(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1)
return tf.saturate_cast(images, tf.uint8)
Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
    Can be used as an output transformation for Network.run().
Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
    Can be used as an output transformation for Network.run().
Convert a minibatch of images from float32 to uint8 with configurable dynamic range . Can be used as an output transformation for Network . run () .
def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
    Can be used as an output transformation for Network.run().
    """
    images = tf.cast(images, tf.float32)
    if shrink > 1:
        ksize = [1, 1, shrink, shrink]
        images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
    if nchw_to_nhwc:
        images = tf.transpose(images, [0, 2, 3, 1])
    scale = 255 / (drange[1] - drange[0])
    images = images * scale + (0.5 - drange[0] * scale)
    return tf.saturate_cast(images, tf.uint8)
def convert_images_to_uint8 ( images , drange = [ - 1 , 1 ] , nchw_to_nhwc = False , shrink = 1 ) : images = tf . cast ( images , tf . float32 ) if shrink > 1 : ksize = [ 1 , 1 , shrink , shrink ] images = tf . nn . avg_pool ( images , ksize = ksize , strides = ksize , padding = "VALID" , data_format = "NCHW" ) if nchw_to_nhwc : images = tf . transpose ( images , [ 0 , 2 , 3 , 1 ] ) scale = 255 / ( drange [ 1 ] - drange [ 0 ] ) images = images * scale + ( 0.5 - drange [ 0 ] * scale ) return tf . saturate_cast ( images , tf . uint8 )