resnetFCN.py 文件源码

def create_classifier(body, data, n_classes, l2_reg=0.):
    # Include last layers
    top = BatchNormalization(mode=0, axis=channel_idx, name="bn7")(body)
    top = Activation('relu', name="relu7")(top)
    top = AtrousConvolution2D(512, 3, 3, 'he_normal', atrous_rate=(12, 12),
                              border_mode='same', name="conv6a",
                              W_regularizer=l2(l2_reg))(top)
    top = Activation('relu', name="conv6a_relu")(top)
    name = "hyperplane_num_cls_%d_branch_%d" % (n_classes, 12)

    def my_init(shape, name=None, dim_ordering='th'):
        return initializations.normal(shape, scale=0.01, name=name)
    top = AtrousConvolution2D(n_classes, 3, 3, my_init,
                              atrous_rate=(12, 12), border_mode='same',
                              name=name, W_regularizer=l2(l2_reg))(top)

    top = Deconvolution2D(n_classes, 16, 16, top._keras_shape, bilinear_init,
                          'linear', border_mode='valid', subsample=(8, 8),
                          bias=False, name="upscaling_"+str(n_classes),
                          W_regularizer=l2(l2_reg))(top)

    top = CropLayer2D(data, name='score')(top)
    top = NdSoftmax()(top)

    return top


# Create model of basic segnet