| # Copyright 2024 Big Vision Authors. | |
| # | |
| # 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. | |
| """Few common utils used in both/all flexi-trainers.""" | |
| import functools | |
| import itertools | |
| import numpy as np | |
| def mkrng(xid, wid, step): | |
| # Need to cap at 0, for example localruns use -1. | |
| rng_key = (max(xid, 0), max(wid, 0), max(step, 0)) | |
| return np.random.default_rng(rng_key) | |
| def mkprob(x): | |
| if x is None: | |
| return x | |
| return np.array(x) / np.sum(x) | |
| def choice(values, ratios, rng=None): | |
| rng = rng or np.random.default_rng() | |
| return rng.choice(values, p=mkprob(ratios)) | |
| def mkpredictfns(predict_fn, config, template="predict_{x}"): | |
| # If we have two flexi args a=[1,2], b=[10,20], then we create a | |
| # predict_fn for all possible combinations, named "predict_a=1_b=10" etc. | |
| all_combinations = [dict(comb) for comb in itertools.product( | |
| *[[(arg, val) for val in config[arg].v] for arg in config] | |
| )] | |
| return { | |
| template.format(x="_".join(f"{k}={v}" for k, v in kw.items())): | |
| functools.partial(predict_fn, **kw) | |
| for kw in all_combinations} | |