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train_music_translation.py
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train_music_translation.py
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import warnings
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.nn.utils import clip_grad_value_
from numba.core.errors import NumbaDeprecationWarning
warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
torch.backends.cudnn.benchmark = True
torch.multiprocessing.set_start_method('spawn', force=True)
import os
from itertools import chain
import numpy as np
from tqdm import tqdm
from dataset_factory import DatasetSet
from models.decoder import Decoder
from models.encoder import Encoder
from models.domain_classifier import DomainClassifier
from utils.helper_functions import cross_entropy_loss, LossMeter, wrap_cuda
from config import config
from utils.logger import Logger
class Trainer:
def __init__(self, config):
self.config = config
self.config.data.n_datasets = len(config.data.datasets)
print("No of datasets used:", self.config.data.n_datasets)
torch.manual_seed(config.env.seed)
torch.cuda.manual_seed(config.env.seed)
self.expPath = self.config.env.expPath
self.logger = Logger("Training", "logs/training.log")
self.data = [DatasetSet(data_path, config.data.seq_len, config.data) for data_path in config.data.datasets]
self.losses_recon = [LossMeter(f'recon {i}') for i in range(self.config.data.n_datasets)]
self.loss_d_right = LossMeter('d')
self.loss_total = [LossMeter(f'total {i}') for i in range(self.config.data.n_datasets)]
self.evals_recon = [LossMeter(f'recon {i}') for i in range(self.config.data.n_datasets)]
self.eval_d_right = LossMeter('eval d')
self.eval_total = [LossMeter(f'eval total {i}') for i in range(self.config.data.n_datasets)]
self.encoder = Encoder(config.encoder)
self.decoders = torch.nn.ModuleList([Decoder(config.decoder) for _ in range(self.config.data.n_datasets)])
self.classifier = DomainClassifier(config.domain_classifier, num_classes=self.config.data.n_datasets)
states = None
if config.env.checkpoint:
checkpoint_args_path = os.path.dirname(config.env.checkpoint) + '/args.pth'
checkpoint_args = torch.load(checkpoint_args_path)
self.start_epoch = checkpoint_args[-1] + 1
states = [torch.load(self.config.env.checkpoint + f'_{i}.pth')
for i in range(self.config.data.n_datasets)]
self.encoder.load_state_dict(states[0]['encoder_state'])
for i in range(self.config.data.n_datasets):
self.decoders[i].load_state_dict(states[i]['decoder_state'])
self.classifier.load_state_dict(states[0]['discriminator_state'])
self.logger.info('Loaded checkpoint parameters')
raise NotImplementedError
else:
self.start_epoch = 0
self.encoder = torch.nn.DataParallel(self.encoder).cuda()
self.classifier = torch.nn.DataParallel(self.classifier).cuda()
for i, decoder in enumerate(self.decoders):
self.decoders[i] = torch.nn.DataParallel(decoder).cuda()
self.model_optimizers = [optim.Adam(chain(self.encoder.parameters(), decoder.parameters()), lr=config.data.lr)
for decoder in self.decoders]
self.classifier_optimizer = optim.Adam(self.classifier.parameters(), lr=config.data.lr)
if config.env.checkpoint and config.env.load_optimizer:
for i in range(self.config.data.n_datasets):
self.model_optimizers[i].load_state_dict(states[i]['model_optimizer_state'])
self.classifier_optimizer.load_state_dict(states[0]['d_optimizer_state'])
self.lr_managers = []
for i in range(self.config.data.n_datasets):
self.lr_managers.append(
torch.optim.lr_scheduler.ExponentialLR(self.model_optimizers[i], config.data.lr_decay))
self.lr_managers[i].last_epoch = self.start_epoch
self.lr_managers[i].step()
def eval_batch(self, x, x_aug, dset_num):
x, x_aug = x.float(), x_aug.float()
z = self.encoder(x)
y = self.decoders[dset_num](x, z)
z_logits = self.classifier(z)
z_classification = torch.max(z_logits, dim=1)[1]
z_accuracy = (z_classification == dset_num).float().mean()
self.eval_d_right.add(z_accuracy.data.item())
# discriminator_right = F.cross_entropy(z_logits, dset_num).mean()
discriminator_right = F.cross_entropy(z_logits, torch.tensor([dset_num] * x.size(0)).long().cuda()).mean()
recon_loss = cross_entropy_loss(y, x)
self.evals_recon[dset_num].add(recon_loss.data.cpu().numpy().mean())
total_loss = discriminator_right.data.item() * self.config.domain_classifier.d_lambda + \
recon_loss.mean().data.item()
self.eval_total[dset_num].add(total_loss)
return total_loss
def train_batch(self, x, x_aug, dset_num):
x, x_aug = x.float(), x_aug.float()
# Optimize D - classifier right
z = self.encoder(x)
z_logits = self.classifier(z)
discriminator_right = F.cross_entropy(z_logits, torch.tensor([dset_num] * x.size(0)).long().cuda()).mean()
