def build_dataloader(cfg, default_args: dict = None): dataloaders = {} if DATALOADERS.get(cfg['type']): packages = DATALOADERS src = 'registry' else: packages = tud src = 'module' if isinstance(default_args.get('dataset'), list): for idx, ds in enumerate(default_args['dataset']): assert isinstance(ds, tud.Dataset) dataloader = build_from_cfg(cfg, packages, dict(dataset=ds, sampler=default_args.get( 'sampler', None)), src=src) if hasattr(ds, 'root'): name = getattr(ds, 'root') else: name = str(idx) dataloaders[name] = dataloader else: dataloaders = build_from_cfg(cfg, packages, default_args, src=src) return dataloaders
def build_dataloader(cfg, default_args=None): try: dataloader = build_from_cfg(cfg, tud, default_args, src='module') except: dataloader = build_from_cfg(cfg, DATALOADERS, default_args) return dataloader
def build_datasets(cfg, default_args=None): if isinstance(cfg, list): datasets = [] for icfg in cfg: ds = build_from_cfg(icfg, DATASETS, default_args) datasets.append(ds) else: datasets = build_from_cfg(cfg, DATASETS, default_args) return datasets
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, src='module') tfs.append(tf) aug = albu.Compose(tfs) return aug
def build_datasets(cfg, default_args=None): datasets = [] for icfg in cfg: ds = build_from_cfg(icfg, DATASETS, default_args) datasets.append(ds) return datasets
def build_datasets(cfg, default_args=None): datasets = [] for icfg in cfg: ds = build_from_cfg(icfg, DATASETS, default_args) logger.info(f'current dataset length {len(ds)}') datasets.append(ds) return datasets
def build_transform(cfg): tfs = [] for icfg in cfg: tf = build_from_cfg(icfg, TRANSFORMS) tfs.append(tf) aug = Compose(tfs) return aug
def get_model(file_config, weights): cfg = Config.fromfile(file_config) deploy_cfg = cfg['deploy'] model = build_from_cfg(deploy_cfg['model'], MODELS) checkpoint = torch.load(weights, map_location='cpu') model.load_state_dict(checkpoint['state_dict']) return model
def build_torch_nn(cfg, default_args=None): module = build_from_cfg(cfg, nn, default_args, 'module') return module
def build_decoder(cfg, default_args=None): decoder = build_from_cfg(cfg, DECODERS, default_args) return decoder
def build_runner(cfg, default_args=None): runner = build_from_cfg(cfg, RUNNERS, default_args) return runner
def build_optimizer(cfg, default_args=None): optim = build_from_cfg(cfg, torch_optim, default_args, 'module') return optim
def build_criterion(cfg): #criterion = CriterionWrapper(cfg) criterion = build_from_cfg(cfg, CRITERIA, src='registry') return criterion
def build_attention(cfg, default_args=None): attention = build_from_cfg(cfg, TRANSFORMER_ATTENTIONS, default_args) return attention
def build_dataloader(cfg, default_args=None): dataloader = build_from_cfg(cfg, DATALOADERS, default_args) return dataloader
def build_brick(cfg, default_args=None): brick = build_from_cfg(cfg, BRICKS, default_args) return brick
def build_criterion(cfg): criterion = build_from_cfg(cfg, CRITERIA, src='registry') return criterion
def build_enhance_module(cfg, default_args=None): enhance_module = build_from_cfg(cfg, ENHANCE_MODULES, default_args) return enhance_module
def build_module(cfg, default_args=None): util = build_from_cfg(cfg, UTILS, default_args) return util
def build_rectificator(cfg, default_args=None): rectificator = build_from_cfg(cfg, RECTIFICATORS, default_args) return rectificator
def build_sampler(cfg, default_args=None): sampler = build_from_cfg(cfg, SAMPLER, default_args) return sampler
def build_metric(cfg, default_args=None): metric = build_from_cfg(cfg, METRICS, default_args) return metric
def build_component(cfg, default_args=None): component = build_from_cfg(cfg, COMPONENT, default_args) return component
def build_body(cfg, default_args=None): body = build_from_cfg(cfg, BODIES, default_args) return body
def build_position_encoder(cfg, default_args=None): position_encoder = build_from_cfg(cfg, POSITION_ENCODERS, default_args) return position_encoder
def build_sequence_encoder(cfg, default_args=None): sequence_encoder = build_from_cfg(cfg, SEQUENCE_ENCODERS, default_args) return sequence_encoder
def build_head(cfg, default_args=None): head = build_from_cfg(cfg, HEADS, default_args) return head
def build_lr_scheduler(cfg, default_args=None): scheduler = build_from_cfg(cfg, LR_SCHEDULERS, default_args, 'registry') return scheduler
def build_converter(cfg, default_args=None): converter = build_from_cfg(cfg, CONVERTERS, default_args=default_args) return converter
def build_backbone(cfg, default_args=None): backbone = build_from_cfg(cfg, BACKBONES, default_args) return backbone