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opencv/modules/dnn/test/test_layers_1d.cpp

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// 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.
//
// Copyright (C) 2024, OpenCV Team, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
namespace opencv_test { namespace {
class Layer_Test_01D: public testing::TestWithParam<tuple<std::vector<int>>>
{
public:
std::vector<int> input_shape;
std::vector<int> output_shape;
float inp_value;
Mat input;
LayerParams lp;
void SetUp()
{
input_shape = get<0>(GetParam());
output_shape = input_shape;
// generate random positeve value from 1 to 10
RNG& rng = TS::ptr()->get_rng();
inp_value = rng.uniform(1.0, 10.0); // random uniform value
input = Mat(input_shape.size(), input_shape.data(), CV_32F, inp_value);
}
void TestLayer(Ptr<Layer> layer, std::vector<Mat> &inputs, const Mat& output_ref){
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
};
TEST_P(Layer_Test_01D, Scale)
{
lp.type = "Scale";
lp.name = "ScaleLayer";
lp.set("axis", 0);
lp.set("mode", "scale");
lp.set("bias_term", false);
Ptr<ScaleLayer> layer = ScaleLayer::create(lp);
Mat weight = Mat(output_shape.size(), output_shape.data(), CV_32F, 2.0);
std::vector<Mat> inputs{input, weight};
Mat output_ref = input.mul(weight);
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, ReLU6)
{
lp.type = "ReLU6";
lp.name = "ReLU6Layer";
lp.set("min_value", 0.0);
lp.set("max_value", 1.0);
Ptr<ReLU6Layer> layer = ReLU6Layer::create(lp);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, 1.0);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Clip)
{
lp.type = "Clip";
lp.name = "ClipLayer";
lp.set("min_value", 0.0);
lp.set("max_value", 1.0);
Ptr<ReLU6Layer> layer = ReLU6Layer::create(lp);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, 1.0);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, ReLU)
{
lp.type = "ReLU";
lp.name = "ReluLayer";
lp.set("negative_slope", 0.0);
Ptr<ReLULayer> layer = ReLULayer::create(lp);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, inp_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Gelu)
{
lp.type = "Gelu";
lp.name = "GeluLayer";
Ptr<GeluLayer> layer = GeluLayer::create(lp);
float value = inp_value * 0.5 * (std::erf(inp_value * 1 / std::sqrt(2.0)) + 1.0);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, GeluApprox)
{
lp.type = "GeluApprox";
lp.name = "GeluApproxLayer";
Ptr<GeluApproximationLayer> layer = GeluApproximationLayer::create(lp);
float value = inp_value * 0.5 * (1.0 + std::tanh(std::sqrt(2.0 / M_PI) * (inp_value + 0.044715 * std::pow(inp_value, 3))));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Sigmoid)
{
lp.type = "Sigmoid";
lp.name = "SigmoidLayer";
Ptr<SigmoidLayer> layer = SigmoidLayer::create(lp);
float value = 1.0 / (1.0 + std::exp(-inp_value));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Tanh)
{
lp.type = "TanH";
lp.name = "TanHLayer";
Ptr<Layer> layer = TanHLayer::create(lp);
float value = std::tanh(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Swish)
{
lp.type = "Swish";
lp.name = "SwishLayer";
Ptr<Layer> layer = SwishLayer::create(lp);
float value = inp_value / (1 + std::exp(-inp_value));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Mish)
{
lp.type = "Mish";
lp.name = "MishLayer";
Ptr<Layer> layer = MishLayer::create(lp);
float value = inp_value * std::tanh(std::log(1 + std::exp(inp_value)));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, ELU)
{
lp.type = "ELU";
lp.name = "EluLayer";
lp.set("alpha", 1.0);
Ptr<Layer> layer = ELULayer::create(lp);
float value = inp_value > 0 ? inp_value : std::exp(inp_value) - 1;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Abs)
