def model_from_config()

in point_e/models/configs.py [0:0]

• 18 lines of code
• 7 McCabe index (conditional complexity)

def model_from_config(config: Dict[str, Any], device: torch.device) -> nn.Module:
    config = config.copy()
    name = config.pop("name")
    if name == "PointDiffusionTransformer":
        return PointDiffusionTransformer(device=device, dtype=torch.float32, **config)
    elif name == "CLIPImagePointDiffusionTransformer":
        return CLIPImagePointDiffusionTransformer(device=device, dtype=torch.float32, **config)
    elif name == "CLIPImageGridPointDiffusionTransformer":
        return CLIPImageGridPointDiffusionTransformer(device=device, dtype=torch.float32, **config)
    elif name == "UpsamplePointDiffusionTransformer":
        return UpsamplePointDiffusionTransformer(device=device, dtype=torch.float32, **config)
    elif name == "CLIPImageGridUpsamplePointDiffusionTransformer":
        return CLIPImageGridUpsamplePointDiffusionTransformer(
            device=device, dtype=torch.float32, **config
        )
    elif name == "CrossAttentionPointCloudSDFModel":
        return CrossAttentionPointCloudSDFModel(device=device, dtype=torch.float32, **config)
    raise ValueError(f"unknown model name: {name}")