def test_resize_and_fuse_features(self):
    batch, height, width, channels = 2, 11, 11, 6
    smaller_height, smaller_width, smaller_channels = 6, 6, 3
    larger_height1, larger_width1 = 21, 21  # Stride 2 conv.
    larger_height2, larger_width2 = 22, 22  # Stride 2 conv.
    larger_height3, larger_width3 = 23, 23  # Conv and resize.

    feature_list = []
    feature_list.append(tf.zeros([batch, smaller_height, smaller_width,
                                  smaller_channels]))
    feature_list.append(tf.zeros([batch, smaller_height, smaller_width,
                                  channels]))
    feature_list.append(tf.zeros([batch, height, width, smaller_channels]))
    feature_list.append(tf.zeros([batch, height, width, channels]))
    feature_list.append(tf.zeros([batch, larger_height1, larger_width1,
                                  channels]))
    feature_list.append(tf.zeros([batch, larger_height1, larger_width1,
                                  smaller_channels]))
    feature_list.append(tf.zeros([batch, larger_height2, larger_width2,
                                  smaller_channels]))
    feature_list.append(tf.zeros([batch, larger_height3, larger_width3,
                                  smaller_channels]))
    layer = resized_fuse.ResizedFuse(name='fuse',
                                     height=height,
                                     width=width,
                                     num_channels=channels)
    output = layer(feature_list)
    self.assertEqual(output.get_shape().as_list(), [batch, height, width,
                                                    channels])