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Configuration error
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from typing import TypeVar, List, Tuple
import torch
from tqdm import tqdm
from abc import abstractmethod
from numpy import inf
from logger import TensorboardWriter
import numpy as np
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model1, model2, model_ema1, model_ema2, train_criterion1,
train_criterion2, metrics, optimizer1, optimizer2, config, val_criterion,
model_ema1_copy, model_ema2_copy):
self.config = config.config
self.logger = config.get_logger('trainer', config['trainer']['verbosity'])
# setup GPU device if available, move model into configured device
self.device, self.device_ids = self._prepare_device(config['n_gpu'])
if len(self.device_ids) > 1:
print('Using Multi-Processing!')
self.model1 = model1.to(self.device+str(self.device_ids[0]))
self.model2 = model2.to(self.device+str(self.device_ids[-1]))
if model_ema1 is not None:
self.model_ema1 = model_ema1.to(self.device+str(self.device_ids[0]))
self.model_ema2_copy = model_ema2_copy.to(self.device+str(self.device_ids[0]))
else:
self.model_ema1 = None
self.model_ema2_copy = None
if model_ema2 is not None:
self.model_ema2 = model_ema2.to(self.device+str(self.device_ids[-1]))
self.model_ema1_copy = model_ema1_copy.to(self.device+str(self.device_ids[-1]))
else:
self.model_ema2 = None
self.model_ema1_copy = None
if self.model_ema1 is not None:
for param in self.model_ema1.parameters():
param.detach_()
for param in self.model_ema2_copy.parameters():
param.detach_()
if self.model_ema2 is not None:
for param in self.model_ema2.parameters():
param.detach_()
for param in self.model_ema1_copy.parameters():
param.detach_()
self.train_criterion1 = train_criterion1.to(self.device+str(self.device_ids[0]))
self.train_criterion2 = train_criterion2.to(self.device+str(self.device_ids[-1]))
self.val_criterion = val_criterion
self.metrics = metrics
self.optimizer1 = optimizer1
self.optimizer2 = optimizer2
cfg_trainer = config['trainer']
self.epochs = cfg_trainer['epochs']
self.save_period = cfg_trainer['save_period']
self.monitor = cfg_trainer.get('monitor', 'off')
# configuration to monitor model performance and save best
if self.monitor == 'off':
self.mnt_mode = 'off'
self.mnt_best = 0
else:
self.mnt_mode, self.mnt_metric = self.monitor.split()
assert self.mnt_mode in ['min', 'max']
self.mnt_best = inf if self.mnt_mode == 'min' else -inf
self.early_stop = cfg_trainer.get('early_stop', inf)
self.start_epoch = 1
self.global_step = 0
self.checkpoint_dir = config.save_dir
# setup visualization writer instance
self.writer = TensorboardWriter(config.log_dir, self.logger, cfg_trainer['tensorboard'])
if config.resume is not None:
self._resume_checkpoint(config.resume)
@abstractmethod
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Current epochs number
"""
raise NotImplementedError
def train(self):
"""
Full training logic
"""
if len(self.device_ids) > 1:
import torch.multiprocessing as mp
mp.set_start_method('spawn', force =True)
not_improved_count = 0
for epoch in tqdm(range(self.start_epoch, self.epochs + 1), desc='Total progress: '):
if epoch <= self.config['trainer']['warmup']:
if len(self.device_ids) > 1:
q1 = mp.Queue()
q2 = mp.Queue()
p1 = mp.Process(target=self._warmup_epoch, args=(epoch, self.model1, self.data_loader1, self.optimizer1, self.train_criterion1, self.lr_scheduler1, self.device+str(self.device_ids[0]), q1 ))
p2 = mp.Process(target=self._warmup_epoch, args=(epoch, self.model2, self.data_loader2, self.optimizer2, self.train_criterion2, self.lr_scheduler2, self.device+str(self.device_ids[-1]), q2))
p1.start()
p2.start()
result1 = q1.get()
result2 = q2.get()
p1.join()
p2.join()
else:
result1 = self._warmup_epoch(epoch, self.model1, self.data_loader1, self.optimizer1, self.train_criterion1, self.lr_scheduler1, self.device+str(self.device_ids[0]))
