adaptor_base.py

# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from argparse import Namespace
from typing import NamedTuple, Optional

import torch
from torch import nn
import torch.nn.functional as F


class AdaptorInput(NamedTuple):
    images: torch.Tensor
    summary: torch.Tensor
    features: torch.Tensor
    feature_fmt: str
    patch_size: int


class RadioOutput(NamedTuple):
    summary: torch.Tensor
    features: torch.Tensor

    def to(self, *args, **kwargs):
        return RadioOutput(
            self.summary.to(*args, **kwargs) if self.summary is not None else None,
            self.features.to(*args, **kwargs) if self.features is not None else None,
        )


class AdaptorModuleBase(nn.Module):
    def __init__(
        self,
        requires_summary_and_spatial: bool,
        handles_summary_and_spatial: bool = False
    ) -> None:
        super().__init__()
        self.requires_summary_and_spatial = requires_summary_and_spatial
        self.handles_summary_and_spatial = handles_summary_and_spatial

        assert not handles_summary_and_spatial or requires_summary_and_spatial, "If handles summary and spatial, must require it too!"


class AdaptorBase(nn.Module):
    def forward(self, input: AdaptorInput) -> RadioOutput:
        raise NotImplementedError("Subclasses must implement this!")