def get_model(args):
# create model
if args.arch == 'wide-resnet':
model = model_wideresnet.WideResNet(args.wrn_depth, args.num_classes,
args.wrn_widen_factor,
drop_rate=args.wrn_droprate)
elif args.arch == 'mlp':
n_units = [int(x) for x in args.mlp_spec.split('x')] # hidden dims
n_units.append(args.num_classes) # output dim
n_units.insert(0, 32 * 32 * 3) # input dim
model = model_mlp.MLP(n_units)
elif args.arch == 'resnet18':
model = model_resnet.resnet18(args.num_classes)
elif args.arch == 'resnet34':
model = model_resnet.resnet34(args.num_classes)
elif args.arch == 'resnet50':
model = model_resnet.resnet50(args.num_classes)
elif args.arch == 'vgg16':
model = model_vgg.vgg16(args.num_classes)
# for training on multiple GPUs.
# Use CUDA_VISIBLE_DEVICES=0,1 to specify which GPUs to use
# model = torch.nn.DataParallel(model).cuda()
model = model.cuda()
return model
apps_train = apps_npb
temp = list(set(libdata.apps) - set(list(apps_train)))
apps_validation = temp
print("validation apps: ", apps_validation)
train_start = time.time()
for hi in machines:
if ml_method == 'xgb':
model = model_xgb.XGBoost()
if ml_method == 'lr':
model = model_lr.LR()
if ml_method == 'svr':
model = model_svr.SVM()
if ml_method == 'gp':
model = model_gp.GPR()
if ml_method == 'mlp':
model = model_mlp.MLP()
model.init(**params)
totrain.append((model, hi, nTrain, dt, apps_train))
lmodels = pool.map(trainModel, totrain)
print("finish: %s\t%d" % (str(datetime.datetime.now()), eval_times))
for i in range(len(lmodels)):
models[i + 1] = lmodels[i]
train_time += (time.time() - train_start)
pool.close()
pool.join()
start = time.time()
#print(start)
res = evalAccuracy(apps_validation=apps_validation)
pred_time += (time.time() - start)
pool = mp.Pool(mp.cpu_count())
# test_resdf = testPop(nTests = NTESTS)