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darts_bohamiann.py
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darts_bohamiann.py
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import time
import os
import logging
import ConfigSpace as CS
import ConfigSpace.hyperparameters as CSH
import logging
logging.basicConfig(level=logging.INFO)
import sys
from fmin.bohamiann import bohamiann
from utils import get_config_dictionary, get_upper_lower, save_results_optimisation
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils
import torchvision
import hpbandster.core.result as hpres
# import hpbandster.visualization as hpvis
import torchvision.transforms as transforms
from torch.autograd import Variable
from torch.utils.data.sampler import SubsetRandomSampler
from settings import get
import utils
import genotypes
from model import NetworkKMNIST as Network
from train import train, infer
from datasets import K49, KMNIST
import pickle
class BohamiannWorker(object):
def __init__(self, run_dir, experiment_no=1, init_channels=get('init_channels'), batch_size=get('batch_size'), split=0.8, dataset=KMNIST, **kwargs):
'''
Worker to implement Bohamiann. Bohamiann uses Bayesian neural networks to model the objective function [1] inside
Bayesian optimization. Bayesian neural networks usually scale better with the number of function evaluations and
the number of dimensions than Gaussian processes.
[1] Bayesian optimization with robust Bayesian neural networks
J. T. Springenberg and A. Klein and S. Falkner and F. Hutter
Advances in Neural Information Processing Systems 29
'''
super().__init__(**kwargs)
self.init_channels = init_channels
self.run_dir = run_dir
data_augmentations = transforms.ToTensor()
self.train_dataset = dataset('./data', True, data_augmentations)
self.test_dataset = dataset('./data', False, data_augmentations)
self.n_classes = self.train_dataset.n_classes
self.split = split
self.batch_size = batch_size
if 'seed' in kwargs:
self.seed = kwargs['seed']
else:
self.seed = 0
self.experiment_no = experiment_no
def compute(self, x, budget, config, **kwargs):
"""
Get model with hyperparameters from config generated by get_configspace()
"""
config = get_config_dictionary(x, config)
print("config", config)
if (len(config.keys()) < len(x)):
return 100
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
gpu = 'cuda:0'
np.random.seed(self.seed)
torch.cuda.set_device(gpu)
cudnn.benchmark = True
torch.manual_seed(self.seed)
cudnn.enabled=True
torch.cuda.manual_seed(self.seed)
logging.info('gpu device = %s' % gpu)
logging.info("config = %s", config)
genotype = eval("genotypes.%s" % 'PCDARTS')
model = Network(self.init_channels, self.n_classes, config['n_conv_layers'], genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if config['optimizer'] == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
lr=config['initial_lr'],
momentum=0.9,
weight_decay=config['weight_decay'],
nesterov=True)
else:
optimizer = get('opti_dict')[config['optimizer']](model.parameters(), lr=config['initial_lr'], weight_decay=config['weight_decay'])
if config['lr_scheduler'] == 'Cosine':
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, int(budget))
elif config['lr_scheduler'] == 'Exponential':
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
indices = list(range(int(self.split*len(self.train_dataset))))
valid_indices = list(range(int(self.split*len(self.train_dataset)), len(self.train_dataset)))
print("Training size=", len(indices))
training_sampler = SubsetRandomSampler(indices)
valid_sampler = SubsetRandomSampler(valid_indices)
train_queue = torch.utils.data.DataLoader(dataset=self.train_dataset,
batch_size=self.batch_size,
sampler=training_sampler)
valid_queue = torch.utils.data.DataLoader(dataset=self.train_dataset,
batch_size=self.batch_size,
sampler=valid_sampler)
for epoch in range(int(budget)):
lr_scheduler.step()
logging.info('epoch %d lr %e', epoch, lr_scheduler.get_lr()[0])
model.drop_path_prob = config['drop_path_prob'] * epoch / int(budget)
train_acc, train_obj = train(train_queue, model, criterion, optimizer, grad_clip=config['grad_clip_value'])
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
return valid_obj # Hyperband always minimizes, so we want to minimise the error, error = 1-accuracy
@staticmethod
def get_configspace():
"""
Define all the hyperparameters that need to be optimised and store them in config
"""
cs = CS.ConfigurationSpace()
n_conv_layers = CSH.UniformIntegerHyperparameter('n_conv_layers', lower=3, upper=6)
initial_lr = CSH.UniformFloatHyperparameter('initial_lr', lower=1e-3, upper=1e-1, default_value='1e-2', log=True)
optimizer = CSH.CategoricalHyperparameter('optimizer', get('opti_dict').keys())
cs.add_hyperparameters([initial_lr, optimizer, n_conv_layers])
lr_scheduler = CSH.CategoricalHyperparameter('lr_scheduler', ['Exponential', 'Cosine'])
weight_decay = CSH.UniformFloatHyperparameter('weight_decay', lower=1e-5, upper=1e-3, default_value=3e-4, log=True)
drop_path_prob = CSH.UniformFloatHyperparameter('drop_path_prob', lower=0, upper=0.4, default_value=0.3, log=False)
grad_clip_value = CSH.UniformIntegerHyperparameter('grad_clip_value', lower=4, upper=8, default_value=5)
cs.add_hyperparameters([lr_scheduler, drop_path_prob, weight_decay, grad_clip_value])
return cs
def run_bohamiann(self, iterations=20):
cs = self.__class__.get_configspace()
lower, upper = get_upper_lower(cs)
results = bohamiann(self.compute, lower, upper, num_iterations=iterations, cs=cs)
if not os.path.exists(self.run_dir):
os.mkdir(self.run_dir)
log_dir = os.path.join(self.run_dir, f'EXP{self.experiment_no}')
if not os.path.exists(log_dir):
os.mkdir(log_dir)
save_results_optimisation(results, log_dir)
x_best = results["x_opt"]
self.experiment_no += 1
print('Best run', x_best)
return x_best
if __name__ =='__main__':
worker = BohamiannWorker('./bohamiann', experiment_no=1)
worker.run_bohamiann()