{
lp.type = "Abs";
lp.name = "AbsLayer";
Ptr<Layer> layer = AbsLayer::create(lp);
float value = std::abs(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, BNLL)
{
lp.type = "BNLL";
lp.name = "BNLLLayer";
Ptr<Layer> layer = BNLLLayer::create(lp);
float value = std::log(1 + std::exp(inp_value));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Ceil)
{
lp.type = "Ceil";
lp.name = "CeilLayer";
Ptr<Layer> layer = CeilLayer::create(lp);
float value = std::ceil(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Floor)
{
lp.type = "Floor";
lp.name = "FloorLayer";
Ptr<Layer> layer = FloorLayer::create(lp);
float value = std::floor(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Log)
{
lp.type = "Log";
lp.name = "LogLayer";
Ptr<Layer> layer = LogLayer::create(lp);
float value = std::log(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Round)
{
lp.type = "Round";
lp.name = "RoundLayer";
Ptr<Layer> layer = RoundLayer::create(lp);
float value = std::round(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Sqrt)
{
lp.type = "Sqrt";
lp.name = "SqrtLayer";
Ptr<Layer> layer = SqrtLayer::create(lp);
float value = std::sqrt(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Acos)
{
lp.type = "Acos";
lp.name = "AcosLayer";
Ptr<Layer> layer = AcosLayer::create(lp);
inp_value = 0.5 + static_cast <float> (inp_value) / (static_cast <float> (RAND_MAX/(1-0.5)));
input = Mat(input_shape.size(), input_shape.data(), CV_32F, inp_value);
float value = std::acos(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Acosh)
{
lp.type = "Acosh";
lp.name = "AcoshLayer";
Ptr<Layer> layer = AcoshLayer::create(lp);
float value = std::acosh(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Asin)
{
lp.type = "Asin";
lp.name = "AsinLayer";
Ptr<Layer> layer = AsinLayer::create(lp);
inp_value = 0.5 + static_cast <float> (inp_value) / (static_cast <float> (RAND_MAX/(1-0.5)));
input = Mat(input_shape.size(), input_shape.data(), CV_32F, inp_value);
float value = std::asin(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Asinh)
{
lp.type = "Asinh";
lp.name = "AsinhLayer";
Ptr<Layer> layer = AsinhLayer::create(lp);
float value = std::asinh(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Atan)
{
lp.type = "Atan";
lp.name = "AtanLayer";
Ptr<Layer> layer = AtanLayer::create(lp);
float value = std::atan(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Cos)
{
lp.type = "Cos";
lp.name = "CosLayer";
Ptr<Layer> layer = CosLayer::create(lp);
float value = std::cos(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Cosh)
{
lp.type = "Cosh";
lp.name = "CoshLayer";
Ptr<Layer> layer = CoshLayer::create(lp);
float value = std::cosh(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Sin)
{
lp.type = "Sin";
lp.name = "SinLayer";
Ptr<Layer> layer = SinLayer::create(lp);
float value = std::sin(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Sinh)
{
lp.type = "Sinh";
lp.name = "SinhLayer";
Ptr<Layer> layer = SinhLayer::create(lp);
float value = std::sinh(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Tan)
{
lp.type = "Tan";
lp.name = "TanLayer";
Ptr<Layer> layer = TanLayer::create(lp);
float value = std::tan(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Erf)
{
lp.type = "Erf";
lp.name = "ErfLayer";
Ptr<Layer> layer = ErfLayer::create(lp);
float out_value = std::erf(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Reciprocal)
{
lp.type = "Reciprocal";
lp.name = "ReciprocalLayer";
Ptr<Layer> layer = ReciprocalLayer::create(lp);
float out_value = 1/inp_value;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, HardSwish)
{
lp.type = "HardSwish";
lp.name = "HardSwishLayer";
