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275 lines
8.4 KiB
C++
275 lines
8.4 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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}}
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