translate.py 文件源码

def translate(U, theta, out_height, out_width):
    num_batch = tf.shape(U)[0]
    height, width, num_ch = U.get_shape()[1:]
    height = height.value
    width = width.value
    num_ch = num_ch.value
    hwc = height*width*num_ch

    nind = tf.range(num_batch)
    x = repeat(tf.range(height), width)
    y = tf.tile(tf.range(width), tf.pack([height]))
    cind = tf.range(num_ch)

    nind = tf.expand_dims(repeat(nind, hwc), 1)
    x = tf.tile(tf.expand_dims(repeat(x, num_ch), 1), tf.pack([num_batch,1]))
    y = tf.tile(tf.expand_dims(repeat(y, num_ch), 1), tf.pack([num_batch,1]))
    cind = tf.tile(tf.expand_dims(cind, 1), tf.pack([num_batch*height*width,1]))

    dx, dy = tf.split(1, 2, theta)
    dx = tf.cast(tf.clip_by_value(dx, 0, out_height-height), 'int32')
    dx = tf.reshape(tf.tile(dx, tf.pack([1,hwc])), [-1,1])
    dy = tf.cast(tf.clip_by_value(dy, 0, out_width-width), 'int32')
    dy = tf.reshape(tf.tile(dy, tf.pack([1,hwc])), [-1,1])
    x = x + dx
    y = y + dy

    tind = tf.concat(1, [nind, x, y, cind])
    val = tf.reshape(U, [-1])
    T = tf.sparse_to_dense(tind,
            tf.pack([num_batch, out_height, out_width, num_ch]),
            val)
    T.set_shape([None, out_height, out_width, num_ch])
    return T