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Sstandardized Logging and Fixed a Bug #71

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Sstandardized Logging and Fixed a Bug #71

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f168c59
Fixed a bug with React
kushagrabhushan Mar 30, 2023
56d871d
Standardized logging across methods
kushagrabhushan Mar 30, 2023
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25 changes: 23 additions & 2 deletions trailmet/algorithms/binarize/BNNBN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,19 @@ def __init__(self, model, dataloaders, **kwargs):
self.weight_decay = self.kwargs.get('weight_decay', '0')
self.learning_rate = self.kwargs.get('learning_rate', '0.001')

def prepare_dirs(self):
if not os.path.exists('log'):
print('Creating Logging Directory...')
os.mkdir('log')
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join('log/log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)

def compress_model(self):
self.prepare_dirs()
if not torch.cuda.is_available():
sys.exit(1)
start_t = time.time()
Expand Down Expand Up @@ -108,6 +120,7 @@ def compress_model(self):

if self.pretrained:
print('* loading pretrained weight {}'.format(self.pretrained))
logging.info(f'loading pretrained weight {self.pretrained}')
pretrain_student = torch.load(args.pretrained)
if 'state_dict' in pretrain_student.keys():
pretrain_student = pretrain_student['state_dict']
Expand All @@ -122,20 +135,24 @@ def compress_model(self):
checkpoint_tar = os.path.join(self.save, 'checkpoint.pth.tar')
if os.path.exists(checkpoint_tar):
print('loading checkpoint {} ..........'.format(checkpoint_tar))
logging.info('loading checkpoint {} ..........'.format(checkpoint_tar))
checkpoint = torch.load(checkpoint_tar)
start_epoch = checkpoint['epoch']
best_top1_acc = checkpoint['best_top1_acc']
self.model_student.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
print("loaded checkpoint {} epoch = {}" .format(checkpoint_tar, checkpoint['epoch']))
logging.info("loaded checkpoint {} epoch = {}" .format(checkpoint_tar, checkpoint['epoch']))
else:
raise ValueError('no checkpoint for resume')

if self.loss_type == 'kd':
if not classes_in_teacher == self.num_classes:
self.validate('teacher', self.val_loader, model_teacher, criterion)


logging.info('epoch, train accuracy, train loss, val accuracy, val loss')

# train the model
epoch = start_epoch
while epoch < self.epochs:
Expand All @@ -150,7 +167,9 @@ def compress_model(self):
raise ValueError('unsupport loss_type')

valid_obj, valid_top1_acc, valid_top5_acc = self.validate(epoch, self.val_loader, self.model_student, criterion)


logging.info("{}, {}, {}, {}, {}".format(epoch, train_top1_acc, train_obj, valid_top1_acc.item(), valid_obj))

is_best = False
if valid_top1_acc > best_top1_acc:
best_top1_acc = valid_top1_acc
Expand All @@ -168,7 +187,9 @@ def compress_model(self):

training_time = (time.time() - start_t) / 3600
print('total training time = {} hours'.format(training_time))
logging.info('total training time = {} hours'.format(training_time))
print('* best acc = {}'.format(best_top1_acc))
logging.info('* best acc = {}'.format(best_top1_acc))


def train_kd(self, epoch, train_loader, model_student, model_teacher, criterion, optimizer, scheduler):
Expand Down
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