added opencl support for faster inferencing on non cuda devices #10

cansik · Feb 17, 2022 · d71ac5e · d71ac5e
1 parent be902ae
commit d71ac5e
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Showing 21 changed files with 55 additions and 71 deletions.
diff --git a/README.md b/README.md
@@ -15,7 +15,7 @@ It is recommended to use the contribution manager in the Processing app to insta
 ![image](https://user-images.githubusercontent.com/5220162/118391536-05b1ea80-b635-11eb-9704-2c5b780008df.png)
 
 ### Manual
-Download the [latest](https://github.com/cansik/deep-vision-processing/releases/tag/v0.8.0-alpha) prebuilt version from the [release](https://github.com/cansik/deep-vision-processing/releases) sections and install it into your Processing library folder.
+Download the [latest](https://github.com/cansik/deep-vision-processing/releases/tag/v0.8.1-alpha) prebuilt version from the [release](https://github.com/cansik/deep-vision-processing/releases) sections and install it into your Processing library folder.
 
 ## Usage
 The base of the library is the `DeepVision` class. It is used to download the pretrained models and create new networks.
@@ -66,8 +66,16 @@ ResultList<ObjectDetectionResult> detections = network.run(myImg);
 
 Please have a look at the specific networks for further information or at the [examples](examples).
 
