from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Tuple
import torch
from torch import nn
@dataclass
class MMArg:
vid: Any
txt: Any
def get_args(key: str, args: List[Any]) -> List[Any]:
return [getattr(v, key) if isinstance(v, MMArg) else v for v in args]
def get_kwargs(key: str, kwargs: Dict[str, Any]) -> Dict[str, Any]:
return {k: getattr(v, key) if isinstance(v, MMArg) else v for k, v in kwargs.items()}
class MMModule(nn.Module):
def __init__(
self,
module: Callable[..., nn.Module],
*args,
shared_weights: bool = False,
**kwargs,
):
super().__init__()
self.shared_weights = shared_weights
if self.shared_weights:
assert get_args("vid", args) == get_args("txt", args)
assert get_kwargs("vid", kwargs) == get_kwargs("txt", kwargs)
self.all = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
else:
self.vid = module(*get_args("vid", args), **get_kwargs("vid", kwargs))
self.txt = module(*get_args("txt", args), **get_kwargs("txt", kwargs))
def forward(
self,
vid: torch.FloatTensor,
txt: torch.FloatTensor,
*args,
**kwargs,
) -> Tuple[
torch.FloatTensor,
torch.FloatTensor,
]:
vid_module = self.vid if not self.shared_weights else self.all
txt_module = self.txt if not self.shared_weights else self.all
vid = vid_module(vid, *get_args("vid", args), **get_kwargs("vid", kwargs))
txt = txt_module(txt, *get_args("txt", args), **get_kwargs("txt", kwargs))
return vid, txt