def __init__(self, in_size, ch, out_size, stride=2):
    super(BottleNeckA, self).__init__()
    initialW = initializers.HeNormal()
    with self.init_scope():
        self.conv1 = L.Convolution2D(in_size, ch, 1, stride, 0, initialW=initialW, nobias=True)
        self.bn1 = L.BatchNormalization(ch)
        self.conv2 = L.Convolution2D(ch, ch, 3, 1, 1, initialW=initialW, nobias=True)
        self.bn2 = L.BatchNormalization(ch)
        self.conv3 = L.Convolution2D(ch, out_size, 1, 1, 0, initialW=initialW, nobias=True)
        self.bn3 = L.BatchNormalization(out_size)
        self.conv4 = L.Convolution2D(in_size, out_size, 1, stride, 0, initialW=initialW, nobias=True)
        self.bn4 = L.BatchNormalization(out_size)
def __init__(self, in_size, ch, out_size, stride=2):
    super(BottleNeckA, self).__init__()
    initialW = initializers.HeNormal()
    with self.init_scope():
        self.conv1 = L.Convolution2D(in_size, ch, 1, stride, 0, initialW=initialW, nobias=True)
        self.bn1 = L.BatchNormalization(ch)
        self.conv2 = L.Convolution2D(ch, ch, 3, 1, 1, initialW=initialW, nobias=True)
        self.bn2 = L.BatchNormalization(ch)
        self.conv3 = L.Convolution2D(ch, out_size, 1, 1, 0, initialW=initialW, nobias=True)
        self.bn3 = L.BatchNormalization(out_size)
        self.conv4 = L.Convolution2D(in_size, out_size, 1, stride, 0, initialW=initialW, nobias=True)
        self.bn4 = L.BatchNormalization(out_size)