java tf plugin

src/main/java/TensorflowExamplePlugin.java

@@ -6,8 +6,16 @@
 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.OperationBuilder;
+import org.tensorflow.Shape;
+import org.tensorflow.Output;
+
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.List;
 
 /**
 
@@ -56,23 +64,152 @@ public class TensorflowExamplePlugin
         }
     }
 
+    /** 
+     * From https://github.com/DIVSIO/tensorflow_java_cli_example/blob/master/src/main/java/divisio/example/tensorflow/cli/RunRegression.java
+     */
+    
+	/**
+	 * wraps a single float in a tensor
+	 * @param f the float to wrap
+	 * @return a tensor containing the float
+	 */
+	private static Tensor<Float> toTensor(final float f, final Collection<Tensor<?>> tensorsToClose) {
+		final Tensor<Float> t = Tensors.create(f);
+		if (tensorsToClose != null) {
+			tensorsToClose.add(t);
+		}
+		return t;
+	}
+
+	private static Tensor<Float> toTensor2DFloat(final float [][] f, final Collection<Tensor<?>> tensorsToClose) {
+		final Tensor<Float> t = Tensors.create(f);
+		if (tensorsToClose != null) {
+			tensorsToClose.add(t);
+		}
+		return t;
+	}
+	
+	private static Tensor<Integer> toTensor1DInt(final int [] f, final Collection<Tensor<?>> tensorsToClose) {
+		final Tensor<Integer> t = Tensors.create(f);
+		if (tensorsToClose != null) {
+			tensorsToClose.add(t);
+		}
+		return t;
+	}
+	
+	private static void closeTensors(final Collection<Tensor<?>> ts) {		
+		for (final Tensor<?> t : ts) {
+			try {
+				t.close();
+			} catch (final Exception e) {
+				System.err.println("Error closing Tensor.");
+				e.printStackTrace();
+			}
+		}
+		ts.clear();
+	}
+	
+	
+	
     public static void main() throws Exception {
 
         final Graph smpb;
 
+        float [][] rv_stage1_out = new float[78408][32];
+        
+        // from: infer_qcds_01.py
         float [][] img_corr2d = new float[78408][324];
         float [][] img_target = new float[78408][  1];
         int     [] img_ntile  = new   int[78408];
-
+        
         // init ntile
         for(int i=0;i<img_ntile.length;i++){
         	img_ntile[i] = i;
         }
 
-        try (SavedModelBundle b = SavedModelBundle.load(EXPORTDIR,PB_TAG)){
-            System.out.println("OK");
-            smpb = b.graph();
+        /*
+         * for feed:
+         *     "ph_corr2d":           img_corr2d
+         *     "ph_target_disparity": img_target
+         *     "ph_ntile":            img_ntile
+         *     
+         *     so it will look like:
+         *     
+         *     https://divis.io/2018/01/enterprise-tensorflow-code-examples/ -> 
+         *         https://github.com/DIVSIO/tensorflow_java_cli_example/blob/master/src/main/java/divisio/example/tensorflow/cli/RunRegression.java
+         *     
+         *     sess.runner()
+         *         .feed("ph_corr2d",img_corr2d)
+         *         .feed("ph_target_disparity",img_target)
+         *         .feed("ph_ntile",img_ntile)
+         *         .fetch("Disparity_net/stage1done:0")
+         *         .run()
+         *         .get(0)
+         */
+        
+        final SavedModelBundle bundle = SavedModelBundle.load(EXPORTDIR,PB_TAG); 
+        
+        final List<Tensor<?>> tensorsToClose = new ArrayList<Tensor<?>>(5);
+        
+        System.out.println("OK");
+        
+        try {
+
+        	// init variable via constant
+        	Tensor<Float> t = toTensor2DFloat(rv_stage1_out, tensorsToClose);
+        	Output builder_init = bundle.graph().opBuilder("Const", "rv_stage1_out_init").setAttr("dtype", t.dataType()).setAttr("value", t).build().output(0);
+        	
+        	// variable
+        	OperationBuilder builder2 = bundle.graph().opBuilder("Variable", "rv_stage1_out");
+        	builder2.addInput(builder_init);
+        	
+        	//Tensor<Float> t = toTensor2DFloat(rv_stage1_out, tensorsToClose);
+        	//builder.setAttr("dtype", t.dataType()).setAttr("shape",t.shape()).build().output(0);
+        	
+        	// stage 1        	
+        	bundle.session().runner()
+	                  .feed("ph_corr2d",toTensor2DFloat(img_corr2d, tensorsToClose))
+	                  .feed("ph_target_disparity",toTensor2DFloat(img_target, tensorsToClose))
+	                  .feed("ph_ntile",toTensor1DInt(img_ntile, tensorsToClose))
+	                  .fetch("Disparity_net/stage1done:0")
+	                  .run()
+	                  .get(0);
+        	
+        	// stage 2
+        	final Tensor<?> result = bundle.session().runner()
+	                  .feed("ph_ntile",toTensor1DInt(img_ntile, tensorsToClose))
+	                  .fetch("Disparity_net/stage2_out_sparse:0")
+	                  .run()
+	                  .get(0);
+        	
+        	tensorsToClose.add(result);
+        	
+        	float [] resultValues = (float[]) result.copyTo(new float[78408]);
+        	
+        	System.out.println("DONE");
+        	
+        } catch (final IllegalStateException ise) {
+        	System.out.println("Very Bad Error (VBE): "+ise);
+        	closeTensors(tensorsToClose);
+        } catch (final NumberFormatException nfe) {
+			//just skip unparsable lines ?!
+		} finally {
+			closeTensors(tensorsToClose);
+		}
+        
+        //try (){
+            
+            //smpb = b.graph();
            
+            //Session sess = b.session();
+            //System.out.println(b.metaGraphDef());
+            
+            //final List<String> labels = tensorFlowService.loadLabels(source,
+    		//		MODEL_NAME, "imagenet_comp_graph_label_strings.txt");
+            //System.out.println("Loaded graph and " + labels.size() + " labels");
+            
+            //output = sess.runner().feed(o, t).fetch().run().get(0).copyTo()
+            
             /*
             try (
             	final Session s = new Session(g);
@@ -84,7 +221,7 @@ public class TensorflowExamplePlugin
             }
             */
             
-        }
+        //}
 
         try (Graph g = new Graph()) {
             final String value = "Hello from " + TensorFlow.version();