Question

我正在使用Google的TensorFlow DeepDream实现（https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb）对目录中的图像进行反复的深梦。

我的代码如下：

model_fn = tensorflow_inception_graph.pb

# creating TensorFlow session and loading the model
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':t_preprocessed})



def render_deepdream(t_obj, img0=img_noise,
                     iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
    t_score = tf.reduce_mean(t_obj) # defining the optimization objective
    t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!

    # split the image into a number of octaves
    img = img0
    octaves = []
    for i in range(octave_n-1):
        hw = img.shape[:2]
        lo = resize(img, np.int32(np.float32(hw)/octave_scale))
        hi = img-resize(lo, hw)
        img = lo
        octaves.append(hi)

    # generate details octave by octave
    for octave in range(octave_n):
        if octave>0:
            hi = octaves[-octave]
            img = resize(img, hi.shape[:2])+hi
        for i in range(iter_n):
            g = calc_grad_tiled(img, t_grad)
            img += g*(step / (np.abs(g).mean()+1e-7))
            #print('.',end = ' ')
        #clear_output()
    #showarray(img/255.0)
    return img/255.0


def morphPicture(filename1,filename2,blend,width):
    img1 = PIL.Image.open(filename1)
    img2 = PIL.Image.open(filename2)
    if width is not 0:
        img2 = resizePicture(filename2,width)
    finalImage= PIL.Image.blend(img1, img2, blend)
    del img1
    del img2
    return finalImage

def save_array(arr, name,direc, ext="png"):
    img = np.uint8(np.clip(arr, 0, 1)*255)
    img =cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    cv2.imwrite("{d}/{n}.{e}".format(d=direc, n=name, e=ext), img)
    del img

framesDir = "my directory"
os.chdir(framesDir)

outputDir ="my directory"
for file in os.listdir(framesDir):
    img0 = PIL.Image.open(file)
    img0 = np.float32(img0)
    dreamedImage = render_deepdream(tf.square(T('mixed4c')),img0,iter_n=3,octave_n=6)
    save_array(dreamedImage,1,outputDir,'jpg')
    break

i=1
j=0
with tf.device('/gpu:0'):
    for file in os.listdir(framesDir):
        if j<=1: #already processed first image so we skip it here
            j+=1
            continue
        else:
            dreamedImage = "my directory"+str(i)+'.jpg' # get the previous deep dreamed frame

            img1 = file # get the next undreamed frame

            morphedImage = morphPicture(dreamedImage,img1,0.5,0) #blend the images
            morphedImage=np.float32(morphedImage)
            dreamedImage = render_deepdream(tf.square(T('mixed4c')),morphedImage,iter_n=3,octave_n=6) #deep dream a 
                                                                                                    #blend of the two frames
            i+=1
            save_array(dreamedImage,i,outputDir,'jpg') #save the dreamed image
            del dreamedImage
            del img1
            del morphedImage


            time.sleep(0.5)

每当我运行代码一个小时以上时，脚本就会因MemoryError错误而停止。我假设某个地方一定有内存泄漏，但是我找不到它。我以为，通过包含多个del语句，我可以摆脱阻塞RAM / CPU的对象，但似乎不起作用。

代码中是否存在明显缺少的对象？还是在我的代码下的某个地方，即在tensorflow中建立？

任何帮助/建议将不胜感激。谢谢。

仅供参考，目录中包含901张图片。我将Windows 7与NVIDIA GeForce GTX 980 Ti一起使用。

Answer 1

99％的情况下，使用tensorflow时，“内存泄漏”实际上是由于在迭代时不断向图形中添加操作的缘故-而不是先构建图形，然后在循环中使用它。

为循环指定设备（with tf.device('/gpu:0）的事实表明情况确实如此：通常为新节点指定设备，因为这不会影响已经定义的节点。

幸运的是，tensorflow有一个方便的工具可以发现这些错误：tf.Graph.finalize。调用该函数时，可以防止将其他节点添加到图形中。最好在迭代之前调用此函数。

因此，在您的情况下，我会在循环之前调用tf.get_default_graph().finalize()并查找可能引发的任何错误。

Tensorflow在计算时内存不足：如何查找内存泄漏？

1 个答案: