Function fpn_model

examples/FasterRCNN/modeling/model_fpn.py:22–68

Args: features ([tf.Tensor]): ResNet features c2-c5 Returns: [tf.Tensor]: FPN features p2-p6

(features)

@layer_register(log_shape=True)
def fpn_model(features):
    """
    Args:
        features ([tf.Tensor]): ResNet features c2-c5

    Returns:
        [tf.Tensor]: FPN features p2-p6
    """
    assert len(features) == 4, features
    num_channel = cfg.FPN.NUM_CHANNEL

    use_gn = cfg.FPN.NORM == 'GN'

    def upsample2x(name, x):
        try:
            resize = tf.compat.v2.image.resize_images
            with tf.name_scope(name):
                shp2d = tf.shape(x)[2:]
                x = tf.transpose(x, [0, 2, 3, 1])
                x = resize(x, shp2d * 2, 'nearest')
                x = tf.transpose(x, [0, 3, 1, 2])
                return x
        except AttributeError:
            return FixedUnPooling(
                name, x, 2, unpool_mat=np.ones((2, 2), dtype='float32'),
                data_format='channels_first')

    with argscope(Conv2D, data_format='channels_first',
                  activation=tf.identity, use_bias=True,
                  kernel_initializer=tf.variance_scaling_initializer(scale=1.)):
        lat_2345 = [Conv2D('lateral_1x1_c{}'.format(i + 2), c, num_channel, 1)
                    for i, c in enumerate(features)]
        if use_gn:
            lat_2345 = [GroupNorm('gn_c{}'.format(i + 2), c) for i, c in enumerate(lat_2345)]
        lat_sum_5432 = []
        for idx, lat in enumerate(lat_2345[::-1]):
            if idx == 0:
                lat_sum_5432.append(lat)
            else:
                lat = lat + upsample2x('upsample_lat{}'.format(6 - idx), lat_sum_5432[-1])
                lat_sum_5432.append(lat)
        p2345 = [Conv2D('posthoc_3x3_p{}'.format(i + 2), c, num_channel, 3)
                 for i, c in enumerate(lat_sum_5432[::-1])]
        if use_gn:
            p2345 = [GroupNorm('gn_p{}'.format(i + 2), c) for i, c in enumerate(p2345)]
        p6 = MaxPooling('maxpool_p6', p2345[-1], pool_size=1, strides=2, data_format='channels_first', padding='VALID')
        return p2345 + [p6]


@under_name_scope()