Ptr<Layer> layer = HardSwishLayer::create(lp);
float out_value = inp_value * std::max(0.0f, std::min(6.0f, inp_value + 3.0f)) / 6.0f;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Softplus)
{
lp.type = "Softplus";
lp.name = "SoftplusLayer";
Ptr<Layer> layer = SoftplusLayer::create(lp);
float out_value = std::log(1 + std::exp(inp_value));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, SoftSign)
{
lp.type = "Softsign";
lp.name = "SoftsignLayer";
Ptr<Layer> layer = SoftsignLayer::create(lp);
float out_value = inp_value / (1 + std::abs(inp_value));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, CELU)
{
lp.type = "CELU";
lp.name = "CeluLayer";
lp.set("alpha", 1.0);
Ptr<Layer> layer = CeluLayer::create(lp);
float out_value = inp_value < 0 ? std::exp(inp_value) - 1 : inp_value;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, HardSigmoid)
{
lp.type = "HardSigmoid";
lp.name = "HardSigmoidLayer";
Ptr<Layer> layer = HardSigmoidLayer::create(lp);
float out_value = std::max(0.0f, std::min(1.0f, 0.2f * inp_value + 0.5f));
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, SELU)
{
lp.type = "SELU";
lp.name = "SeluLayer";
lp.set("alpha", 1.6732631921768188);
lp.set("gamma", 1.0507009873554805);
Ptr<Layer> layer = SeluLayer::create(lp);
double inp_value_double = static_cast<double>(inp_value); // Ensure the input is treated as double for the computation
double value_double = 1.0507009873554805 * (inp_value_double > 0 ? inp_value_double : 1.6732631921768188 * (std::exp(inp_value_double / 1.0) - 1));
float value = static_cast<float>(value_double);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, ThresholdedReLU)
{
lp.type = "ThresholdedRelu";
lp.name = "ThresholdedReluLayer";
lp.set("alpha", 1.0);
Ptr<Layer> layer = ThresholdedReluLayer::create(lp);
float value = inp_value > 1.0 ? inp_value : 0.0;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Power)
{
lp.type = "Power";
lp.name = "PowerLayer";
lp.set("power", 2.0);
lp.set("scale", 1.0);
lp.set("shift", 0.0);
Ptr<Layer> layer = PowerLayer::create(lp);
float value = std::pow(inp_value, 2.0);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Exp)
{
lp.type = "Exp";
lp.name = "ExpLayer";
Ptr<Layer> layer = ExpLayer::create(lp);
float out_value = std::exp(inp_value);
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, out_value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Sign)
{
lp.type = "Sign";
lp.name = "SignLayer";
Ptr<Layer> layer = SignLayer::create(lp);
float value = inp_value > 0 ? 1.0 : 0.0;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, Shrink)
{
lp.type = "Shrink";
lp.name = "ShrinkLayer";
lp.set("lambda", 0.5);
lp.set("bias", 0.5);
Ptr<Layer> layer = ShrinkLayer::create(lp);
float value = inp_value > 0.5 ? inp_value - 0.5 : 0.0;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
TEST_P(Layer_Test_01D, ChannelsPReLU)
{
lp.type = "ChannelsPReLU";
lp.name = "ChannelsPReLULayer";
Mat alpha = Mat(1, 3, CV_32F, 0.5);
lp.blobs.push_back(alpha);
Ptr<Layer> layer = ChannelsPReLULayer::create(lp);
float value = inp_value > 0 ? inp_value : 0.5 * inp_value;
Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, value);
std::vector<Mat> inputs{input};
TestLayer(layer, inputs, output_ref);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Test_01D,
testing::Values(
std::vector<int>{},
std::vector<int>{1}
));
typedef testing::TestWithParam<tuple<std::vector<int>, int>> Layer_Gather_Test;
TEST_P(Layer_Gather_Test, Accuracy_01D) {
std::vector<int> input_shape = get<0>(GetParam());
int axis = get<1>(GetParam());
// skip case when axis > input shape
if (axis > input_shape.size())
return;
LayerParams lp;
lp.type = "Gather";
