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869016d8b1
Fully connected 0D test. #25208 This PR introduces parametrized `0/1D` input support test for `Fullyconnected` layer. ### 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
607 lines
19 KiB
C++
607 lines
19 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2024, OpenCV Team, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include <opencv2/dnn/all_layers.hpp>
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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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namespace opencv_test { namespace {
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typedef testing::TestWithParam<tuple<int>> Layer_1d_Test;
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TEST_P(Layer_1d_Test, Scale)
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{
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int batch_size = get<0>(GetParam());
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LayerParams lp;
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lp.type = "Scale";
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lp.name = "scaleLayer";
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lp.set("axis", 0);
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lp.set("mode", "scale");
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lp.set("bias_term", false);
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Ptr<ScaleLayer> layer = ScaleLayer::create(lp);
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std::vector<int> input_shape = {batch_size, 3};
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std::vector<int> output_shape = {batch_size, 3};
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if (batch_size == 0){
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input_shape.erase(input_shape.begin());
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output_shape.erase(output_shape.begin());
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}
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cv::Mat input = cv::Mat(input_shape, CV_32F, 1.0);
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cv::randn(input, 0.0, 1.0);
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cv::Mat weight = cv::Mat(output_shape, CV_32F, 2.0);
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std::vector<Mat> inputs{input, weight};
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std::vector<Mat> outputs;
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cv::Mat output_ref = input.mul(weight);
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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typedef testing::TestWithParam<tuple<int, int>> Layer_Gather_1d_Test;
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TEST_P(Layer_Gather_1d_Test, Accuracy) {
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int batch_size = get<0>(GetParam());
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int axis = get<1>(GetParam());
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LayerParams lp;
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lp.type = "Gather";
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lp.name = "gatherLayer";
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lp.set("axis", axis);
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lp.set("real_ndims", 1);
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Ptr<GatherLayer> layer = GatherLayer::create(lp);
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std::vector<int> input_shape = {batch_size, 1};
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std::vector<int> indices_shape = {1, 1};
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std::vector<int> output_shape = {batch_size, 1};
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if (batch_size == 0){
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input_shape.erase(input_shape.begin());
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indices_shape.erase(indices_shape.begin());
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output_shape.erase(output_shape.begin());
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} else if (axis == 0) {
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output_shape[0] = 1;
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}
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cv::Mat input = cv::Mat(input_shape, CV_32F, 1.0);
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cv::randu(input, 0.0, 1.0);
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cv::Mat indices = cv::Mat(indices_shape, CV_32S, 0.0);
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cv::Mat output_ref = cv::Mat(output_shape, CV_32F, input(cv::Range::all(), cv::Range(0, 1)).data);
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std::vector<Mat> inputs{input, indices};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Gather_1d_Test, Combine(
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/*input blob shape*/ Values(0, 1, 2, 3),
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/*operation*/ Values(0, 1)
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));
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typedef testing::TestWithParam<tuple<int, int, std::string>> Layer_Arg_1d_Test;
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TEST_P(Layer_Arg_1d_Test, Accuracy) {
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int batch_size = get<0>(GetParam());
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int axis = get<1>(GetParam());
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std::string operation = get<2>(GetParam());
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LayerParams lp;
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lp.type = "Arg";
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lp.name = "arg" + operation + "_Layer";
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lp.set("op", operation);
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lp.set("axis", axis);
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lp.set("keepdims", 1);
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lp.set("select_last_index", 0);
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Ptr<ArgLayer> layer = ArgLayer::create(lp);
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std::vector<int> input_shape = {batch_size, 1};
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std::vector<int> output_shape = {1, 1};
