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
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