def main(config_file, exp_suffix):
# LOAD ARGS
assert config_file is not None, 'Missing cfg file'
cfg_from_file(config_file)
# auto-generate exp name if not specified
if cfg.EXP_NAME == '':
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'
if exp_suffix:
cfg.EXP_NAME += f'_{exp_suffix}'
# auto-generate snapshot path if not specified
if cfg.TEST.SNAPSHOT_DIR[0] == '':
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT,
cfg.EXP_NAME)
os.makedirs(cfg.TEST.SNAPSHOT_DIR[0], exist_ok=True)
print('Using config:')
pprint.pprint(cfg)
# load models
models = []
n_models = len(cfg.TEST.MODEL)
if cfg.TEST.MODE == 'best':
assert n_models == 1, 'Not yet supported'
for i in range(n_models):
if cfg.TEST.MODEL[i] == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL[i])
else:
raise NotImplementedError(f"Not yet supported {cfg.TEST.MODEL[i]}")
models.append(model)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# dataloaders
if cfg.TARGET == 'Mapillary':
test_dataset = MapillaryDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
set=cfg.TEST.SET_TARGET,
crop_size=cfg.TEST.INPUT_SIZE_TARGET,
mean=cfg.TEST.IMG_MEAN,
labels_size=cfg.TEST.OUTPUT_SIZE_TARGET,
scale_label=False)
else:
test_dataset = CityscapesDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TEST.SET_TARGET,
info_path=cfg.TEST.INFO_TARGET,
crop_size=cfg.TEST.INPUT_SIZE_TARGET,
mean=cfg.TEST.IMG_MEAN,
labels_size=cfg.TEST.OUTPUT_SIZE_TARGET,
num_classes=cfg.NUM_CLASSES)
test_loader = data.DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=False,
pin_memory=True)
# eval
evaluate_domain_adaptation(models, test_loader, cfg)
def main():
# LOAD ARGS
args = get_arguments()
config_file = args.cfg
exp_suffix = args.exp_suffix
assert config_file is not None, 'Missing cfg file'
cfg_from_file(config_file)
# auto-generate exp name if not specified
# pdb.set_trace()
if cfg.EXP_NAME == '':
if args.MBT: # when to train a model on pseudo label from MBT
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_MBT_THRESH_{args.thres}_ROUND_{args.round}'
else:
args.LB = str(args.LB).replace('.', '_')
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}_THRESH_{args.thres}_ROUND_{args.round}'
# ----------------------------------------------------------------#
if exp_suffix:
cfg.EXP_NAME += f'_{exp_suffix}'
# auto-generate snapshot path if not specified
# pdb.set_trace()
if cfg.TEST.SNAPSHOT_DIR[0] == '':
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT,
cfg.EXP_NAME)
os.makedirs(cfg.TEST.SNAPSHOT_DIR[0], exist_ok=True)
print('Using config:')
pprint.pprint(cfg)
# load models
models = []
n_models = len(cfg.TEST.MODEL)
if cfg.TEST.MODE == 'best':
assert n_models == 1, 'Not yet supported'
for i in range(n_models):
if cfg.TEST.MODEL[i] == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL[i])
else:
raise NotImplementedError(f"Not yet supported {cfg.TEST.MODEL[i]}")
models.append(model)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# dataloaders
# pdb.set_trace()
# ----------------------------------------------------------------#
test_dataset = CityscapesDataSet(
args=args,
root=cfg.DATA_DIRECTORY_TARGET,
list_path='../ADVENT/advent/dataset/cityscapes_list/{}.txt',
set=cfg.TEST.SET_TARGET,
info_path=cfg.TEST.INFO_TARGET,
crop_size=cfg.TEST.INPUT_SIZE_TARGET,
mean=cfg.TEST.IMG_MEAN,
labels_size=cfg.TEST.OUTPUT_SIZE_TARGET)
test_loader = data.DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=False,
pin_memory=True)
# eval
# pdb.set_trace()
evaluate_domain_adaptation(models, test_loader, cfg)
