def __call__(self, model):
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = self.optimizer_cfg.copy()
# if no paramwise option is specified, just use the global setting
if not self.paramwise_cfg:
optimizer_cfg['params'] = model.parameters()
return build_from_cfg(optimizer_cfg, OPTIMIZERS)
# set param-wise lr and weight decay recursively
params = []
self.add_params(params, model)
optimizer_cfg['params'] = params
return build_from_cfg(optimizer_cfg, OPTIMIZERS)
def __init__(self, transforms):
assert isinstance(transforms, collections.abc.Sequence)
self.transforms = []
for transform in transforms:
if isinstance(transform, dict):
transform = build_from_cfg(transform, PIPELINES)
self.transforms.append(transform)
elif callable(transform):
self.transforms.append(transform)
else:
raise TypeError('transform must be callable or a dict')
def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, is is either a dict
or a list of configs.
registry (:obj:`Registry`): A registry the module belongs to.
default_args (dict, optional): Default arguments to build the module.
Defaults to None.
Returns:
nn.Module: A built nn module.
"""
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
]
return nn.Sequential(*modules)
else:
return build_from_cfg(cfg, registry, default_args)
def build_dataset(cfg, default_args=None):
from .dataset_wrappers import (RepeatDataset)
if cfg['type'] == 'RepeatDataset':
# 将数据集重复times次数，主要用于小数据集，否则每个epoch太短，训练时长会变长
dataset = RepeatDataset(build_dataset(cfg['dataset'], default_args),
cfg['times'])
elif isinstance(cfg.get('ann_file'), (list, tuple)):
# 多个标注文件
dataset = _concat_dataset(cfg, default_args)
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
def build_anchor_generator(cfg, default_args=None):
return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args)
def build_iou_calculator(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, IOU_CALCULATORS, default_args)
def build_assigner(cfg, **default_args):
"""Builder of box assigner."""
return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args)
def build_bbox_coder(cfg, **default_args):
"""Builder of box coder."""
return build_from_cfg(cfg, BBOX_CODERS, default_args)
def build_sampler(cfg, **default_args):
"""Builder of box sampler."""
return build_from_cfg(cfg, BBOX_SAMPLERS, default_args)