我是'm iteratively deepdreaming images in a directory using the Google'的TensorFlow DeepDream实现(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb) .
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)
每当我运行代码超过一个小时,脚本就会因MemoryError而停止 . 我'm assuming there must be a memory leak somewhere, but I'我无法找到它 . 我认为通过包含多个 del
语句,我会摆脱阻塞RAM / CPU的对象,但它似乎没有起作用 .
我的代码中是否存在明显的对象堆积?或者是在我的代码下面的某处,即在tensorflow内?
任何帮助/建议将不胜感激 . 谢谢 .
仅供参考,目录中有901张图片 . 我使用的是Windows 7和NVIDIA GeForce GTX 980 Ti .
1 回答
99%的时候,当使用张量流时,“内存泄漏”实际上是由于在迭代时不断添加到图形中的操作 - 而不是先构建图形,然后在循环中使用它 .
您为循环指定设备(
with tf.device('/gpu:0
)这一事实暗示了这种情况:您通常为新节点指定设备,因为这不会影响已定义的节点 .幸运的是,tensorflow有一个方便的工具来发现这些错误:tf.Graph.finalize . 调用时,此函数可防止将更多节点添加到图形中 . 在迭代之前调用此函数是一种好习惯 .
所以在你的情况下,我会在你的循环之前调用
tf.get_default_graph().finalize()
并查找它可能抛出的任何错误 .