result2 = self._warmup_epoch(epoch, self.model2, self.data_loader2, self.optimizer2, self.train_criterion2, self.lr_scheduler2, self.device+str(self.device_ids[-1]))
if len(self.device_ids) > 1:
self.model_ema1_copy.load_state_dict(self.model_ema1.state_dict())
self.model_ema2_copy.load_state_dict(self.model_ema2.state_dict())
if self.do_validation:
q1 = mp.Queue()
p1 = mp.Process(target=self._valid_epoch, args=(epoch, self.model1, self.model_ema2_copy, self.device+str(self.device_ids[0]),q1))
if self.do_test:
q2 = mp.Queue()
p2 = mp.Process(target=self._test_epoch, args=(epoch, self.model1, self.model_ema2_copy, self.device+str(self.device_ids[0]),q2))
p1.start()
p2.start()
val_log = q1.get()
test_log, test_meta = q2.get()
result1.update(val_log)
result2.update(val_log)
result1.update(test_log)
result2.update(test_log)
p1.join()
p2.join()
else:
if self.do_validation:
val_log = self._valid_epoch(epoch, self.model1, self.model2, self.device+str(self.device_ids[0]))
result1.update(val_log)
result2.update(val_log)
if self.do_test:
test_log, test_meta = self._test_epoch(epoch, self.model1, self.model2, self.device+str(self.device_ids[0]))
result1.update(test_log)
result2.update(test_log)
else:
test_meta = [0,0]
else:
if len(self.device_ids) > 1:
q1 = mp.Queue()
q2 = mp.Queue()
p1 = mp.Process(target=self._train_epoch, args=(epoch, self.model1, self.model_ema1, self.model_ema2_copy, self.data_loader1, self.train_criterion1, self.optimizer1, self.lr_scheduler1, self.device+str(self.device_ids[0]), q1 ))
p2 = mp.Process(target=self._train_epoch, args=(epoch, self.model2, self.model_ema2, self.model_ema1_copy, self.data_loader2, self.train_criterion2, self.optimizer2, self.lr_scheduler2, self.device+str(self.device_ids[-1]), q2 ))
p1.start()
p2.start()
result1 = q1.get()
result2 = q2.get()
p1.join()
p2.join()
else:
result1 = self._train_epoch(epoch, self.model1, self.model_ema1, self.model_ema2, self.data_loader1, self.train_criterion1, self.optimizer1, self.lr_scheduler1, self.device+str(self.device_ids[0]))
result2 = self._train_epoch(epoch, self.model2, self.model_ema2, self.model_ema1, self.data_loader2, self.train_criterion2, self.optimizer2, self.lr_scheduler2, self.device+str(self.device_ids[-1]))
self.global_step += result1['local_step']
if len(self.device_ids) > 1:
self.model_ema1_copy.load_state_dict(self.model_ema1.state_dict())
self.model_ema2_copy.load_state_dict(self.model_ema2.state_dict())
if self.do_validation:
q1 = mp.Queue()
p1 = mp.Process(target=self._valid_epoch, args=(epoch, self.model1, self.model_ema2_copy, self.device+str(self.device_ids[0]),q1))
if self.do_test:
q2 = mp.Queue()
p2 = mp.Process(target=self._test_epoch, args=(epoch, self.model1, self.model_ema2_copy, self.device+str(self.device_ids[0]),q2))
p1.start()
p2.start()
val_log = q1.get()
test_log = q2.get()
result1.update(val_log)
result2.update(val_log)
result1.update(test_log)
result2.update(test_log)
p1.join()
p2.join()
else:
if self.do_validation:
val_log = self._valid_epoch(epoch, self.model1, self.model2, self.device+str(self.device_ids[0]))
result1.update(val_log)
result2.update(val_log)
if self.do_test:
test_log = self._test_epoch(epoch, self.model1, self.model2, self.device+str(self.device_ids[0]))
result1.update(test_log)
result2.update(test_log)
# save logged informations into log dict
log = {'epoch': epoch}
for key, value in result1.items():
if key == 'metrics':
log.update({'Net1' + mtr.__name__: value[i] for i, mtr in enumerate(self.metrics)})
log.update({'Net2' + mtr.__name__: result2[key][i] for i, mtr in enumerate(self.metrics)})
elif key == 'val_metrics':
log.update({'val_' + mtr.__name__: value[i] for i, mtr in enumerate(self.metrics)})
elif key == 'test_metrics':
log.update({'test_' + mtr.__name__: value[i] for i, mtr in enumerate(self.metrics)})
