def test_train_model():
model = ExampleModel()
dataset = ExampleDataset()
datasets = [ExampleDataset(), ExampleDataset()]
cfg = dict(seed=0,
gpus=1,
gpu_ids=[0],
resume_from=None,
load_from=None,
workflow=[('train', 1)],
total_epochs=5,
evaluation=dict(interval=1, key_indicator='acc'),
data=dict(videos_per_gpu=1,
workers_per_gpu=0,
val=dict(type='ExampleDataset')),
optimizer=dict(type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=0.0001),
optimizer_config=dict(grad_clip=dict(max_norm=40, norm_type=2)),
lr_config=dict(policy='step', step=[40, 80]),
omnisource=False,
precise_bn=False,
checkpoint_config=dict(interval=1),
log_level='INFO',
log_config=dict(interval=20,
hooks=[dict(type='TextLoggerHook')]))
with tempfile.TemporaryDirectory() as tmpdir:
# normal train
cfg['work_dir'] = tmpdir
config = Config(cfg)
train_model(model, dataset, config)
with tempfile.TemporaryDirectory() as tmpdir:
# train with validation
cfg['work_dir'] = tmpdir
config = Config(cfg)
train_model(model, dataset, config, validate=True)
with tempfile.TemporaryDirectory() as tmpdir:
# train with Fp16OptimizerHook
cfg['work_dir'] = tmpdir
cfg['fp16'] = dict(loss_scale=512.)
config = Config(cfg)
model.fp16_enabled = None
train_model(model, dataset, config)
with tempfile.TemporaryDirectory() as tmpdir:
cfg['work_dir'] = tmpdir
cfg['omnisource'] = True
config = Config(cfg)
train_model(model, datasets, config)
with tempfile.TemporaryDirectory() as tmpdir:
# train with precise_bn on
cfg['work_dir'] = tmpdir
cfg['precise_bn'] = dict(num_iters=1, interval=1)
config = Config(cfg)
train_model(model, datasets, config)
def Train(self):
self.setup();
print("Starting to train ...");
train_model(self.system_dict["local"]["model"],
self.system_dict["local"]["datasets"],
self.system_dict["local"]["cfg"],
distributed=self.system_dict["params"]["distributed"],
validate=self.system_dict["params"]["val_dataset"])
print("Done");
print("Creating inference config file");
cfg_infer = Config.fromfile(self.system_dict["params"]["inference_config_file"]);
cfg_infer.model.cls_head.num_classes = self.system_dict["params"]["num_classes"]
cfg_infer.dump(self.system_dict["local"]["cfg"].work_dir + "/config.py");
print("Done");
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
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 > config file > default (base 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.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)
# The flag is used to determine whether it is omnisource training
cfg.setdefault('omnisource', False)
# The flag is used to register module's hooks
cfg.setdefault('module_hooks', [])
# 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 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: {cfg.text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['config_name'] = osp.basename(args.config)
meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\'))
model = build_model(cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
register_module_hooks(model.backbone, cfg.module_hooks)
if cfg.omnisource:
# If omnisource flag is set, cfg.data.train should be a list
assert type(cfg.data.train) is list
datasets = [build_dataset(dataset) for dataset in cfg.data.train]
else:
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
# For simplicity, omnisource is not compatiable with val workflow,
# we recommend you to use `--validate`
assert not cfg.omnisource
if args.validate:
warnings.warn('val workflow is duplicated with `--validate`, '
'it is recommended to use `--validate`. see '
'https://github.com/open-mmlab/mmaction2/pull/123')
val_dataset = copy.deepcopy(cfg.data.val)
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmaction version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmaction_version=__version__ +
get_git_hash(digits=7),
config=cfg.text)
train_model(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
timestamp=timestamp,
meta=meta)
def main():
# parse arguments
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
cfg = update_config(cfg, args)
cfg = propagate_root_dir(cfg, args.data_dir)
# init distributed env first, since logger depends on the dist info.
distributed = args.launcher != 'none'
if distributed:
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init 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: {cfg.text}')
if cfg.get('nncf_config'):
check_nncf_is_enabled()
logger.info('NNCF config: {}'.format(cfg.nncf_config))
meta.update(get_nncf_metadata())
# set random seeds
cfg.seed = args.seed
meta['seed'] = args.seed
if cfg.get('seed'):
logger.info(f'Set random seed to {cfg.seed}, deterministic: {args.deterministic}')
set_random_seed(cfg.seed, deterministic=args.deterministic)
# build datasets
datasets = [build_dataset(cfg.data, 'train', dict(logger=logger))]
logger.info(f'Train datasets:\n{str(datasets[0])}')
if len(cfg.workflow) == 2:
if not args.no_validate:
warnings.warn('val workflow is duplicated with `--validate`, '
'it is recommended to use `--validate`. see '
'https://github.com/open-mmlab/mmaction2/pull/123')
datasets.append(build_dataset(copy.deepcopy(cfg.data), 'val', dict(logger=logger)))
logger.info(f'Val datasets:\n{str(datasets[-1])}')
# filter dataset labels
if cfg.get('classes'):
datasets = [dataset.filter(cfg.classes) for dataset in datasets]
# build model
model = build_model(
cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg,
class_sizes=datasets[0].class_sizes,
class_maps=datasets[0].class_maps
)
# define ignore layers
ignore_prefixes = []
if hasattr(cfg, 'reset_layer_prefixes') and isinstance(cfg.reset_layer_prefixes, (list, tuple)):
ignore_prefixes += cfg.reset_layer_prefixes
ignore_suffixes = ['num_batches_tracked']
if hasattr(cfg, 'reset_layer_suffixes') and isinstance(cfg.reset_layer_suffixes, (list, tuple)):
ignore_suffixes += cfg.reset_layer_suffixes
# train model
train_model(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta,
ignore_prefixes=tuple(ignore_prefixes),
ignore_suffixes=tuple(ignore_suffixes)
)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority:
# CLI > config file > default (base 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.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 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: {cfg.text}')
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
model = build_model(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmaction version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmaction_version=__version__,
config=cfg.text)
train_model(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
timestamp=timestamp,
meta=meta)
# The original learning rate (LR) is set for 8-GPU training.
# We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = cfg.optimizer.lr / 8 / 16
cfg.total_epochs = 15
# We can set the checkpoint saving interval to reduce the storage cost
cfg.checkpoint_config.interval = 10
# We can set the log print interval to reduce the the times of printing log
cfg.log_config.interval = 5
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
# We can initialize the logger for training and have a look
# at the final config used for training
print(f'Config:\n{cfg.pretty_text}')
# Build the dataset
datasets = [build_dataset(cfg.data.train)]
# Build the recognizer
model = build_model(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
# Create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
train_model(model, datasets, cfg, distributed=False, validate=True)
