Merge pull request #28678 from omrope79:caffe-importer-cleanup

Caffe importer cleanup #28678

Merge with: https://github.com/opencv/opencv_extra/pull/1324

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/misc/java/test/DnnForwardAndRetrieve.java

@@ -1,67 +1,71 @@
 package org.opencv.test.dnn;
 
+import java.io.File;
 import java.util.ArrayList;
 import java.util.List;
 import org.opencv.core.Core;
 import org.opencv.core.CvType;
 import org.opencv.core.Mat;
-import org.opencv.core.MatOfByte;
-import org.opencv.core.Range;
 import org.opencv.dnn.Dnn;
 import org.opencv.dnn.Net;
 import org.opencv.test.OpenCVTestCase;
 
 public class DnnForwardAndRetrieve extends OpenCVTestCase {
 
+    private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
+    private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
+
+    private String modelFileName = "";
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        String dnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
+        String generalTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
+
+        File model = null;
+
+        if (generalTestDataPath != null) {
+            model = new File(generalTestDataPath, "dnn/onnx/models/split_0.onnx");
+        }
+
+        if ((model == null || !model.isFile()) && dnnTestDataPath != null) {
+            model = new File(dnnTestDataPath, "dnn/onnx/models/split_0.onnx");
+        }
+
+        if (model == null || !model.isFile()) {
+            isTestCaseEnabled = false;
+            return;
+        }
+
+        modelFileName = model.getAbsolutePath();
+    }
+
     public void testForwardAndRetrieve()
     {
-        // Create a simple Caffe prototxt with a Slice layer
-        String prototxt =
-            "input: \"data\"\n" +
-            "layer {\n" +
-            "  name: \"testLayer\"\n" +
-            "  type: \"Slice\"\n" +
-            "  bottom: \"data\"\n" +
-            "  top: \"firstCopy\"\n" +
-            "  top: \"secondCopy\"\n" +
-            "  slice_param {\n" +
-            "    axis: 0\n" +
-            "    slice_point: 2\n" +
-            "  }\n" +
-            "}";
-
-        // Read network from prototxt
-        MatOfByte bufferProto = new MatOfByte();
-        bufferProto.fromArray(prototxt.getBytes());
-        MatOfByte bufferModel = new MatOfByte();
-        Net net = Dnn.readNetFromCaffe(bufferProto, bufferModel, Dnn.ENGINE_CLASSIC);
+        // Verifies forwardAndRetrieve nested list marshalling using a small ONNX model instead of the removed Caffe importer.
+        Net net = Dnn.readNetFromONNX(modelFileName, Dnn.ENGINE_CLASSIC);
         net.setPreferableBackend(Dnn.DNN_BACKEND_OPENCV);
 
-        // Create input data
-        Mat inp = new Mat(4, 5, CvType.CV_32F);
+        // split_0.onnx declares a single 4D input named "image" of shape [1, 3, 2, 2].
+        Mat inp = new Mat(new int[]{1, 3, 2, 2}, CvType.CV_32F);
         Core.randu(inp, -1, 1);
         net.setInput(inp);
 
-        // Define output names
-        List<String> outNames = new ArrayList<>();
-        outNames.add("testLayer");
+        List<String> outNames = net.getUnconnectedOutLayersNames();
+        assertFalse("Model has no output layers", outNames.isEmpty());
 
-        // Forward and retrieve multiple outputs
+        // Forward and retrieve every output blob of the requested layers.
         List<List<Mat>> outBlobs = new ArrayList<>();
         net.forwardAndRetrieve(outBlobs, outNames);
 
-        // Verify results
-        assertEquals(1, outBlobs.size());
-        assertEquals(2, outBlobs.get(0).size());
-
-        // Compare results
-        Mat expectedFirst = inp.rowRange(0, 2);
-        Mat expectedSecond = inp.rowRange(2, 4);
-
-        Mat actualFirst = outBlobs.get(0).get(0);
-        Mat actualSecond = outBlobs.get(0).get(1);
-
-        assertEquals(0, Core.norm(expectedFirst, actualFirst, Core.NORM_INF), EPS);
-        assertEquals(0, Core.norm(expectedSecond, actualSecond, Core.NORM_INF), EPS);
+        // One entry per requested layer name, each holding at least one valid blob.
+        assertEquals(outNames.size(), outBlobs.size());
+        for (List<Mat> blobs : outBlobs) {
+            assertFalse(blobs.isEmpty());
+            for (Mat blob : blobs)
+                assertFalse(blob.empty());
+        }
     }
 }