}
#ifdef HAVE_OPENCL
+ virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
+ {
+ OCL4DNNSoftmaxConfig config;
+
+ config.in_shape = shape(*inputs[0]);
+ config.axis = axisRaw;
+ config.channels = inputs[0]->size[axisRaw];
+ config.logsoftmax = logSoftMax;
+
+ softmaxOp = Ptr<OCL4DNNSoftmax<float> >(new OCL4DNNSoftmax<float>(config));
+ }
+
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays itns)
{
std::vector<UMat> inputs;
outs.getUMatVector(outputs);
itns.getUMatVector(internals);
- if (softmaxOp.empty())
- {
- OCL4DNNSoftmaxConfig config;
-
- config.in_shape = shape(inputs[0]);
- config.axis = axisRaw;
- config.channels = inputs[0].size[axisRaw];
- config.logsoftmax = logSoftMax;
-
- softmaxOp = Ptr<OCL4DNNSoftmax<float> >(new OCL4DNNSoftmax<float>(config));
- }
-
UMat& src = inputs[0];
UMat& dstMat = outputs[0];
namespace opencv_test { namespace {
+CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
+static testing::internal::ParamGenerator<DNNTarget> availableBackends()
+{
+ static std::vector<DNNTarget> targets;
+ if (targets.empty())
+ {
+ targets.push_back(DNN_TARGET_CPU);
+#ifdef HAVE_OPENCL
+ if (cv::ocl::useOpenCL())
+ targets.push_back(DNN_TARGET_OPENCL);
+#endif
+ }
+ return testing::ValuesIn(targets);
+}
+
template<typename TString>
static std::string _tf(TString filename)
{
ASSERT_FALSE(net.empty());
}
-typedef testing::TestWithParam<bool> Reproducibility_AlexNet;
+typedef testing::TestWithParam<tuple<bool, DNNTarget> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
- bool readFromMemory = GetParam();
- Net net;
- {
- const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
- const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
- if (readFromMemory)
- {
- string dataProto;
- ASSERT_TRUE(readFileInMemory(proto, dataProto));
- string dataModel;
- ASSERT_TRUE(readFileInMemory(model, dataModel));
-
- net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
- dataModel.c_str(), dataModel.size());
- }
- else
- net = readNetFromCaffe(proto, model);
- ASSERT_FALSE(net.empty());
- }
-
- Mat sample = imread(_tf("grace_hopper_227.png"));
- ASSERT_TRUE(!sample.empty());
-
- net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
- Mat out = net.forward("prob");
- Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
- normAssert(ref, out);
-}
-
-INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Bool());
-
-typedef testing::TestWithParam<bool> Reproducibility_OCL_AlexNet;
-OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
-{
- bool readFromMemory = GetParam();
+ bool readFromMemory = get<0>(GetParam());
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
ASSERT_FALSE(net.empty());
}
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ net.setPreferableTarget(get<1>(GetParam()));
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
normAssert(ref, out);
}
-OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Bool());
+INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_AlexNet, Combine(testing::Bool(), availableBackends()));
#if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy)
normAssert(ref, out);
}
-TEST(Reproducibility_MobileNet_SSD, Accuracy)
-{
- const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
- const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
- Net net = readNetFromCaffe(proto, model);
-
- Mat sample = imread(_tf("street.png"));
-
- Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
- net.setInput(inp);
- Mat out = net.forward();
-
- Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
- normAssert(ref, out);
-
- // Check that detections aren't preserved.
- inp.setTo(0.0f);
- net.setInput(inp);
- out = net.forward();
-
- const int numDetections = out.size[2];
- ASSERT_NE(numDetections, 0);
- for (int i = 0; i < numDetections; ++i)
- {
- float confidence = out.ptr<float>(0, 0, i)[2];
- ASSERT_EQ(confidence, 0);
- }
-}
-
-OCL_TEST(Reproducibility_MobileNet_SSD, Accuracy)
+typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD;
+TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ net.setPreferableTarget(GetParam());
Mat sample = imread(_tf("street.png"));
inp.setTo(0.0f);
net.setInput(inp);
out = net.forward();
+ out = out.reshape(1, out.total() / 7);
- const int numDetections = out.size[2];
+ const int numDetections = out.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
- float confidence = out.ptr<float>(0, 0, i)[2];
+ float confidence = out.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
-}
-TEST(Reproducibility_ResNet50, Accuracy)
-{
- Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
- findDataFile("dnn/ResNet-50-model.caffemodel", false));
-
- Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
- ASSERT_TRUE(!input.empty());
-
- net.setInput(input);
- Mat out = net.forward();
-
- Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
- normAssert(ref, out);
+ // Check batching mode.
