Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Add ocl accuracy test for a few dnn nets

Browse Source 
They are alexnet, mobilenet-ssd, resnet50, squeezeNet_v1_1,
yolo and fast_neural_style.

Signed-off-by: Li Peng <peng.li@intel.com>
pull/10164/head
Li Peng 7 years ago
parent 807f5e33ad
commit a47fbd2610
3 changed files with 202 additions and 0 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 107
      modules/dnn/test/test_caffe_importer.cpp
  2. 62
      modules/dnn/test/test_darknet_importer.cpp
  3. 33
      modules/dnn/test/test_torch_importer.cpp

107
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -42,6 +42,8 @@
#include "test_precomp.hpp" #include "test_precomp.hpp"
#include "npy_blob.hpp" #include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp> #include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/ts/ocl_test.hpp>
namespace cvtest namespace cvtest
{ {
@ -119,6 +121,43 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Values(true, false)); INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Values(true, false));
typedef testing::TestWithParam<tuple<bool> > Reproducibility_OCL_AlexNet;
OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
{
bool readFromMemory = get<0>(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());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
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);
}
OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Values(true, false));
#if !defined(_WIN32) || defined(_WIN64) #if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy) TEST(Reproducibility_FCN, Accuracy)
{ {
@ -201,6 +240,38 @@ TEST(Reproducibility_MobileNet_SSD, Accuracy)
} }
} }
OCL_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);
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
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);
}
}
TEST(Reproducibility_ResNet50, Accuracy) TEST(Reproducibility_ResNet50, Accuracy)
{ {
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false), Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
@ -216,6 +287,24 @@ TEST(Reproducibility_ResNet50, Accuracy)
normAssert(ref, out); normAssert(ref, out);
} }
OCL_TEST(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);
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);
}
TEST(Reproducibility_SqueezeNet_v1_1, Accuracy) TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
{ {
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false), Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
@ -231,6 +320,24 @@ TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
normAssert(ref, out); normAssert(ref, out);
} }
OCL_TEST(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);
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();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
TEST(Reproducibility_AlexNet_fp16, Accuracy) TEST(Reproducibility_AlexNet_fp16, Accuracy)
{ {
const float l1 = 1e-5; const float l1 = 1e-5;

62
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -184,6 +184,68 @@ TEST(Reproducibility_TinyYoloVoc, Accuracy)
normAssert(ref, detection); normAssert(ref, detection);
} }
OCL_TEST(Reproducibility_YoloVoc, Accuracy)
{
Net net;
{
const string cfg = findDataFile("dnn/yolo-voc.cfg", false);
const string model = findDataFile("dnn/yolo-voc.weights", false);
net = readNetFromDarknet(cfg, model);
ASSERT_FALSE(net.empty());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
// dog416.png is dog.jpg that resized to 416x416 in the lossless PNG format
Mat sample = imread(_tf("dog416.png"));
ASSERT_TRUE(!sample.empty());
Size inputSize(416, 416);
if (sample.size() != inputSize)
resize(sample, sample, inputSize);
net.setInput(blobFromImage(sample, 1 / 255.F), "data");
Mat out = net.forward("detection_out");
Mat detection;
const float confidenceThreshold = 0.24;
for (int i = 0; i < out.rows; i++) {
const int probability_index = 5;
const int probability_size = out.cols - probability_index;
float *prob_array_ptr = &out.at<float>(i, probability_index);
size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
float confidence = out.at<float>(i, (int)objectClass + probability_index);
if (confidence > confidenceThreshold)
detection.push_back(out.row(i));
}
// obtained by: ./darknet detector test ./cfg/voc.data ./cfg/yolo-voc.cfg ./yolo-voc.weights -thresh 0.24 ./dog416.png
// There are 3 objects (6-car, 1-bicycle, 11-dog) with 25 values for each:
// { relative_center_x, relative_center_y, relative_width, relative_height, unused_t0, probability_for_each_class[20] }
float ref_array[] = {
0.740161F, 0.214100F, 0.325575F, 0.173418F, 0.750769F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.000000F, 0.000000F, 0.750469F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.501618F, 0.504757F, 0.461713F, 0.481310F, 0.783550F, 0.000000F, 0.780879F, 0.000000F, 0.000000F,
0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.279968F, 0.638651F, 0.282737F, 0.600284F, 0.901864F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.901615F,
0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F
};
const int number_of_objects = 3;
Mat ref(number_of_objects, sizeof(ref_array) / (number_of_objects * sizeof(float)), CV_32FC1, &ref_array);
normAssert(ref, detection);
}
TEST(Reproducibility_YoloVoc, Accuracy) TEST(Reproducibility_YoloVoc, Accuracy)
{ {
Net net; Net net;

33
modules/dnn/test/test_torch_importer.cpp
Unescape View File

@ -382,6 +382,39 @@ TEST(Torch_Importer, FastNeuralStyle_accuracy)
} }
} }
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);
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);
normAssert(out, refBlob, "", 0.5, 1.1);
}
}
} }
#endif #endif

Write Preview
Loading…
Cancel
Save