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if (batch_size == 0){
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input_shape.erase(input_shape.begin());
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output_shape.erase(output_shape.begin());
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}
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if (axis != 0 && batch_size != 0){
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output_shape[0] = batch_size;
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}
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cv::Mat input = cv::Mat(input_shape, CV_32F, 1);
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cv::Mat output_ref = cv::Mat(output_shape, CV_32F, 0);
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for (int i = 0; i < batch_size; ++i)
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input.at<float>(i, 0) = static_cast<float>(i + 1);
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std::vector<Mat> inputs{input};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Arg_1d_Test, Combine(
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/*input blob shape*/ Values(0, 1, 2, 3),
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/*operation*/ Values(0, 1),
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/*operation*/ Values( "max", "min")
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));
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typedef testing::TestWithParam<tuple<int, std::string>> Layer_NaryElemwise_1d_Test;
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TEST_P(Layer_NaryElemwise_1d_Test, Accuracy) {
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int batch_size = get<0>(GetParam());
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std::string operation = get<1>(GetParam());
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LayerParams lp;
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lp.type = "Eltwise";
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lp.name = operation + "_Layer";
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lp.set("operation", operation);
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Ptr<NaryEltwiseLayer> layer = NaryEltwiseLayer::create(lp);
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std::vector<int> input_shape = {batch_size, 1};
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if (batch_size == 0)
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input_shape.erase(input_shape.begin());
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cv::Mat input1 = cv::Mat(input_shape, CV_32F, 0.0);
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cv::Mat input2 = cv::Mat(input_shape, CV_32F, 0.0);
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cv::randu(input1, 0.0, 1.0);
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cv::randu(input2, 0.0, 1.0);
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cv::Mat output_ref;
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if (operation == "sum") {
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output_ref = input1 + input2;
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} else if (operation == "mul") {
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output_ref = input1.mul(input2);
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} else if (operation == "div") {
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output_ref = input1 / input2;
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} else if (operation == "sub") {
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output_ref = input1 - input2;
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} else {
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output_ref = cv::Mat();
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}
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std::vector<Mat> inputs{input1, input2};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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if (!output_ref.empty()) {
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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} else {
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CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
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}
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_NaryElemwise_1d_Test, Combine(
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/*input blob shape*/ Values(0, 1),
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/*operation*/ Values("div", "mul", "sum", "sub")
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));
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typedef testing::TestWithParam<tuple<int, std::string>> Layer_Elemwise_1d_Test;
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TEST_P(Layer_Elemwise_1d_Test, Accuracy) {
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int batch_size = get<0>(GetParam());
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std::string operation = get<1>(GetParam());
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LayerParams lp;
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lp.type = "Eltwise";
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lp.name = operation + "_Layer";
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lp.set("operation", operation);
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Ptr<EltwiseLayer> layer = EltwiseLayer::create(lp);
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std::vector<int> input_shape = {batch_size, 1};
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if (batch_size == 0)
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input_shape.erase(input_shape.begin());
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cv::Mat input1 = cv::Mat(input_shape, CV_32F, 1.0);
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cv::Mat input2 = cv::Mat(input_shape, CV_32F, 1.0);
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cv::randu(input1, 0.0, 1.0);
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cv::randu(input2, 0.0, 1.0);
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// Dynamically select the operation
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cv::Mat output_ref;
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if (operation == "sum") {
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output_ref = input1 + input2;