lp.name = "GatherLayer";
lp.set("axis", axis);
lp.set("real_ndims", 1);
Ptr<GatherLayer> layer = GatherLayer::create(lp);
cv::Mat input(input_shape.size(), input_shape.data(), CV_32F);
cv::randu(input, 0.0, 1.0);
std::vector<int> indices_shape = {1};
cv::Mat indices = cv::Mat(indices_shape.size(), indices_shape.data(), CV_32S, 0.0);
cv::Mat output_ref;
if (input_shape.size() == 0 || input_shape.size() == 1){
output_ref = input;
} else if (axis == 0){
output_ref = input.row(0);
} else if (axis == 1){
output_ref = input.col(0);
}
std::vector<Mat> inputs{input, indices};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Gather_Test, Combine(
/*input blob shape*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 4}),
std::vector<int>({4, 4})
),
/*axis*/ testing::Values(0, 1)
));
template <typename T>
int arg_op(const std::vector<T>& vec, const std::string& operation) {
CV_Assert(!vec.empty());
if (operation == "max") {
return static_cast<int>(std::distance(vec.begin(), std::max_element(vec.begin(), vec.end())));
} else if (operation == "min") {
return static_cast<int>(std::distance(vec.begin(), std::min_element(vec.begin(), vec.end())));
} else {
CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
}
}
// Test for ArgLayer is disabled because there problem in runLayer function related to type assignment
typedef testing::TestWithParam<tuple<std::vector<int>, std::string>> Layer_Arg_Test;
TEST_P(Layer_Arg_Test, Accuracy_01D) {
std::vector<int> input_shape = get<0>(GetParam());
std::string operation = get<1>(GetParam());
LayerParams lp;
lp.type = "Arg";
lp.name = "Arg" + operation + "_Layer";
int axis = (input_shape.size() == 0 || input_shape.size() == 1 ) ? 0 : 1;
lp.set("op", operation);
lp.set("axis", axis);
lp.set("keepdims", 1);
lp.set("select_last_index", 0);
Ptr<ArgLayer> layer = ArgLayer::create(lp);
cv::Mat input = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
for (int i = 0; i < input.total(); i++){
input.at<float>(i) = i;
}
// create reference output with required shape and values
int index;
cv::Mat output_ref;
std::vector<int> ref_output;
if (input_shape.size() == 2 ){
int rows = input_shape[0];
int cols = input_shape[1];
ref_output.resize(rows);
for (int i = 0; i < rows; i++) {
std::vector<float> row_vec(cols);
for (int j = 0; j < cols; j++) {
row_vec[j] = input.at<float>(i, j);
}
ref_output[i] = (int) arg_op(row_vec, operation);
}
output_ref = cv::Mat(rows, (axis == 1) ? 1 : cols, CV_32S, ref_output.data());
} else if (input_shape.size() <= 1) {
index = arg_op(std::vector<float>(input.begin<float>(), input.end<float>()), operation);
output_ref = cv::Mat(input_shape.size(), input_shape.data(), CV_32FC1, &index);
}
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
// convert output_ref to float to match the output type
output_ref.convertTo(output_ref, CV_64SC1);
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Arg_Test, Combine(
/*input blob shape*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 4}),
std::vector<int>({4, 4})
),
/*operation*/ Values( "max", "min")
));
typedef testing::TestWithParam<tuple<std::vector<int>, std::string>> Layer_NaryElemwise_1d_Test;
TEST_P(Layer_NaryElemwise_1d_Test, Accuracy) {
std::vector<int> input_shape = get<0>(GetParam());
std::string operation = get<1>(GetParam());
LayerParams lp;
lp.type = "NaryEltwise";
lp.name = operation + "_Layer";
lp.set("operation", operation);
Ptr<NaryEltwiseLayer> layer = NaryEltwiseLayer::create(lp);
cv::Mat input1 = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
cv::Mat input2 = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
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(1, outputs.size());
