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Merge pull request #25124 from asmorkalov:as/dnn_1d
Extracted 1d test cases to reduce conflicts with 4.x.
This commit is contained in:
Alexander Smorkalov
GitHub
commit
81956ad83e
@ -4,3 +4,38 @@
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#include "test_precomp.hpp"
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#include "test_precomp.hpp"
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#include "test_common.impl.hpp" // shared with perf tests
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#include "test_common.impl.hpp" // shared with perf tests
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test {
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void runLayer(cv::Ptr<cv::dnn::Layer> layer, std::vector<cv::Mat> &inpBlobs, std::vector<cv::Mat> &outBlobs)
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{
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size_t ninputs = inpBlobs.size();
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std::vector<cv::Mat> inp(ninputs), outp, intp;
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std::vector<cv::dnn::MatShape> inputs, outputs, internals;
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for (size_t i = 0; i < ninputs; i++)
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{
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inp[i] = inpBlobs[i].clone();
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inputs.push_back(cv::dnn::shape(inp[i]));
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}
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layer->getMemoryShapes(inputs, 0, outputs, internals);
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for (size_t i = 0; i < outputs.size(); i++)
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{
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outp.push_back(cv::Mat(outputs[i], CV_32F));
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}
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for (size_t i = 0; i < internals.size(); i++)
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{
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intp.push_back(cv::Mat(internals[i], CV_32F));
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}
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layer->finalize(inp, outp);
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layer->forward(inp, outp, intp);
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size_t noutputs = outp.size();
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outBlobs.resize(noutputs);
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for (size_t i = 0; i < noutputs; i++)
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outBlobs[i] = outp[i];
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}
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}
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@ -238,7 +238,8 @@ protected:
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}
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}
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};
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};
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void runLayer(cv::Ptr<cv::dnn::Layer> layer, std::vector<cv::Mat> &inpBlobs, std::vector<cv::Mat> &outBlobs);
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} // namespace
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} // namespace
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#endif
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#endif
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@ -62,37 +62,6 @@ static String _tf(TString filename)
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return (basetestdir + "dnn/layers/") + filename;
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return (basetestdir + "dnn/layers/") + filename;
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}
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}
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void runLayer(Ptr<Layer> layer, std::vector<Mat> &inpBlobs, std::vector<Mat> &outBlobs)
