pytorch3d/pytorch3d/implicitron/tools/utils.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


import collections
import dataclasses
import time
from contextlib import contextmanager
from typing import Any, Callable, Dict

import torch


@contextmanager
def evaluating(net: torch.nn.Module):
    """Temporarily switch to evaluation mode."""
    istrain = net.training
    try:
        net.eval()
        yield net
    finally:
        if istrain:
            net.train()


def try_to_cuda(t: Any) -> Any:
    """
    Try to move the input variable `t` to a cuda device.

    Args:
        t: Input.

    Returns:
        t_cuda: `t` moved to a cuda device, if supported.
    """
    try:
        t = t.cuda()
    except AttributeError:
        pass
    return t


def try_to_cpu(t: Any) -> Any:
    """
    Try to move the input variable `t` to a cpu device.

    Args:
        t: Input.

    Returns:
        t_cpu: `t` moved to a cpu device, if supported.
    """
    try:
        t = t.cpu()
    except AttributeError:
        pass
    return t


def dict_to_cuda(batch: Dict[Any, Any]) -> Dict[Any, Any]:
    """
    Move all values in a dictionary to cuda if supported.

    Args:
        batch: Input dict.

    Returns:
        batch_cuda: `batch` moved to a cuda device, if supported.
    """
    return {k: try_to_cuda(v) for k, v in batch.items()}


def dict_to_cpu(batch):
    """
    Move all values in a dictionary to cpu if supported.

    Args:
        batch: Input dict.

    Returns:
        batch_cpu: `batch` moved to a cpu device, if supported.
    """
    return {k: try_to_cpu(v) for k, v in batch.items()}


def dataclass_to_cuda_(obj):
    """
    Move all contents of a dataclass to cuda inplace if supported.

    Args:
        batch: Input dataclass.

    Returns:
        batch_cuda: `batch` moved to a cuda device, if supported.
    """
    for f in dataclasses.fields(obj):
        setattr(obj, f.name, try_to_cuda(getattr(obj, f.name)))
    return obj


def dataclass_to_cpu_(obj):
    """
    Move all contents of a dataclass to cpu inplace if supported.

    Args:
        batch: Input dataclass.

    Returns:
        batch_cuda: `batch` moved to a cpu device, if supported.
    """
    for f in dataclasses.fields(obj):
        setattr(obj, f.name, try_to_cpu(getattr(obj, f.name)))
    return obj


# TODO: test it
def cat_dataclass(batch, tensor_collator: Callable):
    """
    Concatenate all fields of a list of dataclasses `batch` to a single
    dataclass object using `tensor_collator`.

    Args:
        batch: Input list of dataclasses.

    Returns:
        concatenated_batch: All elements of `batch` concatenated to a single
            dataclass object.
        tensor_collator: The function used to concatenate tensor fields.
    """

    elem = batch[0]
    collated = {}

    for f in dataclasses.fields(elem):
        elem_f = getattr(elem, f.name)
        if elem_f is None:
            collated[f.name] = None
        elif torch.is_tensor(elem_f):
            collated[f.name] = tensor_collator([getattr(e, f.name) for e in batch])
        elif dataclasses.is_dataclass(elem_f):
            collated[f.name] = cat_dataclass(
                [getattr(e, f.name) for e in batch], tensor_collator
            )
        elif isinstance(elem_f, collections.abc.Mapping):
            collated[f.name] = {
                k: tensor_collator([getattr(e, f.name)[k] for e in batch])
                if elem_f[k] is not None
                else None
                for k in elem_f
            }
        else:
            raise ValueError("Unsupported field type for concatenation")

    return type(elem)(**collated)


class Timer:
    """
    A simple class for timing execution.

    Example:
        ```
        with Timer():
            print("This print statement is timed.")
        ```
    """

    def __init__(self, name="timer", quiet=False):
        self.name = name
        self.quiet = quiet

    def __enter__(self):
        self.start = time.time()
        return self

    def __exit__(self, *args):
        self.end = time.time()
        self.interval = self.end - self.start
        if not self.quiet:
            print("%20s: %1.6f sec" % (self.name, self.interval))