# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-module for utilities for working with dictionaries."""
import collections.abc
import hashlib
import json
import torch
from collections.abc import Iterable, Mapping
from typing import Any
from .array import TENSOR_TYPE_CONVERSIONS, TENSOR_TYPES
from .string import callable_to_string, string_to_callable, string_to_slice
"""
Dictionary <-> Class operations.
"""
[docs]def class_to_dict(obj: object) -> dict[str, Any]:
"""Convert an object into dictionary recursively.
Note:
Ignores all names starting with "__" (i.e. built-in methods).
Args:
obj: An instance of a class to convert.
Raises:
ValueError: When input argument is not an object.
Returns:
Converted dictionary mapping.
"""
# check that input data is class instance
if not hasattr(obj, "__class__"):
raise ValueError(f"Expected a class instance. Received: {type(obj)}.")
# convert object to dictionary
if isinstance(obj, dict):
obj_dict = obj
elif isinstance(obj, torch.Tensor):
# We have to treat torch tensors specially because `torch.tensor.__dict__` returns an empty
# dict, which would mean that a torch.tensor would be stored as an empty dict. Instead we
# want to store it directly as the tensor.
return obj
elif hasattr(obj, "__dict__"):
obj_dict = obj.__dict__
else:
return obj
# convert to dictionary
data = dict()
for key, value in obj_dict.items():
# disregard builtin attributes
if key.startswith("__"):
continue
# check if attribute is callable -- function
if callable(value):
data[key] = callable_to_string(value)
# check if attribute is a dictionary
elif hasattr(value, "__dict__") or isinstance(value, dict):
data[key] = class_to_dict(value)
# check if attribute is a list or tuple
elif isinstance(value, (list, tuple)):
data[key] = type(value)([class_to_dict(v) for v in value])
else:
data[key] = value
return data
[docs]def update_class_from_dict(obj, data: dict[str, Any], _ns: str = "") -> None:
"""Reads a dictionary and sets object variables recursively.
This function performs in-place update of the class member attributes.
Args:
obj: An instance of a class to update.
data: Input dictionary to update from.
_ns: Namespace of the current object. This is useful for nested configuration
classes or dictionaries. Defaults to "".
Raises:
TypeError: When input is not a dictionary.
ValueError: When dictionary has a value that does not match default config type.
KeyError: When dictionary has a key that does not exist in the default config type.
"""
for key, value in data.items():
# key_ns is the full namespace of the key
key_ns = _ns + "/" + key
# check if key is present in the object
if hasattr(obj, key) or isinstance(obj, dict):
obj_mem = obj[key] if isinstance(obj, dict) else getattr(obj, key)
if isinstance(value, Mapping):
# recursively call if it is a dictionary
update_class_from_dict(obj_mem, value, _ns=key_ns)
continue
if isinstance(value, Iterable) and not isinstance(value, str):
# check length of value to be safe
if len(obj_mem) != len(value) and obj_mem is not None:
raise ValueError(
f"[Config]: Incorrect length under namespace: {key_ns}."
f" Expected: {len(obj_mem)}, Received: {len(value)}."
)
if isinstance(obj_mem, tuple):
value = tuple(value)
else:
set_obj = True
# recursively call if iterable contains dictionaries
for i in range(len(obj_mem)):
if isinstance(value[i], dict):
update_class_from_dict(obj_mem[i], value[i], _ns=key_ns)
set_obj = False
# do not set value to obj, otherwise it overwrites the cfg class with the dict
if not set_obj:
continue
elif callable(obj_mem):
# update function name
value = string_to_callable(value)
elif isinstance(value, type(obj_mem)) or value is None:
pass
else:
raise ValueError(
f"[Config]: Incorrect type under namespace: {key_ns}."
f" Expected: {type(obj_mem)}, Received: {type(value)}."
)
# set value
if isinstance(obj, dict):
obj[key] = value
else:
setattr(obj, key, value)
else:
raise KeyError(f"[Config]: Key not found under namespace: {key_ns}.")
"""
Dictionary <-> Hashable operations.
"""
[docs]def dict_to_md5_hash(data: object) -> str:
"""Convert a dictionary into a hashable key using MD5 hash.
Args:
data: Input dictionary or configuration object to convert.
Returns:
A string object of double length containing only hexadecimal digits.
"""
# convert to dictionary
if isinstance(data, dict):
encoded_buffer = json.dumps(data, sort_keys=True).encode()
else:
encoded_buffer = json.dumps(class_to_dict(data), sort_keys=True).encode()
# compute hash using MD5
data_hash = hashlib.md5()
data_hash.update(encoded_buffer)
# return the hash key
return data_hash.hexdigest()
"""
Dictionary operations.