+ ref = ref.reshape(1, numDetections);
+ inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
+ net.setInput(inp);
+ Mat outBatch = net.forward();
+
+ // Output blob has a shape 1x1x2Nx7 where N is a number of detection for
+ // a single sample in batch. The first numbers of detection vectors are batch id.
+ outBatch = outBatch.reshape(1, outBatch.total() / 7);
+ EXPECT_EQ(outBatch.rows, 2 * numDetections);
+ normAssert(outBatch.rowRange(0, numDetections), ref);
+ normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7));
}
+INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, availableBackends());
-OCL_TEST(Reproducibility_ResNet50, Accuracy)
+typedef testing::TestWithParam<DNNTarget> Reproducibility_ResNet50;
+TEST_P(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
ASSERT_TRUE(!input.empty());
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
normAssert(ref, out);
- UMat out_umat;
- net.forward(out_umat);
- normAssert(ref, out_umat, "out_umat");
-
- std::vector<UMat> out_umats;
- net.forward(out_umats);
- normAssert(ref, out_umats[0], "out_umat_vector");
-}
-
-TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
-{
- Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
- findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
-
- Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
- ASSERT_TRUE(!input.empty());
-
- net.setInput(input);
- Mat out = net.forward();
+ if (targetId == DNN_TARGET_OPENCL)
+ {
+ UMat out_umat;
+ net.forward(out_umat);
+ normAssert(ref, out_umat, "out_umat");
- Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
- normAssert(ref, out);
+ std::vector<UMat> out_umats;
+ net.forward(out_umats);
+ normAssert(ref, out_umats[0], "out_umat_vector");
+ }
}
+INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50, availableBackends());
-OCL_TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
+typedef testing::TestWithParam<DNNTarget> Reproducibility_SqueezeNet_v1_1;
+TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
ASSERT_TRUE(!input.empty());
- // Firstly set a wrong input blob and run the model to receive a wrong output.
- net.setInput(input * 2.0f);
- Mat out = net.forward();
-
- // Then set a correct input blob to check CPU->GPU synchronization is working well.
+ Mat out;
+ if (targetId == DNN_TARGET_OPENCL)
+ {
+ // Firstly set a wrong input blob and run the model to receive a wrong output.
+ // Then set a correct input blob to check CPU->GPU synchronization is working well.
+ net.setInput(input * 2.0f);
+ out = net.forward();
+ }
net.setInput(input);
out = net.forward();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
+INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_SqueezeNet_v1_1, availableBackends());
TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
normAssert(out, first_image + second_image);
}
-CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
+CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
+static testing::internal::ParamGenerator<DNNTarget> availableBackends()
+{
+ static std::vector<DNNTarget> targets;
+ if (targets.empty())
+ {
+ targets.push_back(DNN_TARGET_CPU);
+#ifdef HAVE_OPENCL
+ if (cv::ocl::useOpenCL())
+ targets.push_back(DNN_TARGET_OPENCL);
+#endif
+ }
+ return testing::ValuesIn(targets);
+}
+
TEST(Test_TensorFlow, read_inception)
{
Net net;
normAssert(target, output, "", l1, lInf);
}
-TEST(Test_TensorFlow, conv)
-{
- runTensorFlowNet("single_conv");
- runTensorFlowNet("atrous_conv2d_valid");
- runTensorFlowNet("atrous_conv2d_same");
- runTensorFlowNet("depthwise_conv2d");
-}
-
-TEST(Test_TensorFlow, padding)
-{
- runTensorFlowNet("padding_same");
- runTensorFlowNet("padding_valid");
- runTensorFlowNet("spatial_padding");
-}
+typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_layers;
-TEST(Test_TensorFlow, eltwise_add_mul)
+TEST_P(Test_TensorFlow_layers, conv)
{
- runTensorFlowNet("eltwise_add_mul");
+ int targetId = GetParam();
+ runTensorFlowNet("single_conv", targetId);
+ runTensorFlowNet("atrous_conv2d_valid", targetId);
+ runTensorFlowNet("atrous_conv2d_same", targetId);
+ runTensorFlowNet("depthwise_conv2d", targetId);
}
-OCL_TEST(Test_TensorFlow, eltwise_add_mul)
