Python get_bootstrap_dataset_config 예제들

예제 #1

def build_inference_based_loaders(
        cfg: CfgNode, model: torch.nn.Module) -> List[InferenceBasedLoader]:
    loaders = []
    ratios = []
    for dataset_spec in cfg.BOOTSTRAP_DATASETS:
        dataset_cfg = get_bootstrap_dataset_config().clone()
        dataset_cfg.merge_from_other_cfg(CfgNode(dataset_spec))
        loader = build_inference_based_loader(cfg, dataset_cfg, model)
        loaders.append(loader)
        ratios.append(dataset_cfg.RATIO)
    return loaders, ratios

예제 #2

def build_inference_based_loaders(
    cfg: CfgNode, model: torch.nn.Module
) -> Tuple[List[InferenceBasedLoader], List[float]]:
    loaders = []
    ratios = []
    embedder = build_densepose_embedder(cfg).to(device=model.device)  # pyre-ignore[16]
    for dataset_spec in cfg.BOOTSTRAP_DATASETS:
        dataset_cfg = get_bootstrap_dataset_config().clone()
        dataset_cfg.merge_from_other_cfg(CfgNode(dataset_spec))
        loader = build_inference_based_loader(cfg, dataset_cfg, model, embedder)
        loaders.append(loader)
        ratios.append(dataset_cfg.RATIO)
    return loaders, ratios