AutoGL/autogl/module/nas/algorithm/random_search.py

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

from . import register_nas_algo
from .base import BaseNAS
from ..space import BaseSpace
from ..utils import AverageMeterGroup, replace_layer_choice, replace_input_choice, get_module_order, sort_replaced_module
from tqdm import tqdm
from .rl import PathSamplingLayerChoice,PathSamplingInputChoice
import numpy as np
from ....utils import get_logger

LOGGER = get_logger("random_search_NAS")

@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='cuda', num_epochs=400, disable_progress=False):
        super().__init__(device)
        self.num_epochs=num_epochs
        self.disable_progress=disable_progress

    def search(self, space: BaseSpace, dset, estimator):
        self.estimator=estimator
        self.dataset=dset
        self.space=space
        
        self.nas_modules = []
        k2o = get_module_order(self.space)
        replace_layer_choice(self.space, PathSamplingLayerChoice, self.nas_modules)
        replace_input_choice(self.space, 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
        
        #space_size=np.prod(list(selection_range.values()))

        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=self._infer(mask='val')
                    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