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{
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size_t ninputs = inpBlobs.size();
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std::vector<Mat> inp(ninputs), outp, intp;
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std::vector<MatShape> inputs, outputs, internals;
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for (size_t i = 0; i < ninputs; i++)
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{
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inp[i] = inpBlobs[i].clone();
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inputs.push_back(shape(inp[i]));
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}
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layer->getMemoryShapes(inputs, 0, outputs, internals);
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for (size_t i = 0; i < outputs.size(); i++)
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{
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outp.push_back(Mat(outputs[i], CV_32F));
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}
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for (size_t i = 0; i < internals.size(); i++)
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{
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intp.push_back(Mat(internals[i], CV_32F));
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}
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layer->finalize(inp, outp);
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layer->forward(inp, outp, intp);
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size_t noutputs = outp.size();
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outBlobs.resize(noutputs);
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for (size_t i = 0; i < noutputs; i++)
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outBlobs[i] = outp[i];
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}
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class Test_Caffe_layers : public DNNTestLayer
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class Test_Caffe_layers : public DNNTestLayer
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{
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{
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public:
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public:
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@ -618,242 +587,6 @@ TEST(Layer_LSTM_Test_Accuracy_with_, HiddenParams)
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normAssert(h_t_reference, outputs[0]);
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normAssert(h_t_reference, outputs[0]);
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}
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}
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typedef testing::TestWithParam<tuple<int>> Layer_Scale_1d_Test;
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TEST_P(Layer_Scale_1d_Test, Accuracy)
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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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INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Scale_1d_Test,
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/*operation*/ Values(0, 1));
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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_32F, 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_GRU_Test_Accuracy_with_, Pytorch)
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TEST(Layer_GRU_Test_Accuracy_with_, Pytorch)
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{
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{
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Mat Wx = blobFromNPY(_tf("gru.W.npy"));
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Mat Wx = blobFromNPY(_tf("gru.W.npy"));
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248
modules/dnn/test/test_layers_1d.cpp
Normal file
248
modules/dnn/test/test_layers_1d.cpp
Normal file
@ -0,0 +1,248 @@
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// 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);
