script.py 文件源码

def deepdream(net, base_img, iter_n=11, octave_n=4, octave_scale=1.4, 
              end='inception_4c/output', clip=True, **step_params):
    #BACKUP high detail: def deepdream(net, base_img, iter_n=12, octave_n=6, octave_scale=1.6,end='inception_5b/pool_proj', clip=True, **step_params):
    #deepdream(net, base_img, iter_n=10, octave_n=7, octave_scale=1.6,end='prob', clip=False, **step_params):
    #function params>>net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_5b/5x5', 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])



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