def prepare():
from utils.common import get_save_path
from utils.config import configs
from utils.device import set_cuda_visible_devices
# since PyTorch jams device selection, we have to parse args before import torch (issue #26790)
parser = argparse.ArgumentParser()
parser.add_argument('configs', nargs='+')
parser.add_argument('--devices', default=None)
args, opts = parser.parse_known_args()
if args.devices is not None and args.devices != 'cpu':
gpus = set_cuda_visible_devices(args.devices)
else:
gpus = []
print(f'==> loading configs from {args.configs}')
configs.update_from_modules(*args.configs)
# define save path
save_path = get_save_path(*args.configs, prefix='runs')
os.makedirs(save_path, exist_ok=True)
configs.train.save_path = save_path
configs.train.best_checkpoint_path = os.path.join(save_path,
'best.pth.tar')
# override configs with args
configs.update_from_arguments(*opts)
if len(gpus) == 0:
configs.device = 'cpu'
configs.device_ids = []
else:
configs.device = 'cuda'
configs.device_ids = gpus
if 'dataset' in configs.evaluate:
for k, v in configs.evaluate.dataset.items():
configs.dataset[k] = v
if 'best_checkpoint_path' not in configs.evaluate or configs.evaluate.best_checkpoint_path is None:
if 'best_checkpoint_path' in configs.train and configs.train.best_checkpoint_path is not None:
configs.evaluate.best_checkpoint_path = configs.train.best_checkpoint_path
else:
configs.evaluate.best_checkpoint_path = os.path.join(
configs.train.save_path, 'best.pth.tar')
assert configs.evaluate.best_checkpoint_path.endswith('.pth.tar')
configs.evaluate.predictions_path = configs.evaluate.best_checkpoint_path.replace(
'.pth.tar', '.predictions')
configs.evaluate.stats_path = configs.evaluate.best_checkpoint_path.replace(
'.pth.tar', '.eval.npy')
return configs
def prepare():
from utils.common import get_save_path
from utils.config import configs
from utils.device import set_cuda_visible_devices
# since PyTorch jams device selection, we have to parse args before import torch (issue #26790)
parser = argparse.ArgumentParser()
parser.add_argument('configs', nargs='+')
parser.add_argument('--devices', default=None)
parser.add_argument('--evaluate', default=False, action='store_true')
args, opts = parser.parse_known_args()
if args.devices is not None and args.devices != 'cpu':
gpus = set_cuda_visible_devices(args.devices)
else:
gpus = []
print('==> loading configs from {}'.format(args.configs))
configs.update_from_modules(*args.configs)
if args.evaluate and configs.evaluate is not None:
configs.train.batch_size = 10
configs.dataset.split = 'test'
else:
configs.evaluate = None
# define save path
save_path = get_save_path(*args.configs, prefix='runs')
os.makedirs(save_path, exist_ok=True)
configs.train.save_path = save_path
configs.train.checkpoint_path = os.path.join(save_path, 'latest.pth.tar')
configs.train.best_checkpoint_path = os.path.join(save_path,
'best.pth.tar')
# override configs with args
configs.update_from_arguments(*opts)
if len(gpus) == 0:
configs.device = 'cpu'
configs.device_ids = []
else:
configs.device = 'cuda'
configs.device_ids = gpus
configs.train.stats_path = configs.train.best_checkpoint_path.replace(
'pth.tar', 'eval.npy')
return configs
def prepare():
from utils.common import get_save_path
from utils.config import configs
from utils.device import set_cuda_visible_devices
# since PyTorch jams device selection, we have to parse args before import torch (issue #26790)
parser = argparse.ArgumentParser()
parser.add_argument('configs', nargs='+')
parser.add_argument('--devices', default=None)
parser.add_argument('--evaluate', default=False, action='store_true')
args, opts = parser.parse_known_args()
if args.devices is not None and args.devices != 'cpu':
gpus = set_cuda_visible_devices(args.devices)
else:
gpus = []
print(f'==> loading configs from {args.configs}')
configs.update_from_modules(*args.configs)
# define save path
configs.train.save_path = get_save_path(*args.configs, prefix='runs')
# override configs with args
configs.update_from_arguments(*opts)
if len(gpus) == 0:
configs.device = 'cpu'
configs.device_ids = []
else:
configs.device = 'cuda'
configs.device_ids = gpus
if args.evaluate and configs.evaluate.fn is not None:
if 'dataset' in configs.evaluate:
configs.dataset = configs.evaluate.dataset
else:
configs.evaluate = None
if configs.evaluate is None:
metrics = []
if 'metric' in configs.train and configs.train.metric is not None:
metrics.append(configs.train.metric)
if 'metrics' in configs.train and configs.train.metrics is not None:
for m in configs.train.metrics:
if m not in metrics:
metrics.append(m)
configs.train.metrics = metrics
configs.train.metric = None if len(metrics) == 0 else metrics[0]
save_path = configs.train.save_path
configs.train.checkpoint_path = os.path.join(save_path,
'latest.pth.tar')
configs.train.checkpoints_path = os.path.join(save_path, 'latest',
'e{}.pth.tar')
configs.train.best_checkpoint_path = os.path.join(
configs.train.save_path, 'best.pth.tar')
best_checkpoints_dir = os.path.join(save_path, 'best')
configs.train.best_checkpoint_paths = {
m: os.path.join(best_checkpoints_dir,
'best.{}.pth.tar'.format(m.replace('/', '.')))
for m in configs.train.metrics
}
os.makedirs(os.path.dirname(configs.train.checkpoints_path),
exist_ok=True)
os.makedirs(best_checkpoints_dir, exist_ok=True)
else:
if 'best_checkpoint_path' not in configs.evaluate or configs.evaluate.best_checkpoint_path is None:
if 'best_checkpoint_path' in configs.train and configs.train.best_checkpoint_path is not None:
configs.evaluate.best_checkpoint_path = configs.train.best_checkpoint_path
else:
configs.evaluate.best_checkpoint_path = os.path.join(
configs.train.save_path, 'best.pth.tar')
assert configs.evaluate.best_checkpoint_path.endswith('.pth.tar')
configs.evaluate.predictions_path = configs.evaluate.best_checkpoint_path.replace(
'.pth.tar', '.predictions')
configs.evaluate.stats_path = configs.evaluate.best_checkpoint_path.replace(
'.pth.tar', '.eval.npy')
return configs