-### CUDA Support
-With version `0.6.0` it is possible to [download the CUDA bundled libraries](https://github.com/cansik/deep-vision-processing/releases/tag/v0.8.0-alpha). This enables to run most of the DNN's on CUDA enabled graphics cards. For most networks this is necessary to run them in real-time. If you have the cuda-bundled version installed and run deep-vision on a Linux or Windows with an NVIDIA graphics card, you are able to enable the CUDA backend:
+### OpenCL Backend Support
+With version `0.8.1` by default if OpenCL is enabled, it will be used as backend. If CUDA is enabled too, CUDA will be preferred. It is possible to force the CPU backend by setting the following option:
+
+```java
+DeepVision vision = new DeepVision(this);
+vision.setUseDefaultBackend(true);
+```
+
+### CUDA Backend Support
+With version `0.6.0` it is possible to [download the CUDA bundled libraries](https://github.com/cansik/deep-vision-processing/releases/tag/v0.8.1-alpha). This enables to run most of the DNN's on CUDA enabled graphics cards. For most networks this is necessary to run them in real-time. If you have the cuda-bundled version installed and run deep-vision on a Linux or Windows with an NVIDIA graphics card, you are able to enable the CUDA backend:
 
 ```java
 // Second parameter (enableCUDABackend) enables CUDA

diff --git a/build.gradle b/build.gradle
@@ -4,7 +4,7 @@ plugins {
 }
 
 group 'ch.bildspur'
-version '0.8.0'
+version '0.8.1'
 
 sourceCompatibility = 1.8
 

diff --git a/library.properties b/library.properties
@@ -4,7 +4,7 @@ authors=[Florian Bruggisser](https://broox.ch)
 url=https://github.com/cansik/deep-vision-processing
 sentence=Deep computer-vision algorithms for Processing
 paragraph=Run deep neural networks on images in Processing.
-version=008000
-prettyVersion=0.8.0
+version=008010
+prettyVersion=0.8.1
 minRevision=0
 maxRevision=0
diff --git a/src/main/java/ch/bildspur/vision/CRNNNetwork.java b/src/main/java/ch/bildspur/vision/CRNNNetwork.java
@@ -26,10 +26,7 @@ public CRNNNetwork(Path model) {
     public boolean setup() {
         net = readNetFromTorch(model.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/ColorizationNetwork.java b/src/main/java/ch/bildspur/vision/ColorizationNetwork.java
@@ -56,10 +56,7 @@ public boolean setup() {
         outputBlobs.push_back(emptyOutput);
         net.getLayer(new DictValue("conv8_313_rh")).blobs(outputBlobs);
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/DORNDepthEstimationNetwork.java b/src/main/java/ch/bildspur/vision/DORNDepthEstimationNetwork.java
@@ -28,10 +28,7 @@ public DORNDepthEstimationNetwork(Path protoTextPath, Path modelPath) {
     public boolean setup() {
         net = readNetFromCaffe(protoTextPath.toAbsolutePath().toString(), modelPath.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/DeepVision.java b/src/main/java/ch/bildspur/vision/DeepVision.java
@@ -3,16 +3,20 @@
 import ch.bildspur.vision.dependency.Dependency;
 import ch.bildspur.vision.dependency.Repository;
 import ch.bildspur.vision.util.ProcessingUtils;
+import org.bytedeco.opencv.global.opencv_dnn;
+import org.bytedeco.opencv.opencv_dnn.Net;
 import processing.core.PApplet;
 
 import java.io.IOException;
 import java.nio.file.Files;
 import java.nio.file.Paths;
 
 import static org.bytedeco.opencv.global.opencv_core.getCudaEnabledDeviceCount;
+import static org.bytedeco.opencv.global.opencv_core.haveOpenCL;
 
 public class DeepVision {
     public static boolean ENABLE_CUDA_BACKEND = false;
+    public static boolean USE_DEFAULT_BACKEND = false;
 
     private PApplet sketch;
     private boolean storeNetworksInSketch = false;
@@ -26,6 +30,26 @@ public DeepVision(PApplet sketch, boolean enableCUDABackend) {
         ENABLE_CUDA_BACKEND = enableCUDABackend;
     }
 
+    public static void enableDesiredBackend(Net net) {
+        if (USE_DEFAULT_BACKEND) return;
+
+        if(haveOpenCL()) {
+            System.out.println("DNN OpenCL backend enabled");
+            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_OPENCV);
+            net.setPreferableTarget(opencv_dnn.DNN_TARGET_OPENCL);
+        }
+
+        if (DeepVision.ENABLE_CUDA_BACKEND) {
+            System.out.println("DNN CUDA backend enabled");
+            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
+            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
+        }
+    }
+
+    public void setUseDefaultBackend(boolean value) {
+        USE_DEFAULT_BACKEND = value;
+    }
+
     public boolean isCUDABackendEnabled() {
         return ENABLE_CUDA_BACKEND;
     }

diff --git a/src/main/java/ch/bildspur/vision/Face3DDFAV2Network.java b/src/main/java/ch/bildspur/vision/Face3DDFAV2Network.java
@@ -34,10 +34,7 @@ public Face3DDFAV2Network(Path model) {
     public boolean setup() {
         net = readNetFromONNX(model.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/MaskRCNN.java b/src/main/java/ch/bildspur/vision/MaskRCNN.java
@@ -46,10 +46,7 @@ public boolean setup() {
         outNames.push_back("detection_out_final");
         outNames.push_back("detection_masks");
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         this.loadLabels(labelsPath);
 

diff --git a/src/main/java/ch/bildspur/vision/MediaPipeBlazeFaceNetwork.java b/src/main/java/ch/bildspur/vision/MediaPipeBlazeFaceNetwork.java
@@ -43,10 +43,7 @@ public MediaPipeBlazeFaceNetwork(Path modelPath, int width, int height) {
     public boolean setup() {
         net = readNetFromONNX(modelPath.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/MediaPipeFaceLandmark.java b/src/main/java/ch/bildspur/vision/MediaPipeFaceLandmark.java
@@ -36,10 +36,7 @@ public MediaPipeFaceLandmark(Path model, Path config) {
     public boolean setup() {
         net = readNetFromTensorflow(model.toAbsolutePath().toString(), config.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/MidasNetwork.java b/src/main/java/ch/bildspur/vision/MidasNetwork.java
@@ -31,10 +31,7 @@ public MidasNetwork(Path model) {
     public boolean setup() {
         net = readNetFromONNX(model.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/OpenFaceNetwork.java b/src/main/java/ch/bildspur/vision/OpenFaceNetwork.java
@@ -33,10 +33,7 @@ public OpenFaceNetwork(Path modelPath) {
     public boolean setup() {
         net = readNetFromTorch(modelPath.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return !net.empty();
     }

diff --git a/src/main/java/ch/bildspur/vision/SSDMobileNetwork.java b/src/main/java/ch/bildspur/vision/SSDMobileNetwork.java
@@ -45,10 +45,7 @@ public boolean setup() {
 
         outNames = net.getUnconnectedOutLayersNames();
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/StyleTransferNetwork.java b/src/main/java/ch/bildspur/vision/StyleTransferNetwork.java
@@ -29,10 +29,7 @@ public StyleTransferNetwork(Path model) {
     public boolean setup() {
         net = readNetFromTorch(model.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/SuperResolutionNetwork.java b/src/main/java/ch/bildspur/vision/SuperResolutionNetwork.java
@@ -7,6 +7,7 @@
 import org.bytedeco.opencv.global.opencv_dnn;
 import org.bytedeco.opencv.global.opencv_quality;
 import org.bytedeco.opencv.opencv_core.Mat;
+import org.bytedeco.opencv.opencv_dnn.Net;
 import org.bytedeco.opencv.opencv_dnn_superres.DnnSuperResImpl;
 import processing.core.PImage;
 
@@ -33,10 +34,7 @@ public boolean setup() {
         net.readModel(model.toAbsolutePath().toString());
         net.setModel(name, scale);
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(new Net(net));
 
         return true;
     }

diff --git a/src/main/java/ch/bildspur/vision/ULFGFaceDetectionNetwork.java b/src/main/java/ch/bildspur/vision/ULFGFaceDetectionNetwork.java
@@ -54,10 +54,7 @@ public ULFGFaceDetectionNetwork(Path modelPath, int width, int height) {
     public boolean setup() {
         net = readNetFromONNX(modelPath.toAbsolutePath().toString());
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/YOLONetwork.java b/src/main/java/ch/bildspur/vision/YOLONetwork.java
@@ -46,10 +46,7 @@ public boolean setup() {
         // setup output layers
         outNames = net.getUnconnectedOutLayersNames();
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/network/ClassificationNetwork.java b/src/main/java/ch/bildspur/vision/network/ClassificationNetwork.java
@@ -53,10 +53,7 @@ public ClassificationNetwork(int width, int height, boolean convertToGrayScale,
     public boolean setup() {
         net = createNetwork();
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/main/java/ch/bildspur/vision/network/PoseNetwork.java b/src/main/java/ch/bildspur/vision/network/PoseNetwork.java
@@ -43,10 +43,7 @@ public PoseNetwork(Path modelPath, int inputWidth, int inputHeight, double scale
     public boolean setup() {
         net = createNetwork();
 
-        if (DeepVision.ENABLE_CUDA_BACKEND) {
-            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
-            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
-        }
+        DeepVision.enableDesiredBackend(net);
 
         if (net.empty()) {
             System.out.println("Can't load network!");

diff --git a/src/test/java/ch/bildspur/vision/test/MaskRCNNWebcamTest.java b/src/test/java/ch/bildspur/vision/test/MaskRCNNWebcamTest.java
@@ -4,7 +4,6 @@
 import ch.bildspur.video.Capture;
 import ch.bildspur.vision.DeepVisionPreview;
 import ch.bildspur.vision.MaskRCNN;
-import ch.bildspur.vision.result.ObjectDetectionResult;
 import ch.bildspur.vision.result.ObjectSegmentationResult;
 import ch.bildspur.vision.test.tools.StopWatch;
 import ch.bildspur.vision.util.CvProcessingUtils;