def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottle2neck):
# dcn in Res2Net bottle2neck is in ModuleList
for n in m.convs:
if hasattr(n, 'conv_offset'):
constant_init(n.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottle2neck):
constant_init(m.norm3, 0)
else:
raise TypeError('pretrained must be a str or None')
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.features.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError('pretrained must be a str or None')
for m in self.extra.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
constant_init(self.l2_norm, self.l2_norm.scale)
def init_weights(self, pretrained: dict):
if pretrained is not None:
assert isinstance(pretrained, dict)
logger = get_root_logger()
print_log(f'load decoder weight from: {pretrained}', logger=logger)
self.transformer.init_weights(
pretrained=pretrained['transformer_pretrained'])
def init_weights(self, pretrained=None):
"""Initialize the weights in Encoder
"""
if pretrained is not None:
assert isinstance(pretrained, dict)
logger = get_root_logger()
print_log(f'load encoder weight from: {pretrained}',
logger=logger)
self.backbone.init_weights(pretrained=pretrained['backbone_pretrained'])
def init_weights(self, pretrained: str):
if pretrained is not None:
logger = get_root_logger()
load_checkpoint(self,
pretrained,
strict=False,
logger=logger,
map_location=lambda storage, loc: storage)
else:
"""Initialize the weights of FPN module."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
def init_weights(self, pretrained: dict):
"""Initialize the weights in Captioning Model.
Args:
pretrained (dict, optional): Path Dict to pre-trained weights.
Defaults to None.
"""
if pretrained is not None:
assert isinstance(pretrained, dict)
logger = get_root_logger()
print_log(f'load weight from: {pretrained}', logger=logger)
self.encoder.init_weights(pretrained['encoder_pretrained'])
self.decoder.init_weights(pretrained['decoder_pretrained'])
def train_caption_model(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed) for ds in dataset
]
# put model on gpus
if distributed:
#find_unused_parameters = cfg.get('find_unused_parameters', False)
find_unused_parameters = cfg.get('find_unused_parameters', True)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("N_PARAMETERS", n_parameters)
print('--------------------------------------')
# build runner
# AdamW Optimizer
# TODO -> build_optimizer 구현
param_dicts = [
{"names": [n for n, p in model.named_parameters() \
if "backbone" in n and p.requires_grad],
"params": [p for n, p in model.named_parameters() \
if "backbone" in n and p.requires_grad],
"lr": cfg.lr_dict.lr_backbone},
{"names": [n for n, p in model.named_parameters() \
if "backbone" not in n and p.requires_grad],
"params": [p for n, p in model.named_parameters() \
if "backbone" not in n and p.requires_grad]},
]
#optimizer = build_optimizer(model, cfg.optimizer)
optimizer = torch.optim.AdamW(param_dicts,
lr=cfg.lr_dict.lr,
weight_decay=cfg.weight_decay)
# nondistubuted -> TextGenerateRunner
# distributed -> EpochBasedRunner
if not distributed:
runner = TextGenerateRunner(model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
# default 50 batch 마다 하나의 샘플에 대해서 문장 생성함
runner.set_gen_iter(cfg.log_config.interval)
# set tokenizer for train sample generation
runner.set_tokenizer(dataset[0].tokenizer)
# set decoding method for train sample generation
runner.set_decoding_cfg(cfg.train_cfg.decoding_cfg)
else: # distributed
runner = EpochBasedRunner(model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
# an ugly workaround to make .log and .log.json filenames the same
# TODO -> Docker 시간 설정
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=distributed)
elif distributed and 'type' not in cfg.optimizer_config:
optimizer_config = OptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = cfg.optimizer_config
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
if distributed:
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
# TODO : Support batch_size > 1 in validation
val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
if val_samples_per_gpu > 1:
# Replace 'ImageToTensor' to 'DefaultFormatBundle'
cfg.data.val.pipeline = replace_ImageToTensor(
cfg.data.val.pipeline)
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=val_samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# import modules from string list.
if cfg.get('custom_imports', None):
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports'])
# 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('checkpoints', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./checkpoints',
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 the logger before other steps
#TODO timezone 변경
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
meta['config'] = cfg.pretty_text
# 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}, '
f'deterministic: {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)
# get model by task
model = build_caption(cfg.model)
# Including tokenizer config in dataset config
cfg.data.train.tokenizer = cfg.tokenizer
cfg.data.val.tokenizer = cfg.tokenizer
cfg.data.test.tokenizer = cfg.tokenizer
# build dataset
datasets = [build_dataset(cfg.data.train)]
# debug
#x = datasets[0].__getitem__(0)
# for i in range(100):
# x = datasets[0].__getitem__(i)
# not used
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:
cfg.checkpoint_config.meta = dict(mmcap_version=__version__ +
get_git_hash()[:7]) #,
# train
train_caption_model(model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)