}
}
+ void init_buffer_sort(cldnn::memory input_buff)
+ {
+ auto input_data_ptr = input_buff.pointer<T>();
+
+ EXPECT_EQ((int)input_buff.count(), 128);
+
+ T* input_data = input_data_ptr.data();
+ input_data[0] = 8;
+ input_data[1] = 3;
+ input_data[16] = 0; input_data[17] = 0; input_data[18] = 0.6f; input_data[19] = 0.55f; input_data[20] = 0.55f; input_data[21] = 0.85f; input_data[22] = 0.85f;
+ input_data[23] = 0; input_data[24] = 0; input_data[25] = 0.4f; input_data[26] = 0.15f; input_data[27] = 0.55f; input_data[28] = 0.45f; input_data[29] = 0.85f;
+ input_data[30] = 0; input_data[31] = 0; input_data[32] = 0.2f; input_data[33] = 0.55f; input_data[34] = 0.15f; input_data[35] = 0.85f; input_data[36] = 0.45f;
+ input_data[37] = 0; input_data[38] = 0; input_data[39] = 0.0f; input_data[40] = 0.15f; input_data[41] = 0.15f; input_data[42] = 0.45f; input_data[43] = 0.45f;
+ input_data[44] = 0; input_data[45] = 1; input_data[46] = 1.0f; input_data[47] = 0.20f; input_data[48] = 0.20f; input_data[49] = 0.50f; input_data[50] = 0.50f;
+ input_data[51] = 0; input_data[52] = 1; input_data[53] = 0.8f; input_data[54] = 0.50f; input_data[55] = 0.20f; input_data[56] = 0.80f; input_data[57] = 0.50f;
+ input_data[58] = 0; input_data[59] = 1; input_data[60] = 0.6f; input_data[61] = 0.20f; input_data[62] = 0.50f; input_data[63] = 0.50f; input_data[64] = 0.80f;
+ input_data[65] = 0; input_data[66] = 1; input_data[67] = 0.4f; input_data[68] = 0.50f; input_data[69] = 0.50f; input_data[70] = 0.80f; input_data[71] = 0.80f;
+ input_data[72] = 1; input_data[73] = 0; input_data[74] = 1.0f; input_data[75] = 0.25f; input_data[76] = 0.25f; input_data[77] = 0.55f; input_data[78] = 0.55f;
+ input_data[79] = 1; input_data[80] = 0; input_data[81] = 0.4f; input_data[82] = 0.45f; input_data[83] = 0.45f; input_data[84] = 0.75f; input_data[85] = 0.75f;
+ input_data[86] = -1; input_data[87] = 0; input_data[88] = 0; input_data[89] = 0; input_data[90] = 0; input_data[91] = 0; input_data[92] = 0;
+ input_data[93] = -1; input_data[94] = 0; input_data[95] = 0; input_data[96] = 0; input_data[97] = 0; input_data[98] = 0; input_data[99] = 0;
+ input_data[100] = 1; input_data[101] = 1; input_data[102] = 0.6f; input_data[103] = 0.40f; input_data[104] = 0.40f; input_data[105] = 0.70f; input_data[106] = 0.70f;
+ input_data[107] = -1; input_data[108] = 0; input_data[109] = 0; input_data[110] = 0; input_data[111] = 0; input_data[112] = 0; input_data[113] = 0;
+ input_data[114] = -1; input_data[115] = 0; input_data[116] = 0; input_data[117] = 0; input_data[118] = 0; input_data[119] = 0; input_data[120] = 0;
+ input_data[121] = -1; input_data[122] = 0; input_data[123] = 0; input_data[124] = 0; input_data[125] = 0; input_data[126] = 0; input_data[127] = 0;
+ }
+
void check_results(const memory& output, const int num, const std::string values)
{
assert(num < output.get_layout().size.spatial[1]);
EXPECT_TRUE(floating_point_equal(data[num * output.get_layout().size.spatial[0] + i], (T)(float)atof(items[i].c_str())));
}
}
- static const int num_of_images = 2;
- static const int num_classes = 2;
- static const int num_priors = 4;
- static const int img_size = 300;
- const float nms_threshold;
-};
-typedef ::testing::Types<float, FLOAT16> detection_output_test_types;
-TYPED_TEST_CASE(detection_output_test, detection_output_test_types);
+ void setup_basic(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 150;
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
-TYPED_TEST(detection_output_test, test_setup_basic)
-{
- const bool share_location = true;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 150;
+ 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()));
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4} });
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k));
- 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()));
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k));
- network network(engine, topology);
- 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);
+ 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 outputs = network.execute();
-TYPED_TEST(detection_output_test, test_forward_share_location)
-{
- const bool share_location = true;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 4;
- const int background_label_id = 0;
+ EXPECT_EQ(outputs.size(), size_t(1));
+ EXPECT_EQ(outputs.begin()->first, "detection_output");
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4} });
+ 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);
+ }
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ void setup_two_layers(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 150;
- 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()));
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- network network(engine, topology);
- 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);
+ 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()));
- auto outputs = network.execute();
+ topology.add(detection_output("detection_output_1", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k));
+ topology.add(detection_output("detection_output_2", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k));
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- 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);
+ 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 output_prim = outputs.begin()->second.get_memory();
-
- this->check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
- this->check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
