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")
[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
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, 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:]