def test_default_config(self):
"""Test creating Config object"""
version = 'configs/defaults/binary-cifar-classification.yml'
cfg = Config(version)
self.assertIn('data', dir(cfg))
self.assertIn('model', dir(cfg))
self.assertIn('network', dir(cfg))
def setUpClass(cls):
version = 'configs/defaults/binary-cifar-classification.yml'
cls.cfg = Config(version)
cls.cfg.data['dataset']['params'] = {
'val': {
'fraction': 0.1
}
}
cls.cfg.num_workers = 10
def main(args):
seed_everything(args.seed)
config = Config(args.version)
set_logger(join(config.log_dir, 'train.log'))
logging.info(args)
if args.wandb:
os.environ['WANDB_ENTITY'] = config.entity
os.environ['WANDB_PROJECT'] = config.project
os.environ['WANDB_DIR'] = dirname(config.checkpoint_dir)
run_name = args.version.replace('/', '_')
wandb.init(name=run_name,
dir=dirname(config.checkpoint_dir),
notes=config.description,
resume=args.resume,
id=args.id)
wandb.config.update(config.__dict__,
allow_val_change=config.allow_val_change)
config.num_workers = args.num_workers
train(config, args.debug, args.overfit_batch, args.wandb)
def main(args):
version = args.version
config = Config(version)
version = splitext(version)[0]
set_logger(join(config.log_dir, 'eval.log'))
logging.info(args)
if args.bs is not None:
config.model['batch_size'] = args.bs
# add checkpoint loading values
load_epoch = args.epoch
load_best = args.best
config.model['load']['version'] = version
config.model['load']['epoch'] = load_epoch
config.model['load']['load_best'] = load_best
# ensures that the epoch_counter attribute is set to the
# epoch number being loaded
config.model['load']['resume_epoch'] = True
if args.wandb:
# set up wandb
os.environ['WANDB_ENTITY'] = config.entity
os.environ['WANDB_PROJECT'] = config.project
os.environ['WANDB_DIR'] = dirname(config.checkpoint_dir)
run_name = '_'.join(['evaluation', version.replace('/', '_')])
wandb.init(name=run_name,
dir=dirname(config.checkpoint_dir),
notes=config.description)
wandb.config.update(config.__dict__)
config.num_workers = args.num_workers
evaluate(config, args.mode, args.wandb, args.ignore_cache, args.n_tta)
print(len(prediction_val), len(val))
print()
val = pd.merge(prediction_val, val)
print('Performance without using SWA')
val_preds = val['target'].values
val_labels = val['label'].values
roc = roc_auc_score(val_labels, val_preds)
print(roc)
# In[9]:
config = Config(join('/workspace/coreml', config_name + '.yml'))
# In[19]:
set_logger(join(config.log_dir, 'debug.log'))
# In[10]:
val_dataloader, _ = get_dataloader(
config.data, 'val',
config.model['batch_size'],
num_workers=10,