- this->check_results(output_prim, 2, "0 1 0.6 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 3, "0 1 0.4 0.55 0.55 0.85 0.85");
- this->check_results(output_prim, 4, "1 1 0.6 0.45 0.45 0.75 0.75");
- this->check_results(output_prim, 5, "1 1 0.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
-}
+ auto outputs = network.execute();
-TYPED_TEST(detection_output_test, test_forward_num_detections_greater_than_keep_top_k)
-{
- const bool share_location = true;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 1;
- const int background_label_id = 0;
+ EXPECT_EQ(outputs.size(), size_t(2));
+ unsigned i = 1;
+ for (auto it = outputs.begin(); it != outputs.begin(); it++)
+ {
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4} });
+ EXPECT_EQ(it->first, "detection_output_" + std::to_string(i));
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ EXPECT_EQ(it->second.get_memory().get_layout().size.batch[0], 1);
+ EXPECT_EQ(it->second.get_memory().get_layout().size.feature[0], 1);
+ EXPECT_EQ(it->second.get_memory().get_layout().size.spatial[1], keep_top_k * this->num_of_images);
+ EXPECT_EQ(it->second.get_memory().get_layout().size.spatial[0], 7);
+ i++;
+ }
+ }
- 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()));
+ void forward_share_location(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 4;
+ const int background_label_id = 0;
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- network network(engine, topology);
- 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);
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- auto outputs = network.execute();
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ 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()));
- 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);
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- auto output_prim = outputs.begin()->second.get_memory();
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- this->check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
- this->check_results(output_prim, 1, "1 1 0.6 0.45 0.45 0.75 0.75");
-}
+ 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);
-TYPED_TEST(detection_output_test, test_forward_num_detections_smaller_than_keep_top_k)
-{
- const bool share_location = true;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 6;
- const int background_label_id = 0;
+ auto outputs = network.execute();
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4} });
+ EXPECT_EQ(outputs.size(), size_t(1));
+ EXPECT_EQ(outputs.begin()->first, "detection_output");
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ 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);
- 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()));
+ auto output_prim = outputs.begin()->second.get_memory();
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- network network(engine, topology);
- 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);
+ check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
+ check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
+ check_results(output_prim, 2, "0 1 0.6 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 3, "0 1 0.4 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 4, "1 1 0.6 0.45 0.45 0.75 0.75");
+ check_results(output_prim, 5, "1 1 0.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ }
- auto outputs = network.execute();
+ void forward_num_detections_greater_than_keep_top_k(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 1;
+ const int background_label_id = 0;
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- 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);
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
- auto output_prim = outputs.begin()->second.get_memory();
+ 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()));
- this->check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
- this->check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
- this->check_results(output_prim, 2, "0 1 0.6 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 3, "0 1 0.4 0.55 0.55 0.85 0.85");
- this->check_results(output_prim, 4, "1 1 0.6 0.45 0.45 0.75 0.75");
- this->check_results(output_prim, 5, "1 1 0.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 8, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 9, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 10, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 11, "-1 0 0 0 0 0 0");
-}
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
-TYPED_TEST(detection_output_test, test_forward_share_location_top_k)
-{
- const bool share_location = true;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 2;
- const int top_k = 2;
- const int background_label_id = 0;
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
+ 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);
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ auto outputs = network.execute();
- 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()));
+ EXPECT_EQ(outputs.size(), size_t(1));
+ EXPECT_EQ(outputs.begin()->first, "detection_output");
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- network network(engine, topology);
- 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);
+ 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 outputs = network.execute();
+ auto output_prim = outputs.begin()->second.get_memory();
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
