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# Copyright (c) Facebook, Inc. and its 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.
# author: adiyoss
import json
import logging
from pathlib import Path
import os
import time
import torch
import torch.nn.functional as F
from . import augment, distrib, pretrained
from .enhance import enhance
from .evaluate import evaluate
from .stft_loss import MultiResolutionSTFTLoss
from .utils import bold, copy_state, pull_metric, serialize_model, swap_state, LogProgress
logger = logging.getLogger(__name__)
class Solver(object):
def __init__(self, data, model, optimizer, args):
self.tr_loader = data['tr_loader']
self.cv_loader = data['cv_loader']
self.tt_loader = data['tt_loader']
self.model = model
self.dmodel = distrib.wrap(model)
self.optimizer = optimizer
# data augment
augments = []
if args.remix:
augments.append(augment.Remix())
if args.bandmask:
augments.append(augment.BandMask(args.bandmask, sample_rate=args.sample_rate))
if args.shift:
augments.append(augment.Shift(args.shift, args.shift_same))
if args.revecho:
augments.append(
augment.RevEcho(args.revecho))
self.augment = torch.nn.Sequential(*augments)
# Training config
self.device = args.device
self.epochs = args.epochs
# Checkpoints
self.continue_from = args.continue_from
self.eval_every = args.eval_every
self.checkpoint = args.checkpoint
if self.checkpoint:
self.checkpoint_file = Path(args.checkpoint_file)
self.best_file = Path(args.best_file)
logger.debug("Checkpoint will be saved to %s", self.checkpoint_file.resolve())
self.history_file = args.history_file
self.best_state = None
self.restart = args.restart
self.history = [] # Keep track of loss
self.samples_dir = args.samples_dir # Where to save samples
self.num_prints = args.num_prints # Number of times to log per epoch
self.args = args
self.mrstftloss = MultiResolutionSTFTLoss(factor_sc=args.stft_sc_factor,
factor_mag=args.stft_mag_factor)
self._reset()
def _serialize(self):
package = {}
package['model'] = serialize_model(self.model)
package['optimizer'] = self.optimizer.state_dict()
package['history'] = self.history
package['best_state'] = self.best_state
package['args'] = self.args
tmp_path = str(self.checkpoint_file) + ".tmp"
torch.save(package, tmp_path)
# renaming is sort of atomic on UNIX (not really true on NFS)
# but still less chances of leaving a half written checkpoint behind.
os.rename(tmp_path, self.checkpoint_file)
# Saving only the latest best model.
model = package['model']
model['state'] = self.best_state
tmp_path = str(self.best_file) + ".tmp"
torch.save(model, tmp_path)
os.rename(tmp_path, self.best_file)
def _reset(self):
"""_reset."""
load_from = None
load_best = False
keep_history = True
# Reset
if self.checkpoint and self.checkpoint_file.exists() and not self.restart:
load_from = self.checkpoint_file
elif self.continue_from:
load_from = self.continue_from
load_best = self.args.continue_best
keep_history = False
if load_from:
logger.info(f'Loading checkpoint model: {load_from}')
package = torch.load(load_from, 'cpu')
if load_best:
self.model.load_state_dict(package['best_state'])
else:
self.model.load_state_dict(package['model']['state'])
if 'optimizer' in package and not load_best:
self.optimizer.load_state_dict(package['optimizer'])
if keep_history:
self.history = package['history']
self.best_state = package['best_state']
continue_pretrained = self.args.continue_pretrained
if continue_pretrained:
logger.info("Fine tuning from pre-trained model %s", continue_pretrained)
model = getattr(pretrained, self.args.continue_pretrained)()
self.model.load_state_dict(model.state_dict())
def train(self):
# Optimizing the model
if self.history:
logger.info("Replaying metrics from previous run")
for epoch, metrics in enumerate(self.history):
info = " ".join(f"{k.capitalize()}={v:.5f}" for k, v in metrics.items())
logger.info(f"Epoch {epoch + 1}: {info}")
for epoch in range(len(self.history), self.epochs):
# Train one epoch
self.model.train()
start = time.time()
logger.info('-' * 70)
logger.info("Training...")
