Merge pull request #24977 from Abdurrahheem:ash/primitive_1d_tests

Primitive 1D Tests #24977

This PR is designed to add tests for 1D inputs for layer, which is required after introducing 1d support in 5.x. Currently tests are written for following layers: 

- [x] `Add`, `Sub`
- [x]  `Product`, `Div`
- [x]  `Min`, `Max`
- [x] `Argmin`, `Argmax`
- [x] `Gather` 

This list is to be extended for more layer such `gemm`, `conv` etc.

### 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
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/src/layers/eltwise_layer.cpp

@@ -191,19 +191,19 @@ public:
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
         CV_Assert(inputs.size() >= 2);
-        CV_Assert(inputs[0].size() >= 2);
+        CV_Assert(inputs[0].size() >= 1);
         CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
         CV_Assert(op == SUM || coeffs.size() == 0);
 
         int dims = inputs[0].size();
         // Number of channels in output shape is determined by the first input tensor.
         bool variableChannels = false;
-        int numChannels = inputs[0][1];
+        int numChannels = (dims == 1) ? inputs[0][0] : inputs[0][1];
         for (size_t i = 1; i < inputs.size(); i++)
         {
             CV_Assert(inputs[0][0] == inputs[i][0]);  // batch sizes are equal
 
-            int input_channels = inputs[i][1];
+            int input_channels = (dims == 1) ? inputs[i][0] : inputs[i][1];
             if (numChannels != input_channels)
                 variableChannels = true;
 
@@ -235,13 +235,13 @@ public:
         outputs.assign(1, inputs[0]);
         outputs[0][1] = numChannels;
 
-        if (dims > 2)
+        if (dims >= 1)
         {
             size_t vecIdx = 0;
             bool isVecFound = false;
             for (size_t i = 0; i < inputs.size(); i++)
             {
-                bool allOnes = isAllOnes(inputs[i], 2, dims);
+                bool allOnes = isAllOnes(inputs[i], (dims != 1) ? 2 : 1, dims);
                 if (!allOnes && !isVecFound)
                 {
                     vecIdx = i;
@@ -277,7 +277,7 @@ public:
         for (size_t i = 0; i < inputs.size(); i++)
         {
             MatShape inpShape = shape(inputs[i].size);
-            if (isAllOnes(inpShape, 2, inputs[i].dims))
+            if (isAllOnes(inpShape, 0, inputs[i].dims))
             {
                 hasVecInput = true;
                 return;
@@ -310,10 +310,13 @@ public:
                         int nstripes)
         {
             const EltwiseOp op = self.op;
-            CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 5, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
+            CV_Check(dst.dims, 1 <= dst.dims && dst.dims <= 5, "");
+            CV_CheckTypeEQ(dst.type(), CV_32FC1, "");
+            CV_Assert(dst.isContinuous());
             CV_Assert(self.coeffs.empty() || self.coeffs.size() == (size_t)nsrcs);
             CV_CheckGE(nsrcs, 2, "");
 
+            if (dst.dims != 1)
                 CV_Assert(self.outputChannels == dst.size[1]);
 
             EltwiseInvoker p(self);

modules/dnn/test/test_layers.cpp

@@ -618,6 +618,204 @@ TEST(Layer_LSTM_Test_Accuracy_with_, HiddenParams)
     normAssert(h_t_reference, outputs[0]);
 }
 