+ check_results(output_prim, 1, "1 1 0.6 0.45 0.45 0.75 0.75");
+ }
- 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);
+ void forward_num_detections_smaller_than_keep_top_k(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 6;
+ const int background_label_id = 0;
- auto output_prim = outputs.begin()->second.get_memory();
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- this->check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
- this->check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
- this->check_results(output_prim, 2, "1 1 0.6 0.45 0.45 0.75 0.75");
- this->check_results(output_prim, 3, "-1 0 0 0 0 0 0");
-}
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
-TYPED_TEST(detection_output_test, test_forward_no_share_location)
-{
- const bool share_location = false;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 10;
- const int background_label_id = -1;
+ 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()));
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- 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()));
+ 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 1 1.0 0.15 0.15 0.45 0.45");
+ check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
+ check_results(output_prim, 2, "0 1 0.6 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 3, "0 1 0.4 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 4, "1 1 0.6 0.45 0.45 0.75 0.75");
+ check_results(output_prim, 5, "1 1 0.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 8, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 9, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 10, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 11, "-1 0 0 0 0 0 0");
+ }
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- network network(engine, topology);
- 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);
+ void test_forward_share_location_top_k(bool runOnGPU)
+ {
+ const bool share_location = true;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 2;
+ const int top_k = 2;
+ const int background_label_id = 0;
- auto outputs = network.execute();
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
- 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);
+ 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()));
- auto output_prim = outputs.begin()->second.get_memory();
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- this->check_results(output_prim, 0, "0 0 0.6 0.55 0.55 0.85 0.85");
- this->check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 2, "0 0 0.2 0.55 0.15 0.85 0.45");
- this->check_results(output_prim, 3, "0 0 0.0 0.15 0.15 0.45 0.45");
- this->check_results(output_prim, 4, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 5, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 6, "0 1 0.6 0.20 0.50 0.50 0.80");
- this->check_results(output_prim, 7, "0 1 0.4 0.50 0.50 0.80 0.80");
- this->check_results(output_prim, 8, "1 0 1.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 9, "1 0 0.4 0.45 0.45 0.75 0.75");
- this->check_results(output_prim, 10, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 11, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 12, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 13, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 14, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 15, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 16, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 17, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 18, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 19, "-1 0 0 0 0 0 0");
-}
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
-TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k)
-{
- const bool share_location = false;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 4;
- const int background_label_id = -1;
- const int top_k = 2;
+ 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);
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
+ auto outputs = network.execute();
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ EXPECT_EQ(outputs.size(), size_t(1));
+ EXPECT_EQ(outputs.begin()->first, "detection_output");
- 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()));
+ 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);
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- network network(engine, topology);
- 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 output_prim = outputs.begin()->second.get_memory();
- auto outputs = network.execute();
+ check_results(output_prim, 0, "0 1 1.0 0.15 0.15 0.45 0.45");
+ check_results(output_prim, 1, "0 1 0.8 0.55 0.15 0.85 0.45");
+ check_results(output_prim, 2, "1 1 0.6 0.45 0.45 0.75 0.75");
+ check_results(output_prim, 3, "-1 0 0 0 0 0 0");
+ }
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ void forward_no_share_location(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 10;
+ const int background_label_id = -1;
- 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);
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- auto output_prim = outputs.begin()->second.get_memory();
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
- this->check_results(output_prim, 0, "0 0 0.6 0.55 0.55 0.85 0.85");
- this->check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
-}
+ 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()));
-TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0)
-{
- const bool share_location = false;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 5;