def main(config_file, exp_suffix):
# LOAD ARGS
assert config_file is not None, 'Missing cfg file'
cfg_from_file(config_file)
cfg.NUM_WORKERS = args.num_workers
### dataset settings
cfg.SOURCE = args.source
cfg.TARGET = args.target
## source config
if cfg.SOURCE == 'GTA':
cfg.DATA_LIST_SOURCE = str(project_root / 'advent/dataset/gta5_list/{}.txt')
cfg.DATA_DIRECTORY_SOURCE = str(project_root / 'data/GTA5')
elif cfg.SOURCE == 'SYNTHIA':
raise NotImplementedError(f"Not yet supported {cfg.SOURCE} dataset")
else:
raise NotImplementedError(f"Not yet supported {cfg.SOURCE} dataset")
## target config
if cfg.TARGET == 'Cityscapes':
cfg.DATA_LIST_TARGET = str(project_root / 'advent/dataset/cityscapes_list/{}.txt')
cfg.DATA_DIRECTORY_TARGET = str(project_root / 'data/cityscapes')
cfg.EXP_ROOT = project_root / 'experiments_G2C'
cfg.EXP_ROOT_SNAPSHOT = osp.join(cfg.EXP_ROOT, 'snapshots_G2C')
cfg.EXP_ROOT_LOGS = osp.join(cfg.EXP_ROOT, 'logs_G2C')
cfg.TEST.INPUT_SIZE_TARGET = (1024, 512)
cfg.TEST.OUTPUT_SIZE_TARGET = (2048, 1024)
cfg.TEST.INFO_TARGET = str(project_root / 'advent/dataset/cityscapes_list/info.json')
elif cfg.TARGET == 'BDD':
cfg.DATA_LIST_TARGET = str(project_root / 'advent/dataset/compound_list/{}.txt')
cfg.DATA_DIRECTORY_TARGET = str(project_root / 'data/bdd/Compound')
cfg.EXP_ROOT = project_root / 'experiments'
cfg.EXP_ROOT_SNAPSHOT = osp.join(cfg.EXP_ROOT, 'snapshots')
cfg.EXP_ROOT_LOGS = osp.join(cfg.EXP_ROOT, 'logs')
cfg.TEST.INPUT_SIZE_TARGET = (960, 540)
cfg.TEST.OUTPUT_SIZE_TARGET = (1280, 720)
cfg.TEST.INFO_TARGET = str(project_root / 'advent/dataset/compound_list/info.json')
else:
raise NotImplementedError(f"Not yet supported {cfg.TARGET} dataset")
# auto-generate exp name if not specified
if cfg.EXP_NAME == '':
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{cfg.TRAIN.OCDA_METHOD}'
if exp_suffix:
cfg.EXP_NAME += f'_{exp_suffix}'
# auto-generate snapshot path if not specified
if cfg.TEST.SNAPSHOT_DIR[0] == '':
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
os.makedirs(cfg.TEST.SNAPSHOT_DIR[0], exist_ok=True)
print('Using config:')
pprint.pprint(cfg)
# load models
models = []
n_models = len(cfg.TEST.MODEL)
if cfg.TEST.MODE == 'best':
assert n_models == 1, 'Not yet supported'
for i in range(n_models):
if cfg.TEST.MODEL[i] == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL[i])
elif cfg.TRAIN.MODEL == 'DeepLabv2_VGG':
model = get_deeplab_v2_vgg(cfg=cfg, num_classes=cfg.NUM_CLASSES, pretrained_model=cfg.TRAIN_VGG_PRE_MODEL)
else:
raise NotImplementedError(f"Not yet supported {cfg.TEST.MODEL[i]}")
models.append(model)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# dataloaders
if cfg.TARGET == 'Cityscapes':
test_dataset = CityscapesDataSet(root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TEST.SET_TARGET,
info_path=cfg.TEST.INFO_TARGET,
crop_size=cfg.TEST.INPUT_SIZE_TARGET,
mean=cfg.TEST.IMG_MEAN,
labels_size=cfg.TEST.OUTPUT_SIZE_TARGET)
test_loader = data.DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=False,
pin_memory=True)
elif cfg.TARGET == 'BDD':
test_dataset = BDDdataset(root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TEST.SET_TARGET,
info_path=cfg.TEST.INFO_TARGET,
crop_size=cfg.TEST.INPUT_SIZE_TARGET,
mean=cfg.TEST.IMG_MEAN,
labels_size=cfg.TEST.OUTPUT_SIZE_TARGET)
test_loader = data.DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=False,
pin_memory=True)
else:
raise NotImplementedError(f"Not yet supported {cfg.TARGET} datasets")
# eval
evaluate_domain_adaptation(models, test_loader, cfg)