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(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}'
print('Using config:')
pprint.pprint(cfg)
# load model
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.ESL.MULTI_LEVEL)
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.ESL.SET_TARGET,
crop_size=cfg.ESL.INPUT_SIZE_TARGET,
mean=cfg.ESL.IMG_MEAN,
labels_size=cfg.ESL.OUTPUT_SIZE_TARGET)
else:
test_dataset = CityscapesDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.ESL.SET_TARGET,
info_path=cfg.ESL.INFO_TARGET,
crop_size=cfg.ESL.INPUT_SIZE_TARGET,
mean=cfg.ESL.IMG_MEAN,
labels_size=cfg.ESL.OUTPUT_SIZE_TARGET,
num_classes=cfg.NUM_CLASSES)
test_loader = data.DataLoader(test_dataset,
batch_size=cfg.ESL.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=False,
pin_memory=True)
# eval
extract_pseudo_labels(model, test_loader, cfg)
def main():
# LOAD ARGS
args = get_arguments()
print('Called with args:')
print(args)
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
# 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 args.exp_suffix:
cfg.EXP_NAME += f'_{args.exp_suffix}'
# auto-generate snapshot path if not specified
if cfg.TRAIN.SNAPSHOT_DIR == '':
cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)
# tensorboard
if args.tensorboard:
if cfg.TRAIN.TENSORBOARD_LOGDIR == '':
cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(cfg.EXP_ROOT_LOGS,
'tensorboard',
cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)
if args.viz_every_iter is not None:
cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter
else:
cfg.TRAIN.TENSORBOARD_LOGDIR = ''
print('Using config:')
pprint.pprint(cfg)
# INIT
_init_fn = None
if not args.random_train:
torch.manual_seed(cfg.TRAIN.RANDOM_SEED)
torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)
np.random.seed(cfg.TRAIN.RANDOM_SEED)
random.seed(cfg.TRAIN.RANDOM_SEED)
def _init_fn(worker_id):
np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# LOAD SEGMENTATION NET
assert osp.exists(
cfg.TRAIN.RESTORE_FROM), f'Missing init model {cfg.TRAIN.RESTORE_FROM}'
if cfg.TRAIN.MODEL == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TRAIN.MULTI_LEVEL)
saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
if 'DeepLab_resnet_pretrained_imagenet' in cfg.TRAIN.RESTORE_FROM:
new_params = model.state_dict().copy()
for i in saved_state_dict:
i_parts = i.split('.')
if not i_parts[1] == 'layer5':
new_params['.'.join(i_parts[1:])] = saved_state_dict[i]
model.load_state_dict(new_params)
else:
model.load_state_dict(saved_state_dict)
else:
raise NotImplementedError(f"Not yet supported {cfg.TRAIN.MODEL}")
print('Model loaded')
# DATALOADERS
source_dataset = GTA5DataSet(root=cfg.DATA_DIRECTORY_SOURCE,
list_path=cfg.DATA_LIST_SOURCE,
set=cfg.TRAIN.SET_SOURCE,
max_iters=cfg.TRAIN.MAX_ITERS *
cfg.TRAIN.BATCH_SIZE_SOURCE,
crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,
mean=cfg.TRAIN.IMG_MEAN)
source_loader = data.DataLoader(source_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn)
target_dataset = CityscapesDataSet(root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=cfg.TRAIN.MAX_ITERS *
cfg.TRAIN.BATCH_SIZE_TARGET,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn)
with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, 'train_cfg.yml'),
'w') as yaml_file:
yaml.dump(cfg, yaml_file, default_flow_style=False)
# UDA TRAINING
train_domain_adaptation(model, source_loader, target_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(args):
# load configuration file
device = cfg.GPU_ID
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
if not os.path.exists(save_dir % (args.FDA_mode, args.round)):
os.mkdir(save_dir % (args.FDA_mode, args.round))
# ----------------------------------------------------------------#
args.LB = str(args.MBT) # set args.LB = 'MBT'
SRC_IMG_MEAN = np.asarray(cfg.TRAIN.IMG_MEAN, dtype=np.float32)
SRC_IMG_MEAN = torch.reshape(torch.from_numpy(SRC_IMG_MEAN), (1, 3, 1, 1))
###################### here, replace by restoring three different model#####################
if args.round == 0: # first round of SSL
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}'
elif args.round > 0: # when SSL round is higher than 0
# SOURCE and TARGET are no longer GTA and Cityscape, but are easy and hard split
cfg.SOURCE = 'CityscapesEasy'
cfg.TARGET = 'CityscapesHard'
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}_THRESH_{str(thresholding)}_ROUND_{args.round - 1}'
else:
raise KeyError()
##########################################################################################################################################
# ----------------------------------------------------------------#
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
# load model with parameters trained from Inter-domain adaptation
model_gen = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL)
restore_from = osp.join(cfg.TEST.SNAPSHOT_DIR[0],
f'model_{args.best_iter}.pth')
print("Loading the generator:", restore_from)
load_checkpoint_for_evaluation(model_gen, restore_from, device)
# load data
target_dataset = CityscapesDataSet(args=args,
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=None,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=None)
target_loader_iter = enumerate(target_loader)
# upsampling layer
input_size_target = cfg.TRAIN.INPUT_SIZE_TARGET
interp_target = nn.Upsample(size=(input_size_target[1],
input_size_target[0]),
mode='bilinear',
align_corners=True)
