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import torch
import pytorch_lightning as pl
from torch.optim.lr_scheduler import ReduceLROnPlateau
from collections.abc import MutableMapping
# from speechbrain.processing.speech_augmentation import SpeedPerturb
def flatten_dict(d, parent_key="", sep="_"):
"""Flattens a dictionary into a single-level dictionary while preserving
parent keys. Taken from
`SO <https://stackoverflow.com/questions/6027558/flatten-nested-dictionaries-compressing-keys>`_
Args:
d (MutableMapping): Dictionary to be flattened.
parent_key (str): String to use as a prefix to all subsequent keys.
sep (str): String to use as a separator between two key levels.
Returns:
dict: Single-level dictionary, flattened.
"""
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, MutableMapping):
items.extend(flatten_dict(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
class AudioLightningModuleMultiDecoder(pl.LightningModule):
def __init__(
self,
audio_model=None,
video_model=None,
optimizer=None,
loss_func=None,
train_loader=None,
val_loader=None,
test_loader=None,
scheduler=None,
config=None,
):
super().__init__()
self.audio_model = audio_model
self.video_model = video_model
self.optimizer = optimizer
self.loss_func = loss_func
self.train_loader = train_loader
self.val_loader = val_loader
self.test_loader = test_loader
self.scheduler = scheduler
self.config = {} if config is None else config
# Speed Aug
# self.speedperturb = SpeedPerturb(
# self.config["datamodule"]["data_config"]["sample_rate"],
# speeds=[95, 100, 105],
# perturb_prob=1.0
# )
# Save lightning"s AttributeDict under self.hparams
self.default_monitor = "val_loss/dataloader_idx_0"
self.save_hyperparameters(self.config_to_hparams(self.config))
# self.print(self.audio_model)
self.validation_step_outputs = []
self.test_step_outputs = []
def forward(self, wav, mouth=None):
"""Applies forward pass of the model.
Returns:
:class:`torch.Tensor`
"""
if self.training:
return self.audio_model(wav)
else:
ests, num_blocks = self.audio_model(wav)
assert ests.shape[0] % num_blocks == 0
batch_size = int(ests.shape[0] / num_blocks)
return ests[-batch_size:]
def training_step(self, batch, batch_nb):
mixtures, targets, _ = batch
new_targets = []
min_len = -1
# if self.config["training"]["SpeedAug"] == True:
# with torch.no_grad():
# for i in range(targets.shape[1]):
# new_target = self.speedperturb(targets[:, i, :])
# new_targets.append(new_target)
# if i == 0:
# min_len = new_target.shape[-1]
# else:
# if new_target.shape[-1] < min_len:
# min_len = new_target.shape[-1]
# targets = torch.zeros(
# targets.shape[0],
# targets.shape[1],
# min_len,
# device=targets.device,
# dtype=torch.float,
# )
# for i, new_target in enumerate(new_targets):
# targets[:, i, :] = new_targets[i][:, 0:min_len]
# mixtures = targets.sum(1)
# print(mixtures.shape)
est_sources, num_blocks = self(mixtures)
assert est_sources.shape[0] % num_blocks == 0
batch_size = int(est_sources.shape[0] / num_blocks)
ests_sources_each_block = []
for i in range(num_blocks):
ests_sources_each_block.append(est_sources[i * batch_size : (i + 1) * batch_size])
loss = self.loss_func["train"](ests_sources_each_block, targets)
self.log(
"train_loss",
loss,
on_epoch=True,
prog_bar=True,
sync_dist=True,
logger=True,
)
return {"loss": loss}
def validation_step(self, batch, batch_nb, dataloader_idx):
# cal val loss
if dataloader_idx == 0:
mixtures, targets, _ = batch
# print(mixtures.shape)
est_sources = self(mixtures)
loss = self.loss_func["val"](est_sources, targets)
self.log(
"val_loss",
loss,
on_epoch=True,
prog_bar=True,
sync_dist=True,
logger=True,
)
self.validation_step_outputs.append(loss)
return {"val_loss": loss}
# cal test loss
if (self.trainer.current_epoch) % 10 == 0 and dataloader_idx == 1:
mixtures, targets, _ = batch
# print(mixtures.shape)
est_sources = self(mixtures)
tloss = self.loss_func["val"](est_sources, targets)
self.log(
"test_loss",
tloss,
on_epoch=True,
prog_bar=True,
sync_dist=True,
logger=True,
)
self.test_step_outputs.append(tloss)
return {"test_loss": tloss}
def on_validation_epoch_end(self):
# val
avg_loss = torch.stack(self.validation_step_outputs).mean()
val_loss = torch.mean(self.all_gather(avg_loss))
self.log(
"lr",
self.optimizer.param_groups[0]["lr"],
on_epoch=True,
prog_bar=True,
sync_dist=True,
)
self.logger.experiment.log(
{"learning_rate": self.optimizer.param_groups[0]["lr"], "epoch": self.current_epoch}
)
self.logger.experiment.log(
{"val_pit_sisnr": -val_loss, "epoch": self.current_epoch}
)
# test
if (self.trainer.current_epoch) % 10 == 0:
avg_loss = torch.stack(self.test_step_outputs).mean()
test_loss = torch.mean(self.all_gather(avg_loss))
self.logger.experiment.log(
{"test_pit_sisnr": -test_loss, "epoch": self.current_epoch}
)
self.validation_step_outputs.clear() # free memory
self.test_step_outputs.clear() # free memory
def configure_optimizers(self):
"""Initialize optimizers, batch-wise and epoch-wise schedulers."""
if self.scheduler is None:
return self.optimizer
if not isinstance(self.scheduler, (list, tuple)):
self.scheduler = [self.scheduler] # support multiple schedulers
epoch_schedulers = []
for sched in self.scheduler:
if not isinstance(sched, dict):
if isinstance(sched, ReduceLROnPlateau):
sched = {"scheduler": sched, "monitor": self.default_monitor}
epoch_schedulers.append(sched)
else:
sched.setdefault("monitor", self.default_monitor)
sched.setdefault("frequency", 1)
# Backward compat
if sched["interval"] == "batch":
sched["interval"] = "step"
assert sched["interval"] in [
"epoch",
"step",
], "Scheduler interval should be either step or epoch"
epoch_schedulers.append(sched)
return [self.optimizer], epoch_schedulers
# def lr_scheduler_step(self, scheduler, optimizer_idx, metric):
# if metric is None:
# scheduler.step()
# else:
# scheduler.step(metric)
def train_dataloader(self):
"""Training dataloader"""
return self.train_loader
def val_dataloader(self):
"""Validation dataloader"""
return [self.val_loader, self.test_loader]
def on_save_checkpoint(self, checkpoint):
"""Overwrite if you want to save more things in the checkpoint."""
checkpoint["training_config"] = self.config
return checkpoint
@staticmethod
def config_to_hparams(dic):
"""Sanitizes the config dict to be handled correctly by torch
SummaryWriter. It flatten the config dict, converts ``None`` to
``"None"`` and any list and tuple into torch.Tensors.
Args:
dic (dict): Dictionary to be transformed.
Returns:
dict: Transformed dictionary.
"""
dic = flatten_dict(dic)
for k, v in dic.items():
if v is None:
dic[k] = str(v)
elif isinstance(v, (list, tuple)):
dic[k] = torch.tensor(v)
return dic
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