nnUtils.py 文件源码

def BinarizedWeightOnlySpatialConvolution(nOutputPlane, kW, kH, dW=1, dH=1,
        padding='VALID', bias=True, reuse=None, name='BinarizedWeightOnlySpatialConvolution'):
    '''
    This function is used only at the first layer of the model as we dont want to binarized the RGB images
    '''
    def bc_conv2d(x, is_training=True):
        nInputPlane = x.get_shape().as_list()[3]
        with tf.variable_op_scope([x], None, name, reuse=reuse):
            w = tf.get_variable('weight', [kH, kW, nInputPlane, nOutputPlane],
                            initializer=tf.contrib.layers.xavier_initializer_conv2d())
            bin_w = binarize(w)
            out = tf.nn.conv2d(x, bin_w, strides=[1, dH, dW, 1], padding=padding)
            if bias:
                b = tf.get_variable('bias', [nOutputPlane],initializer=tf.zeros_initializer)
                out = tf.nn.bias_add(out, b)
            return out
    return bc_conv2d