- const int background_label_id = 0;
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+ 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 0.6 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 2, "0 0 0.2 0.55 0.15 0.85 0.45");
+ check_results(output_prim, 3, "0 0 0.0 0.15 0.15 0.45 0.45");
+ check_results(output_prim, 4, "0 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 5, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 6, "0 1 0.6 0.20 0.50 0.50 0.80");
+ check_results(output_prim, 7, "0 1 0.4 0.50 0.50 0.80 0.80");
+ check_results(output_prim, 8, "1 0 1.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 9, "1 0 0.4 0.45 0.45 0.75 0.75");
+ check_results(output_prim, 10, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 11, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 12, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 13, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 14, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 15, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 16, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 17, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 18, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 19, "-1 0 0 0 0 0 0");
+ }
- 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()));
+ void forward_no_share_location_top_k(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 4;
+ const int background_label_id = -1;
+ const int top_k = 2;
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold));
- network network(engine, topology);
- 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);
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
- auto outputs = network.execute();
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ 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()));
- 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);
+ topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- auto output_prim = outputs.begin()->second.get_memory();
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
- this->check_results(output_prim, 0, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 1, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 2, "0 1 0.6 0.20 0.50 0.50 0.80");
- this->check_results(output_prim, 3, "0 1 0.4 0.50 0.50 0.80 0.80");
- this->check_results(output_prim, 4, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 5, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 8, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 9, "-1 0 0 0 0 0 0");
+ 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 0.6 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ }
+
+ void forward_no_share_location_neg_0(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 5;
+ const int background_label_id = 0;
+
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { 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, this->nms_threshold));
+
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
+
+ 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 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 1, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 2, "0 1 0.6 0.20 0.50 0.50 0.80");
+ check_results(output_prim, 3, "0 1 0.4 0.50 0.50 0.80 0.80");
+ check_results(output_prim, 4, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 5, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 8, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 9, "-1 0 0 0 0 0 0");
+ }
+
+ void forward_no_share_location_neg_0_top_k(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 2;
+ const int background_label_id = 0;
+ const int top_k = 2;
+
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { 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, this->nms_threshold, top_k));
+
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
+
+ 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 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 1, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 2, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 3, "-1 0 0 0 0 0 0");
+ }
+
+ void forward_no_share_location_top_k_input_padding(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 4;
+ const int background_label_id = -1;
+ const int top_k = 2;
+
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { 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(reorder("input_location_padded", "input_location", input_location.get_layout().with_padding({ { 0, 0, 12, 3 },{ 0, 0, 5, 11 } })));
+ topology.add(reorder("input_confidence_padded", "input_confidence", input_location.get_layout().with_padding({ { 0, 0, 2, 7 },{ 0, 0, 13, 1 } })));
+
+ topology.add(detection_output("detection_output", "input_location_padded", "input_confidence_padded", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
+
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
+
+ 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 0.6 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ }
+
+ void test_forward_no_share_location_top_k_faster_rcnn_case(bool runOnGPU)
+ {
+ const bool share_location = false;
+ const int num_loc_classes = share_location ? 1 : this->num_classes;
+ const int keep_top_k = 4;
+ const int background_label_id = -1;
+ const int top_k = 2;
+ const float eta = 1.0f;
+ const prior_box_code_type code_type = prior_box_code_type::corner;
+ const bool variance_encoded_in_target = true;
+ const float confidence_threshold = -std::numeric_limits<float>::max();
+ const int32_t prior_info_size = 5;