+TEST_P(Test_TensorFlow_layers, padding)
{
- runTensorFlowNet("eltwise_add_mul", DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ runTensorFlowNet("padding_same", targetId);
+ runTensorFlowNet("padding_valid", targetId);
+ runTensorFlowNet("spatial_padding", targetId);
}
-TEST(Test_TensorFlow, pad_and_concat)
+TEST_P(Test_TensorFlow_layers, eltwise_add_mul)
{
- runTensorFlowNet("pad_and_concat");
+ runTensorFlowNet("eltwise_add_mul", GetParam());
}
-TEST(Test_TensorFlow, batch_norm)
+TEST_P(Test_TensorFlow_layers, pad_and_concat)
{
- runTensorFlowNet("batch_norm");
- runTensorFlowNet("fused_batch_norm");
- runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true);
- runTensorFlowNet("mvn_batch_norm");
- runTensorFlowNet("mvn_batch_norm_1x1");
+ runTensorFlowNet("pad_and_concat", GetParam());
}
-OCL_TEST(Test_TensorFlow, batch_norm)
+TEST_P(Test_TensorFlow_layers, batch_norm)
{
- runTensorFlowNet("batch_norm", DNN_TARGET_OPENCL);
- runTensorFlowNet("fused_batch_norm", DNN_TARGET_OPENCL);
- runTensorFlowNet("batch_norm_text", DNN_TARGET_OPENCL, true);
+ int targetId = GetParam();
+ runTensorFlowNet("batch_norm", targetId);
+ runTensorFlowNet("fused_batch_norm", targetId);
+ runTensorFlowNet("batch_norm_text", targetId, true);
+ runTensorFlowNet("mvn_batch_norm", targetId);
+ runTensorFlowNet("mvn_batch_norm_1x1", targetId);
}
-TEST(Test_TensorFlow, pooling)
+TEST_P(Test_TensorFlow_layers, pooling)
{
- runTensorFlowNet("max_pool_even");
- runTensorFlowNet("max_pool_odd_valid");
- runTensorFlowNet("max_pool_odd_same");
- runTensorFlowNet("ave_pool_same");
+ int targetId = GetParam();
+ runTensorFlowNet("max_pool_even", targetId);
+ runTensorFlowNet("max_pool_odd_valid", targetId);
+ runTensorFlowNet("ave_pool_same", targetId);
+ runTensorFlowNet("max_pool_odd_same", targetId);
}
-TEST(Test_TensorFlow, deconvolution)
+TEST_P(Test_TensorFlow_layers, deconvolution)
{
- runTensorFlowNet("deconvolution");
- runTensorFlowNet("deconvolution_same");
- runTensorFlowNet("deconvolution_stride_2_same");
- runTensorFlowNet("deconvolution_adj_pad_valid");
- runTensorFlowNet("deconvolution_adj_pad_same");
+ int targetId = GetParam();
+ runTensorFlowNet("deconvolution", targetId);
+ runTensorFlowNet("deconvolution_same", targetId);
+ runTensorFlowNet("deconvolution_stride_2_same", targetId);
+ runTensorFlowNet("deconvolution_adj_pad_valid", targetId);
+ runTensorFlowNet("deconvolution_adj_pad_same", targetId);
}
-OCL_TEST(Test_TensorFlow, deconvolution)
+TEST_P(Test_TensorFlow_layers, matmul)
{
- runTensorFlowNet("deconvolution", DNN_TARGET_OPENCL);
- runTensorFlowNet("deconvolution_same", DNN_TARGET_OPENCL);
- runTensorFlowNet("deconvolution_stride_2_same", DNN_TARGET_OPENCL);
- runTensorFlowNet("deconvolution_adj_pad_valid", DNN_TARGET_OPENCL);
- runTensorFlowNet("deconvolution_adj_pad_same", DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ runTensorFlowNet("matmul", targetId);
+ runTensorFlowNet("nhwc_reshape_matmul", targetId);
+ runTensorFlowNet("nhwc_transpose_reshape_matmul", targetId);
}
-TEST(Test_TensorFlow, matmul)
+TEST_P(Test_TensorFlow_layers, reshape)
{
- runTensorFlowNet("matmul");
- runTensorFlowNet("nhwc_reshape_matmul");
- runTensorFlowNet("nhwc_transpose_reshape_matmul");
+ int targetId = GetParam();
+ runTensorFlowNet("shift_reshape_no_reorder", targetId);
+ runTensorFlowNet("reshape_reduce", targetId);
+ runTensorFlowNet("flatten", targetId, true);
}
-TEST(Test_TensorFlow, defun)
-{
- runTensorFlowNet("defun_dropout");
-}
+INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, availableBackends());
-TEST(Test_TensorFlow, reshape)
-{
- runTensorFlowNet("shift_reshape_no_reorder");
- runTensorFlowNet("reshape_reduce");
- runTensorFlowNet("flatten", DNN_TARGET_CPU, true);
-}
+typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets;
-TEST(Test_TensorFlow, fp16)
-{
- const float l1 = 1e-3;
- const float lInf = 1e-2;
- runTensorFlowNet("fp16_single_conv", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, false, l1, lInf);
-}
-
-TEST(Test_TensorFlow, quantized)
-{
- runTensorFlowNet("uint8_single_conv");
-}
-
-TEST(Test_TensorFlow, MobileNet_SSD)
+TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