# ---------------------------------------------------------------------------------------------------------------#
# step 1. entropy-ranking: split the target dataset into easy and hard cases.
entropy_list = []
for index in tqdm(range(len(target_loader))):
_, batch = target_loader_iter.__next__()
image, _, _, name = batch
# normalize the image before fed into the trained model
B, C, H, W = image.shape
mean_image = SRC_IMG_MEAN.repeat(B, 1, H, W)
if args.FDA_mode == 'on':
image -= mean_image
elif args.FDA_mode == 'off':
# no need to perform normalization again since that has been done already in dataset class(GTA5, cityscapes) when args.FDA_mode = 'off'
image = image
else:
raise KeyError()
with torch.no_grad():
_, pred_trg_main = model_gen(
image.cuda(device)) # shape(pred_trg_main) = (1, 19, 65, 129)
pred_trg_main = interp_target(
pred_trg_main) # shape(pred_trg_main) = (1, 19, 512, 1024)
if args.normalize == True:
normalizor = (11 -
len(find_rare_class(pred_trg_main))) / 11.0 + 0.5
else:
normalizor = 1
pred_trg_entropy = prob_2_entropy(F.softmax(pred_trg_main))
entropy_list.append(
(name[0], pred_trg_entropy.mean().item() * normalizor))
# colorize_save(pred_trg_main, name[0], args.FDA_mode)
# split the enntropy_list into
_, easy_split = cluster_subdomain(entropy_list, args, thresholding)
# ---------------------------------------------------------------------------------------------------------------#
# step2. apply thresholding(either top 66% or confidence score above 0.9) to easy-split target dataset and save them.
predicted_label = np.zeros(
(len(easy_split), 512,
1024)) # (512, 1024) is the size of target output
predicted_prob = np.zeros((len(easy_split), 512, 1024))
image_name = []
idx = 0
target_loader_iter = enumerate(target_loader)
for index in tqdm(range(len(target_loader))):
_, batch = target_loader_iter.__next__()
image, _, _, name = batch
if name[0] not in easy_split: # only compute the images that belongs to easy-split
continue
# normalize the image before fed into the trained model
B, C, H, W = image.shape
mean_image = SRC_IMG_MEAN.repeat(B, 1, H, W)
if args.FDA_mode == 'on':
image -= mean_image
elif args.FDA_mode == 'off':
# no need to perform normalization again since that has been done already in dataset class(GTA5, cityscapes) when args.FDA_mode = 'off'
image = image
else:
raise KeyError()
with torch.no_grad():
_, pred_trg_main = model_gen(
image.cuda(device)) # shape(pred_trg_main) = (1, 19, 65, 129)
pred_trg_main = F.softmax(interp_target(pred_trg_main), dim=1).cpu(
).data[0].numpy() # shape(pred_trg_main) = (1, 19, 512, 1024)
pred_trg_main = pred_trg_main.transpose(
1, 2, 0) # shape(pred_trg_main) = (512, 1024, 19)
label, prob = np.argmax(pred_trg_main,
axis=2), np.max(pred_trg_main, axis=2)
predicted_label[idx] = label
predicted_prob[idx] = prob
image_name.append(name[0])
idx += 1
assert len(easy_split) == len(
image_name) # check whether all images in easy-split are processed
# compute the threshold for each label
thres = []
for i in range(cfg.NUM_CLASSES):
x = predicted_prob[predicted_label == i]
if len(x) == 0:
thres.append(0)
continue
x = np.sort(x)
thres.append(
x[np.int(np.round(len(x) * 0.66))]
) # thres contains the thresholding values by labels in corresponding entry:thres[label]
print(thres)
thres = np.array(thres)
thres[thres > 0.9] = 0.9
print(thres)
colorize_save_with_thresholding(easy_split, thres, predicted_label,
predicted_prob, image_name, args)
def main(args):
# load configuration file
device = cfg.GPU_ID
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
if not os.path.exists(save_dir % (args.FDA_mode)):
os.mkdir(save_dir % (args.FDA_mode))
# ----------------------------------------------------------------#
args.LB = str(args.LB).replace('.', '_')
SRC_IMG_MEAN = np.asarray(cfg.TRAIN.IMG_MEAN, dtype=np.float32)
SRC_IMG_MEAN = torch.reshape(torch.from_numpy(SRC_IMG_MEAN), (1, 3, 1, 1))
if args.round == 0: # first round of SSL
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}'
elif args.round > 0: # when SSL round is higher than 0
# SOURCE and TARGET are no longer GTA and Cityscape, but are easy and hard split
cfg.SOURCE = 'CityscapesEasy'