train_loss = self._run_one_epoch(epoch)
logger.info(
bold(f'Train Summary | End of Epoch {epoch + 1} | '
f'Time {time.time() - start:.2f}s | Train Loss {train_loss:.5f}'))
if self.cv_loader:
# Cross validation
logger.info('-' * 70)
logger.info('Cross validation...')
self.model.eval()
with torch.no_grad():
valid_loss = self._run_one_epoch(epoch, cross_valid=True)
logger.info(
bold(f'Valid Summary | End of Epoch {epoch + 1} | '
f'Time {time.time() - start:.2f}s | Valid Loss {valid_loss:.5f}'))
else:
valid_loss = 0
best_loss = min(pull_metric(self.history, 'valid') + [valid_loss])
metrics = {'train': train_loss, 'valid': valid_loss, 'best': best_loss}
# Save the best model
if valid_loss == best_loss:
logger.info(bold('New best valid loss %.4f'), valid_loss)
self.best_state = copy_state(self.model.state_dict())
# evaluate and enhance samples every 'eval_every' argument number of epochs
# also evaluate on last epoch
if (epoch + 1) % self.eval_every == 0 or epoch == self.epochs - 1:
# Evaluate on the testset
logger.info('-' * 70)
logger.info('Evaluating on the test set...')
# We switch to the best known model for testing
with swap_state(self.model, self.best_state):
pesq, stoi = evaluate(self.args, self.model, self.tt_loader)
metrics.update({'pesq': pesq, 'stoi': stoi})
# enhance some samples
logger.info('Enhance and save samples...')
enhance(self.args, self.model, self.samples_dir)
self.history.append(metrics)
info = " | ".join(f"{k.capitalize()} {v:.5f}" for k, v in metrics.items())
logger.info('-' * 70)
logger.info(bold(f"Overall Summary | Epoch {epoch + 1} | {info}"))
if distrib.rank == 0:
json.dump(self.history, open(self.history_file, "w"), indent=2)
# Save model each epoch
if self.checkpoint:
self._serialize()
logger.debug("Checkpoint saved to %s", self.checkpoint_file.resolve())
def _run_one_epoch(self, epoch, cross_valid=False):
total_loss = 0
data_loader = self.tr_loader if not cross_valid else self.cv_loader
# get a different order for distributed training, otherwise this will get ignored
data_loader.epoch = epoch
label = ["Train", "Valid"][cross_valid]
name = label + f" | Epoch {epoch + 1}"
logprog = LogProgress(logger, data_loader, updates=self.num_prints, name=name)
for i, data in enumerate(logprog):
noisy, clean = [x.to(self.device) for x in data]
if not cross_valid:
sources = torch.stack([noisy - clean, clean])
sources = self.augment(sources)
noise, clean = sources
noisy = noise + clean
estimate = self.dmodel(noisy)
# apply a loss function after each layer
with torch.autograd.set_detect_anomaly(True):
if self.args.loss == 'l1':
loss = F.l1_loss(clean, estimate)
elif self.args.loss == 'l2':
loss = F.mse_loss(clean, estimate)
elif self.args.loss == 'huber':
loss = F.smooth_l1_loss(clean, estimate)
else:
raise ValueError(f"Invalid loss {self.args.loss}")
# MultiResolution STFT loss
if self.args.stft_loss:
sc_loss, mag_loss = self.mrstftloss(estimate.squeeze(1), clean.squeeze(1))
loss += sc_loss + mag_loss
# optimize model in training mode
if not cross_valid:
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
total_loss += loss.item()
logprog.update(loss=format(total_loss / (i + 1), ".5f"))
# Just in case, clear some memory
del loss, estimate
return distrib.average([total_loss / (i + 1)], i + 1)[0]
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