}
Net net = readNetFromTensorflow(netPath, netConfig);
+
+ net.setPreferableTarget(GetParam());
+
net.setInput(inp);
std::vector<Mat> output;
net.forward(output, outNames);
- normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1));
+ normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
}
-TEST(Test_TensorFlow, Inception_v2_SSD)
+TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
+ net.setPreferableTarget(GetParam());
+
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
normAssert(detections, ref);
}
-OCL_TEST(Test_TensorFlow, MobileNet_SSD)
-{
- std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
- std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
- std::string imgPath = findDataFile("dnn/street.png", false);
-
- Mat inp;
- resize(imread(imgPath), inp, Size(300, 300));
- inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
-
- std::vector<String> outNames(3);
- outNames[0] = "concat";
- outNames[1] = "concat_1";
- outNames[2] = "detection_out";
-
- std::vector<Mat> target(outNames.size());
- for (int i = 0; i < outNames.size(); ++i)
- {
- std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
- target[i] = blobFromNPY(path);
- }
-
- Net net = readNetFromTensorflow(netPath, netConfig);
-
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
-
- net.setInput(inp);
-
- std::vector<Mat> output;
- net.forward(output, outNames);
-
- normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
- normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
- normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
-}
-
-OCL_TEST(Test_TensorFlow, Inception_v2_SSD)
+TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
- std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
- std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
+ std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
+ std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
Net net = readNetFromTensorflow(model, proto);
- Mat img = imread(findDataFile("dnn/street.png", false));
- Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
+ Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
+ Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ net.setPreferableTarget(GetParam());
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
- out = out.reshape(1, out.total() / 7);
- Mat detections;
- for (int i = 0; i < out.rows; ++i)
- {
- if (out.at<float>(i, 2) > 0.5)
- detections.push_back(out.row(i).colRange(1, 7));
- }
+ // References are from test for Caffe model.
+ Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
+ 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
+ 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
+ 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
+ 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
+ 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
+ normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
+}
- Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
- 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
- 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
- 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
- 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
- normAssert(detections, ref);
+INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableBackends());
+
+TEST(Test_TensorFlow, defun)
+{
+ runTensorFlowNet("defun_dropout");
+}
+
+TEST(Test_TensorFlow, fp16)
+{
+ const float l1 = 1e-3;
+ const float lInf = 1e-2;
+ runTensorFlowNet("fp16_single_conv", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, false, l1, lInf);
+}
+
+TEST(Test_TensorFlow, quantized)
+{
+ runTensorFlowNet("uint8_single_conv");
}
TEST(Test_TensorFlow, lstm)
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
}
-TEST(Test_TensorFlow, opencv_face_detector_uint8)
-{
- std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
- std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
-
- Net net = readNetFromTensorflow(model, proto);
- Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
- Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
-
- net.setInput(blob);
- // Output has shape 1x1xNx7 where N - number of detections.
- // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
- Mat out = net.forward();
-
- // References are from test for Caffe model.
- Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
- 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
- 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
- 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
- 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
- 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
- normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
-}
-
}
return findDataFile(path, false);
}
+CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
+static testing::internal::ParamGenerator<DNNTarget> availableBackends()
+{
+ static std::vector<DNNTarget> targets;
+ if (targets.empty())
+ {
+ targets.push_back(DNN_TARGET_CPU);
+#ifdef HAVE_OPENCL
+ if (cv::ocl::useOpenCL())
+ targets.push_back(DNN_TARGET_OPENCL);
+#endif
+ }
+ return testing::ValuesIn(targets);
+}
+
TEST(Torch_Importer, simple_read)
{
Net net;
}
}
-TEST(Torch_Importer, run_convolution)
-{
- runTorchNet("net_conv");
-}
-
-OCL_TEST(Torch_Importer, run_convolution)
-{
- runTorchNet("net_conv", DNN_TARGET_OPENCL);
-}
-
-TEST(Torch_Importer, run_pool_max)
-{
- runTorchNet("net_pool_max", DNN_TARGET_CPU, "", true);
-}
-
-OCL_TEST(Torch_Importer, run_pool_max)
-{
- runTorchNet("net_pool_max", DNN_TARGET_OPENCL, "", true);
-}
-
-TEST(Torch_Importer, run_pool_ave)
-{
- runTorchNet("net_pool_ave");
-}
-
-OCL_TEST(Torch_Importer, run_pool_ave)
-{
- runTorchNet("net_pool_ave", DNN_TARGET_OPENCL);
-}
+typedef testing::TestWithParam<DNNTarget> Test_Torch_layers;
-TEST(Torch_Importer, run_reshape)
+TEST_P(Test_Torch_layers, run_convolution)
{
- runTorchNet("net_reshape");
- runTorchNet("net_reshape_batch");
- runTorchNet("net_reshape_single_sample");
- runTorchNet("net_reshape_channels", DNN_TARGET_CPU, "", false, true);
-}
-
-TEST(Torch_Importer, run_linear)
-{
- runTorchNet("net_linear_2d");
-}
-
-TEST(Torch_Importer, run_paralel)
-{
- runTorchNet("net_parallel", DNN_TARGET_CPU, "l5_torchMerge");
+ runTorchNet("net_conv", GetParam());
}
-TEST(Torch_Importer, run_concat)
+TEST_P(Test_Torch_layers, run_pool_max)
{
- runTorchNet("net_concat", DNN_TARGET_CPU, "l5_torchMerge");
- runTorchNet("net_depth_concat", DNN_TARGET_CPU, "", false, true);
+ runTorchNet("net_pool_max", GetParam(), "", true);
}
-OCL_TEST(Torch_Importer, run_concat)
+TEST_P(Test_Torch_layers, run_pool_ave)
{
- runTorchNet("net_concat", DNN_TARGET_OPENCL, "l5_torchMerge");
- runTorchNet("net_depth_concat", DNN_TARGET_OPENCL, "", false, true);
+ runTorchNet("net_pool_ave", GetParam());
}
-TEST(Torch_Importer, run_deconv)
+TEST_P(Test_Torch_layers, run_reshape)
{
- runTorchNet("net_deconv");
+ int targetId = GetParam();
+ runTorchNet("net_reshape", targetId);
+ runTorchNet("net_reshape_batch", targetId);
+ runTorchNet("net_reshape_single_sample", targetId);
+ runTorchNet("net_reshape_channels", targetId, "", false, true);
}
-OCL_TEST(Torch_Importer, run_deconv)
+TEST_P(Test_Torch_layers, run_linear)
{
- runTorchNet("net_deconv", DNN_TARGET_OPENCL);
+ runTorchNet("net_linear_2d", GetParam());
}
-TEST(Torch_Importer, run_batch_norm)
+TEST_P(Test_Torch_layers, run_concat)
{
- runTorchNet("net_batch_norm", DNN_TARGET_CPU, "", false, true);
+ int targetId = GetParam();
+ runTorchNet("net_concat", targetId, "l5_torchMerge");
+ runTorchNet("net_depth_concat", targetId, "", false, true);
}
-OCL_TEST(Torch_Importer, run_batch_norm)
+TEST_P(Test_Torch_layers, run_deconv)
{
- runTorchNet("net_batch_norm", DNN_TARGET_OPENCL, "", false, true);