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} else if (operation == "max") {
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output_ref = cv::max(input1, input2);
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} else if (operation == "min") {
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output_ref = cv::min(input1, input2);
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} else if (operation == "prod") {
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output_ref = input1.mul(input2);
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} else if (operation == "div") {
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output_ref = input1 / input2;
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} else {
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output_ref = cv::Mat();
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}
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std::vector<Mat> inputs{input1, input2};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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if (!output_ref.empty()) {
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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} else {
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CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
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}
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Elemwise_1d_Test, Combine(
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/*input blob shape*/ Values(0, 1, 2, 3),
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/*operation*/ Values("div", "prod", "max", "min", "sum")
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));
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TEST(Layer_Reshape_Test, Accuracy)
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{
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LayerParams lp;
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lp.type = "Reshape";
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lp.name = "ReshapeLayer";
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lp.set("axis", 0); // Set axis to 0 to start reshaping from the first dimension
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lp.set("num_axes", -1); // Set num_axes to -1 to indicate all following axes are included in the reshape
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int newShape[] = {1};
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lp.set("dim", DictValue::arrayInt(newShape, 1));
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Ptr<ReshapeLayer> layer = ReshapeLayer::create(lp);
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std::vector<int> input_shape = {0};
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Mat input(0, input_shape.data(), CV_32F);
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randn(input, 0.0, 1.0);
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Mat output_ref(1, newShape, CV_32F, input.data);
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std::vector<Mat> inputs{input};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Split_Test;
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TEST_P(Layer_Split_Test, Accuracy_01D)
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{
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LayerParams lp;
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lp.type = "Split";
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lp.name = "SplitLayer";
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int top_count = 2; // 2 is for simplicity
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lp.set("top_count", top_count);
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Ptr<SplitLayer> layer = SplitLayer::create(lp);
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std::vector<int> input_shape = std::get<0>(GetParam());
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Mat input(input_shape.size(), input_shape.data(), CV_32F);
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cv::randn(input, 0.0, 1.0);
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Mat output_ref = Mat(input_shape.size(), input_shape.data(), CV_32F, input.data);
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std::vector<Mat> inputs{input};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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for (int i = 0; i < top_count; i++)
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{
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ASSERT_EQ(shape(output_ref), shape(outputs[i]));
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normAssert(output_ref, outputs[i]);
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}
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}
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INSTANTIATE_TEST_CASE_P(/*nothting*/, Layer_Split_Test,
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testing::Values(
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std::vector<int>({}),
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std::vector<int>({1}),
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std::vector<int>({1, 4}),
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std::vector<int>({1, 5}),
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std::vector<int>({4, 1}),
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std::vector<int>({4, 5})
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));
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typedef testing::TestWithParam<tuple<std::vector<int>, std::vector<int>>> Layer_Expand_Test;
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TEST_P(Layer_Expand_Test, Accuracy_ND) {
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std::vector<int> input_shape = get<0>(GetParam());
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std::vector<int> target_shape = get<1>(GetParam());
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if (input_shape.size() >= target_shape.size()) // Skip if input shape is already larger than target shape
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return;
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LayerParams lp;
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lp.type = "Expand";
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lp.name = "ExpandLayer";
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lp.set("shape", DictValue::arrayInt(&target_shape[0], target_shape.size()));
