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import logging
import os
import time
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
from utils.nn.model_utils import print_arch, num_params
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.epoch_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):
for n, m in self.model.named_children():
num_params(m, model_name=n)
if torch.__version__.split(".")[0] == '2' and hparams.get("torch_compile", False):
self.model = torch.compile(self.model, mode='default')
def on_train_end(self):
pass
def on_epoch_start(self):
self.epoch_training_losses_meter = {'total_loss': AvgrageMeter()}
def on_epoch_end(self):
loss_outputs = {k: v.avg for k, v in self.epoch_training_losses_meter.items()}
print(f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}")
loss_outputs = {"epoch_mean/"+k:v for k,v in loss_outputs.items()}
return 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}
"""
# perform the main training step in a specific task
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)
# add to epoch meter
for k, v in log_outputs.items():
if '/' in k:
k_split = k.split("/")
assert len(k_split) == 2, "we only support one `/` in tag_name, i.e., `<tag>/<sub_tag>`"
k = k.replace("/", "_")
if k not in self.epoch_training_losses_meter:
self.epoch_training_losses_meter[k] = AvgrageMeter()
if not np.isnan(v):
self.epoch_training_losses_meter[k].update(v)
if optimizer_idx >= 0:
for params_group_i in range(len(self.trainer.optimizers[optimizer_idx].param_groups)):
log_outputs[f'lr/optimizer{optimizer_idx}_params_group{params_group_i}'] = self.trainer.optimizers[optimizer_idx].param_groups[params_group_i]['lr']
# add to progress bar
progress_bar_log = {}
for k, v in log_outputs.items():
if '/' in k:
k_split = k.split("/")
assert len(k_split) == 2, "we only support one `/` in tag_name, i.e., `<tag>/<sub_tag>`"
k = k.replace("/", "_")
assert k not in progress_bar_log, f"we got duplicate tags in log_outputs, check this `{k}`"
progress_bar_log[k] = v
# add to progress bar
tb_log = {}
for k, v in log_outputs.items():
if '/' in k:
tb_log[k] = v
else:
tb_log[f'tr/{k}'] = v
if not isinstance(total_loss, torch.Tensor):
return {'loss': None}
self.epoch_training_losses_meter['total_loss'].update(total_loss.item())
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 output is None or len(output) == 0:
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, 10) 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):
def is_port_in_use(port: int) -> bool:
import socket
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
return s.connect_ex(('localhost', port)) == 0
os.environ['MASTER_PORT'] = str(random.randint(10000, 11000))
while is_port_in_use(int(os.environ['MASTER_PORT'])):
print(f"| Port {os.environ['MASTER_PORT']} is in use. Change another port...")
os.environ['MASTER_PORT'] = str(random.randint(10000, 11000))
time.sleep(1)
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
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