+ runTorchNet("net_deconv", GetParam());
}
-TEST(Torch_Importer, net_prelu)
+TEST_P(Test_Torch_layers, run_batch_norm)
{
- runTorchNet("net_prelu");
+ runTorchNet("net_batch_norm", GetParam(), "", false, true);
}
-TEST(Torch_Importer, net_cadd_table)
+TEST_P(Test_Torch_layers, net_prelu)
{
- runTorchNet("net_cadd_table");
+ runTorchNet("net_prelu", GetParam());
}
-TEST(Torch_Importer, net_softmax)
+TEST_P(Test_Torch_layers, net_cadd_table)
{
- runTorchNet("net_softmax");
- runTorchNet("net_softmax_spatial");
+ runTorchNet("net_cadd_table", GetParam());
}
-OCL_TEST(Torch_Importer, net_softmax)
+TEST_P(Test_Torch_layers, net_softmax)
{
- runTorchNet("net_softmax", DNN_TARGET_OPENCL);
- runTorchNet("net_softmax_spatial", DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ runTorchNet("net_softmax", targetId);
+ runTorchNet("net_softmax_spatial", targetId);
}
-TEST(Torch_Importer, net_logsoftmax)
+TEST_P(Test_Torch_layers, net_logsoftmax)
{
runTorchNet("net_logsoftmax");
runTorchNet("net_logsoftmax_spatial");
}
-OCL_TEST(Torch_Importer, net_logsoftmax)
+TEST_P(Test_Torch_layers, net_lp_pooling)
{
- runTorchNet("net_logsoftmax", DNN_TARGET_OPENCL);
- runTorchNet("net_logsoftmax_spatial", DNN_TARGET_OPENCL);
+ int targetId = GetParam();
+ runTorchNet("net_lp_pooling_square", targetId, "", false, true);
+ runTorchNet("net_lp_pooling_power", targetId, "", false, true);
}
-TEST(Torch_Importer, net_lp_pooling)
+TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
- runTorchNet("net_lp_pooling_square", DNN_TARGET_CPU, "", false, true);
- runTorchNet("net_lp_pooling_power", DNN_TARGET_CPU, "", false, true);
+ runTorchNet("net_conv_gemm_lrn", GetParam(), "", false, true);
}
-TEST(Torch_Importer, net_conv_gemm_lrn)
+TEST_P(Test_Torch_layers, net_inception_block)
{
- runTorchNet("net_conv_gemm_lrn", DNN_TARGET_CPU, "", false, true);
+ runTorchNet("net_inception_block", GetParam(), "", false, true);
}
-TEST(Torch_Importer, net_inception_block)
+TEST_P(Test_Torch_layers, net_normalize)
{
- runTorchNet("net_inception_block", DNN_TARGET_CPU, "", false, true);
+ runTorchNet("net_normalize", GetParam(), "", false, true);
}
-TEST(Torch_Importer, net_normalize)
+TEST_P(Test_Torch_layers, net_padding)
{
- runTorchNet("net_normalize", DNN_TARGET_CPU, "", false, true);
+ int targetId = GetParam();
+ runTorchNet("net_padding", targetId, "", false, true);
+ runTorchNet("net_spatial_zero_padding", targetId, "", false, true);
+ runTorchNet("net_spatial_reflection_padding", targetId, "", false, true);
}
-OCL_TEST(Torch_Importer, net_normalize)
+TEST_P(Test_Torch_layers, net_non_spatial)
{
- runTorchNet("net_normalize", DNN_TARGET_OPENCL, "", false, true);
+ runTorchNet("net_non_spatial", GetParam(), "", false, true);
}
-TEST(Torch_Importer, net_padding)
-{
- runTorchNet("net_padding", DNN_TARGET_CPU, "", false, true);
- runTorchNet("net_spatial_zero_padding", DNN_TARGET_CPU, "", false, true);
- runTorchNet("net_spatial_reflection_padding", DNN_TARGET_CPU, "", false, true);
-}
-
-TEST(Torch_Importer, net_non_spatial)
-{
- runTorchNet("net_non_spatial", DNN_TARGET_CPU, "", false, true);
-}
+INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, availableBackends());
-OCL_TEST(Torch_Importer, net_non_spatial)
-{
- runTorchNet("net_non_spatial", DNN_TARGET_OPENCL, "", false, true);
-}
+typedef testing::TestWithParam<DNNTarget> Test_Torch_nets;
-TEST(Torch_Importer, ENet_accuracy)
-{
- Net net;
- {
- const string model = findDataFile("dnn/Enet-model-best.net", false);
- net = readNetFromTorch(model, true);
