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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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140 changes: 68 additions & 72 deletions ...xamples/src/main/java/org/tensorflow/model/examples/cnn/fastrcnn/FasterRcnnInception.java
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Original file line number Diff line number Diff line change
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 @@ -269,79 +270,74 @@ public static void main(String[] params) {
//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")) {
int numDetects = (int) numDetections.getFloat(0);
if (numDetects > 0) {
ArrayList<FloatNdArray> boxArray = new ArrayList<>();
//TODO tf.image.combinedNonMaxSuppression
for (int n = 0; n < numDetects; n++) {
//put probability and position in outputMap
float detectionScore = detectionScores.getFloat(0, n);
//only include those classes with detection score greater than 0.3f
if (detectionScore > 0.3f) {
boxArray.add(detectionBoxes.get(0, n));
}
Result result = model.function("serving_default").call(feedDict);
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This result needs to be in a try with resources so that all the outputs are closed.

//detection_classes, detectionBoxes, num_detections. are model output names
TFloat32 detectionBoxes = (TFloat32) result.get("detection_boxes").orElseThrow(() -> new RuntimeException("model output exception detection_classes 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<>();
//TODO tf.image.combinedNonMaxSuppression
for (int n = 0; n < numDetects; n++) {
//put probability and position in outputMap
float detectionScore = detectionScores.getFloat(0, n);
//only include those classes with detection score greater than 0.3f
if (detectionScore > 0.3f) {
boxArray.add(detectionBoxes.get(0, n));
}
//2-D. A list of RGBA colors to cycle through for the boxes.
Operand<TFloat32> colors = tf.constant(new float[][]{
{0.9f, 0.3f, 0.3f, 0.0f},
{0.3f, 0.3f, 0.9f, 0.0f},
{0.3f, 0.9f, 0.3f, 0.0f}
});
Shape boxesShape = Shape.of(1, boxArray.size(), 4);
int boxCount = 0;
//3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding boxes
try (TFloat32 boxes = TFloat32.tensorOf(boxesShape)) {
//batch size of 1
boxes.setFloat(1, 0, 0, 0);
for (FloatNdArray floatNdArray : boxArray) {
boxes.set(floatNdArray, 0, boxCount);
boxCount++;
}
//Placeholders for boxes and path to outputimage
Placeholder<TFloat32> boxesPlaceHolder = tf.placeholder(TFloat32.class, Placeholder.shape(boxesShape));
Placeholder<TString> outImagePathPlaceholder = tf.placeholder(TString.class);
//Create JPEG from the Tensor with quality of 100%
EncodeJpeg.Options jpgOptions = EncodeJpeg.quality(100L);
//convert the 4D input image to normalised 0.0f - 1.0f
//Draw bounding boxes using boxes tensor and list of colors
//multiply by 255 then reshape and recast to TUint8 3D tensor
WriteFile writeFile = tf.io.writeFile(outImagePathPlaceholder,
tf.image.encodeJpeg(
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)
),
boxesPlaceHolder, colors),
tf.constant(255.0f)
),
tf.array(
imageShape.asArray()[0],
imageShape.asArray()[1],
imageShape.asArray()[2]
)
), TUint8.class),
jpgOptions));
//output the JPEG to file
s.runner().feed(outImagePathPlaceholder, TString.scalarOf(outputImagePath))
.feed(boxesPlaceHolder, boxes)
.addTarget(writeFile).run();
}
//2-D. A list of RGBA colors to cycle through for the boxes.
Operand<TFloat32> colors = tf.constant(new float[][]{
{0.9f, 0.3f, 0.3f, 0.0f},
{0.3f, 0.3f, 0.9f, 0.0f},
{0.3f, 0.9f, 0.3f, 0.0f}
});
Shape boxesShape = Shape.of(1, boxArray.size(), 4);
int boxCount = 0;
//3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding boxes
try (TFloat32 boxes = TFloat32.tensorOf(boxesShape)) {
//batch size of 1
boxes.setFloat(1, 0, 0, 0);
for (FloatNdArray floatNdArray : boxArray) {
boxes.set(floatNdArray, 0, boxCount);
boxCount++;
}
//Placeholders for boxes and path to outputimage
Placeholder<TFloat32> boxesPlaceHolder = tf.placeholder(TFloat32.class, Placeholder.shape(boxesShape));
Placeholder<TString> outImagePathPlaceholder = tf.placeholder(TString.class);
//Create JPEG from the Tensor with quality of 100%
EncodeJpeg.Options jpgOptions = EncodeJpeg.quality(100L);
//convert the 4D input image to normalised 0.0f - 1.0f
//Draw bounding boxes using boxes tensor and list of colors
//multiply by 255 then reshape and recast to TUint8 3D tensor
WriteFile writeFile = tf.io.writeFile(outImagePathPlaceholder,
tf.image.encodeJpeg(
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)
),
boxesPlaceHolder, colors),
tf.constant(255.0f)
),
tf.array(
imageShape.asArray()[0],
imageShape.asArray()[1],
imageShape.asArray()[2]
)
), TUint8.class),
jpgOptions));
//output the JPEG to file
s.runner().feed(outImagePathPlaceholder, TString.scalarOf(outputImagePath))
.feed(boxesPlaceHolder, boxes)
.addTarget(writeFile).run();
}
}
}
}
}
}
}
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