|
||||||
|
|
||||||
|
std::vector<int> input_shape = {batch_size, 3};
|
||||||
|
std::vector<int> output_shape = {batch_size, 3};
|
||||||
|
|
||||||
|
if (batch_size == 0){
|
||||||
|
input_shape.erase(input_shape.begin());
|
||||||
|
output_shape.erase(output_shape.begin());
|
||||||
|
}
|
||||||
|
|
||||||
|
cv::Mat input = cv::Mat(input_shape, CV_32F, 1.0);
|
||||||
|
cv::randn(input, 0.0, 1.0);
|
||||||
|
cv::Mat weight = cv::Mat(output_shape, CV_32F, 2.0);
|
||||||
|
|
||||||
|
std::vector<Mat> inputs{input, weight};
|
||||||
|
std::vector<Mat> outputs;
|
||||||
|
|
||||||
|
cv::Mat output_ref = input.mul(weight);
|
||||||
|
runLayer(layer, inputs, outputs);
|
||||||
|
|
||||||
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
||||||
|
normAssert(output_ref, outputs[0]);
|
||||||
|
}
|
||||||
|
|
||||||
|
typedef testing::TestWithParam<tuple<int, int>> Layer_Gather_1d_Test;
|
||||||
|
TEST_P(Layer_Gather_1d_Test, Accuracy) {
|
||||||
|
|
||||||
|
int batch_size = get<0>(GetParam());
|
||||||
|
int axis = get<1>(GetParam());
|
||||||
|
|
||||||
|
LayerParams lp;
|
||||||
|
lp.type = "Gather";
|
||||||
|
lp.name = "gatherLayer";
|
||||||
|
lp.set("axis", axis);
|
||||||
|
lp.set("real_ndims", 1);
|
||||||
|
|
||||||
|
Ptr<GatherLayer> layer = GatherLayer::create(lp);
|
||||||
|
|
||||||
|
std::vector<int> input_shape = {batch_size, 1};
|
||||||
|
std::vector<int> indices_shape = {1, 1};
|
||||||
|
std::vector<int> output_shape = {batch_size, 1};
|
||||||
|
|
||||||
|
if (batch_size == 0){
|
||||||
|
input_shape.erase(input_shape.begin());
|
||||||
|
indices_shape.erase(indices_shape.begin());
|
||||||
|
output_shape.erase(output_shape.begin());
|
||||||
|
} else if (axis == 0) {
|
||||||
|
output_shape[0] = 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
cv::Mat input = cv::Mat(input_shape, CV_32F, 1.0);
|
||||||
|
cv::randu(input, 0.0, 1.0);
|
||||||
|
cv::Mat indices = cv::Mat(indices_shape, CV_32F, 0.0);
|
||||||
|
cv::Mat output_ref = cv::Mat(output_shape, CV_32F, input(cv::Range::all(), cv::Range(0, 1)).data);
|
||||||
|
|
||||||
|
std::vector<Mat> inputs{input, indices};
|
||||||
|
std::vector<Mat> outputs;
|
||||||
|
|
||||||
|
runLayer(layer, inputs, outputs);
|
||||||
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
||||||
|
normAssert(output_ref, outputs[0]);
|
||||||
|
}
|
||||||
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Gather_1d_Test, Combine(
|
||||||
|
/*input blob shape*/ Values(0, 1, 2, 3),
|
||||||
|
/*operation*/ Values(0, 1)
|
||||||
|
));
|
||||||
|
|
||||||
|
typedef testing::TestWithParam<tuple<int, int, std::string>> Layer_Arg_1d_Test;
|
||||||
|
TEST_P(Layer_Arg_1d_Test, Accuracy) {
|
||||||
|
|
||||||
|
int batch_size = get<0>(GetParam());
|
||||||
|
int axis = get<1>(GetParam());
|
||||||
|
std::string operation = get<2>(GetParam());
|
||||||
|
|
||||||
|
LayerParams lp;
|
||||||
|
lp.type = "Arg";
|
||||||
|
lp.name = "arg" + operation + "_Layer";
|
||||||
|
lp.set("op", operation);
|
||||||
|
lp.set("axis", axis);
|
||||||
|
lp.set("keepdims", 1);
|
||||||
|
lp.set("select_last_index", 0);
|
||||||
|
|
||||||
|
Ptr<ArgLayer> layer = ArgLayer::create(lp);
|
||||||
|
|
||||||
|
std::vector<int> input_shape = {batch_size, 1};
|
||||||
|
std::vector<int> output_shape = {1, 1};
|
||||||
|
|
||||||
|
if (batch_size == 0){
|
||||||
|
input_shape.erase(input_shape.begin());
|
||||||
|
output_shape.erase(output_shape.begin());
|
||||||
|
}
|
||||||
|
|
||||||
|
if (axis != 0 && batch_size != 0){
|
||||||
|
output_shape[0] = batch_size;
|
||||||
|
}
|
||||||
|
|
||||||
|
cv::Mat input = cv::Mat(input_shape, CV_32F, 1);
|
||||||
|
cv::Mat output_ref = cv::Mat(output_shape, CV_32F, 0);
|
||||||
|
|
||||||
|
for (int i = 0; i < batch_size; ++i)
|
||||||
|
input.at<float>(i, 0) = static_cast<float>(i + 1);
|
||||||
|
|
||||||
|
std::vector<Mat> inputs{input};
|
||||||
|
std::vector<Mat> outputs;
|
||||||
|
|
||||||
|
runLayer(layer, inputs, outputs);
|
||||||
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
||||||
|
normAssert(output_ref, outputs[0]);
|
||||||
|
}
|
||||||
|
|
||||||
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Arg_1d_Test, Combine(
|
||||||
|