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*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 4}),
std::vector<int>({4, 1})),
/*operation*/ testing::Values("div", "mul", "sum", "sub")
));
typedef testing::TestWithParam<tuple<std::vector<int>, std::string>> Layer_Elemwise_1d_Test;
TEST_P(Layer_Elemwise_1d_Test, Accuracy_01D) {
std::vector<int> input_shape = 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);
cv::Mat input1(input_shape.size(), input_shape.data(), CV_32F);
cv::Mat input2(input_shape.size(), input_shape.data(), CV_32F);
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(1, outputs.size());
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*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({4}),
std::vector<int>({1, 4}),
std::vector<int>({4, 1})),
/*operation*/ testing::Values("div", "prod", "max", "min", "sum")
));
TEST(Layer_Reshape_Test, Accuracy_1D)
{
LayerParams lp;
lp.type = "Reshape";
lp.name = "ReshapeLayer";
lp.set("axis", 0); // Set axis to 0 to start reshaping from the first dimension
lp.set("num_axes", -1); // Set num_axes to -1 to indicate all following axes are included in the reshape
int newShape[] = {1};
lp.set("dim", DictValue::arrayInt(newShape, 1));
Ptr<ReshapeLayer> layer = ReshapeLayer::create(lp);
std::vector<int> input_shape = {0};
Mat input(0, input_shape.data(), CV_32F);
randn(input, 0.0, 1.0);
Mat output_ref(1, newShape, CV_32F, input.data);
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Split_Test;
TEST_P(Layer_Split_Test, Accuracy_01D)
{
LayerParams lp;
lp.type = "Split";
lp.name = "SplitLayer";
int top_count = 2; // 2 is for simplicity
lp.set("top_count", top_count);
Ptr<SplitLayer> layer = SplitLayer::create(lp);
std::vector<int> input_shape = std::get<0>(GetParam());
Mat input(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0.0, 1.0);
Mat output_ref = Mat(input_shape.size(), input_shape.data(), CV_32F, input.data);
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(outputs.size(), top_count);
for (int i = 0; i < top_count; i++)
{
ASSERT_EQ(shape(outputs[i]), shape(output_ref));
normAssert(output_ref, outputs[i]);
}
}
INSTANTIATE_TEST_CASE_P(/*nothting*/, Layer_Split_Test,
testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 4}),
std::vector<int>({1, 5}),
std::vector<int>({4, 1}),
std::vector<int>({4, 5})
));
typedef testing::TestWithParam<tuple<std::vector<int>, std::vector<int>>> Layer_Expand_Test;
TEST_P(Layer_Expand_Test, Accuracy_ND) {
std::vector<int> input_shape = get<0>(GetParam());
std::vector<int> target_shape = get<1>(GetParam());
if (input_shape.size() >= target_shape.size()) // Skip if input shape is already larger than target shape
return;
LayerParams lp;
lp.type = "Expand";
lp.name = "ExpandLayer";
lp.set("shape", DictValue::arrayInt(&target_shape[0], target_shape.size()));
Ptr<ExpandLayer> layer = ExpandLayer::create(lp);
Mat input(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0.0, 1.0);
cv::Mat output_ref(target_shape, CV_32F, input.data);
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Expand_Test, Combine(
/*input blob shape*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 1}),
std::vector<int>({1, 1, 1})
),
/*output blob shape*/ testing::Values(
std::vector<int>({1}),
std::vector<int>({1, 1}),
std::vector<int>({1, 1, 1}),
std::vector<int>({1, 1, 1, 1})
)
));
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Concat_Test;
TEST_P(Layer_Concat_Test, Accuracy_01D)
{
LayerParams lp;
lp.type = "Concat";
lp.name = "ConcatLayer";
lp.set("axis", 0);
Ptr<ConcatLayer> layer = ConcatLayer::create(lp);
std::vector<int> input_shape = get<0>(GetParam());
std::vector<int> output_shape = {3};