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Ptr<ExpandLayer> layer = ExpandLayer::create(lp);
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Mat input(input_shape.size(), input_shape.data(), CV_32F);
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cv::randn(input, 0.0, 1.0);
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cv::Mat output_ref(target_shape, CV_32F, input.data);
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std::vector<Mat> inputs{input};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(outputs.size(), 1);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Expand_Test, Combine(
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/*input blob shape*/ testing::Values(
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std::vector<int>({}),
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std::vector<int>({1}),
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std::vector<int>({1, 1}),
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std::vector<int>({1, 1, 1})
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),
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/*output blob shape*/ testing::Values(
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std::vector<int>({1}),
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std::vector<int>({1, 1}),
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std::vector<int>({1, 1, 1}),
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std::vector<int>({1, 1, 1, 1})
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)
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));
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typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Concat_Test;
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TEST_P(Layer_Concat_Test, Accuracy_01D)
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{
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LayerParams lp;
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lp.type = "Concat";
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lp.name = "ConcatLayer";
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lp.set("axis", 0);
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Ptr<ConcatLayer> layer = ConcatLayer::create(lp);
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std::vector<int> input_shape = get<0>(GetParam());
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std::vector<int> output_shape = {3};
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Mat input1(input_shape.size(), input_shape.data(), CV_32F, 1.0);
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Mat input2(input_shape.size(), input_shape.data(), CV_32F, 2.0);
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Mat input3(input_shape.size(), input_shape.data(), CV_32F, 3.0);
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float data[] = {1.0, 2.0, 3.0};
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Mat output_ref(output_shape, CV_32F, data);
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std::vector<Mat> inputs{input1, input2, input3};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Concat_Test,
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/*input blob shape*/ testing::Values(
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std::vector<int>({}),
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std::vector<int>({1})
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));
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typedef testing::TestWithParam<tuple<std::vector<int>, int>> Layer_Softmax_Test;
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TEST_P(Layer_Softmax_Test, Accuracy_01D) {
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int axis = get<1>(GetParam());
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std::vector<int> input_shape = get<0>(GetParam());
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if ((input_shape.size() == 0 && axis == 1) ||
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(!input_shape.empty() && input_shape.size() == 2 && input_shape[0] > 1 && axis == 1) ||
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(!input_shape.empty() && input_shape[0] > 1 && axis == 0)) // skip since not valid case
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return;
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LayerParams lp;
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lp.type = "Softmax";
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lp.name = "softmaxLayer";
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lp.set("axis", axis);
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Ptr<SoftmaxLayer> layer = SoftmaxLayer::create(lp);
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Mat input = Mat(input_shape.size(), input_shape.data(), CV_32F);
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cv::randn(input, 0.0, 1.0);
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Mat output_ref;
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cv::exp(input, output_ref);
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if (axis == 1){
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cv::divide(output_ref, cv::sum(output_ref), output_ref);
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} else {
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cv::divide(output_ref, output_ref, output_ref);
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}
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std::vector<Mat> inputs{input};
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std::vector<Mat> outputs;
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(outputs.size(), 1);
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ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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normAssert(output_ref, outputs[0]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Softmax_Test, Combine(
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/*input blob shape*/
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testing::Values(
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std::vector<int>({}),
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std::vector<int>({1}),
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std::vector<int>({4}),
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std::vector<int>({1, 4}),
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std::vector<int>({4, 1})
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),
