build_inception_v4.py 文件源码

def _block_c(net, scope='BlockC'):
    # 8 x 8 x 1536 grid
    # default padding = SAME
    # default stride = 1
    with tf.variable_scope(scope):
        with tf.variable_scope('Br1_Pool'):
            br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
            br1 = layers.conv2d(br1, 256, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br2_1x1'):
            br2 = layers.conv2d(net, 256, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br3_3x3'):
            br3 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
            br3a = layers.conv2d(br3, 256, [1, 3], scope='Conv2_1x3')
            br3b = layers.conv2d(br3, 256, [3, 1], scope='Conv3_3x1')
        with tf.variable_scope('Br4_7x7Dbl'):
            br4 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
            br4 = layers.conv2d(br4, 448, [1, 7], scope='Conv2_1x7')
            br4 = layers.conv2d(br4, 512, [7, 1], scope='Conv3_7x1')
            br4a = layers.conv2d(br4, 256, [1, 7], scope='Conv4a_1x7')
            br4b = layers.conv2d(br4, 256, [7, 1], scope='Conv4b_7x1')
        net = tf.concat(3, [br1, br2, br3a, br3b, br4a, br4b], name='Concat1')
        # 8 x 8 x 1536
    return net