Spaces:
Runtime error
Runtime error
File size: 5,284 Bytes
5325fcc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
# 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
|