cfg.TARGET = 'CityscapesHard'
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}_THRESH_{str(thresholding)}_ROUND_{args.round - 1}'
else:
raise KeyError()
#cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}_{args.FDA_mode}_LB_{args.LB}'
# ----------------------------------------------------------------#
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
# load model with parameters trained from Inter-domain adaptation
model_gen = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL)
restore_from = osp.join(cfg.TEST.SNAPSHOT_DIR[0],
f'model_{args.best_iter}.pth')
print("Loading the generator:", restore_from)
load_checkpoint_for_evaluation(model_gen, restore_from, device)
# load data
target_dataset = CityscapesDataSet(args=args,
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=None,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=None)
target_loader_iter = enumerate(target_loader)
# upsampling layer
input_size_target = cfg.TRAIN.INPUT_SIZE_TARGET
interp_target = nn.Upsample(size=(input_size_target[1],
input_size_target[0]),
mode='bilinear',
align_corners=True)
entropy_list = []
for index in tqdm(range(len(target_loader))):
_, batch = target_loader_iter.__next__()
image, _, _, name = batch
# ----------------------------------------------------------------#
"""
normalize the image before fed into the trained model
"""
B, C, H, W = image.shape
mean_image = SRC_IMG_MEAN.repeat(B, 1, H, W)
if args.FDA_mode == 'on':
image -= mean_image
elif args.FDA_mode == 'off':
# no need to perform normalization again since that has been done already in dataset class(GTA5, cityscapes) when args.FDA_mode = 'off'
image = image
else:
raise KeyError()
# ----------------------------------------------------------------#
with torch.no_grad():
_, pred_trg_main = model_gen(image.cuda(device))
pred_trg_main = interp_target(pred_trg_main)
if args.normalize == True:
normalizor = (11 -
len(find_rare_class(pred_trg_main))) / 11.0 + 0.5
else:
normalizor = 1
pred_trg_entropy = prob_2_entropy(F.softmax(pred_trg_main))
entropy_list.append(
(name[0], pred_trg_entropy.mean().item() * normalizor))
colorize_save(pred_trg_main, name[0], args)
# split the enntropy_list into
cluster_subdomain(entropy_list, args, thresholding)
def main(args):
# load configuration file
device = cfg.GPU_ID
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
if not os.path.exists('./color_masks'):
os.mkdir('./color_masks')
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'
cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
# load model with parameters trained from Inter-domain adaptation
model_gen = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TEST.MULTI_LEVEL)
restore_from = osp.join(cfg.TEST.SNAPSHOT_DIR[0],
f'model_{args.best_iter}.pth')
print("Loading the generator:", restore_from)
load_checkpoint_for_evaluation(model_gen, restore_from, device)
# load data
target_dataset = CityscapesDataSet(root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=None,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=None)
target_loader_iter = enumerate(target_loader)
# upsampling layer
input_size_target = cfg.TRAIN.INPUT_SIZE_TARGET
interp_target = nn.Upsample(size=(input_size_target[1],
input_size_target[0]),
mode='bilinear',
align_corners=True)
entropy_list = []
for index in tqdm(range(len(target_loader))):
_, batch = target_loader_iter.__next__()
image, _, _, name = batch
with torch.no_grad():
_, pred_trg_main = model_gen(image.cuda(device))
pred_trg_main = interp_target(pred_trg_main)
if args.normalize == True:
normalizor = (11 -
len(find_rare_class(pred_trg_main))) / 11.0 + 0.5
else:
normalizor = 1
pred_trg_entropy = prob_2_entropy(F.softmax(pred_trg_main))
entropy_list.append(
(name[0], pred_trg_entropy.mean().item() * normalizor))
colorize_save(pred_trg_main, name[0])
# split the enntropy_list into
cluster_subdomain(entropy_list, args.lambda1)
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)
def main():
# LOAD ARGS
args = get_arguments()
print('Called with args:')
print(args)
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