Mat input1(input_shape.size(), input_shape.data(), CV_32F, 1.0);
Mat input2(input_shape.size(), input_shape.data(), CV_32F, 2.0);
Mat input3(input_shape.size(), input_shape.data(), CV_32F, 3.0);
float data[] = {1.0, 2.0, 3.0};
Mat output_ref(output_shape, CV_32F, data);
std::vector<Mat> inputs{input1, input2, input3};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Concat_Test,
/*input blob shape*/ testing::Values(
std::vector<int>({}),
std::vector<int>({1})
));
typedef testing::TestWithParam<tuple<std::vector<int>, int>> Layer_Softmax_Test;
TEST_P(Layer_Softmax_Test, Accuracy_01D) {
int axis = get<1>(GetParam());
std::vector<int> input_shape = get<0>(GetParam());
if ((input_shape.size() == 0 && axis == 1) ||
(!input_shape.empty() && input_shape.size() == 2 && input_shape[0] > 1 && axis == 1) ||
(!input_shape.empty() && input_shape[0] > 1 && axis == 0)) // skip since not valid case
return;
LayerParams lp;
lp.type = "Softmax";
lp.name = "softmaxLayer";
lp.set("axis", axis);
Ptr<SoftmaxLayer> layer = SoftmaxLayer::create(lp);
Mat input = Mat(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0.0, 1.0);
Mat output_ref;
cv::exp(input, output_ref);
if (axis == 1){
cv::divide(output_ref, cv::sum(output_ref), output_ref);
} else {
cv::divide(output_ref, output_ref, output_ref);
}
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Softmax_Test, Combine(
/*input blob shape*/
testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({4}),
std::vector<int>({1, 4}),
std::vector<int>({4, 1})
),
/*Axis */
testing::Values(0, 1)
));
typedef testing::TestWithParam<std::tuple<std::tuple<int, std::vector<int>>, std::string>> Layer_Scatter_Test;
TEST_P(Layer_Scatter_Test, Accuracy1D) {
auto tup = get<0>(GetParam());
int axis = get<0>(tup);
std::vector<int> input_shape = get<1>(tup);
std::string opr = get<1>(GetParam());
LayerParams lp;
lp.type = "Scatter";
lp.name = "ScatterLayer";
lp.set("axis", axis);
lp.set("reduction", opr);
Ptr<ScatterLayer> layer = ScatterLayer::create(lp);
cv::Mat input = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0.0, 1.0);
int indices[] = {3, 2, 1, 0};
cv::Mat indices_mat(input_shape.size(), input_shape.data(), CV_32S, indices);
cv::Mat output(input_shape.size(), input_shape.data(), CV_32F, 0.0);
// create reference output
cv::Mat output_ref(input_shape, CV_32F, 0.0);
for (int i = 0; i < ((input_shape.size() == 1) ? input_shape[0] : input_shape[1]); i++){
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(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Scatter_Test, Combine(
/*input blob shape*/ testing::Values(std::make_tuple(0, std::vector<int>{4}),
std::make_tuple(1, std::vector<int>{1, 4})),
/*reduce*/ testing::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(1, outputs.size());
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);
ASSERT_EQ(outputs.size(), splits);
for (int i = 0; i < splits; ++i){
ASSERT_EQ(shape(outputs[i]), shape(output_refs[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_Padding_Test;
TEST_P(Layer_Padding_Test, Accuracy_01D){
std::vector<int> input_shape = get<0>(GetParam());
float pad_value = 10;
LayerParams lp;
lp.type = "Padding";
lp.name = "PaddingLayer";
std::vector<int> paddings = {5, 3}; // Pad before and pad after for one dimension
lp.set("paddings", DictValue::arrayInt(paddings.data(), paddings.size()));
lp.set("value", pad_value);
lp.set("input_dims", (input_shape.size() == 1) ? -1 : 0);
Ptr<PaddingLayer> layer = PaddingLayer::create(lp);
cv::Mat input(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0.0, 1.0);
// Fill in the padding values manually
// Create output ref shape depending on the input shape and input_dims
