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# 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
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