cfg.NUM_WORKERS = args.num_workers
if args.option is not None:
cfg.TRAIN.OPTION = args.option
cfg.TRAIN.LAMBDA_BOUNDARY = args.LAMBDA_BOUNDARY
cfg.TRAIN.LAMBDA_DICE = args.LAMBDA_DICE
## gan method settings
cfg.GAN = args.gan
if cfg.GAN == 'gan':
cfg.TRAIN.LAMBDA_ADV_MAIN = 0.001 # GAN
elif cfg.GAN == 'lsgan':
cfg.TRAIN.LAMBDA_ADV_MAIN = 0.01 # LS-GAN
else:
raise NotImplementedError(f"Not Supported gan method")
### 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')
cfg.TRAIN.INPUT_SIZE_SOURCE = (1280, 720)
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.TRAIN.INPUT_SIZE_TARGET = (1024, 512)
cfg.TRAIN.INFO_TARGET = str(project_root /
'advent/dataset/cityscapes_list/info.json')
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.TRAIN.INPUT_SIZE_TARGET = (960, 540)
cfg.TRAIN.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 args.exp_suffix:
cfg.EXP_NAME += f'_{args.exp_suffix}'
# auto-generate snapshot path if not specified
if cfg.TRAIN.SNAPSHOT_DIR == '':
cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)
# tensorboard
if args.tensorboard:
if cfg.TRAIN.TENSORBOARD_LOGDIR == '':
cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(cfg.EXP_ROOT_LOGS,
'tensorboard',
cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)
if args.viz_every_iter is not None:
cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter
else:
cfg.TRAIN.TENSORBOARD_LOGDIR = ''
print('Using config:')
pprint.pprint(cfg)
# INIT
_init_fn = None
if not args.random_train:
torch.manual_seed(cfg.TRAIN.RANDOM_SEED)
torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)
np.random.seed(cfg.TRAIN.RANDOM_SEED)
random.seed(cfg.TRAIN.RANDOM_SEED)
def _init_fn(worker_id):
np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# LOAD SEGMENTATION NET
if cfg.TRAIN.MODEL == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TRAIN.MULTI_LEVEL)
saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
if 'DeepLab_resnet_pretrained_imagenet' in cfg.TRAIN.RESTORE_FROM:
new_params = model.state_dict().copy()
for i in saved_state_dict:
i_parts = i.split('.')
if not i_parts[1] == 'layer5':
new_params['.'.join(i_parts[1:])] = saved_state_dict[i]
model.load_state_dict(new_params)
else:
model.load_state_dict(saved_state_dict)
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)
if cfg.TRAIN.SELF_TRAINING:
path = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.TRAIN.RESTORE_FROM_SELF)
saved_state_dict = torch.load(path)
model.load_state_dict(saved_state_dict, strict=False)
trg_list = cfg.DATA_LIST_TARGET_ORDER
print("self-training model loaded: {} ".format(path))
else:
trg_list = cfg.DATA_LIST_TARGET
else:
raise NotImplementedError(f"Not yet supported {cfg.TRAIN.MODEL}")
print("model: ")
print(model)
print('Model loaded')
######## DATALOADERS ########
# GTA5: 24,966: 274,626 / 24,966 = 11 epoch
# self-training : target data shuffle
shuffle = cfg.TRAIN.SHUFFLE
if cfg.TRAIN.SELF_TRAINING:
max_iteration = None
else:
max_iteration = cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_SOURCE
source_dataset = GTA5DataSet(root=cfg.DATA_DIRECTORY_SOURCE,
list_path=cfg.DATA_LIST_SOURCE,
set=cfg.TRAIN.SET_SOURCE,
max_iters=max_iteration,
crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,
mean=cfg.TRAIN.IMG_MEAN)
source_loader = data.DataLoader(source_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn)
if cfg.TARGET == "BDD":
# GTA5: 14,697: 264,546 / 14,697 = 18 epoch
target_dataset = BDDdataset(root=cfg.DATA_DIRECTORY_TARGET,
list_path=trg_list,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=max_iteration,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=shuffle,
pin_memory=True,
worker_init_fn=_init_fn)
elif cfg.TARGET == 'Cityscapes':
target_dataset = CityscapesDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_TARGET,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN)
target_loader = data.DataLoader(target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn)
else:
raise NotImplementedError(f"Not yet supported {cfg.TARGET} datasets")
with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, 'train_cfg.yml'),