/*input blob shape*/ Values(0, 1, 2, 3),
|
||||||
|
/*operation*/ Values(0, 1),
|
||||||
|
/*operation*/ Values( "max", "min")
|
||||||
|
));
|
||||||
|
|
||||||
|
typedef testing::TestWithParam<tuple<int, std::string>> Layer_NaryElemwise_1d_Test;
|
||||||
|
TEST_P(Layer_NaryElemwise_1d_Test, Accuracy) {
|
||||||
|
|
||||||
|
int batch_size = get<0>(GetParam());
|
||||||
|
std::string operation = get<1>(GetParam());
|
||||||
|
|
||||||
|
LayerParams lp;
|
||||||
|
lp.type = "Eltwise";
|
||||||
|
lp.name = operation + "_Layer";
|
||||||
|
lp.set("operation", operation);
|
||||||
|
Ptr<NaryEltwiseLayer> layer = NaryEltwiseLayer::create(lp);
|
||||||
|
|
||||||
|
std::vector<int> input_shape = {batch_size, 1};
|
||||||
|
if (batch_size == 0)
|
||||||
|
input_shape.erase(input_shape.begin());
|
||||||
|
|
||||||
|
cv::Mat input1 = cv::Mat(input_shape, CV_32F, 0.0);
|
||||||
|
cv::Mat input2 = cv::Mat(input_shape, CV_32F, 0.0);
|
||||||
|
cv::randu(input1, 0.0, 1.0);
|
||||||
|
cv::randu(input2, 0.0, 1.0);
|
||||||
|
|
||||||
|
cv::Mat output_ref;
|
||||||
|
if (operation == "sum") {
|
||||||
|
output_ref = input1 + input2;
|
||||||
|
} else if (operation == "mul") {
|
||||||
|
output_ref = input1.mul(input2);
|
||||||
|
} else if (operation == "div") {
|
||||||
|
output_ref = input1 / input2;
|
||||||
|
} else if (operation == "sub") {
|
||||||
|
output_ref = input1 - input2;
|
||||||
|
} else {
|
||||||
|
output_ref = cv::Mat();
|
||||||
|
}
|
||||||
|
std::vector<Mat> inputs{input1, input2};
|
||||||
|
std::vector<Mat> outputs;
|
||||||
|
|
||||||
|
runLayer(layer, inputs, outputs);
|
||||||
|
if (!output_ref.empty()) {
|
||||||
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
||||||
|
normAssert(output_ref, outputs[0]);
|
||||||
|
} else {
|
||||||
|
CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_NaryElemwise_1d_Test, Combine(
|
||||||
|
/*input blob shape*/ Values(0, 1),
|
||||||
|
/*operation*/ Values("div", "mul", "sum", "sub")
|
||||||
|
));
|
||||||
|
|
||||||
|
typedef testing::TestWithParam<tuple<int, std::string>> Layer_Elemwise_1d_Test;
|
||||||
|
TEST_P(Layer_Elemwise_1d_Test, Accuracy) {
|
||||||
|
|
||||||
|
int batch_size = get<0>(GetParam());
|
||||||
|
std::string operation = get<1>(GetParam());
|
||||||
|
|
||||||
|
LayerParams lp;
|
||||||
|
lp.type = "Eltwise";
|
||||||
|
lp.name = operation + "_Layer";
|
||||||
|
lp.set("operation", operation);
|
||||||
|
Ptr<EltwiseLayer> layer = EltwiseLayer::create(lp);
|
||||||
|
|
||||||
|
std::vector<int> input_shape = {batch_size, 1};
|
||||||
|
if (batch_size == 0)
|
||||||
|
input_shape.erase(input_shape.begin());
|
||||||
|
|
||||||
|
cv::Mat input1 = cv::Mat(input_shape, CV_32F, 1.0);
|
||||||
|
cv::Mat input2 = cv::Mat(input_shape, CV_32F, 1.0);
|
||||||
|
cv::randu(input1, 0.0, 1.0);
|
||||||
|
cv::randu(input2, 0.0, 1.0);
|
||||||
|
|
||||||
|
// Dynamically select the operation
|
||||||
|
cv::Mat output_ref;
|
||||||
|
if (operation == "sum") {
|
||||||
|
output_ref = input1 + input2;
|
||||||
|
} else if (operation == "max") {
|
||||||
|
output_ref = cv::max(input1, input2);
|
||||||
|
} else if (operation == "min") {
|
||||||
|
output_ref = cv::min(input1, input2);
|
||||||
|
} else if (operation == "prod") {
|
||||||
|
output_ref = input1.mul(input2);
|
||||||
|
} else if (operation == "div") {
|
||||||
|
output_ref = input1 / input2;
|
||||||
|
} else {
|
||||||
|
output_ref = cv::Mat();
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
std::vector<Mat> inputs{input1, input2};
|
||||||
|
std::vector<Mat> outputs;
|
||||||
|
|
||||||
|
runLayer(layer, inputs, outputs);
|
||||||
|
|
||||||
|
if (!output_ref.empty()) {
|
||||||
|
ASSERT_EQ(shape(output_ref), shape(outputs[0]));
|
||||||
|
normAssert(output_ref, outputs[0]);
|
||||||
|
} else {
|
||||||
|
CV_Error(Error::StsAssert, "Provided operation: " + operation + " is not supported. Please check the test instantiation.");
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Layer_Elemwise_1d_Test, Combine(
|
||||||
|
/*input blob shape*/ Values(0, 1, 2, 3),
|
||||||
|
/*operation*/ Values("div", "prod", "max", "min", "sum")
|
||||||
|
));
|
||||||
|
|
||||||
|
}}
|
||||||
Loading…
Reference in New Issue
Block a user