def build_lr_scheduler(cfg, default_args=None): if LR_SCHEDULERS.get(cfg['type']): scheduler = build_from_cfg(cfg, LR_SCHEDULERS, default_args, 'registry') else: default_args = dict(optimizer=default_args.get('optimizer')) scheduler = build_from_cfg(cfg, lr_scheduler, default_args, 'module') return scheduler
def build_encoder(cfg, default_args=None): backbone = build_from_cfg(cfg['backbone'], BACKBONES, default_args) enhance_cfg = cfg.get('enhance') if enhance_cfg: enhance_module = build_from_cfg(enhance_cfg, ENHANCE_MODULES, default_args) encoder = nn.Sequential(backbone, enhance_module) else: encoder = backbone return encoder
def build_transform(cfgs): tfs = [] for cfg in cfgs: if TRANSFORMS.get(cfg['type']): tf = build_from_cfg(cfg, TRANSFORMS) else: tf = build_from_cfg(cfg, albu, mode='module') tfs.append(tf) aug = albu.Compose(tfs) return aug
def build_transform(cfg): tfs = [] for icfg in cfg: tf = build_from_cfg(icfg, albu, method='module') tfs.append(tf) aug = albu.Compose(tfs) return aug
def build_transform(cfg): tfs = [] for icfg in cfg: tf = build_from_cfg(icfg, TRANSFORMS) tfs.append(tf) aug = Compose(tfs) return aug
def build_module(cfg, default_args=None): try: module = build_from_cfg(cfg, UTILS, default_args) except KeyError as error: if ' is not in the ' not in error.args[0]: raise KeyError from error if ' registry' not in error.args[0]: raise KeyError from error module = build_torch_nn(cfg, default_args=default_args) return module
def build_dataset(cfg, default_args=None): dataset = build_from_cfg(cfg, DATASETS, default_args) return dataset
def build_enhance_module(cfg, default_args=None): #import pdb #pdb.set_trace() enhance_module = build_from_cfg(cfg, ENHANCE_MODULES, default_args) return enhance_module
def __init__(self, cfg): super().__init__() #self.criterion = build_from_cfg(cfg, nn, method='module') self.criterion = build_from_cfg(cfg, CRITERIA, method='registry')
def build_head(cfg, default_args=None): #import pdb #pdb.set_trace() head = build_from_cfg(cfg, HEADS, default_args) return head
def build_runner(cfg, default_args=None): runner = build_from_cfg(cfg, RUNNERS, default_args) return runner
def build_optim(cfg, default_args=None): optim = build_from_cfg(cfg, torch_optim, default_args, 'module') return optim
def build_lr_scheduler(cfg, default_args=None): scheduler = build_from_cfg(cfg, torch_lr_scheduler, default_args, 'module') return scheduler
def build_decoder(cfg, default_args=None): decoder = build_from_cfg(cfg, DECODERS, default_args) return decoder
def build_lr_scheduler(cfg, default_args=None): scheduler = build_from_cfg(cfg, LR_SCHEDULERS, default_args, 'registry') return scheduler
def build_enhance_module(cfg, default_args=None): enhance_module = build_from_cfg(cfg, ENHANCE_MODULES, default_args) return enhance_module
def build_criterion(cfg): #criterion = CriterionWrapper(cfg) criterion = build_from_cfg(cfg, CRITERIA, src='registry') return criterion
def build_dataloader(cfg, default_args): loader = build_from_cfg(cfg, torch_data, default_args, 'module') return loader
def build_torch_nn(cfg, default_args=None): module = build_from_cfg(cfg, nn, default_args, 'module') return module
def build_backbone(cfg, default_args=None): backbone = build_from_cfg(cfg, BACKBONES, default_args) return backbone
def build_module(cfg, default_args=None): util = build_from_cfg(cfg, UTILS, default_args) return util
def build_head(cfg, default_args=None): head = build_from_cfg(cfg, HEADS, default_args) return head
def build_brick(cfg, default_args=None): brick = build_from_cfg(cfg, BRICKS, default_args) return brick
def build_backbone(cfg, default_args=None): #import pdb #pdb.set_trace() backbone = build_from_cfg(cfg, BACKBONES, default_args) return backbone