'w') as yaml_file:
yaml.dump(cfg, yaml_file, default_flow_style=False)
# UDA TRAINING
train_domain_adaptation(model, source_loader, target_loader, cfg)
def main():
# LOAD ARGS
args = get_arguments()
print("Called with args:")
print(args)
assert args.cfg is not None, "Missing cfg file"
cfg_from_file(args.cfg)
# 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 args.exp_suffix:
cfg.EXP_NAME += f"_{args.exp_suffix}"
# auto-generate snapshot path if not specified
if cfg.TRAIN.SNAPSHOT_DIR == "":
cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)
# tensorboard
if args.tensorboard:
if cfg.TRAIN.TENSORBOARD_LOGDIR == "":
cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(
cfg.EXP_ROOT_LOGS, "tensorboard", cfg.EXP_NAME
)
os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)
if args.viz_every_iter is not None:
cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter
else:
cfg.TRAIN.TENSORBOARD_LOGDIR = ""
print("Using config:")
pprint.pprint(cfg)
# INIT
_init_fn = None
if not args.random_train:
torch.manual_seed(cfg.TRAIN.RANDOM_SEED)
torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)
np.random.seed(cfg.TRAIN.RANDOM_SEED)
random.seed(cfg.TRAIN.RANDOM_SEED)
def _init_fn(worker_id):
np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)
if os.environ.get("DADA_DRY_RUN", "0") == "1":
return
# LOAD SEGMENTATION NET
assert osp.exists(
cfg.TRAIN.RESTORE_FROM
), f"Missing init model {cfg.TRAIN.RESTORE_FROM}"
if cfg.TRAIN.MODEL == "DeepLabv2_depth":
model = get_deeplab_v2_depth(
num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TRAIN.MULTI_LEVEL
)
saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
if "DeepLab_resnet_pretrained_imagenet" in cfg.TRAIN.RESTORE_FROM:
new_params = model.state_dict().copy()
for i in saved_state_dict:
i_parts = i.split(".")
if not i_parts[1] == "layer5":
new_params[".".join(i_parts[1:])] = saved_state_dict[i]
model.load_state_dict(new_params)
else:
model.load_state_dict(saved_state_dict)
elif cfg.TRAIN.MODEL == "DeepLabv2":
model = get_deeplab_v2(
num_classes=cfg.NUM_CLASSES,
multi_level=cfg.TRAIN.MULTI_LEVEL
)
saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
if "DeepLab_resnet_pretrained_imagenet" in cfg.TRAIN.RESTORE_FROM:
new_params = model.state_dict().copy()
for i in saved_state_dict:
i_parts = i.split(".")
if not i_parts[1] == "layer5":
new_params[".".join(i_parts[1:])] = saved_state_dict[i]
model.load_state_dict(new_params)
else:
model.load_state_dict(saved_state_dict)
else:
raise NotImplementedError(f"Not yet supported {cfg.TRAIN.MODEL}")
print("Model loaded")
# DATALOADERS
source_dataset = SYNTHIADataSetDepth(
root=cfg.DATA_DIRECTORY_SOURCE,
list_path=cfg.DATA_LIST_SOURCE,
set=cfg.TRAIN.SET_SOURCE,
num_classes=cfg.NUM_CLASSES,
max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_SOURCE,
crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,
mean=cfg.TRAIN.IMG_MEAN,
use_depth=cfg.USE_DEPTH,
)
source_loader = data.DataLoader(
source_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn,
)
if cfg.TARGET == 'Cityscapes':
target_dataset = CityscapesDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_TARGET,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN
)
elif cfg.TARGET == 'Mapillary':
target_dataset = MapillaryDataSet(
root=cfg.DATA_DIRECTORY_TARGET,
list_path=cfg.DATA_LIST_TARGET,
set=cfg.TRAIN.SET_TARGET,
info_path=cfg.TRAIN.INFO_TARGET,
max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_TARGET,
crop_size=cfg.TRAIN.INPUT_SIZE_TARGET,
mean=cfg.TRAIN.IMG_MEAN,
scale_label=True
)
else:
raise NotImplementedError(f"Not yet supported dataset {cfg.TARGET}")
target_loader = data.DataLoader(
target_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_TARGET,
num_workers=cfg.NUM_WORKERS,
shuffle=True,
pin_memory=True,
worker_init_fn=_init_fn,
)
with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, "train_cfg.yml"), "w") as yaml_file:
yaml.dump(cfg, yaml_file, default_flow_style=False)
# UDA TRAINING
if cfg.USE_DEPTH:
train_domain_adaptation_with_depth(model, source_loader, target_loader, cfg)
else:
train_domain_adaptation(model, source_loader, target_loader, cfg)