init - 初始化项目

modules/dnn/perf/perf_layer.cpp

@@ -0,0 +1,95 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "perf_precomp.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+
+namespace opencv_test {
+
+struct Layer_Slice : public TestBaseWithParam<tuple<Backend, Target> >
+{
+    template<int DIMS>
+    void test_slice(const int* inputShape, const int* begin, const int* end)
+    {
+        int backendId = get<0>(GetParam());
+        int targetId = get<1>(GetParam());
+
+        Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0));
+        for (int i = 0; i < (int)input.total(); ++i)
+            input.ptr<float>()[i] = (float)(i & 4095);
+
+        std::vector<Range> range(DIMS);
+        for (int i = 0; i < DIMS; ++i)
+            range[i] = Range(begin[i], end[i]);
+
+        Net net;
+        LayerParams lp;
+        lp.type = "Slice";
+        lp.name = "testLayer";
+        lp.set("begin", DictValue::arrayInt<int*>((int*)&begin[0], DIMS));
+        lp.set("end", DictValue::arrayInt<int*>((int*)&end[0], DIMS));
+        net.addLayerToPrev(lp.name, lp.type, lp);
+
+        // warmup
+        {
+            net.setInput(input);
+            net.setPreferableBackend(backendId);
+            net.setPreferableTarget(targetId);
+            Mat out = net.forward();
+
+            EXPECT_GT(cv::norm(out, NORM_INF), 0);
+#if 0
+            //normAssert(out, input(range));
+            cout << input(range).clone().reshape(1, 1) << endl;
+            cout << out.reshape(1, 1) << endl;
+#endif
+        }
+
+        TEST_CYCLE()
+        {
+            Mat res = net.forward();
+        }
+
+        SANITY_CHECK_NOTHING();
+    }
+};
+
+
+
+PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_1)
+{
+    const int inputShape[4] = {1, 64, 104, 104};
+    const int begin[] = {0, 32, 0, 0};
+    const int end[] = {1, 64, 104, 104};
+    test_slice<4>(inputShape, begin, end);
+}
+
+PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_2)
+{
+    const int inputShape[4] = {1, 128, 52, 52};
+    const int begin[] = {0, 64, 0, 0};
+    const int end[] = {1, 128, 52, 52};
+    test_slice<4>(inputShape, begin, end);
+}
+
+PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_3)
+{
+    const int inputShape[4] = {1, 256, 26, 26};
+    const int begin[] = {0, 128, 0, 0};
+    const int end[] = {1, 256, 26, 26};
+    test_slice<4>(inputShape, begin, end);
+}
+
+
+PERF_TEST_P_(Layer_Slice, FastNeuralStyle_eccv16)
+{
+    const int inputShape[4] = {1, 128, 80, 100};
+    const int begin[] = {0, 0, 2, 2};
+    const int end[] = {1, 128, 76, 96};
+    test_slice<4>(inputShape, begin, end);
+}
+
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
+
+} // namespace