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evaluate.py
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evaluate.py
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import torch
from torch.utils.data import DataLoader
from utils.tools import to_device, log, synth_one_sample
from model import SpecDiffGANLoss
from dataset import Dataset
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def evaluate(args, model, diff_d, spec_d, step, configs, logger=None, vocoder=None, losses=None):
preprocess_config, model_config, train_config = configs
# Get dataset
dataset = Dataset(
"val.txt", args, preprocess_config, model_config, train_config, sort=False, drop_last=False
)
batch_size = train_config["optimizer"]["batch_size"]
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=dataset.collate_fn,
)
# Get loss function
Loss = SpecDiffGANLoss(args, preprocess_config, model_config, train_config).to(device)
loss_sums = [{k : 0 for k in loss.keys()} if isinstance(loss, dict) else 0 for loss in losses]
for batchs in loader:
for batch in batchs:
batch = to_device(batch, device)
with torch.no_grad():
#######################
# Evaluate Discriminator #
#######################
# Forward
output, *_ = model(*(batch[2:]))
xs, spk_emb, t, mel_masks = *(output[1:4]), output[9]
x_ts, x_t_prevs, x_t_prev_preds, spk_emb, t = \
[x.detach() if x is not None else x for x in (list(xs) + [spk_emb, t])]
D_fake_diff = diff_d(x_ts, x_t_prev_preds, t)
D_real_diff = diff_d(x_ts, x_t_prevs, t)
D_fake_spec_feats, D_fake_spec = spec_d(output[0], spk_emb)
D_real_spec_feats, D_real_spec = spec_d(batch[6], spk_emb)
D_loss_real, D_loss_fake = Loss.d_loss_fn(D_real_diff[-1], D_real_spec, D_fake_diff[-1], D_fake_spec)
D_loss = D_loss_real + D_loss_fake
#######################
# Evaluate Generator #
#######################
# Forward
output, p_targets, coarse_mels = model(*(batch[2:]))
# Update Batch
batch[9] = p_targets
(x_ts, x_t_prevs, x_t_prev_preds), spk_emb, t, mel_masks = *(output[1:4]), output[9]
D_fake_diff = diff_d(x_ts, x_t_prev_preds, t)
D_real_diff = diff_d(x_ts, x_t_prevs, t)
D_fake_spec_feats, D_fake_spec = spec_d(output[0], spk_emb)
D_real_spec_feats, D_real_spec = spec_d(batch[6], spk_emb)
adv_loss = Loss.g_loss_fn(D_fake_diff[-1], D_fake_spec)
(
fm_loss,
recon_loss,
mel_loss,
pitch_loss,
energy_loss,
duration_loss,
) = Loss(
model,
batch,
output,
coarse_mels,
(D_real_diff, D_real_spec_feats, D_fake_diff, D_fake_spec_feats),
)
G_loss = recon_loss + fm_loss + adv_loss
losses = [D_loss + G_loss, D_loss, G_loss, recon_loss, fm_loss, adv_loss, mel_loss, pitch_loss, energy_loss, duration_loss]
for i in range(len(losses)):
if isinstance(losses[i], dict):
for k in loss_sums[i].keys():
loss_sums[i][k] += losses[i][k].item() * len(batch[0])
else:
loss_sums[i] += losses[i].item() * len(batch[0])
loss_means = []
loss_means_msg = []
for loss_sum in loss_sums:
if isinstance(loss_sum, dict):
loss_mean = {k : v / len(dataset) for k, v in loss_sum.items()}
loss_means.append(loss_mean)
loss_means_msg.append(sum(loss_mean.values()))
else:
loss_means.append(loss_sum / len(dataset))
loss_means_msg.append(loss_sum / len(dataset))
loss_means_msg = loss_means_msg[0:2] + loss_means_msg[5:]
message = "Validation Step {}, Total Loss: {:.4f}, D_loss: {:.4f}, adv_loss: {:.4f}, mel_loss: {:.4f}, pitch_loss: {:.4f}, energy_loss: {:.4f}, duration_loss: {:.4f}".format(
*([step] + [loss_mean for loss_mean in loss_means_msg])
)
figs, wav_reconstruction, wav_prediction, tag = synth_one_sample(
args,
batch,
output,
coarse_mels,
vocoder,
model_config,
preprocess_config,
model.diffusion,
)
sampling_rate = preprocess_config["preprocessing"]["audio"]["sampling_rate"]
if logger is not None:
log(logger, step, losses=loss_means)
log(
logger,
step,
figs=figs,
tag="Validation",
)
log(
logger,
step,
audio=wav_reconstruction,
sampling_rate=sampling_rate,
tag="Validation/reconstructed",
)
log(
logger,
step,
audio=wav_prediction,
sampling_rate=sampling_rate,
tag="Validation/synthesized",
)
return message