import logging
import os
import random
import subprocess
import sys
from datetime import datetime
import numpy as np
import torch.utils.data
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from utils.commons.dataset_utils import data_loader
from utils.commons.hparams import hparams
from utils.commons.meters import AvgrageMeter
from utils.commons.tensor_utils import tensors_to_scalars
from utils.commons.trainer import Trainer
torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
class BaseTask(nn.Module):
def __init__(self, *args, **kwargs):
super(BaseTask, self).__init__()
self.current_epoch = 0
self.global_step = 0
self.trainer = None
self.use_ddp = False
self.gradient_clip_norm = hparams['clip_grad_norm']
self.gradient_clip_val = hparams.get('clip_grad_value', 0)
self.model = None
self.training_losses_meter = None
self.logger: SummaryWriter = None
######################
# build model, dataloaders, optimizer, scheduler and tensorboard
######################
def build_model(self):
raise NotImplementedError
@data_loader
def train_dataloader(self):
raise NotImplementedError
@data_loader
def test_dataloader(self):
raise NotImplementedError
@data_loader
def val_dataloader(self):
raise NotImplementedError
def build_scheduler(self, optimizer):
return None
def build_optimizer(self, model):
raise NotImplementedError
def configure_optimizers(self):
optm = self.build_optimizer(self.model)
self.scheduler = self.build_scheduler(optm)
if isinstance(optm, (list, tuple)):
return optm
return [optm]
def build_tensorboard(self, save_dir, name, **kwargs):
log_dir = os.path.join(save_dir, name)
os.makedirs(log_dir, exist_ok=True)
self.logger = SummaryWriter(log_dir=log_dir, **kwargs)
######################
# training
######################
def on_train_start(self):
pass
def on_train_end(self):
pass
def on_epoch_start(self):
self.training_losses_meter = {'total_loss': AvgrageMeter()}
def on_epoch_end(self):
loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
print(f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}")
def _training_step(self, sample, batch_idx, optimizer_idx):
"""
:param sample:
:param batch_idx:
:return: total loss: torch.Tensor, loss_log: dict
"""
raise NotImplementedError
def training_step(self, sample, batch_idx, optimizer_idx=-1):
"""
:param sample:
:param batch_idx:
:param optimizer_idx:
:return: {'loss': torch.Tensor, 'progress_bar': dict, 'tb_log': dict}
"""
loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
if loss_ret is None:
return {'loss': None}
total_loss, log_outputs = loss_ret
log_outputs = tensors_to_scalars(log_outputs)
for k, v in log_outputs.items():
if k not in self.training_losses_meter:
self.training_losses_meter[k] = AvgrageMeter()
if not np.isnan(v):
self.training_losses_meter[k].update(v)
self.training_losses_meter['total_loss'].update(total_loss.item())
if optimizer_idx >= 0:
log_outputs[f'lr_{optimizer_idx}'] = self.trainer.optimizers[optimizer_idx].param_groups[0]['lr']
progress_bar_log = log_outputs
tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
return {
'loss': total_loss,
'progress_bar': progress_bar_log,
'tb_log': tb_log
}
def on_before_optimization(self, opt_idx):
if self.gradient_clip_norm > 0:
torch.nn.utils.clip_grad_norm_(self.parameters(), self.gradient_clip_norm)
if self.gradient_clip_val > 0:
torch.nn.utils.clip_grad_value_(self.parameters(), self.gradient_clip_val)
def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
if self.scheduler is not None:
self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
######################
# validation
######################
def validation_start(self):
pass
def validation_step(self, sample, batch_idx):
"""
:param sample:
:param batch_idx:
:return: output: {"losses": {...}, "total_loss": float, ...} or (total loss: torch.Tensor, loss_log: dict)
"""
raise NotImplementedError
def validation_end(self, outputs):
"""
:param outputs:
:return: loss_output: dict
"""
all_losses_meter = {'total_loss': AvgrageMeter()}
for output in outputs:
if len(output) == 0 or output is None:
continue
if isinstance(output, dict):
assert 'losses' in output, 'Key "losses" should exist in validation output.'
n = output.pop('nsamples', 1)
losses = tensors_to_scalars(output['losses'])
total_loss = output.get('total_loss', sum(losses.values()))
else:
assert len(output) == 2, 'Validation output should only consist of two elements: (total_loss, losses)'
n = 1
total_loss, losses = output
losses = tensors_to_scalars(losses)
if isinstance(total_loss, torch.Tensor):
total_loss = total_loss.item()
for k, v in losses.items():
if k not in all_losses_meter:
all_losses_meter[k] = AvgrageMeter()
all_losses_meter[k].update(v, n)
all_losses_meter['total_loss'].update(total_loss, n)
loss_output = {k: round(v.avg, 4) for k, v in all_losses_meter.items()}
print(f"| Validation results@{self.global_step}: {loss_output}")
return {
'tb_log': {f'val/{k}': v for k, v in loss_output.items()},
'val_loss': loss_output['total_loss']
}
######################
# testing
######################
def test_start(self):
pass
def test_step(self, sample, batch_idx):
return self.validation_step(sample, batch_idx)
def test_end(self, outputs):
return self.validation_end(outputs)
######################
# start training/testing
######################
@classmethod
def start(cls):
os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
random.seed(hparams['seed'])
np.random.seed(hparams['seed'])
work_dir = hparams['work_dir']
trainer = Trainer(
work_dir=work_dir,
val_check_interval=hparams['val_check_interval'],
tb_log_interval=hparams['tb_log_interval'],
max_updates=hparams['max_updates'],
num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams['validate'] else 10000,
accumulate_grad_batches=hparams['accumulate_grad_batches'],
print_nan_grads=hparams['print_nan_grads'],
resume_from_checkpoint=hparams.get('resume_from_checkpoint', 0),
amp=hparams['amp'],
monitor_key=hparams['valid_monitor_key'],
monitor_mode=hparams['valid_monitor_mode'],
num_ckpt_keep=hparams['num_ckpt_keep'],
save_best=hparams['save_best'],
seed=hparams['seed'],
debug=hparams['debug']
)
if not hparams['infer']: # train
trainer.fit(cls)
else:
trainer.test(cls)
def on_keyboard_interrupt(self):
pass