Source code for autogl.module.nas.algorithm.random_search

import torch
import torch.nn as nn
import torch.nn.functional as F

from . import register_nas_algo
from .base import BaseNAS
from import BaseSpace
from ..utils import (
from tqdm import tqdm
from .rl import PathSamplingLayerChoice, PathSamplingInputChoice
import numpy as np
from ....utils import get_logger

LOGGER = get_logger("random_search_NAS")

[docs]@register_nas_algo("random") class RandomSearch(BaseNAS): """ Uniformly random architecture search Parameters ---------- device : str or torch.device The device of the whole process, e.g. "cuda", torch.device("cpu") num_epochs : int Number of epochs planned for training. disable_progeress: boolean Control whether show the progress bar. """ def __init__(self, device="auto", num_epochs=400, disable_progress=False, hardware_metric_limit=None): super().__init__(device) self.num_epochs = num_epochs self.disable_progress = disable_progress self.hardware_metric_limit = hardware_metric_limit
[docs] def search(self, space: BaseSpace, dset, estimator): self.estimator = estimator self.dataset = dset = space self.nas_modules = [] k2o = get_module_order( replace_layer_choice(, PathSamplingLayerChoice, self.nas_modules) replace_input_choice(, PathSamplingInputChoice, self.nas_modules) self.nas_modules = sort_replaced_module(k2o, self.nas_modules) selection_range = {} for k, v in self.nas_modules: selection_range[k] = len(v) self.selection_dict = selection_range # arch_perfs = [] cache = {} with tqdm(range(self.num_epochs), disable=self.disable_progress) as bar: for i in bar: selection = self.sample() vec = tuple(list(selection.values())) if vec not in cache: self.arch = space.parse_model(selection, self.device) metric, loss, hardware_metric = self._infer(mask="val") if self.hardware_metric_limit is None or hardware_metric[0] < self.hardware_metric_limit: arch_perfs.append([metric, selection]) cache[vec] = metric bar.set_postfix(acc=metric, max_acc=max(cache.values())) selection = arch_perfs[np.argmax([x[0] for x in arch_perfs])][1] arch = space.parse_model(selection, self.device) return arch
def sample(self): # uniformly sample selection = {} for k, v in self.selection_dict.items(): selection[k] = np.random.choice(range(v)) return selection def _infer(self, mask="train"): metric, loss = self.estimator.infer(self.arch._model, self.dataset, mask=mask) return metric[0], loss, metric[1:]