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[IE][VPU][Tests]: Fix ROIPooling test (#3705)

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* fix initialization bug of spatial_scale in tests (affected input generating)
* fix input generating for bilinear ROI Pooling
* correct parameters for myriad tests:
    * myriad plugin does not support batch for this layer;
    * decrease threshold since myriad uses fp16 calculations
This commit is contained in:
Andrey Sokolov 2021-01-20 14:17:19 +03:00 committed by GitHub
parent 278773945c
commit db5cd6751d
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 30 additions and 18 deletions
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2
inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/roi_pooling.cpp
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@ -12,7 +12,7 @@ using namespace LayerTestsDefinitions;
namespace {
const std::vector<std::vector<size_t>> inShapes = {
{3, 4, 50, 50}
{1, 4, 50, 50}
};
const std::vector<std::vector<size_t>> pooledShapes_max = {

2
inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp
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@ -32,8 +32,6 @@ std::vector<std::string> disabledTestPatterns() {
R"(.*DSR_NonMaxSuppression.*NBoxes=(5|20|200).*)",
// TODO: Issue: 42721
R"(.*(DSR_GatherND).*)",
// TODO: Issue 43781
".*ROIPoolingLayerTest.*",
// TODO: Issue 26090
".*DSR_GatherStaticDataDynamicIdx.*f32.*1.3.200.304.*",
// TODO: Issue 46755

12
inference-engine/tests/functional/shared_test_classes/src/single_layer/roi_pooling.cpp
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@ -41,8 +41,11 @@ namespace LayerTestsDefinitions {
inputs.clear();
auto feat_map_shape = cnnNetwork.getInputShapes().begin()->second;
const int height = pool_method == ngraph::helpers::ROIPoolingTypes::ROI_MAX ? feat_map_shape[2] / spatial_scale : 1;
const int width = pool_method == ngraph::helpers::ROIPoolingTypes::ROI_MAX ? feat_map_shape[3] / spatial_scale : 1;
const auto is_roi_max_mode = (pool_method == ngraph::helpers::ROIPoolingTypes::ROI_MAX);
const int height = is_roi_max_mode ? feat_map_shape[2] / spatial_scale : 1;
const int width = is_roi_max_mode ? feat_map_shape[3] / spatial_scale : 1;
size_t it = 0;
for (const auto &input : cnnNetwork.getInputsInfo()) {
@ -53,7 +56,7 @@ namespace LayerTestsDefinitions {
blob = make_blob_with_precision(info->getTensorDesc());
blob->allocate();
CommonTestUtils::fill_data_roi(blob->buffer(), blob->size(), feat_map_shape[0] - 1,
height, width, 1.0f);
height, width, 1.0f, is_roi_max_mode);
} else {
blob = GenerateInput(*info);
}
@ -69,7 +72,8 @@ namespace LayerTestsDefinitions {
InferenceEngine::SizeVector coordsShape;
InferenceEngine::SizeVector poolShape;
InferenceEngine::Precision netPrecision;
float spatial_scale;
threshold = 0.08f;
std::tie(inputShape, coordsShape, poolShape, spatial_scale, pool_method, netPrecision, targetDevice) = this->GetParam();

32
inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp
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@ -123,32 +123,42 @@ static void fill_data_bbox(float *data, size_t size, int height, int width, floa
}
}
static void fill_data_roi(float *data, size_t size, const uint32_t range, const int height, const int width, const float omega, const int seed = 1) {
static void fill_data_roi(float *data, size_t size, const uint32_t range, const int height, const int width, const float omega,
const bool is_roi_max_mode, const int seed = 1) {
std::default_random_engine random(seed);
std::uniform_int_distribution<int32_t> distribution(0, range);
float center_h = (height - 1.0f) / 2;
float center_w = (width - 1.0f) / 2;
const int max_y = (is_roi_max_mode) ? (height - 1) : 1;
const int max_x = (is_roi_max_mode) ? (width - 1) : 1;
float center_h = (max_y) / 2.0f;
float center_w = (max_x) / 2.0f;
for (size_t i = 0; i < size; i += 5) {
data[i] = static_cast<float>(distribution(random));
data[i + 1] = std::floor(center_w + width * 0.6f * sin(static_cast<float>(i+1) * omega));
data[i + 3] = std::floor(center_w + width * 0.6f * sin(static_cast<float>(i+3) * omega));
const float x0 = (center_w + width * 0.3f * sin(static_cast<float>(i + 1) * omega));
const float x1 = (center_w + width * 0.3f * sin(static_cast<float>(i + 3) * omega));
data[i + 1] = is_roi_max_mode ? std::floor(x0) : x0;
data[i + 3] = is_roi_max_mode ? std::floor(x1) : x1;
if (data[i + 3] < data[i + 1]) {
std::swap(data[i + 1], data[i + 3]);
}
if (data[i + 1] < 0)
data[i + 1] = 0;
if (data[i + 3] > width - 1)
data[i + 3] = static_cast<float>(width - 1);
if (data[i + 3] > max_x)
data[i + 3] = static_cast<float>(max_x);
data[i + 2] = std::floor(center_h + height * 0.6f * sin(static_cast<float>(i+2) * omega));
data[i + 4] = std::floor(center_h + height * 0.6f * sin(static_cast<float>(i+4) * omega));
const float y0 = (center_h + height * 0.3f * sin(static_cast<float>(i + 2) * omega));
const float y1 = (center_h + height * 0.3f * sin(static_cast<float>(i + 4) * omega));
data[i + 2] = is_roi_max_mode ? std::floor(y0) : y0;
data[i + 4] = is_roi_max_mode ? std::floor(y1) : y1;
if (data[i + 4] < data[i + 2]) {
std::swap(data[i + 2], data[i + 4]);
}
if (data[i + 2] < 0)
data[i + 2] = 0;
if (data[i + 4] > height - 1)
data[i + 4] = static_cast<float>(height - 1);
if (data[i + 4] > max_y)
data[i + 4] = static_cast<float>(max_y);
}
}