本文介绍了一种在多线程环境下使用TensorFlow进行模型推理时遇到的问题及解决方案。通过保存和重新加载TensorFlow图，可以避免在不同线程中使用同一模型时出现的错误。
解决办法：
config = tf.ConfigProto()
#config.gpu_options.per_process_gpu_memory_fraction = 0.15
tf.Session(config=config)
global model
model = VGG16(weights='imagenet', input_shape=(224, 224, 3), pooling='max', include_top=False)
global graph
graph = tf.get_default_graph()
I had this problem when doing inference in a different thread than where I loaded my model. Here's how I fixed the problem:
Right after loading or constructing your model, save the TensorFlow graph:
graph = tf.get_default_graph()
In the other thread (or perhaps in an asynchronous event handler), do:
global graph
with graph.as_default():
(... do inference here ...)
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