std::vector<int> output_shape;
if (input_shape.size() == 0){
output_shape = {1 + paddings[0] + paddings[1]};
} else if (input_shape.size() == 1){
output_shape = {input_shape[0] + paddings[0] + paddings[1]};
} else {
output_shape = {input_shape[0], input_shape[1] + paddings[0] + paddings[1]};
}
cv::Mat output_ref(output_shape.size(), output_shape.data(), CV_32F, pad_value);
if (input_shape.size() == 0){
output_ref.at<float>(paddings[0]) = input.at<float>(0);
} else if (input_shape.size() == 1){
for (int i = 0; i < input_shape[0]; ++i){
output_ref.at<float>(i + paddings[0]) = input.at<float>(i);
}
} else {
for (int i = 0; i < input_shape[0]; ++i){
for (int j = 0; j < input_shape[1]; ++j){
output_ref.at<float>(i, j + paddings[0]) = input.at<float>(i, j);
}
}
}
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Padding_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_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 = input_shape.size();
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
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})
));
typedef testing::TestWithParam<std::vector<int>> Layer_BatchNorm_Test;
TEST_P(Layer_BatchNorm_Test, Accuracy_01D)
{
std::vector<int> input_shape = GetParam();
// Layer parameters
LayerParams lp;
lp.type = "BatchNorm";
lp.name = "BatchNormLayer";
lp.set("has_weight", false);
lp.set("has_bias", false);
RNG& rng = TS::ptr()->get_rng();
float inp_value = rng.uniform(0.0, 10.0);
Mat meanMat(input_shape.size(), input_shape.data(), CV_32F, inp_value);
Mat varMat(input_shape.size(), input_shape.data(), CV_32F, inp_value);
vector<Mat> blobs = {meanMat, varMat};
lp.blobs = blobs;
// Create the layer
Ptr<Layer> layer = BatchNormLayer::create(lp);
Mat input(input_shape.size(), input_shape.data(), CV_32F, 1.0);
cv::randn(input, 0, 1);
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
//create output_ref to compare with outputs
Mat output_ref = input.clone();
cv::sqrt(varMat + 1e-5, varMat);
output_ref = (output_ref - meanMat) / varMat;
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothting*/, Layer_BatchNorm_Test,
testing::Values(
std::vector<int>({}),
std::vector<int>({4}),
std::vector<int>({1, 4}),
std::vector<int>({4, 1})
));
typedef testing::TestWithParam<tuple<std::vector<int>>> Layer_Const_Test;
TEST_P(Layer_Const_Test, Accuracy_01D)
{
std::vector<int> input_shape = get<0>(GetParam());
LayerParams lp;
lp.type = "Const";
lp.name = "ConstLayer";
Mat constBlob = Mat(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(constBlob, 0.0, 1.0);
Mat output_ref = constBlob.clone();
lp.blobs.push_back(constBlob);
Ptr<Layer> layer = ConstLayer::create(lp);
std::vector<Mat> inputs; // No inputs are needed for a ConstLayer
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Const_Test, testing::Values(
std::vector<int>({}),
std::vector<int>({1}),
std::vector<int>({1, 4}),
std::vector<int>({4, 1})
));
typedef testing::TestWithParam<std::vector<int>> Layer_Tile_Test;
TEST_P(Layer_Tile_Test, Accuracy_01D){
std::vector<int> input_shape = GetParam();
std::vector<int> repeats = {2, 2};
LayerParams lp;
lp.type = "Tile";
lp.name = "TileLayer";
lp.set("repeats", DictValue::arrayInt(repeats.data(), repeats.size()));
Ptr<TileLayer> layer = TileLayer::create(lp);
cv::Mat input = cv::Mat(input_shape.size(), input_shape.data(), CV_32F);
cv::randn(input, 0, 1);
std::vector<Mat> inputs{input};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
// Manually create the expected output for verification
cv::Mat output_ref = input.clone();
for (int i = 0; i < repeats.size(); ++i) {
cv::Mat tmp;
cv::repeat(output_ref, (i == 0 ? repeats[i] : 1), (i == 1 ? repeats[i] : 1), tmp);