+typedef testing::TestWithParam<tuple<int, int>> Layer_Gather_1d_Test;
+TEST_P(Layer_Gather_1d_Test, Accuracy) {
+
+    int batch_size = get<0>(GetParam());
+    int axis = get<1>(GetParam());
+
+    LayerParams lp;
+    lp.type = "Gather";
+    lp.name = "gatherLayer";
+    lp.set("axis", axis);
+    lp.set("real_ndims", 1);
+
+    Ptr<GatherLayer> layer = GatherLayer::create(lp);
+
+    std::vector<int> input_shape = {batch_size, 1};
+    std::vector<int> indices_shape = {1, 1};
+    std::vector<int> output_shape = {batch_size, 1};
+
+    if (batch_size == 0){
+        input_shape.erase(input_shape.begin());
+        indices_shape.erase(indices_shape.begin());
+        output_shape.erase(output_shape.begin());
+    } else if (axis == 0) {
+        output_shape[0] = 1;
+    }
+
+    cv::Mat input = cv::Mat(input_shape, CV_32F, 1.0);
+    cv::randu(input, 0.0, 1.0);
+    cv::Mat indices = cv::Mat(indices_shape, CV_32F, 0.0);
+    cv::Mat output_ref = cv::Mat(output_shape, CV_32F, input(cv::Range::all(), cv::Range(0, 1)).data);
+
+    std::vector<Mat> inputs{input, indices};
+    std::vector<Mat> outputs;
+
+    runLayer(layer, inputs, outputs);
+    ASSERT_EQ(shape(output_ref), shape(outputs[0]));
+    normAssert(output_ref, outputs[0]);
+}
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Gather_1d_Test, Combine(
+/*input blob shape*/    Values(0, 1, 2, 3),
+/*operation*/           Values(0, 1)
+));
+
+typedef testing::TestWithParam<tuple<int, int, std::string>> Layer_Arg_1d_Test;
+TEST_P(Layer_Arg_1d_Test, Accuracy) {
+
+    int batch_size = get<0>(GetParam());
+    int axis = get<1>(GetParam());
+    std::string operation = get<2>(GetParam());
+
+    LayerParams lp;
+    lp.type = "Arg";
+    lp.name = "arg" + operation + "_Layer";
+    lp.set("op", operation);
+    lp.set("axis", axis);
+    lp.set("keepdims", 1);
+    lp.set("select_last_index", 0);
+
+    Ptr<ArgLayer> layer = ArgLayer::create(lp);
+
+    std::vector<int> input_shape = {batch_size, 1};
+    std::vector<int> output_shape = {1, 1};
+
+    if (batch_size == 0){
+        input_shape.erase(input_shape.begin());
+        output_shape.erase(output_shape.begin());
+    }
+
+    if (axis != 0 && batch_size != 0){
+        output_shape[0] = batch_size;
+    }
+
+    cv::Mat input = cv::Mat(input_shape, CV_32F, 1);
+    cv::Mat output_ref = cv::Mat(output_shape,  CV_32F, 0);
+
+    for (int i = 0; i < batch_size; ++i)
+        input.at<float>(i, 0) = static_cast<float>(i + 1);
+
+    std::vector<Mat> inputs{input};
+    std::vector<Mat> outputs;
+
+    runLayer(layer, inputs, outputs);
+    ASSERT_EQ(shape(output_ref), shape(outputs[0]));
+    normAssert(output_ref, outputs[0]);
+}
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Arg_1d_Test, Combine(
+/*input blob shape*/    Values(0, 1, 2, 3),
+/*operation*/           Values(0, 1),
+/*operation*/           Values( "max", "min")
+));
+
+typedef testing::TestWithParam<tuple<int, std::string>> Layer_NaryElemwise_1d_Test;
+TEST_P(Layer_NaryElemwise_1d_Test, Accuracy) {
+
+    int batch_size = get<0>(GetParam());
+    std::string operation = get<1>(GetParam());
+
+    LayerParams lp;
+    lp.type = "Eltwise";
+    lp.name = operation + "_Layer";
+    lp.set("operation", operation);
+    Ptr<NaryEltwiseLayer> layer = NaryEltwiseLayer::create(lp);
+
+    std::vector<int> input_shape = {batch_size, 1};
+    if (batch_size == 0)
+        input_shape.erase(input_shape.begin());
+
+    cv::Mat input1 = cv::Mat(input_shape, CV_32F, 0.0);
+    cv::Mat input2 = cv::Mat(input_shape, CV_32F, 0.0);
+    cv::randu(input1, 0.0, 1.0);
+    cv::randu(input2, 0.0, 1.0);
+
+    cv::Mat output_ref;
+    if (operation == "sum") {
+        output_ref = input1 + input2;
+    } else if (operation == "mul") {
+        output_ref = input1.mul(input2);
+    } else if (operation == "div") {
+        output_ref = input1 / input2;
+    } else if (operation == "sub") {
+        output_ref = input1 - input2;
+    } else {
+        output_ref = cv::Mat();
+    }
+    std::vector<Mat> inputs{input1, input2};
+    std::vector<Mat> outputs;
+
+    runLayer(layer, inputs, outputs);
+    if (!output_ref.empty()) {
+        ASSERT_EQ(shape(output_ref), shape(outputs[0]));
+        normAssert(output_ref, outputs[0]);
+    } else {
+        CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
+    }
+}
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_NaryElemwise_1d_Test, Combine(
+/*input blob shape*/    Values(0, 1),
+/*operation*/           Values("div", "mul", "sum", "sub")
+));
+
+typedef testing::TestWithParam<tuple<int, std::string>> Layer_Elemwise_1d_Test;
+TEST_P(Layer_Elemwise_1d_Test, Accuracy) {
+
+    int batch_size = get<0>(GetParam());
+    std::string operation = get<1>(GetParam());
+
+    LayerParams lp;
+    lp.type = "Eltwise";
+    lp.name = operation + "_Layer";
+    lp.set("operation", operation);
+    Ptr<EltwiseLayer> layer = EltwiseLayer::create(lp);
+
+    std::vector<int> input_shape = {batch_size, 1};
+    if (batch_size == 0)
+        input_shape.erase(input_shape.begin());
+
+    cv::Mat input1 = cv::Mat(input_shape, CV_32F, 1.0);
+    cv::Mat input2 = cv::Mat(input_shape, CV_32F, 1.0);
+    cv::randu(input1, 0.0, 1.0);
+    cv::randu(input2, 0.0, 1.0);
+
+    // Dynamically select the operation
+    cv::Mat output_ref;
+    if (operation == "sum") {
+        output_ref = input1 + input2;
+    } else if (operation == "max") {
+        output_ref = cv::max(input1, input2);
+    } else if (operation == "min") {
+        output_ref = cv::min(input1, input2);
+    } else if (operation == "prod") {
+        output_ref = input1.mul(input2);
+    } else if (operation == "div") {
+        output_ref = input1 / input2;
+    } else {
+        output_ref = cv::Mat();
+    }
+
+
+    std::vector<Mat> inputs{input1, input2};
+    std::vector<Mat> outputs;
+
+    runLayer(layer, inputs, outputs);
+
+    if (!output_ref.empty()) {
+        ASSERT_EQ(shape(output_ref), shape(outputs[0]));
+        normAssert(output_ref, outputs[0]);
+    } else {
+        CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
+    }
+}
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Elemwise_1d_Test, Combine(
+/*input blob shape*/    Values(0, 1, 2, 3),
+/*operation*/           Values("div", "prod", "max", "min", "sum")
+));
+
 TEST(Layer_GRU_Test_Accuracy_with_, Pytorch)
 {
     Mat Wx = blobFromNPY(_tf("gru.W.npy"));