def _test_randperm_with_generator(test_case, N, device, dtype):
generator = flow.Generator()
generator.manual_seed(0)
y_1 = flow.randperm(N, device=device, dtype=dtype, generator=generator)
generator.manual_seed(0)
y_2 = flow.randperm(N, device=device, dtype=dtype, generator=generator)
test_case.assertTrue(np.allclose(y_1.numpy(), y_2.numpy()))
test_case.assertTrue(
y_1.device == flow.device(device) and y_2.device == flow.device(device)
)
test_case.assertTrue(y_1.dtype == dtype and y_2.dtype == dtype)
def _test_randperm_backward(test_case, N, device, dtype):
dtype = flow.float32 # fix dtype here as reduce_sum doesn't support all dtypes yet
x = flow.randperm(N, device=device, dtype=dtype)
x.requires_grad = True
y = x.sum()
y.backward()
test_case.assertTrue(np.allclose(x.grad.numpy(), np.ones(N), 1e-05, 1e-05))
def test_consistent_different_types(test_case):
for dtype in [
flow.uint8,
flow.int8,
flow.int32,
flow.int64,
flow.float32,
flow.float64,
]:
placement = flow.placement("cpu", {0: [0]})
sbp = (flow.sbp.broadcast,)
x = flow.randperm(10, placement=placement, sbp=sbp, dtype=dtype)
test_case.assertEqual(x.dtype, dtype)
test_case.assertEqual(x.sbp, sbp)
test_case.assertEqual(x.placement, placement)
def test_consistent_naive(test_case):
placement = flow.placement("cpu", {0: [0]})
sbp = (flow.sbp.broadcast,)
x = flow.randperm(10, placement=placement, sbp=sbp)
test_case.assertEqual(x.sbp, sbp)
test_case.assertEqual(x.placement, placement)
def _test_randperm_randomness(test_case, N, device, dtype):
n = np.random.randint(100, 1000)
x1 = flow.randperm(n, device=device)
x2 = flow.randperm(n, device=device)
test_case.assertFalse(np.all(x1.numpy() == x2.numpy()))
def train(self):
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
c_lr = self.c_lr
start_iters = 0
if self.resume_iters:
pass
norm = Normalizer()
data_iter = iter(self.data_loader)
print("Start training......")
start_time = datetime.now()
for i in range(start_iters, self.num_iters):
# Preprocess input data
# Fetch real images and labels.
try:
x_real, speaker_idx_org, label_org = next(data_iter)
except:
data_iter = iter(self.data_loader)
x_real, speaker_idx_org, label_org = next(data_iter)
# Generate target domain labels randomly.
rand_idx = flow.randperm(label_org.size(0))
label_trg = label_org[rand_idx]
speaker_idx_trg = speaker_idx_org[rand_idx]
x_real = x_real.to(self.device)
# Original domain one-hot labels.
label_org = label_org.to(self.device)
# Target domain one-hot labels.
label_trg = label_trg.to(self.device)
speaker_idx_org = speaker_idx_org.to(self.device)
speaker_idx_trg = speaker_idx_trg.to(self.device)
# Train the discriminator
# Compute loss with real audio frame.
CELoss = nn.CrossEntropyLoss()
cls_real = self.C(x_real)
cls_loss_real = CELoss(input=cls_real, target=speaker_idx_org)
self.reset_grad()
cls_loss_real.backward()
self.c_optimizer.step()
# Logging.
loss = {}
loss["C/C_loss"] = cls_loss_real.item()
out_r = self.D(x_real, label_org)
# Compute loss with fake audio frame.
x_fake = self.G(x_real, label_trg)
out_f = self.D(x_fake.detach(), label_trg)
d_loss_t = nn.BCEWithLogitsLoss()(
input=out_f, target=flow.zeros_like(
out_f).float()) + nn.BCEWithLogitsLoss()(
input=out_r, target=flow.ones_like(out_r).float())
out_cls = self.C(x_fake)
d_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# Compute loss for gradient penalty.
