| |
| |
| |
|
|
| 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 |
|
|
