adv_criterion(loss_type='mse')
t0 = torch.randn(1, 2, 0)
t1 = torch.FloatTensor([1.0, 1.0, 1.0])
t2 = torch.FloatTensor([2.0, 5.0, 5.0])
assert adv_loss(t0).item() == 0.0
assert adv_loss(t1).item() == 0.0
assert adv_loss(t2).item() == 11.0
class TestDiscriminatorAdversarialLoss:
def _disc_loss(self, loss_type: str, fake: torch.Tensor, real: torch.Tensor):
disc_loss_real = get_real_criterion(loss_type)
disc_loss_fake = get_fake_criterion(loss_type)
loss = disc_loss_fake(fake) + disc_loss_real(real)
return loss
def test_hinge_discriminator_adv_loss(self):
loss_type = 'hinge'
t0 = torch.FloatTensor([0.0, 0.0, 0.0])
t1 = torch.FloatTensor([1.0, 2.0, 3.0])
assert self._disc_loss(loss_type, t0, t0).item() == 2.0
assert self._disc_loss(loss_type, t1, t1).item() == 3.0
def test_mse_discriminator_adv_loss(self):
loss_type = 'mse'
t0 = torch.FloatTensor([0.0, 0.0, 0.0])
t1 = torch.FloatTensor([1.0, 1.0, 1.0])
assert self._disc_loss(loss_type, t0, t0).item() == 1.0
assert self._disc_loss(loss_type, t1, t0).item() == 2.0
class TestFeatureMatchingLoss:
def test_features_matching_loss_base(self):
ft_matching_loss = FeatureMatchingLoss()
length = random.randrange(1, 100_000)
t1 = torch.randn(1, 2, length)
loss = ft_matching_loss([t1], [t1])
assert isinstance(loss, torch.Tensor)
assert loss.item() == 0.0
def test_features_matching_loss_raises_exception(self):
ft_matching_loss = FeatureMatchingLoss()
length = random.randrange(1, 100_000)
t1 = torch.randn(1, 2, length)
t2 = torch.randn(1, 2, length + 1)
with pytest.raises(AssertionError):
ft_matching_loss([], [])
with pytest.raises(AssertionError):
ft_matching_loss([t1], [t1, t1])
with pytest.raises(AssertionError):
ft_matching_loss([t1], [t2])
def test_features_matching_loss_output(self):
loss_nonorm = FeatureMatchingLoss(normalize=False)
loss_layer_normed = FeatureMatchingLoss(normalize=True)
length = random.randrange(1, 100_000)
t1 = torch.randn(1, 2, length)
t2 = torch.randn(1, 2, length)
assert loss_nonorm([t1, t2], [t1, t2]).item() == 0.0
assert loss_layer_normed([t1, t2], [t1, t2]).item() == 0.0
t3 = torch.FloatTensor([1.0, 2.0, 3.0])
t4 = torch.FloatTensor([2.0, 10.0, 3.0])
assert loss_nonorm([t3], [t4]).item() == 3.0
assert loss_nonorm([t3, t3], [t4, t4]).item() == 6.0
assert loss_layer_normed([t3], [t4]).item() == 3.0
assert loss_layer_normed([t3, t3], [t4, t4]).item() == 3.0
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