output_ref = tmp;
}
ASSERT_EQ(outputs.size(), 1);
ASSERT_EQ(shape(outputs[0]), shape(output_ref));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Tile_Test,
/*input blob shape*/ testing::Values(
std::vector<int>({}),
std::vector<int>({2}),
std::vector<int>({2, 1}),
std::vector<int>({1, 2}),
std::vector<int>({2, 2})
));
typedef testing::TestWithParam<tuple<std::vector<int>, std::vector<int>, std::string>> Layer_Einsum_Test;
TEST_P(Layer_Einsum_Test, Accuracy_01D)
{
auto tup = GetParam();
std::vector<int> input_shape1 = std::get<0>(tup);
std::vector<int> input_shape2 = std::get<1>(tup);
std::string equation = std::get<2>(tup);
LayerParams lp;
lp.type = "Einsum";
lp.name = "EinsumLayer";
lp.set("equation", equation);
lp.set("inputSize", 2);
lp.set("outputSize", 1);
lp.set("inputShapes0", DictValue::arrayInt(&input_shape1[0], input_shape1.size()));
lp.set("inputShapes1", DictValue::arrayInt(&input_shape2[0], input_shape2.size()));
Ptr<Layer> layer = EinsumLayer::create(lp);
cv::Mat input1(input_shape1.size(), input_shape1.data(), CV_32F);
cv::Mat input2(input_shape2.size(), input_shape2.data(), CV_32F);
cv::randn(input1, 0.0, 1.0); cv::randn(input2, 0.0, 1.0);
std::vector<Mat> inputs = {input1, input2};
std::vector<Mat> outputs;
runLayer(layer, inputs, outputs);
ASSERT_EQ(1, outputs.size());
// create output_ref to compare with outputs
cv::Mat output_ref;
int size[] = {1};
if(equation == ",->" || equation == "i,->i" || equation == ",i->i" || equation == "ij,->ij"){
output_ref = input1.mul(input2);
if (equation == ",i->i")
output_ref = output_ref.reshape(1, 1, size);
} else if (equation == "i,i->i"){
output_ref = input1.mul(input2);
} else if (equation == "i,i->"){
output_ref = input1.mul(input2);
cv::Scalar sum = cv::sum(output_ref);
output_ref = cv::Mat(0, nullptr, CV_32F, sum[0]);
} else if (equation == "ij,ij->ij"){
output_ref = input1.mul(input2);
} else if (equation == "ij,ij->i"){
output_ref = input1.mul(input2);
if (input_shape1[0] == 1){
cv::Scalar sum = cv::sum(output_ref);
output_ref = cv::Mat(1, size, CV_32F, sum[0]);
} else if (input_shape1[1] == 1){
size[0] = input_shape1[0];
output_ref = output_ref.reshape(1, 1, size);
} else {
cv::reduce(output_ref, output_ref, 1, cv::REDUCE_SUM, CV_32F);
size[0] = input_shape1[0];
output_ref = output_ref.reshape(1, 1, size);
}
} else {
output_ref = cv::Mat();
}
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
normAssert(output_ref, outputs[0]);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Einsum_Test, testing::Values(
std::make_tuple(std::vector<int>({}), std::vector<int>({}), ",->"),
std::make_tuple(std::vector<int>({1}), std::vector<int>({}), "i,->i"),
std::make_tuple(std::vector<int>({}), std::vector<int>({1}), ",i->i"),
std::make_tuple(std::vector<int>({4, 1}), std::vector<int>({}), "ij,->ij"),
// std::make_tuple(std::vector<int>({}), std::vector<int>({4, 1}), ",ij->ij")), // mul function of arithm_op can not handle cases with different number of channels
std::make_tuple(std::vector<int>({1}), std::vector<int>({1}), "i,i->i"),
std::make_tuple(std::vector<int>({1}), std::vector<int>({1}), "i,i->"),
std::make_tuple(std::vector<int>({4}), std::vector<int>({4}), "i,i->i"),
std::make_tuple(std::vector<int>({4}), std::vector<int>({4}), "i,i->"),
std::make_tuple(std::vector<int>({1, 4}), std::vector<int>({1, 4}), "ij,ij->ij"),
std::make_tuple(std::vector<int>({4, 1}), std::vector<int>({4, 1}), "ij,ij->ij"),
std::make_tuple(std::vector<int>({1, 4}), std::vector<int>({1, 4}), "ij,ij->i"),
std::make_tuple(std::vector<int>({4, 1}), std::vector<int>({4, 1}), "ij,ij->i"),
std::make_tuple(std::vector<int>({4, 4}), std::vector<int>({4, 4}), "ij,ij->i")
));
}}