"""
[docs]def convert_dict_to_backend(
data: dict, backend: str = "numpy", array_types: Iterable[str] = ("numpy", "torch", "warp")
) -> dict:
"""Convert all arrays or tensors in a dictionary to a given backend.
This function iterates over the dictionary, converts all arrays or tensors with the given types to
the desired backend, and stores them in a new dictionary. It also works with nested dictionaries.
Currently supported backends are "numpy", "torch", and "warp".
Note:
This function only converts arrays or tensors. Other types of data are left unchanged. Mutable types
(e.g. lists) are referenced by the new dictionary, so they are not copied.
Args:
data: An input dict containing array or tensor data as values.
backend: The backend ("numpy", "torch", "warp") to which arrays in this dict should be converted.
Defaults to "numpy".
array_types: A list containing the types of arrays that should be converted to
the desired backend. Defaults to ("numpy", "torch", "warp").
Raises:
ValueError: If the specified ``backend`` or ``array_types`` are unknown, i.e. not in the list of supported
backends ("numpy", "torch", "warp").
Returns:
The updated dict with the data converted to the desired backend.
"""
# THINK: Should we also support converting to a specific device, e.g. "cuda:0"?
# Check the backend is valid.
if backend not in TENSOR_TYPE_CONVERSIONS:
raise ValueError(f"Unknown backend '{backend}'. Supported backends are 'numpy', 'torch', and 'warp'.")
# Define the conversion functions for each backend.
tensor_type_conversions = TENSOR_TYPE_CONVERSIONS[backend]
# Parse the array types and convert them to the corresponding types: "numpy" -> np.ndarray, etc.
parsed_types = list()
for t in array_types:
# Check type is valid.
if t not in TENSOR_TYPES:
raise ValueError(f"Unknown array type: '{t}'. Supported array types are 'numpy', 'torch', and 'warp'.")
# Exclude types that match the backend, since we do not need to convert these.
if t == backend:
continue
# Convert the string types to the corresponding types.
parsed_types.append(TENSOR_TYPES[t])
# Convert the data to the desired backend.
output_dict = dict()
for key, value in data.items():
# Obtain the data type of the current value.
data_type = type(value)
# -- arrays
if data_type in parsed_types:
# check if we have a known conversion.
if data_type not in tensor_type_conversions:
raise ValueError(f"No registered conversion for data type: {data_type} to {backend}!")
# convert the data to the desired backend.
output_dict[key] = tensor_type_conversions[data_type](value)
# -- nested dictionaries
elif isinstance(data[key], dict):
output_dict[key] = convert_dict_to_backend(value)
# -- everything else
else:
output_dict[key] = value
return output_dict
[docs]def update_dict(orig_dict: dict, new_dict: collections.abc.Mapping) -> dict:
"""Updates existing dictionary with values from a new dictionary.
This function mimics the dict.update() function. However, it works for
nested dictionaries as well.
Args:
orig_dict: The original dictionary to insert items to.
new_dict: The new dictionary to insert items from.
Returns:
The updated dictionary.
"""
for keyname, value in new_dict.items():
if isinstance(value, collections.abc.Mapping):
orig_dict[keyname] = update_dict(orig_dict.get(keyname, {}), value)
else:
orig_dict[keyname] = value
return orig_dict
[docs]def replace_slices_with_strings(data: dict) -> dict:
"""Replace slice objects with their string representations in a dictionary.
Args:
data: The dictionary to process.
Returns:
The dictionary with slice objects replaced by their string representations.
"""
if isinstance(data, dict):
return {k: replace_slices_with_strings(v) for k, v in data.items()}
elif isinstance(data, slice):
return f"slice({data.start},{data.stop},{data.step})"
else:
return data
[docs]def replace_strings_with_slices(data: dict) -> dict:
"""Replace string representations of slices with slice objects in a dictionary.
Args:
data: The dictionary to process.
Returns:
The dictionary with string representations of slices replaced by slice objects.
"""
if isinstance(data, dict):
return {k: replace_strings_with_slices(v) for k, v in data.items()}
elif isinstance(data, str) and data.startswith("slice("):
return string_to_slice(data)
else:
return data
[docs]def print_dict(val, nesting: int = -4, start: bool = True):
"""Outputs a nested dictionary."""
if isinstance(val, dict):
if not start:
print("")
nesting += 4
for k in val:
print(nesting * " ", end="")
print(k, end=": ")
print_dict(val[k], nesting, start=False)
else:
# deal with functions in print statements
if callable(val):
print(callable_to_string(val))
else:
print(val)
