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Let switch CPU/OpenCL targets for models from Intel's Model Optimizer

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This commit is contained in:
Dmitry Kurtaev
2018-04-18 17:26:54 +03:00
parent 0477284b0a
commit 3b4a292ca9
6 changed files with 148 additions and 88 deletions
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modules/dnn/test/test_caffe_importer.cpp
+18 -27
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@@ -167,7 +167,7 @@ TEST(Reproducibility_SSD, Accuracy)
Mat out = net.forward("detection_out");
Mat ref = blobFromNPY(_tf("ssd_out.npy"));
normAssert(ref, out);
normAssertDetections(ref, out);
}
typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD;
@@ -186,7 +186,7 @@ TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
normAssert(ref, out);
normAssertDetections(ref, out);
// Check that detections aren't preserved.
inp.setTo(0.0f);
@@ -403,14 +403,13 @@ TEST_P(opencv_face_detector, Accuracy)
// 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();
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);
Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4);
}
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
Combine(
@@ -426,14 +425,14 @@ TEST(Test_Caffe, FasterRCNN_and_RFCN)
"resnet50_rfcn_final.caffemodel"};
std::string protos[] = {"faster_rcnn_vgg16.prototxt", "faster_rcnn_zf.prototxt",
"rfcn_pascal_voc_resnet50.prototxt"};
Mat refs[] = {(Mat_<float>(3, 6) << 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
12, 0.993028, 133.221, 189.377, 350.994, 563.166),
(Mat_<float>(3, 6) << 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
12, 0.967198, 138.588, 206.843, 329.766, 553.176),
(Mat_<float>(2, 6) << 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
12, 0.94786, 132.093, 223.903, 338.077, 566.16)};
Mat refs[] = {(Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166),
(Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
0, 12, 0.967198, 138.588, 206.843, 329.766, 553.176),
(Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16)};
for (int i = 0; i < 3; ++i)
{
std::string proto = findDataFile("dnn/" + protos[i], false);
@@ -450,15 +449,7 @@ TEST(Test_Caffe, FasterRCNN_and_RFCN)
// 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 j = 0; j < out.rows; ++j)
{
if (out.at<float>(j, 2) > 0.8)
detections.push_back(out.row(j).colRange(1, 7));
}
normAssert(detections, refs[i], ("model name: " + models[i]).c_str(), 1e-3, 1e-3);
normAssertDetections(refs[i], out, ("model name: " + models[i]).c_str(), 0.8);
}
}