nerfstudio/nerfactory/utils/misc.py

# Copyright 2022 The Plenoptix Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Miscellaneous helper code.
"""

import hashlib
import json
from math import floor, log
from typing import Any, Callable, Dict, Union

import torch


def get_dict_to_torch(stuff, device: Union[torch.device, str] = "cpu"):
    """Set everything in the dict to the specified torch device."""
    if isinstance(stuff, dict):
        for k, v in stuff.items():
            stuff[k] = get_dict_to_torch(v, device)
        return stuff
    if isinstance(stuff, torch.Tensor):
        return stuff.to(device)
    return stuff


def get_dict_to_cpu(stuff):
    """Set everything in the dict to CPU."""
    if isinstance(stuff, dict):
        for k, v in stuff.items():
            stuff[k] = get_dict_to_cpu(v)
        return stuff
    if isinstance(stuff, torch.Tensor):
        return stuff.detach().cpu()
    return stuff


def is_not_none(var):
    """Return True if the variable var is None."""
    return not isinstance(var, type(None))


def get_masked_dict(d, mask):
    """Return a masked dictionary.
    TODO(ethan): add more asserts/checks so this doesn't have unpredictable behavior."""
    masked_dict = {}
    for key, value in d.items():
        masked_dict[key] = value[mask]
    return masked_dict


def get_hash_str_from_dict(dictionary: Dict[str, Any]) -> str:
    """MD5 hash of a dictionary. Based on
    https://www.doc.ic.ac.uk/~nuric/coding/how-to-hash-a-dictionary-in-python.html"""
    dhash = hashlib.md5()
    encoded = json.dumps(dictionary, sort_keys=True).encode()
    dhash.update(encoded)
    return dhash.hexdigest()


class IterableWrapper:  # pylint: disable=too-few-public-methods
    """A helper that will allow an instance of a class to return multiple kinds of iterables bound
    to different functions of that class.

    To use this, take an instance of a class. From that class, pass in the <instance>.<new_iter_function>
    and <instance>.<new_next_function> to the IterableWrapper constructor. By passing in the instance's
    functions instead of just the class's functions, the self argument should automatically be accounted
    for.

    Args:
        new_iter (callable): function that will be called instead as the __iter__() function
        new_next (callable): function that will be called instead as the __next__() function
        length (int): length of the iterable. If -1, the iterable will be infinite.


    Attributes:
        new_iter (callable): object's pointer to the function we are calling for __iter__()
        new_next (callable): object's pointer to the function we are calling for __next__()
        length (int): length of the iterable. If -1, the iterable will be infinite.
        i (int): current index of the iterable.

    """

    i: int

    def __init__(self, new_iter: Callable, new_next: Callable, length: int = -1):
        self.new_iter = new_iter
        self.new_next = new_next
        self.length = length

    def __next__(self):
        if self.length != -1 and self.i >= self.length:
            raise StopIteration
        self.i += 1
        return self.new_next()

    def __iter__(self):
        self.new_iter()
        self.i = 0
        return self


def human_format(num):
    """Format a number in a more human readable way"""
    units = ["", "K", "M", "B", "T", "P"]
    k = 1000.0
    magnitude = int(floor(log(num, k)))
    return f"{(num / k**magnitude):.2f} {units[magnitude]}"


def scale_dict(dictionary: Dict[Any, Any], coefficients: Dict[str, float]) -> Dict[Any, Any]:
    """Scale a dictionary in-place given a coefficients dictionary.

    Args:
        dictionary: input dict to be scaled.
        coefficients: scalar dict config for holding coefficients.

    Returns:
        Input dict scaled by coefficients.
    """
    for key in dictionary:
        if key in coefficients:
            dictionary[key] *= coefficients[key]
    return dictionary