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[CPU] Fix RegionYolo (v3) node and Ngraph reference implementations (#5748)

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Egor Duplensky 2021-05-26 11:44:36 +03:00 committed by GitHub
parent d25e149f76
commit e754a6b46f
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 71 additions and 14 deletions
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17
inference-engine/src/mkldnn_plugin/nodes/mkldnn_region_yolo_node.cpp
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@ -369,31 +369,38 @@ inline void MKLDNNRegionYoloNode::calculate_logistic(size_t start_index, int cou
void MKLDNNRegionYoloNode::execute(mkldnn::stream strm) {
auto inputDesc = getParentEdgeAt(0)->getDesc();
auto outputDesc = getChildEdgeAt(0)->getDesc();
size_t mask_size = mask.size();
size_t IW = (inputDesc.getDims().size() > 3) ? inputDesc.getDims()[3] : 1;
size_t IH = (inputDesc.getDims().size() > 2) ? inputDesc.getDims()[2] : 1;
size_t IC = (inputDesc.getDims().size() > 1) ? inputDesc.getDims()[1] : 1;
size_t B = (inputDesc.getDims().size() > 0) ? inputDesc.getDims()[0] : 1;
size_t IC = (inputDesc.getDims().size() > 1) ? inputDesc.getDims()[1] : 1;
size_t IH = (inputDesc.getDims().size() > 2) ? inputDesc.getDims()[2] : 1;
size_t IW = (inputDesc.getDims().size() > 3) ? inputDesc.getDims()[3] : 1;
size_t mask_size = mask.size();
int end_index = 0;
int num_ = 0;
int output_size = 0;
if (do_softmax) {
// Region layer (Yolo v2)
end_index = IW * IH;
num_ = num;
output_size = B * IH * IW * IC; // different shape combinations with the same overall size;
} else {
// Yolo layer (Yolo v3)
end_index = IW * IH * (classes + 1);
num_ = mask_size;
output_size = B * IH * IW * mask_size * (classes + coords + 1);
}
if (output_size != getChildEdgeAt(0)->getMemoryPtr()->GetElementsCount())
IE_THROW() << "Incorrect layer configuration or output dimensions. " << output_size << " != " << getChildEdgeAt(0)->getMemoryPtr()->GetElementsCount();
size_t inputs_size = IH * IW * num_ * (classes + coords + 1);
size_t total_size = 2 * IH * IW;
const auto *src_data = reinterpret_cast<const uint8_t *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
auto *dst_data = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
cpu_convert(src_data, dst_data, inputDesc.getPrecision(), outputDesc.getPrecision(), B * IC * IH * IW);
cpu_convert(src_data, dst_data, inputDesc.getPrecision(), outputDesc.getPrecision(), output_size);
for (int b = 0; b < B; b++) {
for (int n = 0; n < num_; n++) {

16
inference-engine/tests/functional/plugin/cpu/single_layer_tests/region_yolo.cpp
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@ -69,11 +69,14 @@ protected:
selectedType = getPrimitiveType() + "_" + inPrc.name();
auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inPrc);
auto param = std::make_shared<ngraph::op::Parameter>(ngPrc, inputShape);
auto region_yolo = std::make_shared<ngraph::op::v0::RegionYolo>(param, attributes.coordinates, attributes.classes, attributes.num_regions,
const auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inPrc);
auto paramRegionYolo = ngraph::builder::makeParams(ngPrc, {inputShape});
const auto region_yolo = std::make_shared<ngraph::op::v0::RegionYolo>(paramRegionYolo[0],
attributes.coordinates, attributes.classes, attributes.num_regions,
attributes.do_softmax, mask, attributes.start_axis, attributes.end_axis);
function = std::make_shared<ngraph::Function>(std::make_shared<ngraph::opset1::Result>(region_yolo), ngraph::ParameterVector{param}, "RegionYolo");
function = makeNgraphFunction(ngPrc, paramRegionYolo, region_yolo, "RegionYolo");
}
};
@ -99,7 +102,10 @@ const std::vector<ngraph::Shape> inShapes_mxnet = {
{1, 75, 26, 26},
{1, 75, 16, 16},
{1, 75, 13, 13},
{1, 75, 8, 8}
{1, 75, 8, 8},
{1, 303, 7, 7},
{1, 303, 14, 14},
{1, 303, 28, 28},
};
const std::vector<ngraph::Shape> inShapes_v3 = {

7
ngraph/core/reference/include/ngraph/runtime/reference/region_yolo.hpp
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@ -86,24 +86,27 @@ namespace ngraph
const auto mask_size = mask.size();
std::copy(input, input + shape_size(input_shape), output);
int num_regions = 0;
int end_index = 0;
int output_size = 0;
if (do_softmax)
{
// Region layer (Yolo v2)
num_regions = regions;
end_index = width * height;
output_size = shape_size(input_shape);
}
else
{
// Yolo layer (Yolo v3)
num_regions = mask_size;
end_index = width * height * (classes + 1);
output_size = width * height * num_regions * (classes + coords + 1);
}
std::copy(input, input + output_size, output);
const int inputs_size = width * height * num_regions * (classes + coords + 1);
for (int batch_idx = 0; batch_idx < batches; batch_idx++)

41
ngraph/test/backend/region_yolo.in.cpp
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@ -70,3 +70,44 @@ NGRAPH_TEST(${BACKEND_NAME}, region_yolo_v3_mxnet)
shape, TEST_FILES, "region_out_yolov3_mxnet.data");
test_case.run_with_tolerance_as_fp(1.0e-4f);
}
NGRAPH_TEST(${BACKEND_NAME}, region_yolo_v3_mxnet_2)
{
const size_t num = 1;
const size_t coords = 4;
const size_t classes = 1;
const size_t batch = 1;
const size_t channels = 8;
const size_t width = 2;
const size_t height = 2;
const std::vector<int64_t> mask{0};
const int axis = 1;
const int end_axis = 3;
Shape input_shape{batch, channels, height, width};
Shape output_shape{batch, (classes + coords + 1) * mask.size(), height, width};
const auto A = make_shared<op::Parameter>(element::f32, input_shape);
const auto R = make_shared<op::v0::RegionYolo>(A, coords, classes, num, false, mask, axis, end_axis);
const auto f = make_shared<Function>(R, ParameterVector{A});
EXPECT_EQ(R->get_output_shape(0), output_shape);
auto test_case = test::TestCase<TestEngine>(f);
std::vector<float> input {
0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f,
0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f,
0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f,
0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f
};
std::vector<float> output {
0.52497f, 0.54983f, 0.57444f, 0.59868f, 0.62245f, 0.64565f, 0.66818f, 0.68997f,
0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f,
0.52497f, 0.54983f, 0.57444f, 0.59868f, 0.62245f, 0.64565f, 0.66818f, 0.68997f,
};
test_case.add_input<float>(input);
test_case.add_expected_output<float>(output);
test_case.run_with_tolerance_as_fp(1.0e-4f);
}