guided_dreams.py 文件源码

def deepdream(net, base_img, iter_n=5, octave_n=11, octave_scale=1.4, end='inception_3b/5x5_reduce', clip=True,
              **step_params):
    # prepare base images for all octaves
    octaves = [preprocess(net, base_img)]
    for i in xrange(octave_n - 1):
        octaves.append(nd.zoom(octaves[-1], (1, 1.0 / octave_scale, 1.0 / octave_scale), order=1))

    src = net.blobs['data']
    detail = np.zeros_like(octaves[-1])  # allocate image for network-produced details
    for octave, octave_base in enumerate(octaves[::-1]):
        h, w = octave_base.shape[-2:]
        if octave > 0:
            # upscale details from the previous octave
            h1, w1 = detail.shape[-2:]
            detail = nd.zoom(detail, (1, 1.0 * h / h1, 1.0 * w / w1), order=1)

        src.reshape(1, 3, h, w)  # resize the network's input image size
        src.data[0] = octave_base + detail
        for i in xrange(iter_n):
            make_step(net, end=end, clip=clip, **step_params)

            # visualization
            vis = deprocess(net, src.data[0])
            if not clip:  # adjust image contrast if clipping is disabled
                vis = vis * (255.0 / np.percentile(vis, 99.98))
            showarray(vis)
            print octave, i, end, vis.shape
            clear_output(wait=True)

        # extract details produced on the current octave
        detail = src.data[0] - octave_base
    # returning the resulting image
    return deprocess(net, src.data[0])