else:
log['Net1'+key] = value
log['Net2'+key] = result2[key]
# print logged informations to the screen
for key, value in log.items():
self.logger.info(' {:15s}: {}'.format(str(key), value))
# evaluate model performance according to configured metric, save best checkpoint as model_best
best = False
if self.mnt_mode != 'off':
try:
# check whether model performance improved or not, according to specified metric(mnt_metric)
improved = (self.mnt_mode == 'min' and log[self.mnt_metric] <= self.mnt_best) or \
(self.mnt_mode == 'max' and log[self.mnt_metric] >= self.mnt_best)
except KeyError:
self.logger.warning("Warning: Metric '{}' is not found. "
"Model performance monitoring is disabled.".format(self.mnt_metric))
self.mnt_mode = 'off'
improved = False
if improved:
self.mnt_best = log[self.mnt_metric]
not_improved_count = 0
best = True
else:
not_improved_count += 1
if not_improved_count > self.early_stop:
self.logger.info("Validation performance didn\'t improve for {} epochs. "
"Training stops.".format(self.early_stop))
break
if epoch % self.save_period == 0:
self._save_checkpoint(epoch, save_best=best)
def _prepare_device(self, n_gpu_use):
"""
setup GPU device if available, move model into configured device
"""
n_gpu = torch.cuda.device_count()
if n_gpu_use > 0 and n_gpu == 0:
self.logger.warning("Warning: There\'s no GPU available on this machine,"
"training will be performed on CPU.")
n_gpu_use = 0
if n_gpu_use > n_gpu:
self.logger.warning("Warning: The number of GPU\'s configured to use is {}, but only {} are available "
"on this machine.".format(n_gpu_use, n_gpu))
n_gpu_use = n_gpu
device = 'cuda:'#torch.device('cuda:' if n_gpu_use > 0 else 'cpu')
list_ids = list(range(n_gpu_use))
return device, list_ids
def _save_checkpoint(self, epoch, save_best=False):
"""
Saving checkpoints
:param epoch: current epoch number
:param log: logging information of the epoch
:param save_best: if True, rename the saved checkpoint to 'model_best.pth'
"""
arch = type(self.model1).__name__
state = {
'arch': arch,
'epoch': epoch,
'state_dict1': self.model1.state_dict(),
'state_dict2': self.model2.state_dict(),
'optimizer1': self.optimizer1.state_dict(),
'optimizer2': self.optimizer2.state_dict(),
'monitor_best': self.mnt_best
#'config': self.config
}
filename = str(self.checkpoint_dir / 'checkpoint-epoch{}.pth'.format(epoch))
torch.save(state, filename)
self.logger.info("Saving checkpoint: {} ...".format(filename))
if save_best:
best_path = str(self.checkpoint_dir / 'model_best.pth')
torch.save(state, best_path)
self.logger.info("Saving current best: model_best.pth at: {} ...".format(best_path))
def _resume_checkpoint(self, resume_path):
"""
Resume from saved checkpoints
:param resume_path: Checkpoint path to be resumed
"""
resume_path = str(resume_path)
self.logger.info("Loading checkpoint: {} ...".format(resume_path))
checkpoint = torch.load(resume_path)
self.start_epoch = checkpoint['epoch'] + 1
self.mnt_best = checkpoint['monitor_best']
# load architecture params from checkpoint.
if checkpoint['config']['arch'] != self.config['arch1']:
self.logger.warning("Warning: Architecture configuration given in config file is different from that of "
"checkpoint. This may yield an exception while state_dict is being loaded.")
self.model.load_state_dict(checkpoint['state_dict'])
# load optimizer state from checkpoint only when optimizer type is not changed.
if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:
self.logger.warning("Warning: Optimizer type given in config file is different from that of checkpoint. "
"Optimizer parameters not being resumed.")
else:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.logger.info("Checkpoint loaded. Resume training from epoch {}".format(self.start_epoch))
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