def __init__(self, opt):
super().__init__()
with self.init_scope():
self.detecter = L.VGG16Layers().to_gpu(0)
self.layer_names = ['conv1_2', 'conv2_2', 'conv3_3', 'conv4_3', 'conv5_3']
if opt.perceptual_model == 'VGG19':
self.detecter = L.VGG19Layers().to_gpu(0)
self.layer_name = ['conv1_2', 'conv2_2', 'conv3_4', 'conv4_4', 'conv5_4']
self.weight = [32 ** -1,
16 ** -1,
8 ** -1,
4 ** -1,
1]
self.coef = opt.perceptual_coef
self.criterion = F.mean_absolute_error
if opt.perceptual_mode == 'MAE':
self.criterion = F.mean_absolute_error
if opt.perceptual_mode == 'MSE':
self.criterion = F.mean_squared_error
self.coef *= 0.5
def get_vgg19(batchsize):
model = L.VGG19Layers(pretrained_model=None)
model = Wrapper(model, 'fc8')
x = np.random.uniform(size=(batchsize, 3, 224, 224)).astype('f')
x = chainer.as_variable(x)
t = np.random.randint(size=(batchsize, ), low=0,
high=1000).astype(np.int32)
t = chainer.as_variable(t)
return [x, t], model
def __init__(self, last_only=False):
super(VGG, self).__init__()
self.last_only = last_only
with self.init_scope():
self.base = L.VGG19Layers()
def __init__(self):
super(VGG, self).__init__()
with self.init_scope():
self.base = L.VGG19Layers()