- ASSERT_FALSE(net.empty());
- }
-
- Mat sample = imread(_tf("street.png", false));
- Mat inputBlob = blobFromImage(sample, 1./255);
-
- net.setInput(inputBlob, "");
- Mat out = net.forward();
- Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
- // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
- // thresholds for ENet must be changed. Accuracy of resuults was checked on
- // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
- normAssert(ref, out, "", 0.00044, 0.44);
-
- const int N = 3;
- for (int i = 0; i < N; i++)
- {
- net.setInput(inputBlob, "");
- Mat out = net.forward();
- normAssert(ref, out, "", 0.00044, 0.44);
- }
-}
-
-TEST(Torch_Importer, OpenFace_accuracy)
+TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
Net net = readNetFromTorch(model);
- Mat sample = imread(findDataFile("cv/shared/lena.png", false));
- Mat sampleF32(sample.size(), CV_32FC3);
- sample.convertTo(sampleF32, sampleF32.type());
- sampleF32 /= 255;
- resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
-
- Mat inputBlob = blobFromImage(sampleF32);
-
- net.setInput(inputBlob);
- Mat out = net.forward();
-
- Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
- normAssert(out, outRef);
-}
-
-OCL_TEST(Torch_Importer, OpenFace_accuracy)
-{
- const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
- Net net = readNetFromTorch(model);
-
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ net.setPreferableTarget(GetParam());
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
Mat sampleF32(sample.size(), CV_32FC3);
normAssert(out, outRef);
}
-OCL_TEST(Torch_Importer, ENet_accuracy)
+TEST_P(Test_Torch_nets, ENet_accuracy)
{
Net net;
{
ASSERT_TRUE(!net.empty());
}
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+ net.setPreferableTarget(GetParam());
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255);
// -median_filter 0 \
// -image_size 0 \
// -model models/instance_norm/feathers.t7
-TEST(Torch_Importer, FastNeuralStyle_accuracy)
+TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"};
const string model = findDataFile(models[i], false);
Net net = readNetFromTorch(model);
+ net.setPreferableTarget(GetParam());
+
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
}
}
-OCL_TEST(Torch_Importer, FastNeuralStyle_accuracy)
-{
- std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
- "dnn/fast_neural_style_instance_norm_feathers.t7"};
- std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
-
- for (int i = 0; i < 2; ++i)
- {
- const string model = findDataFile(models[i], false);
- Net net = readNetFromTorch(model);
-
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
- net.setPreferableTarget(DNN_TARGET_OPENCL);
+INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, availableBackends());
- Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
- Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
-
- net.setInput(inputBlob);
- Mat out = net.forward();
-
- // Deprocessing.
- getPlane(out, 0, 0) += 103.939;
- getPlane(out, 0, 1) += 116.779;
- getPlane(out, 0, 2) += 123.68;
- out = cv::min(cv::max(0, out), 255);
-
- Mat ref = imread(findDataFile(targets[i]));
- Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
+// TODO: fix OpenCL and add to the rest of tests
+TEST(Torch_Importer, run_paralel)
+{
+ runTorchNet("net_parallel", DNN_TARGET_CPU, "l5_torchMerge");
+}
- normAssert(out, refBlob, "", 0.5, 1.1);
- }
+TEST(Torch_Importer, DISABLED_run_paralel)
+{
+ runTorchNet("net_parallel", DNN_TARGET_OPENCL, "l5_torchMerge");
}
}
projects / platform / upstream / opencv.git / commitdiff