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[GPU] Add TC for MXNet-style NMS model(decrease_label_id) (#10624)

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- Add TC for decrease_label_id=true to cover MXNet-style NMS models
- Fix segfault issue that occurs when data precision is fp16

Signed-off-by: Andrew Kwangwoong Park <andrew.kwangwoong.park@intel.com>
Signed-off-by: Andrew Park <andrew.park@intel.com>
This commit is contained in:
Andrew Kwangwoong Park 2022-05-06 10:36:46 +09:00 committed by GitHub
parent 1331eabbed
commit caccde6a82
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 82 additions and 1 deletions
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10
src/plugins/intel_gpu/src/graph/impls/cpu/detection_output.cpp
View File

@ -707,14 +707,22 @@ struct detection_output_impl : typed_primitive_impl<detection_output> {
scoreIndexPairs.push_back(score_index_per_prior);
} else {
for (int prior = 0; prior < num_of_priors; ++prior) {
for (int cls = 0; cls < num_classes; ++cls) {
int idx_start = (background_label_id == 0 ? 1 : 0);
float max_score = 0;
int max_cls = 0;
for (int cls = idx_start; cls < num_classes; ++cls) {
float score = static_cast<float>(confidence_data[idx]);
if (score > confidence_threshold) {
label_to_scores[cls].emplace_back(score, prior);
if ((cls == idx_start) || score > max_score) {
max_score = score; max_cls = cls;
}
}
idx += stride;
}
score_index_per_prior.emplace_back(std::make_pair(max_score, std::make_pair(max_cls, prior)));
}
scoreIndexPairs.push_back(score_index_per_prior);
}
}
}

73
src/plugins/intel_gpu/tests/test_cases/detection_output_test.cpp
View File

@ -397,6 +397,75 @@ public:
check_results(output_prim, 3, "-1 0 0 0 0 0 0");
}
void test_forward_decrease_label_id() {
const bool share_location = true;
const int num_loc_classes = share_location ? 1 : this->num_classes;
const int keep_top_k = 5;
const int background_label_id = 0;
const float nms_threshold = 0.4f;
const int top_k = 5;
const float eta = 1.f;
const prior_box_code_type code_type = prior_box_code_type::corner;
const bool variance_encoded_in_target = false;
const float confidence_threshold = 0.02;
const int32_t prior_info_size = 4;
const int32_t prior_coordinates_offset = 0;
const bool prior_is_normalized = true;
const int32_t input_width = 1;
const int32_t input_height = 1;
const bool decrease_label_id = true;
auto& engine = get_test_engine();
cldnn::memory::ptr input_location = engine.allocate_memory({ type_to_data_type<T>::value, format::bfyx,
{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
cldnn::memory::ptr input_confidence = engine.allocate_memory({ type_to_data_type<T>::value, format::bfyx,
{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
cldnn::memory::ptr input_prior_box = engine.allocate_memory({ type_to_data_type<T>::value, format::bfyx,
{ 1, 2, 1, this->num_priors * 4 } });
this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
topology topology;
topology.add(input_layout("input_location", input_location->get_layout()));
topology.add(input_layout("input_confidence", input_confidence->get_layout()));
topology.add(input_layout("input_prior_box", input_prior_box->get_layout()));
topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box",
this->num_classes, keep_top_k, share_location, background_label_id, nms_threshold,
top_k, eta, code_type, variance_encoded_in_target, confidence_threshold, prior_info_size,
prior_coordinates_offset, prior_is_normalized, input_width, input_height, decrease_label_id
));
build_options opts;
network network(engine, topology, opts);
network.set_input_data("input_location", input_location);
network.set_input_data("input_confidence", input_confidence);
network.set_input_data("input_prior_box", input_prior_box);
auto outputs = network.execute();
EXPECT_EQ(outputs.size(), size_t(1));
EXPECT_EQ(outputs.begin()->first, "detection_output");
EXPECT_EQ(outputs.begin()->second.get_memory()->get_layout().size.batch[0], 1);
EXPECT_EQ(outputs.begin()->second.get_memory()->get_layout().size.feature[0], 1);
EXPECT_EQ(outputs.begin()->second.get_memory()->get_layout().size.spatial[1], keep_top_k * this->num_of_images);
EXPECT_EQ(outputs.begin()->second.get_memory()->get_layout().size.spatial[0], 7);
auto output_prim = outputs.begin()->second.get_memory();
check_results(output_prim, 0, "0 0 1.0 0.55 0.15 0.85 0.45");
check_results(output_prim, 1, "0 0 0.8 0.55 0.55 0.85 0.85");
check_results(output_prim, 2, "0 0 0.2 0.15 0.55 0.45 0.85");
check_results(output_prim, 3, "1 0 1.0 0.25 0.25 0.55 0.55");
check_results(output_prim, 4, "1 0 0.8 0.25 0.45 0.55 0.75");
check_results(output_prim, 5, "1 0 0.2 0.45 0.45 0.75 0.75");
check_results(output_prim, 6, "-1 0 0 0 0 0 0");
check_results(output_prim, 7, "0 0 0 0 0 0 0");
check_results(output_prim, 8, "0 0 0 0 0 0 0");
check_results(output_prim, 9, "0 0 0 0 0 0 0");
}
void forward_no_share_location() {
const bool share_location = false;
const int num_loc_classes = share_location ? 1 : this->num_classes;
@ -748,6 +817,10 @@ TYPED_TEST(detection_output_test, test_forward_share_location_top_k) {
this->test_forward_share_location_top_k();
}
TYPED_TEST(detection_output_test, test_forward_decrease_label_id) {
this->test_forward_decrease_label_id();
}
TYPED_TEST(detection_output_test, test_forward_no_share_location) {
this->forward_no_share_location();
}