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/*Axis */
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testing::Values(0, 1)
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));
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typedef testing::TestWithParam<tuple<std::vector<int>, std::string>> Layer_Scatter_Test;
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TEST_P(Layer_Scatter_Test, Accuracy1D) {
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std::vector<int> input_shape = get<0>(GetParam());
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std::string opr = get<1>(GetParam());
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LayerParams lp;
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lp.type = "Scatter";
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lp.name = "addLayer";
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lp.set("axis", 0);
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lp.set("reduction", opr);
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Ptr<ScatterLayer> layer = ScatterLayer::create(lp);
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cv::Mat input = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
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cv::randn(input, 0.0, 1.0);
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int indices[] = {3, 2, 1, 0};
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cv::Mat indices_mat(input_shape.size(), input_shape.data(), CV_32S, indices);
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cv::Mat output(input_shape.size(), input_shape.data(), CV_32F, 0.0);
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// create reference output
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cv::Mat output_ref(input_shape, CV_32F, 0.0);
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for (int i = 0; i < input_shape[0]; i++){
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output_ref.at<float>(indices[i]) = input.at<float>(i);
|
|
}
|
|
|
|
if (opr == "add"){
|
|
output_ref += output;
|
|
} else if (opr == "mul"){
|
|
output_ref = output.mul(output_ref);
|
|
} else if (opr == "max"){
|
|
cv::max(output_ref, output, output_ref);
|
|
} else if (opr == "min"){
|
|
cv::min(output_ref, output, output_ref);
|
|
}
|
|
|
|
std::vector<Mat> inputs{output, indices_mat, input};
|
|
std::vector<Mat> outputs;
|
|
runLayer(layer, inputs, outputs);
|
|
ASSERT_EQ(outputs.size(), 1);
|
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Scatter_Test, Combine(
|
|
/*input blob shape*/ testing::Values(std::vector<int>{4},
|
|
std::vector<int>{1, 4}),
|
|
/*reduce*/ Values("none", "add", "mul", "max", "min")
|
|
));
|
|
|
|
|
|
|
|
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Permute_Test;
|
|
TEST_P(Layer_Permute_Test, Accuracy_01D)
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "Permute";
|
|
lp.name = "PermuteLayer";
|
|
|
|
int order[] = {0}; // Since it's a 0D tensor, the order remains [0]
|
|
lp.set("order", DictValue::arrayInt(order, 1));
|
|
Ptr<PermuteLayer> layer = PermuteLayer::create(lp);
|
|
|
|
std::vector<int> input_shape = get<0>(GetParam());
|
|
|
|
Mat input = Mat(input_shape.size(), input_shape.data(), CV_32F);
|
|
cv::randn(input, 0.0, 1.0);
|
|
Mat output_ref = input.clone();
|
|
|
|
std::vector<Mat> inputs{input};
|
|
std::vector<Mat> outputs;
|
|
|
|
runLayer(layer, inputs, outputs);
|
|
ASSERT_EQ(outputs.size(), 1);
|
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
|
normAssert(output_ref, outputs[0]);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Permute_Test,
|
|
/*input blob shape*/ testing::Values(
|
|
std::vector<int>{},
|
|
std::vector<int>{1},
|
|
std::vector<int>{1, 4},
|
|
std::vector<int>{4, 1}
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Slice_Test;
|
|
TEST_P(Layer_Slice_Test, Accuracy_1D){
|
|
|
|
LayerParams lp;
|
|
lp.type = "Slice";
|
|
lp.name = "SliceLayer";
|
|
|
|
std::vector<int> input_shape = get<0>(GetParam());
|
|
|
|
int splits = 2;
|
|
int axis = (input_shape.size() > 1 ) ? 1 : 0;
|
|
|
|
lp.set("axis", axis);
|
|
lp.set("num_split", splits);
|
|
|
|
Ptr<SliceLayer> layer = SliceLayer::create(lp);
|
|
std::vector<int> output_shape;
|
|
if (input_shape.size() > 1)
|
|
output_shape = {1, input_shape[1] / splits};
|
|
else
|
|
output_shape = {input_shape[0] / splits};
|
|
|
|
cv::Mat input = cv::Mat(input_shape, CV_32F);
|
|
cv::randu(input, 0.0, 1.0);
|
|
|
|
std::vector<cv::Mat> output_refs;
|
|
for (int i = 0; i < splits; ++i){
|
|
output_refs.push_back(cv::Mat(output_shape, CV_32F));
|
|
if (input_shape.size() > 1 ) {
|
|
for (int j = 0; j < output_shape[1]; ++j){
|
|
output_refs[i].at<float>(j) = input.at<float>(i * output_shape[1] + j);
|
|
}
|
|
} else {
|
|
for (int j = 0; j < output_shape[0]; ++j){
|
|
output_refs[i].at<float>(j) = input.at<float>(i * output_shape[0] + j);
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<Mat> inputs{input};
|
|
std::vector<Mat> outputs;
|
|
runLayer(layer, inputs, outputs);
|
|
|
|
for (int i = 0; i < splits; ++i){
|
|
ASSERT_EQ(shape(output_refs[i]), shape(outputs[i]));
|
|
normAssert(output_refs[i], outputs[i]);
|
|
}
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Slice_Test,
|
|
/*input blob shape*/ testing::Values(
|
|
std::vector<int>({4}),
|
|
std::vector<int>({1, 4})
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_FullyConnected_Test;
|
|
TEST_P(Layer_FullyConnected_Test, Accuracy_01D)
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "InnerProduct";
|
|
lp.name = "InnerProductLayer";
|
|
lp.set("num_output", 1);
|
|
lp.set("bias_term", false);
|
|
lp.set("axis", 0);
|
|
|
|
std::vector<int> input_shape = get<0>(GetParam());
|
|
|
|
RNG& rng = TS::ptr()->get_rng();
|
|
float inp_value = rng.uniform(0.0, 10.0);
|
|
Mat weights(std::vector<int>{total(input_shape), 1}, CV_32F, inp_value);
|
|
lp.blobs.push_back(weights);
|
|
|
|
Ptr<Layer> layer = LayerFactory::createLayerInstance("InnerProduct", lp);
|
|
|
|
Mat input(input_shape.size(), input_shape.data(), CV_32F);
|
|
randn(input, 0, 1);
|
|
Mat output_ref = input.reshape(1, 1) * weights;
|
|
output_ref.dims = 1;
|
|
|
|
std::vector<Mat> inputs{input};
|
|
std::vector<Mat> outputs;
|
|
runLayer(layer, inputs, outputs);
|
|
normAssert(output_ref, outputs[0]);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/*nothting*/, Layer_FullyConnected_Test,
|
|
testing::Values(
|
|
std::vector<int>({}),
|
|
std::vector<int>({1}),
|
|
std::vector<int>({4})
|
|
));
|
|
|
|
}}
|