NVIDIA Modulus Sym v1.3.0
Sym v1.3.0

deeplearning/modulus/modulus-sym-v130/_modules/modulus/sym/key.html

Source code for modulus.sym.key

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Key
"""

from typing import Union, List
from functools import reduce
from .constants import diff_str, NO_OP_SCALE


[docs]class Key(object): """ Class describing keys used for graph unroll. The most basic key is just a simple string however you can also add dimension information and even information on how to scale inputs to networks. Parameters ---------- name : str String used to refer to the variable (e.g. 'x', 'y'...). size : int=1 Dimension of variable. derivatives : List=[] This signifies that this key holds a derivative with respect to that key. scale: (float, float) Characteristic location and scale of quantity: used for normalisation. """ def __init__(self, name, size=1, derivatives=[], base_unit=None, scale=NO_OP_SCALE): super(Key, self).__init__() self.name = name self.size = size self.derivatives = derivatives self.base_unit = base_unit self.scale = scale @classmethod def from_str(cls, name): split_name = name.split(diff_str) var_name = split_name[0] diff_names = Key.convert_list(split_name[1:]) return cls(var_name, size=1, derivatives=diff_names) @classmethod def from_tuple(cls, name_size): split_name = name_size[0].split(diff_str) var_name = split_name[0] diff_names = Key.convert_list(split_name[1:]) return cls(var_name, size=name_size[1], derivatives=diff_names) @classmethod def convert(cls, name_or_tuple): if isinstance(name_or_tuple, str): key = Key.from_str(name_or_tuple) elif isinstance(name_or_tuple, tuple): key = cls.from_tuple(name_or_tuple) elif isinstance(name_or_tuple, cls): key = name_or_tuple else: raise ValueError("can only convert string or tuple to key") return key @staticmethod def convert_list(ls): keys = [] for name_or_tuple in ls: keys.append(Key.convert(name_or_tuple)) return keys
[docs] @staticmethod def convert_config(key_cfg: Union[List, str]): """Converts a config input/output key string/list into a key This provides a quick alternative method for defining keys in models Parameters ---------- key_cfg : Union[List, str] Config list or string Returns ------- List[Key] List of keys generated Example ------- The following are some config examples for constructing keys in the YAML file. Defining input/output keys with size of 1 >>> arch: >>> full_connected: >>> input_keys: input >>> output_keys: output Defining input/output keys with different sizes >>> arch: >>> full_connected: >>> input_keys: [input, 2] # Key('input',size=2) >>> output_keys: [output, 3] # Key('output',size=3) Multiple input/output keys with size of 1 >>> arch: >>> full_connected: >>> input_keys: [a, b, c] >>> output_keys: [u, w, v] Multiple input/output keys with different sizes >>> arch: >>> full_connected: >>> input_keys: [[a,2], [b,3]] # Key('a',size=2), Key('b',size=3) >>> output_keys: [[u,3],w] # Key('u',size=3), Key('w',size=1) """ # Just single key name if isinstance(key_cfg, str): keys = [Key.convert(key_cfg.lstrip())] # Multiple keys elif isinstance(key_cfg, list): keys = [] for cfg_obj in key_cfg: if isinstance(cfg_obj, str): key = Key.convert(cfg_obj) keys.append(key) elif isinstance(cfg_obj, int) and len(keys) > 0: keys[-1].size = cfg_obj elif isinstance(cfg_obj, list): key_name = cfg_obj[0] key = Key.convert(key_name) try: key_size = int(cfg_obj[1]) key.size = key_size except: key.size = 1 keys.append(key) # Manually provided elif isinstance(cfg_obj, Key): keys.append(cfg_obj) else: raise ValueError(f"Invalid key parameter set in config {key_cfg}") else: raise ValueError(f"Invalid key parameter set in config {key_cfg}") return keys

@property def unit(self): return self.base_unit / reduce( lambda x, y: x.base_unit * y.base_unit, self.derivatives ) def __str__(self): diff_str = "".join(["__" + x.name for x in self.derivatives]) return self.name + diff_str def __repr__(self): return str(self) def __eq__(self, obj): return isinstance(obj, Key) and str(self) == str(obj) def __lt__(self, obj): assert isinstance(obj, Key) return str(self) < str(obj) def __gt__(self, obj): assert isinstance(obj, Key) return str(self) > str(obj) def __hash__(self): return hash(str(self))

def _length_key_list(list_keys): length = 0 for key in list_keys: length += key.size return length

© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.