tf-saved-model.java

import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.Tensors;
import org.tensorflow.TensorFlow;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.SavedModelBundle.Loader;
import org.tensorflow.framework.SignatureDef;
import org.tensorflow.framework.MetaGraphDef;
import org.tensorflow.framework.TensorInfo;
import org.tensorflow.example.*;
import java.util.Map;
import java.util.Arrays;




public class HelloTensorFlow {
  private static void printSignature(SavedModelBundle model) throws Exception {
    MetaGraphDef m = MetaGraphDef.parseFrom(model.metaGraphDef());
    SignatureDef sig = m.getSignatureDefOrThrow("serving_default");
    int numInputs = sig.getInputsCount();
    int i = 1;
    System.out.println("MODEL SIGNATURE");
    System.out.println("Inputs:");
    for (Map.Entry<String, TensorInfo> entry : sig.getInputsMap().entrySet()) {
      TensorInfo t = entry.getValue();
      System.out.printf(
          "%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
          i++, numInputs, entry.getKey(), t.getName(), t.getDtype());
    }
    int numOutputs = sig.getOutputsCount();
    i = 1;
    System.out.println("Outputs:");
    for (Map.Entry<String, TensorInfo> entry : sig.getOutputsMap().entrySet()) {
      TensorInfo t = entry.getValue();
      System.out.printf(
          "%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
          i++, numOutputs, entry.getKey(), t.getName(), t.getDtype());
    }
    System.out.println("-----------------------------------------------");
  }
  
  public static void main(String[] args) throws Exception {
    
      
      SavedModelBundle bundle = SavedModelBundle.load("./exports/1554425274/", "serve");
      System.out.println(bundle);
      
      printSignature(bundle);
      
      Graph graph = bundle.graph();
      System.out.println(graph);
      

        Int64List int64List = Int64List.newBuilder().addValue(i).build();
        Feature offset = Feature.newBuilder().setInt64List(int64List).build();
      
        Features features = Features.newBuilder()
                  .putFeature("rid", offset)
                  .build();
        Example example = Example.newBuilder().setFeatures(features).build();
        System.out.println(example);
        
      
      System.out.println(examples);
      
      Tensor<String> inputBatch = Tensors.create(new byte[][] {example.toByteArray()});
      System.out.println(inputBatch);
      
      final String input_tensor = "input_example_tensor";
      final String output_tensor = "groupwise_dnn_v2/accumulate_scores/truediv";

      Tensor<Float> result = bundle.session().runner()
                      .feed(input_tensor, inputBatch)
                      .fetch(output_tensor)
                      .run().get(0)
                      .expect(Float.class);

      System.out.println(Arrays.deepToString(result.copyTo(new float[1][1])));

      // float[][] scores = result.copyTo(new float[1][1]);
      // for(int i=0; i<scores[0].length; i++){
      //     System.out.println(scores[i][0]);
      // }
      
  }
}