Source code for physicsnemo.datapipes.transforms.base

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"""
Transform base class - The foundation for all data transformations.
"""

from __future__ import annotations

from abc import ABC, abstractmethod
from typing import Any, Optional

import torch
from tensordict import TensorDict




class Transform(ABC):
    """
    Abstract base class for all transforms.

    Transforms operate on a TensorDict and return a modified TensorDict.
    They are designed to run on GPU tensors for maximum performance.
    Metadata is not passed to transforms (handled separately by Dataset/DataLoader).

    Subclasses must implement:

    - ``__call__(data: TensorDict) -> TensorDict``

    Optionally override:

    - ``extra_repr() -> str``: For custom repr output
    - ``state_dict() -> dict``: For serialization
    - ``load_state_dict(state_dict: dict)``: For deserialization

    Examples
    --------
    >>> class MyTransform(Transform):
    ...     def __init__(self, scale: float):
    ...         super().__init__()
    ...         self.scale = scale
    ...
    ...     def __call__(self, data: TensorDict) -> TensorDict:
    ...         # Apply transformation to all tensors
    ...         return data.apply(lambda x: x * self.scale)
    """

    def __init__(self) -> None:
        """Initialize the transform."""
        self._device: Optional[torch.device] = None

    @abstractmethod
    def __call__(self, data: TensorDict) -> TensorDict:
        """
        Apply the transform to a TensorDict.

        Parameters
        ----------
        data : TensorDict
            Input TensorDict to transform.

        Returns
        -------
        TensorDict
            Transformed TensorDict.
        """
        raise NotImplementedError



    def to(self, device: torch.device | str) -> Transform:
        """
        Move any internal tensors to the specified device.

        This default implementation automatically moves any tensor attributes
        found in self.__dict__ to the specified device. Override this method
        if your transform requires custom device handling.

        Parameters
        ----------
        device : torch.device or str
            Target device.

        Returns
        -------
        Transform
            Self for chaining.
        """
        self._device = torch.device(device) if isinstance(device, str) else device
        for name, value in self.__dict__.items():
            if isinstance(value, torch.Tensor):
                setattr(self, name, value.to(self._device))
        return self


    @property
    def device(self) -> torch.device | None:
        """
        The device this transform operates on.

        Returns
        -------
        torch.device or None
            The device, or None if not set.
        """
        return self._device



    def extra_repr(self) -> str:
        """
        Return extra information for repr.

        Override this to add transform-specific info to the repr.

        Returns
        -------
        str
            Extra representation string.
        """
        return ""




    def state_dict(self) -> dict[str, Any]:
        """
        Return a dictionary containing the transform's state.

        Override this for transforms with learnable or configurable state.

        Returns
        -------
        dict[str, Any]
            State dictionary.
        """
        return {}




    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
        """
        Load state from a state dictionary.

        Override this to restore transform state.

        Parameters
        ----------
        state_dict : dict[str, Any]
            State dictionary to load from.
        """
        pass


    def __repr__(self) -> str:
        """
        Return string representation.

        Returns
        -------
        str
            String representation of the transform.
        """
        return f"{self.__class__.__name__}({self.extra_repr()})"