Merge remote-tracking branch 'upstream/3.4' into merge-3.4

2019-10-24 18:17:40 +00:00
parent 87a692b89e eabe679f78
commit 055ffc0425
61 changed files with 1438 additions and 394 deletions
@@ -1493,4 +1493,62 @@ TEST(Layer_Test_Convolution, relu_fusion)
    normAssert(input, output);
 }

+typedef testing::TestWithParam<tuple<bool, tuple<Backend, Target> > > Layer_Test_Eltwise_unequal;
+TEST_P(Layer_Test_Eltwise_unequal, Accuracy)
+{
+    bool weighted = get<0>(GetParam());
+    int backendId = get<0>(get<1>(GetParam()));
+    int targetId = get<1>(get<1>(GetParam()));
+
+    if (backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
+
+    Net net;
+    LayerParams lp;
+    lp.type = "Eltwise";
+    lp.name = "testLayer";
+
+    const int inpShapes[][4] = {{1, 4, 2, 2}, {1, 5, 2, 2}, {1, 3, 2, 2}};
+    std::vector<String> inpNames(3);
+    std::vector<Mat> inputs(3);
+    size_t numOutValues = 1*4*2*2;  // By the first input
+
+    std::vector<float> weights(3, 1);
+    if (weighted)
+    {
+        for (int i = 0; i < inputs.size(); ++i)
+            randu(Mat(1, 1, CV_32F, &weights[i]), -1, 1);
+        lp.set("coeff", DictValue::arrayReal<float*>(&weights[0], weights.size()));
+    }
+
+    int eltwiseId = net.addLayer(lp.name, lp.type, lp);
+    for (int i = 0; i < inputs.size(); ++i)
+    {
+        inputs[i].create(4, inpShapes[i], CV_32F);
+        randu(inputs[i], 0, 255);
+        inpNames[i] = format("input_%d", i);
+        net.connect(0, i, eltwiseId, i);
+    }
+    Mat ref(1, numOutValues, CV_32F, Scalar(0));
+
+    net.setInputsNames(inpNames);
+    for (int i = 0; i < inputs.size(); ++i)
+    {
+        net.setInput(inputs[i], inpNames[i]);
+        if (numOutValues >= inputs[i].total())
+            ref.colRange(0, inputs[i].total()) += weights[i] * inputs[i].reshape(1, 1);
+        else
+            ref += weights[i] * inputs[i].reshape(1, 1).colRange(0, numOutValues);
+    }
+
+    net.setPreferableBackend(backendId);
+    net.setPreferableTarget(targetId);
+    Mat out = net.forward();
+    normAssert(out.reshape(1, 1), ref);
+}
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
+    testing::Bool(),
+    dnnBackendsAndTargets()
+));
+
 }} // namespace