Function resnet_shortcut

examples/FasterRCNN/modeling/backbone.py:124–134

(l, n_out, stride, activation=tf.identity)

def resnet_shortcut(l, n_out, stride, activation=tf.identity):
    n_in = l.shape[1]
    if n_in != n_out:   # change dimension when channel is not the same
        # TF's SAME mode output ceil(x/stride), which is NOT what we want when x is odd and stride is 2
        # In FPN mode, the images are pre-padded already.
        if not cfg.MODE_FPN and stride == 2:
            l = l[:, :, :-1, :-1]
        return Conv2D('convshortcut', l, n_out, 1,
                      strides=stride, activation=activation)
    else:
        return l


def resnet_bottleneck(l, ch_out, stride):