def register_logger_hooks(self, log_config):
if log_config is None:
return
log_interval = log_config['interval']
for info in log_config['hooks']:
logger_hook = cv_core.build_from_cfg(
info, HOOKS, default_args=dict(interval=log_interval))
self.register_hook(logger_hook, priority='VERY_LOW')
def register_checkpoint_hook(self, checkpoint_config):
if checkpoint_config is None:
return
if isinstance(checkpoint_config, dict):
checkpoint_config.setdefault('type', 'CheckpointHook')
hook = cv_core.build_from_cfg(checkpoint_config, HOOKS)
else:
hook = checkpoint_config
self.register_hook(hook)
def register_optimizer_hook(self, optimizer_config):
if optimizer_config is None:
return
if isinstance(optimizer_config, dict):
optimizer_config.setdefault('type', 'OptimizerHook')
hook = cv_core.build_from_cfg(optimizer_config, HOOKS)
else:
hook = optimizer_config
self.register_hook(hook)
def resize_test():
#
transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
transform = cv_core.build_from_cfg(transform, PIPELINES)
input_shape = (60, 84, 3)
img = np.zeros(input_shape, dtype=np.uint8)
output = transform(dict(img=img))
print(output['img_shape'])
transform = dict(
type='Resize',
img_scale=(1333, 800),
ratio_range=(0.9, 1.1),
keep_ratio=True)
transform = cv_core.build_from_cfg(transform, PIPELINES)
input_shape = (60, 84, 3)
img = np.zeros(input_shape, dtype=np.uint8)
output = transform(dict(img=img))
print(output['img_shape'])
def build_activation_layer(cfg):
"""Build activation layer.
Args:
cfg (dict): The activation layer config, which should contain:
- type (str): Layer type.
- layer args: Args needed to instantiate an activation layer.
Returns:
nn.Module: Created activation layer.
"""
return build_from_cfg(cfg, ACTIVATION_LAYERS)
def register_hook_from_cfg(self, hook_cfg):
"""Register a hook from its cfg.
Args:
hook_cfg (dict): Hook config. It should have at least keys 'type'
and 'priority' indicating its type and priority.
Notes:
The specific hook class to register should not use 'type' and
'priority' arguments during initialization.
"""
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = cv_core.build_from_cfg(hook_cfg, HOOKS)
self.register_hook(hook, priority=priority)
def register_lr_hook(self, lr_config):
if isinstance(lr_config, dict):
assert 'policy' in lr_config
policy_type = lr_config.pop('policy')
# If the type of policy is all in lower case, e.g., 'cyclic',
# then its first letter will be capitalized, e.g., to be 'Cyclic'.
# This is for the convenient usage of Lr updater.
# Since this is not applicable for `
# CosineAnnealingLrUpdater`,
# the string will not be changed if it contains capital letters.
if policy_type == policy_type.lower():
policy_type = policy_type.title()
hook_type = policy_type + 'LrUpdaterHook'
lr_config['type'] = hook_type
hook = cv_core.build_from_cfg(lr_config, HOOKS)
else:
hook = lr_config
self.register_hook(hook)
def train_detector(model,
dataset,
cfg,
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,
len(cfg.gpu_ids),
seed=cfg.seed) for ds in dataset
]
# 作用很大，不仅仅是做dataparallel，还包括对DataContainer数据解码
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
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
runner.timestamp = timestamp
if '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))
# register eval hooks
if validate:
# 验证评估模式采用的是hook模式注册
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
runner.register_hook(EvalHook(val_dataloader, **eval_cfg))
# 用户自定义的hook列表
if cfg.get('custom_hooks', None):
custom_hooks = cfg.custom_hooks
assert isinstance(custom_hooks, list), \
f'custom_hooks expect list type, but got {type(custom_hooks)}'
for hook_cfg in cfg.custom_hooks:
assert isinstance(hook_cfg, dict), \
'Each item in custom_hooks expects dict type, but got ' \
f'{type(hook_cfg)}'
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
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)