def main(args):
cfg = mmcv.Config.fromfile(
f'./experiments/config_standfordbackground_{args.version}.py')
if args.config:
print(cfg.pretty_text)
set_random_seed(cfg.seed, deterministic=False)
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
# Build the detector
model = build_segmentor(cfg.model)
if args.model:
print(model)
# Launch training
if args.train:
print("Start training...")
# Create work_dir
mmcv.mkdir_or_exist(os.path.abspath(cfg.work_dir))
# Training process
train_segmentor(model,
datasets,
cfg,
distributed=False,
validate=True,
meta={
"CLASSES": classes,
"PALETTE": palette,
})
# evaluation
if args.evaluation:
eval_ = evaluate_dataset(checkpoint="{}/iter_{}.pth".format(
cfg.work_dir, args.evaluation),
device='cuda:{}'.format(cfg.gpu_ids[0]),
config=cfg)
print(f"Overall accuracy: {eval_[0]}")
print(f"Accuracies: {eval_[1]}")
print(f"IoUs: {eval_[2]}")
print(f"mIoU: {eval_[3]}")
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpus is not None:
cfg.gpu_ids = range(1)
warnings.warn('`--gpus` is deprecated because we only support '
'single GPU mode in non-distributed training. '
'Use `gpus=1` now.')
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids[0:1]
warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
'Because we only support single GPU mode in '
'non-distributed training. Use the first GPU '
'in `gpu_ids` now.')
if args.gpus is None and args.gpu_ids is None:
cfg.gpu_ids = [args.gpu_id]
cfg.auto_resume = args.auto_resume
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# gpu_ids is used to calculate iter when resuming checkpoint
_, world_size = get_dist_info()
cfg.gpu_ids = range(world_size)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# set multi-process settings
setup_multi_processes(cfg)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
seed = init_random_seed(args.seed)
seed = seed + dist.get_rank() if args.diff_seed else seed
logger.info(f'Set random seed to {seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(seed, deterministic=args.deterministic)
cfg.seed = seed
meta['seed'] = seed
meta['exp_name'] = osp.basename(args.config)
model = build_segmentor(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
model.init_weights()
# SyncBN is not support for DP
if not distributed:
warnings.warn(
'SyncBN is only supported with DDP. To be compatible with DP, '
'we convert SyncBN to BN. Please use dist_train.sh which can '
'avoid this error.')
model = revert_sync_batchnorm(model)
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
# passing checkpoint meta for saving best checkpoint
meta.update(cfg.checkpoint_config.meta)
train_segmentor(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)
model = build_segmentor(cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_segmentor(model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
cfg.data.test.pipeline[1].img_ratios = [
0.5, 0.75, 1.0, 1.25, 1.5, 1.75
]
cfg.data.test.pipeline[1].flip = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init the logger before other steps
logger = None
if args.eval:
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'test_{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# set random seeds
if args.seed is not None:
set_random_seed(args.seed, deterministic=args.deterministic)
if logger is not None:
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
else:
print(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.val, dict(test_mode=True))
data_loader = build_dataloader(
dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']
model.PALETTE = checkpoint['meta']['PALETTE']
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, logger, **kwargs)
# We can still use the pre-trained Mask RCNN model though we do not need to
# use the mask branch
cfg.load_from = 'checkpoints/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth'
# Set up working dir to save files and logs.
cfg.work_dir = './work_dirs/dent3_tensor_org'
cfg.total_iters = 80000
cfg.log_config.interval = 10
cfg.evaluation.interval = 1000
cfg.checkpoint_config.interval = 1000
# Set seed to facitate reproducing the result
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
cfg.runner.max_iters = 80000
# Let's have a look at the final config used for training
print(f'Config:\n{cfg.pretty_text}')
sys.exit()
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.apis import train_segmentor
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
def main():
cfg, config_fn = get_cfg()
_, config_name, _ = get_file_name_extension(config_fn)
# dataset_name = "SV3_roads"
dataset_name = "SN7_buildings"
args_work_dir = path_join("C:/_koray/korhun/mmsegmentation/data/space", dataset_name + "_" + config_name)
args_resume_from = path_join(args_work_dir, "latest.pth")
if not os.path.isfile(args_resume_from):
args_resume_from = None
args_launcher = "none"
args_seed = None
args_deterministic = False
args_no_validate = False
if not os.path.isdir(args_work_dir):
create_dir(args_work_dir)
# cfg = Config.fromfile(args_config)
# if args.options is not None:
# cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.work_dir = args_work_dir
# if args.load_from is not None:
# cfg.load_from = args.load_from
if args_resume_from is not None:
cfg.resume_from = args_resume_from
# if args.gpu_ids is not None:
# cfg.gpu_ids = args.gpu_ids
# else:
# cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
cfg.gpu_ids = range(0, 1)
# init distributed env first, since logger depends on the dist info.
if args_launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args_launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, config_name))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args_seed is not None:
logger.info(f'Set random seed to {args_seed}, deterministic: '
f'{args_deterministic}')
set_random_seed(args_seed, deterministic=args_deterministic)
cfg.seed = args_seed
meta['seed'] = args_seed
meta['exp_name'] = config_name
model = build_segmentor(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
logger.info(model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
PALETTE=datasets[0].PALETTE)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_segmentor(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args_no_validate),
timestamp=timestamp,
meta=meta)
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line)
meta['env_info'] = env_info
meta['exp_name'] = os.path.basename(config_file)
# log some basic info
logger.info(f'Distributed training: {distributed}')
# set random seeds
seed = 0
if seed is not None:
logger.info(f'Set random seed to {seed}, deterministic: ' f'{True}')
set_random_seed(seed, deterministic=True)
cfg.seed = seed
meta['seed'] = seed
# get gflops for model
# os.system('python tools/get_flops.py configs/hrnet/parkinglot.py --shape 1024 512')
# train and eval
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.apis import inference_segmentor, init_segmentor, train_segmentor
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