loss = discriminator_right * self.config.domain_classifier.d_lambda
self.loss_d_right.add(loss.data.item())
self.classifier_optimizer.zero_grad()
loss.backward()
if self.config.domain_classifier.grad_clip is not None:
clip_grad_value_(self.classifier.parameters(), self.config.domain_classifier.grad_clip)
self.classifier_optimizer.step()
# optimize G - reconstructs well, classifier wrong
z = self.encoder(x_aug)
y = self.decoders[dset_num](x, z)
z_logits = self.classifier(z)
discriminator_wrong = - F.cross_entropy(z_logits, torch.tensor([dset_num] * x.size(0)).long().cuda()).mean()
if not (-100 < discriminator_right.data.item() < 100):
self.logger.debug(f'z_logits: {z_logits.detach().cpu().numpy()}')
self.logger.debug(f'dset_num: {dset_num}')
recon_loss = cross_entropy_loss(y, x)
self.losses_recon[dset_num].add(recon_loss.data.cpu().numpy().mean())
loss = (recon_loss.mean() + self.config.domain_classifier.d_lambda * discriminator_wrong)
self.model_optimizers[dset_num].zero_grad()
loss.backward()
if self.config.domain_classifier.grad_clip is not None:
clip_grad_value_(self.encoder.parameters(), self.config.domain_classifier.grad_clip)
clip_grad_value_(self.decoders[dset_num].parameters(), self.config.domain_classifier.grad_clip)
self.model_optimizers[dset_num].step()
self.loss_total[dset_num].add(loss.data.item())
return loss.data.item()
def train_epoch(self, epoch):
for meter in self.losses_recon:
meter.reset()
self.loss_d_right.reset()
for i in range(len(self.loss_total)):
self.loss_total[i].reset()
self.encoder.train()
self.classifier.train()
for decoder in self.decoders:
decoder.train()
n_batches = self.config.data.epoch_len
with tqdm(total=n_batches, desc='Train epoch %d' % epoch) as train_enum:
for batch_num in range(n_batches):
if self.config.data.short and batch_num == 3:
break
dset_num = batch_num % self.config.data.n_datasets
x, x_aug = next(self.data[dset_num].train_iter)
x = wrap_cuda(x)
x_aug = wrap_cuda(x_aug)
batch_loss = self.train_batch(x, x_aug, dset_num)
train_enum.set_description(f'Train (loss: {batch_loss:.2f}) epoch {epoch}')
train_enum.update()
def evaluate_epoch(self, epoch):
for meter in self.evals_recon:
meter.reset()
self.eval_d_right.reset()
for i in range(len(self.eval_total)):
self.eval_total[i].reset()
self.encoder.eval()
self.classifier.eval()
for decoder in self.decoders:
decoder.eval()
n_batches = int(np.ceil(self.config.data.epoch_len / 10))
with tqdm(total=n_batches) as valid_enum, \
torch.no_grad():
for batch_num in range(n_batches):
if self.config.data.short and batch_num == 10:
break
dset_num = batch_num % self.config.data.n_datasets
x, x_aug = next(self.data[dset_num].valid_iter)
x = wrap_cuda(x)
x_aug = wrap_cuda(x_aug)
batch_loss = self.eval_batch(x, x_aug, dset_num)
valid_enum.set_description(f'Test (loss: {batch_loss:.2f}) epoch {epoch}')
valid_enum.update()
@staticmethod
def format_losses(meters):
losses = [meter.summarize_epoch() for meter in meters]
return ', '.join('{:.4f}'.format(x) for x in losses)
def train_losses(self):
meters = [*self.losses_recon, self.loss_d_right]
return self.format_losses(meters)
def eval_losses(self):
meters = [*self.evals_recon, self.eval_d_right]
return self.format_losses(meters)
def train(self):
best_eval = [float('inf') for _ in range(self.config.data.n_datasets)]
# Begin!
for epoch in range(self.start_epoch, self.start_epoch + self.config.env.epochs):
self.train_epoch(epoch)
self.evaluate_epoch(epoch)
self.logger.info(f'Epoch %s - Train loss: (%s), Test loss (%s)',
epoch, self.train_losses(), self.eval_losses())
for i in range(len(self.lr_managers)):
self.lr_managers[i].step()
for dataset_id in range(self.config.data.n_datasets):
val_loss = self.eval_total[dataset_id].summarize_epoch()
if val_loss < best_eval[dataset_id]:
self.save_model(f'bestmodel_{dataset_id}.pth', dataset_id)
best_eval[dataset_id] = val_loss
if not self.config.env.save_per_epoch:
self.save_model(f'lastmodel_{dataset_id}.pth', dataset_id)
else:
self.save_model(f'lastmodel_{epoch}_rank_{dataset_id}.pth', dataset_id)
torch.save([self.config, epoch], '%s/args.pth' % self.expPath)
self.logger.debug('Ended epoch')
def save_model(self, filename, decoder_id):
save_path = self.expPath / filename
torch.save({'encoder_state': self.encoder.module.state_dict(),
'decoder_state': self.decoders[decoder_id].module.state_dict(),
'discriminator_state': self.classifier.module.state_dict(),
'model_optimizer_state': self.model_optimizers[decoder_id].state_dict(),
'dataset': decoder_id,
'd_optimizer_state': self.classifier_optimizer.state_dict()
},
save_path)
self.logger.debug(f'Saved model to {save_path}')
def main():
Trainer(config).train()
if __name__ == '__main__':
main()