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Adapt to TFJava 0.5.0 #29

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f527fbc
update version 0.4.0 -> 0.5.0
Jzow Aug 1, 2023
8b242c4
Fix compilation errors caused by modified versions
Jzow Aug 1, 2023
e2f1013
Handling Code Formatting
Jzow Aug 1, 2023
05061cf
Handling Code Formatting
Jzow Aug 1, 2023
af3001f
fix 2023-08-02
Jzow Aug 2, 2023
139ee26
fix Import all util packages
Jzow Aug 2, 2023
50b5da1
Output Map update -> Result Object
Jzow Aug 4, 2023
5562984
result add try-with-resources close outputs
Jzow Aug 4, 2023
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2 changes: 1 addition & 1 deletion tensorflow-examples/pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
<!-- The maven compiler plugin defaults to a lower version -->
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<tensorflow.version>0.4.0</tensorflow.version>
<tensorflow.version>0.5.0</tensorflow.version>
</properties>

<dependencies>
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34 changes: 16 additions & 18 deletions ...xamples/src/main/java/org/tensorflow/model/examples/cnn/fastrcnn/FasterRcnnInception.java
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Expand Up @@ -100,16 +100,17 @@ The given SavedModel SignatureDef contains the following output(s):
but again the actual tensor is DT_FLOAT according to saved_model_cli.
*/


import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import java.util.TreeMap;

import org.tensorflow.Graph;
import org.tensorflow.Operand;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.Result;
import org.tensorflow.ndarray.FloatNdArray;
import org.tensorflow.ndarray.Shape;
import org.tensorflow.op.Ops;
Expand Down Expand Up @@ -228,16 +229,16 @@ public class FasterRcnnInception {
};

public static void main(String[] params) {

if (params.length != 2) {
throw new IllegalArgumentException("Exactly 2 parameters required !");
}

//my output image
String outputImagePath = params[1];
//my test image
String imagePath = params[0];
// get path to model folder
String modelPath = "models/faster_rcnn_inception_resnet_v2_1024x1024";
String modelPath = "models/faster_rcnn_inception_resnet_v2_1024x1024_1";
// load saved model
SavedModelBundle model = SavedModelBundle.load(modelPath, "serve");
//create a map of the COCO 2017 labels
Expand Down Expand Up @@ -268,17 +269,14 @@ public static void main(String[] params) {
Map<String, Tensor> feedDict = new HashMap<>();
//The given SavedModel SignatureDef input
feedDict.put("input_tensor", reshapeTensor);
//The given SavedModel MetaGraphDef key
Map<String, Tensor> outputTensorMap = model.function("serving_default").call(feedDict);
//detection_classes, detectionBoxes etc. are model output names
try (TFloat32 detectionClasses = (TFloat32) outputTensorMap.get("detection_classes");
TFloat32 detectionBoxes = (TFloat32) outputTensorMap.get("detection_boxes");
TFloat32 rawDetectionBoxes = (TFloat32) outputTensorMap.get("raw_detection_boxes");
TFloat32 numDetections = (TFloat32) outputTensorMap.get("num_detections");
TFloat32 detectionScores = (TFloat32) outputTensorMap.get("detection_scores");
TFloat32 rawDetectionScores = (TFloat32) outputTensorMap.get("raw_detection_scores");
TFloat32 detectionAnchorIndices = (TFloat32) outputTensorMap.get("detection_anchor_indices");
TFloat32 detectionMulticlassScores = (TFloat32) outputTensorMap.get("detection_multiclass_scores")) {
//detection_classes, detectionBoxes, num_detections. are model output names
try (Result result = model.function("serving_default").call(feedDict);
TFloat32 detectionBoxes = (TFloat32) result.get("detection_boxes")
.orElseThrow(() -> new RuntimeException("model output exception detection_boxes key is null"));
TFloat32 numDetections = (TFloat32) result.get("num_detections")
.orElseThrow(() -> new RuntimeException("model output exception num_detections key is null"));
TFloat32 detectionScores = (TFloat32) result.get("detection_scores")
.orElseThrow(() -> new RuntimeException("model output exception detection_scores key is null"))) {
int numDetects = (int) numDetections.getFloat(0);
if (numDetects > 0) {
ArrayList<FloatNdArray> boxArray = new ArrayList<>();
Expand Down Expand Up @@ -320,9 +318,9 @@ public static void main(String[] params) {
tf.dtypes.cast(tf.reshape(
tf.math.mul(
tf.image.drawBoundingBoxes(tf.math.div(
tf.dtypes.cast(tf.constant(reshapeTensor),
TFloat32.class),
tf.constant(255.0f)
tf.dtypes.cast(tf.constant(reshapeTensor),
TFloat32.class),
tf.constant(255.0f)
),
boxesPlaceHolder, colors),
tf.constant(255.0f)
Expand All @@ -344,4 +342,4 @@ public static void main(String[] params) {
}
}
}
}
}
10 changes: 5 additions & 5 deletions ...rc/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java
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Original file line number Diff line number Diff line change
Expand Up @@ -16,9 +16,9 @@
*/
package org.tensorflow.model.examples.regression.linear;

import java.util.List;
import java.util.Random;
import org.tensorflow.Graph;
import org.tensorflow.Result;
import org.tensorflow.Session;
import org.tensorflow.framework.optimizers.GradientDescent;
import org.tensorflow.framework.optimizers.Optimizer;
Expand Down Expand Up @@ -108,13 +108,13 @@ public static void main(String[] args) {
}

// Extract linear regression model weight and bias values
List<?> tensorList = session.runner()
Result tensorList = session.runner()
.fetch(WEIGHT_VARIABLE_NAME)
.fetch(BIAS_VARIABLE_NAME)
.run();

try (TFloat32 weightValue = (TFloat32)tensorList.get(0);
TFloat32 biasValue = (TFloat32)tensorList.get(1)) {
try (TFloat32 weightValue = (TFloat32) tensorList.get(0);
TFloat32 biasValue = (TFloat32) tensorList.get(1)) {

System.out.println("Weight is " + weightValue.getFloat());
System.out.println("Bias is " + biasValue.getFloat());
Expand All @@ -126,7 +126,7 @@ public static void main(String[] args) {

try (TFloat32 xTensor = TFloat32.scalarOf(x);
TFloat32 yTensor = TFloat32.scalarOf(predictedY);
TFloat32 yPredictedTensor = (TFloat32)session.runner()
TFloat32 yPredictedTensor = (TFloat32) session.runner()
.feed(xData.asOutput(), xTensor)
.feed(yData.asOutput(), yTensor)
.fetch(yPredicted)
Expand Down