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Configuration error
Configuration error
| # EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction | |
| # Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han | |
| # International Conference on Computer Vision (ICCV), 2023 | |
| import os | |
| from inspect import signature | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| __all__ = [ | |
| "is_parallel", | |
| "get_device", | |
| "get_same_padding", | |
| "resize", | |
| "build_kwargs_from_config", | |
| "load_state_dict_from_file", | |
| ] | |
| def is_parallel(model: nn.Module) -> bool: | |
| return isinstance( | |
| model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) | |
| ) | |
| def get_device(model: nn.Module) -> torch.device: | |
| return model.parameters().__next__().device | |
| def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]: | |
| if isinstance(kernel_size, tuple): | |
| return tuple([get_same_padding(ks) for ks in kernel_size]) | |
| else: | |
| assert kernel_size % 2 > 0, "kernel size should be odd number" | |
| return kernel_size // 2 | |
| def resize( | |
| x: torch.Tensor, | |
| size: any or None = None, | |
| scale_factor: list[float] or None = None, | |
| mode: str = "bicubic", | |
| align_corners: bool or None = False, | |
| ) -> torch.Tensor: | |
| if mode in {"bilinear", "bicubic"}: | |
| return F.interpolate( | |
| x, | |
| size=size, | |
| scale_factor=scale_factor, | |
| mode=mode, | |
| align_corners=align_corners, | |
| ) | |
| elif mode in {"nearest", "area"}: | |
| return F.interpolate(x, size=size, scale_factor=scale_factor, mode=mode) | |
| else: | |
| raise NotImplementedError(f"resize(mode={mode}) not implemented.") | |
| def build_kwargs_from_config(config: dict, target_func: callable) -> dict[str, any]: | |
| valid_keys = list(signature(target_func).parameters) | |
| kwargs = {} | |
| for key in config: | |
| if key in valid_keys: | |
| kwargs[key] = config[key] | |
| return kwargs | |
| def load_state_dict_from_file( | |
| file: str, only_state_dict=True | |
| ) -> dict[str, torch.Tensor]: | |
| file = os.path.realpath(os.path.expanduser(file)) | |
| checkpoint = torch.load(file, map_location="cpu") | |
| if only_state_dict and "state_dict" in checkpoint: | |
| checkpoint = checkpoint["state_dict"] | |
| return checkpoint | |