import argparse
import nasspace
import datasets
import random
import numpy as np
import torch
import os
from scores import get_score_func
from scipy import stats
from pycls.models.nas.nas import Cell
from utils import add_dropout, init_network
parser = argparse.ArgumentParser(description='NAS Without Training')
parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
type=str, help='path to API')
parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file')
parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate')
parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch')
parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use')
parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
parser.add_argument('--GPU', default='0', type=str)
parser.add_argument('--seed', default=1, type=int)
parser.add_argument('--init', default='', type=str)
parser.add_argument('--trainval', action='store_true')
parser.add_argument('--dropout', action='store_true')
parser.add_argument('--dataset', default='cifar10', type=str)
parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network')
parser.add_argument('--n_samples', default=100, type=int)
parser.add_argument('--n_runs', default=500, type=int)
parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)')
parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)')
parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)')
parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
# Reproducibility
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
def get_batch_jacobian(net, x, target, device, args=None):
net.zero_grad()
x.requires_grad_(True)
y, out = net(x)
y.backward(torch.ones_like(y))
jacob = x.grad.detach()
return jacob, target.detach(), y.detach(), out.detach()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
savedataset = args.dataset
dataset = 'fake' if 'fake' in args.dataset else args.dataset
args.dataset = args.dataset.replace('fake', '')
if args.dataset == 'cifar10':
args.dataset = args.dataset + '-valid'
searchspace = nasspace.get_search_space(args)
if 'valid' in args.dataset:
args.dataset = args.dataset.replace('-valid', '')
train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
os.makedirs(args.save_loc, exist_ok=True)
filename = f'{args.save_loc}/{args.save_string}_{args.score}_{args.nasspace}_{savedataset}{"_" + args.init + "_" if args.init != "" else args.init}_{"_dropout" if args.dropout else ""}_{args.augtype}_{args.sigma}_{args.repeat}_{args.trainval}_{args.batch_size}_{args.maxofn}_{args.seed}'
accfilename = f'{args.save_loc}/{args.save_string}_accs_{args.nasspace}_{savedataset}_{args.trainval}'
if args.dataset == 'cifar10':
acc_type = 'ori-test'
val_acc_type = 'x-valid'
else:
acc_type = 'x-test'
val_acc_type = 'x-valid'
scores = np.zeros(len(searchspace))
try:
accs = np.load(accfilename + '.npy')
except:
accs = np.zeros(len(searchspace))
for i, (uid, network) in enumerate(searchspace):
# Reproducibility
try:
if args.dropout:
add_dropout(network, args.sigma)
if args.init != '':
init_network(network, args.init)
if 'hook_' in args.score:
network.K = np.zeros((args.batch_size, args.batch_size))
def counting_forward_hook(module, inp, out):
try:
if not module.visited_backwards:
return
if isinstance(inp, tuple):
inp = inp[0]
inp = inp.view(inp.size(0), -1)
x = (inp > 0).float()
K = x @ x.t()
K2 = (1.-x) @ (1.-x.t())
network.K = network.K + K.cpu().numpy() + K2.cpu().numpy()
except:
pass
def counting_backward_hook(module, inp, out):
module.visited_backwards = True
for name, module in network.named_modules():
if 'ReLU' in str(type(module)):
#hooks[name] = module.register_forward_hook(counting_hook)
module.register_forward_hook(counting_forward_hook)
module.register_backward_hook(counting_backward_hook)
network = network.to(device)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
s = []
for j in range(args.maxofn):
data_iterator = iter(train_loader)
x, target = next(data_iterator)
x2 = torch.clone(x)
x2 = x2.to(device)
x, target = x.to(device), target.to(device)
jacobs, labels, y, out = get_batch_jacobian(network, x, target, device, args)
if 'hook_' in args.score:
network(x2.to(device))
s.append(get_score_func(args.score)(network.K, target))
else:
s.append(get_score_func(args.score)(jacobs, labels))
scores[i] = np.mean(s)
accs[i] = searchspace.get_final_accuracy(uid, acc_type, args.trainval)
accs_ = accs[~np.isnan(scores)]
scores_ = scores[~np.isnan(scores)]
numnan = np.isnan(scores).sum()
tau, p = stats.kendalltau(accs_[:max(i-numnan, 1)], scores_[:max(i-numnan, 1)])
print(f'{tau}')
if i % 1000 == 0:
np.save(filename, scores)
np.save(accfilename, accs)
except Exception as e:
print(e)
accs[i] = searchspace.get_final_accuracy(uid, acc_type, args.trainval)
scores[i] = np.nan
np.save(filename, scores)
np.save(accfilename, accs)