I use custom model for classification in Tensor flow Camera Demo.
I generated a .pb file (serialized protobuf file) and I could display the huge graph it contains.
To convert this graph to a optimized graph, as given in [https://www.oreilly.com/learning/tensorflow-on-android], the following procedure could be used:
$ bazel-bin/tensorflow/python/tools/optimize_for_inference \ --input=tf_files/retrained_graph.pb \ --output=tensorflow/examples/android/assets/retrained_graph.pb --input_names=Mul \ --output_names=final_result
Here how to find the input_names and output_names from the graph display.
When I dont use proper names, I get device crash:
E/TensorFlowInferenceInterface(16821): Failed to run TensorFlow inference with inputs:[AvgPool], outputs:[predictions] E/AndroidRuntime(16821): FATAL EXCEPTION: inference E/AndroidRuntime(16821): java.lang.IllegalArgumentException: Incompatible shapes: [1,224,224,3] vs. [32,1,1,2048] E/AndroidRuntime(16821): [[Node: dropout/dropout/mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](dropout/dropout/div, dropout/dropout/Floor)]]
>>> import tensorflow as tf >>> gf = tf.GraphDef() >>> gf.ParseFromString(open('/your/path/to/graphname.pb','rb').read())
>>> [n.name + '=>' + n.op for n in gf.node if n.op in ( 'Softmax','Placeholder')]
Then, you can get result similar to this:
But I’m not sure it’s the problem of node names regarding the error messages.
I guess you provided wrong arguements when loading the graph file or your generated graph file is something wrong?
Check this part:
E/AndroidRuntime(16821): java.lang.IllegalArgumentException: Incompatible shapes: [1,224,224,3] vs. [32,1,1,2048]
if you’re using (re)trained graph , then try this:
[n.name + '=>' + n.op for n in gf.node if n.op in ( 'Softmax','Mul')]
It seems that (re)trained graph saves input/output op name as “Mul” and “Softmax”, while optimized and/or quantized graph saves them as “Placeholder” and “Softmax”.
BTW, using retrained graph in mobile environment is not recommended according to Peter Warden’s post: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ . It’s better to use quantized or memmapped graph due to performance and file size issue, I couldn’t find out how to load memmapped graph in android though…:(
(no problem loading optimized / quantized graph in android)
Recently I came across this option directly from tensorflow:
bazel build tensorflow/tools/graph_transforms:summarize_graph bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=custom_graph_name.pb
I wrote a simple script to analyze the dependency relations in a computational graph (usually a DAG, directly acyclic graph). It’s so obvious that the inputs are the nodes that lack a input. However, outputs can be defined as any nodes in a graph because, in the weirdest but still valid case, outputs can be inputs while the other nodes are all dummy. I still define the output operations as nodes without output in the code. You could neglect it at your willing.
import tensorflow as tf def load_graph(frozen_graph_filename): with tf.io.gfile.GFile(frozen_graph_filename, "rb") as f: graph_def = tf.compat.v1.GraphDef() graph_def.ParseFromString(f.read()) with tf.Graph().as_default() as graph: tf.import_graph_def(graph_def) return graph def analyze_inputs_outputs(graph): ops = graph.get_operations() outputs_set = set(ops) inputs =  for op in ops: if len(op.inputs) == 0 and op.type != 'Const': inputs.append(op) else: for input_tensor in op.inputs: if input_tensor.op in outputs_set: outputs_set.remove(input_tensor.op) outputs = list(outputs_set) return (inputs, outputs)