How to convert a TensorFlow SavedModel graph to a Caffe model?

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I want to use MMdnn to convert a tensorflow ResNet model to other frameworks. It seems that I can only use mmconvert to read from a .pb frozen graph file.

However, when using tf.estimator.Estimator, the .pb file that it creates is a SavedModelDef. I understand this to be a wrapper around the tf GraphDef. Thus the GraphDef .pb file can be extracted from the SavedModel using freeze_graph.py.

From there, I will need the name of the input node in the tf GraphDef. But I'm unsure how to identify the name from looking at the .pbtxt. The tf.Estimator inputs with a tf.Dataset object, according to the framework.

I'm guessing there should be a tf.Placeholder somewhere that accepts the input. But I'm not sure how to find what the input node actually is.

tensorflow protocol-buffers
1 Answers

Answering my own question here. The freeze_graph utility that comes with tensorflow is useful for extracting the graphdef from the tf SavedModel format.

To find the name of the input node, make sure to saved the tf SavedModel in pbtxt format. Open it up and look for the first node of your compute graph, e.g. if using tf resnet, the first nodes will be named resnet_model/*. Find the node that feeds this node, and you will have the name of the input node to specify to MMdnn tools. I expected this to be a tf.Placeholder that the Estimator adds for inputs. This node was just named Placeholder, so that's what I specified as the input node.

First extract the compute graph.

freeze_graph --input_saved_model_dir <path/to/saved_model_dir> --output_node_names softmax --output_graph ./graph_def.pb

Then use MMdnn to convert it to caffe.

mmconvert -sf tensorflow -iw ./graph_def.pb --inNodeName Placeholder --inputShape 224,224,3 --dstNodeName softmax -df caffe -om tf_resnet
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