alpha = flow.rand(x_real.size(0), 1, 1, 1).to(self.device)
x_hat = ((alpha * x_real +
(1 - alpha) * x_fake).detach().requires_grad_(True))
out_src = self.D(x_hat, label_trg)
# TODO: Second-order derivation is not currently supported in oneflow, so gradient penalty cannot be used temporarily.
if self.use_gradient_penalty:
d_loss_gp = self.gradient_penalty(out_src, x_hat)
d_loss = d_loss_t + self.lambda_cls * d_loss_cls + 5 * d_loss_gp
else:
d_loss = d_loss_t + self.lambda_cls * d_loss_cls
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
loss["D/D_loss"] = d_loss.item()
# Train the generator
if (i + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(x_real, label_trg)
g_out_src = self.D(x_fake, label_trg)
g_loss_fake = nn.BCEWithLogitsLoss()(
input=g_out_src, target=flow.ones_like(g_out_src).float())
out_cls = self.C(x_real)
g_loss_cls = CELoss(input=out_cls, target=speaker_idx_org)
# Target-to-original domain.
x_reconst = self.G(x_fake, label_org)
g_loss_rec = nn.L1Loss()(x_reconst, x_real)
# Original-to-Original domain(identity).
x_fake_iden = self.G(x_real, label_org)
id_loss = nn.L1Loss()(x_fake_iden, x_real)
# Backward and optimize.
g_loss = (g_loss_fake + self.lambda_cycle * g_loss_rec +
self.lambda_cls * g_loss_cls +
self.lambda_identity * id_loss)
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss["G/loss_fake"] = g_loss_fake.item()
loss["G/loss_rec"] = g_loss_rec.item()
loss["G/loss_cls"] = g_loss_cls.item()
loss["G/loss_id"] = id_loss.item()
loss["G/g_loss"] = g_loss.item()
# Miscellaneous
# Print out training information.
if (i + 1) % self.log_step == 0:
et = datetime.now() - start_time
et = str(et)[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, i + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
# Translate fixed images for debugging.
if (i + 1) % self.sample_step == 0:
with flow.no_grad():
d, speaker = TestSet(self.test_dir).test_data()
target = random.choice(
[x for x in speakers if x != speaker])
label_t = self.spk_enc.transform([target])[0]
label_t = np.asarray([label_t])
for filename, content in d.items():
f0 = content["f0"]
ap = content["ap"]
sp_norm_pad = self.pad_coded_sp(
content["coded_sp_norm"])
convert_result = []
for start_idx in range(
0, sp_norm_pad.shape[1] - FRAMES + 1, FRAMES):
one_seg = sp_norm_pad[:,
start_idx:start_idx + FRAMES]
one_seg = flow.Tensor(one_seg).to(self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = flow.Tensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).detach().cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:,
0:content["coded_sp_norm"].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(
f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap,
SAMPLE_RATE)
name = f"{speaker}-{target}_iter{i+1}_{filename}"
path = os.path.join(self.sample_dir, name)
print(f"[save]:{path}")
sf.write(path, wav, SAMPLE_RATE)
# Save model checkpoints.
if (i + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
"{}-G".format(i + 1))
D_path = os.path.join(self.model_save_dir,
"{}-D".format(i + 1))
C_path = os.path.join(self.model_save_dir,
"{}-C".format(i + 1))
flow.save(self.G.state_dict(), G_path)
flow.save(self.D.state_dict(), D_path)
flow.save(self.C.state_dict(), C_path)
print("Saved model checkpoints into {}...".format(
self.model_save_dir))
# Decay learning rates.
if (i + 1) % self.lr_update_step == 0 and (i + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= self.g_lr / float(self.num_iters_decay)
d_lr -= self.d_lr / float(self.num_iters_decay)
c_lr -= self.c_lr / float(self.num_iters_decay)
self.update_lr(g_lr, d_lr, c_lr)
print("Decayed learning rates, g_lr: {}, d_lr: {}.".format(
g_lr, d_lr))
def build(self):
x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype)
return x
def _test_consistent_randperm(test_case, N, placement, sbp, dtype):
x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype)
# TODO:Synchronously get a global random seed, and then each rank sets its own seed in manual_seeds
test_case.assertEqual(x.dtype, dtype)
test_case.assertEqual(x.sbp, sbp)
test_case.assertEqual(x.placement, placement)
def test_global_naive(test_case):
placement = flow.placement("cpu", ranks=[0])
sbp = (flow.sbp.broadcast,)
x = flow.randperm(10, placement=placement, sbp=sbp)
test_case.assertEqual(x.sbp, sbp)
test_case.assertEqual(x.placement, placement)
def _test_randperm_large_seq_randomness(test_case, N, device, dtype):
n = 65536
x1 = flow.randperm(n, device=device)
x2 = flow.randperm(n, device=device)
test_case.assertFalse(np.all(x1.numpy() == x2.numpy()))
def test_non_default_device(test_case):
x = flow.randperm(3, device="cuda:1")
test_case.assertEqual(x.device, flow.device("cuda:1"))
