def __init__(self, data, target, n_outputs, gpu=-1):
self.model = alex.Alex(n_outputs)
self.model_name = 'alex'
if gpu >= 0:
self.model.to_gpu()
self.gpu = gpu
self.x_feature = data
self.y_feature = target
# lossが発散したので学習率を変更できるように
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model)
parser.add_argument('--arch', '-a', default='alexnet',
help='Convnet architecture \
(alex, googlenet, vgga, overfeat)')
parser.add_argument('--batchsize', '-B', type=int, default=128,
help='minibatch size')
parser.add_argument('--gpu', '-g', default=0, type=int,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
xp = cuda.cupy if args.gpu >= 0 else np
# Prepare model
print(args.arch)
if args.arch == 'alexnet':
import alex
model = alex.Alex()
elif args.arch == 'googlenet':
import googlenet
model = googlenet.GoogLeNet()
elif args.arch == 'vgga':
import vgga
model = vgga.vgga()
elif args.arch == 'overfeat':
import overfeat
model = overfeat.overfeat()
else:
raise ValueError('Invalid architecture name')
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
def __init__(self, node_name: str):
super().__init__(node_name)
self.model_path = "{}/AlexlikeMSGD.model".format(MODEL_PATH)
self.alex = chainer.links.Classifier(alex.Alex())
self.create_subscription(Image, "/gender_predictor/color/image", self.callback_image, 1)
self.pub_result = self.create_publisher(PredictResult, "/gender_predictor/result", 10)
parser.add_argument('--gpu', '-g', type=int, default=0, help='gpu to use')
parser.add_argument('--cudnn',
'-c',
action='store_true',
help='True if using cudnn')
parser.add_argument('--batchsize',
'-b',
type=int,
default=None,
help='batchsize. If None, '
'batchsize is architecture-specific batchsize is used.')
args = parser.parse_args()
if args.model == 'alex':
import alex
model = alex.Alex(args.batchsize, args.cudnn)
elif args.model == 'overfeat':
import overfeat
model = overfeat.Overfeat(args.batchsize, args.cudnn)
elif args.model == 'vgg':
import vgg
model = vgg.VGG(args.batchsize, args.cudnn)
elif args.model.startswith('conv'):
import conv
number = args.model[4:]
model = getattr(conv, 'Conv{}'.format(number))(args.batchsize, args.cudnn)
else:
raise ValueError('Invalid model name')
print('Architecture\t{}'.format(args.model))
print('Iteration\t{}'.format(args.iteration))
