def main():
parser = argparse.ArgumentParser(description="PyTorch Target Pseudo Label Testing")
parser.add_argument("-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = cfg.OUTPUT_DIR
if save_dir:
mkdir(save_dir)
logger = setup_logger("pseudo_label", save_dir, 0)
logger.info(cfg)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
thres_const = get_threshold(cfg)
test(cfg, thres_const)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Semantic Segmentation Training")
parser.add_argument(
"-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("FADA", output_dir, args.local_rank)
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
fea, cls = train(cfg, args.local_rank, args.distributed)
if not args.skip_test:
run_test(cfg, fea, cls, args.local_rank, args.distributed)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Semantic Segmentation Testing")
parser.add_argument(
"-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument('--saveres',
action="store_true",
help='save the result')
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
path, model = os.path.split(cfg.resume)
save_dir = os.path.join(path, os.path.splitext(model)[0])
if save_dir:
mkdir(save_dir)
logger = setup_logger("FADA", save_dir, 0)
logger.info(cfg)
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
if os.path.isdir(cfg.resume):
test_all(cfg, args.saveres)
else:
test(cfg, args.saveres)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Semantic Segmentation Training")
parser.add_argument(
"-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("semantic_dist_init", output_dir, 0)
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
semantic_dist_init(cfg)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Semantic Segmentation Testing")
parser.add_argument(
"-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("TEST", save_dir, 0)
logger.info(cfg)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
if os.path.isdir(cfg.resume):
test_all(cfg)
else:
test(cfg)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Semantic Segmentation Testing")
parser.add_argument(
"-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("TSNE", save_dir, 0)
logger.info(cfg)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
logger = logging.getLogger("TSNE.tester")
logger.info("Start")
device = torch.device(cfg.MODEL.DEVICE)
feature_extractor = build_feature_extractor(cfg)
feature_extractor.to(device)
classifier = build_classifier(cfg)
classifier.to(device)
if cfg.resume:
logger.info("Loading checkpoint from {}".format(cfg.resume))
checkpoint = torch.load(cfg.resume, map_location=torch.device('cpu'))
feature_extractor_weights = strip_prefix_if_present(
checkpoint['feature_extractor'], 'module.')
feature_extractor.load_state_dict(feature_extractor_weights)
classifier_weights = strip_prefix_if_present(checkpoint['classifier'],
'module.')
classifier.load_state_dict(classifier_weights)
feature_extractor.eval()
classifier.eval()
torch.cuda.empty_cache()
dataset_name = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
test_data = build_dataset(cfg, mode='test', is_source=False)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=cfg.TSNE.BATCH_SIZE,
shuffle=False,
num_workers=4,
pin_memory=True,
sampler=None)
for batch in tqdm(test_loader):
x, y, name = batch
x = x.cuda(non_blocking=True)
y = y.cuda(non_blocking=True).long()
pred, feat, outs = inference(feature_extractor, classifier, x, y)
filename = name[0] if len(
name[0].split("/")) < 2 else name[0].split("/")[1]
# draw t-sne
B, A, Ht, Wt = outs.size()
tSNE_features = outs.permute(0, 2, 3,
1).contiguous().view(B * Ht * Wt, A)
tSNE_labels = F.interpolate(y.unsqueeze(0).float(),
size=(Ht, Wt),
mode='nearest').squeeze(0).long()
tSNE_labels = tSNE_labels.contiguous().view(B * Ht * Wt, )
mask = (tSNE_labels != cfg.INPUT.IGNORE_LABEL
) # remove IGNORE_LABEL pixels
tSNE_labels = tSNE_labels[mask]
tSNE_features = tSNE_features[mask]
draw(tSNE_features=tSNE_features,
tSNE_labels=tSNE_labels,
name=filename,
cfg=cfg)