+ const int32_t prior_coordinates_offset = 1;
+ const bool prior_is_normalized = true;
+
+ const auto& engine = get_test_engine();
+ cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
+ cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
+ cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx,{ 1, 1, 1, this->num_priors * prior_info_size } });
+
+ this->init_buffers(input_prior_box, input_confidence, input_location, share_location, variance_encoded_in_target,
+ prior_info_size, prior_coordinates_offset, prior_is_normalized);
+
+ 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(reorder("input_location_padded", "input_location", input_location.get_layout().with_padding({ { 0, 0, 12, 3 },{ 0, 0, 5, 11 } })));
+ topology.add(reorder("input_confidence_padded", "input_confidence", input_location.get_layout().with_padding({ { 0, 0, 2, 7 },{ 0, 0, 13, 1 } })));
+
+ topology.add(detection_output("detection_output", "input_location_padded", "input_confidence_padded", "input_prior_box",
+ this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k,
+ eta, code_type, variance_encoded_in_target, confidence_threshold, prior_info_size, prior_coordinates_offset,
+ prior_is_normalized, this->img_size, this->img_size
+ ));
+
+ build_options opts;
+ if (runOnGPU)
+ {
+ opts.set_option(build_option::detection_output_gpu(true));
+ }
+
+ 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 0.6 0.55 0.55 0.85 0.85");
+ check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
+ check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
+ check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
+ check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
+ check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
+ check_results(output_prim, 6, "-1 0 0 0 0 0 0");
+ check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ }
+
+ static const int num_of_images = 2;
+ static const int num_classes = 2;
+ static const int num_priors = 4;
+ static const int img_size = 300;
+ const float nms_threshold;
+};
+
+typedef ::testing::Types<float, FLOAT16> detection_output_test_types;
+TYPED_TEST_CASE(detection_output_test, detection_output_test_types);
+
+
+TYPED_TEST(detection_output_test, test_setup_basic)
+{
+ this->setup_basic(false);
}
-TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0_top_k)
+TYPED_TEST(detection_output_test, test_setup_basic_gpu)
{
- const bool share_location = false;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 2;
- const int background_label_id = 0;
- const int top_k = 2;
+ this->setup_basic(true);
+}
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx, { this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx, { this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx, { 1, 2, 1, this->num_priors * 4 } });
+TYPED_TEST(detection_output_test, test_setup_two_layers)
+{
+ this->setup_two_layers(false);
+}
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location);
+TYPED_TEST(detection_output_test, test_setup_two_layers_gpu)
+{
+ this->setup_two_layers(true);
+}
- 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()));
+TYPED_TEST(detection_output_test, test_forward_share_location)
+{
+ this->forward_share_location(false);
+}
- topology.add(detection_output("detection_output", "input_location", "input_confidence", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- network network(engine, topology);
- 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);
+TYPED_TEST(detection_output_test, test_forward_share_location_gpu)
+{
+ this->forward_share_location(true);
+}
- auto outputs = network.execute();
+TYPED_TEST(detection_output_test, test_forward_num_detections_greater_than_keep_top_k)
+{
+ this->forward_num_detections_greater_than_keep_top_k(false);
+}
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+TYPED_TEST(detection_output_test, test_forward_num_detections_greater_than_keep_top_k_gpu)
+{
+ this->forward_num_detections_greater_than_keep_top_k(true);
+}
- 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);
+TYPED_TEST(detection_output_test, test_forward_num_detections_smaller_than_keep_top_k)
+{
+ this->forward_num_detections_smaller_than_keep_top_k(false);
+}
- auto output_prim = outputs.begin()->second.get_memory();
+TYPED_TEST(detection_output_test, test_forward_num_detections_smaller_than_keep_top_k_gpu)
+{
+ this->forward_num_detections_smaller_than_keep_top_k(true);
+}
- this->check_results(output_prim, 0, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 1, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 2, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 3, "-1 0 0 0 0 0 0");
+TYPED_TEST(detection_output_test, test_forward_share_location_top_k)
+{
+ this->test_forward_share_location_top_k(false);
}
-TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_input_padding)
+TYPED_TEST(detection_output_test, test_forward_share_location_top_k_gpu)
{
- const bool share_location = false;
- const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 4;
- const int background_label_id = -1;
- const int top_k = 2;
+ this->test_forward_share_location_top_k(true);
+}
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 2, 1, this->num_priors * 4 } });
+TYPED_TEST(detection_output_test, test_forward_no_share_location)
+{
+ this->forward_no_share_location(false);
+}
- 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(reorder("input_location_padded", "input_location", input_location.get_layout().with_padding({ { 0, 0, 12, 3 },{ 0, 0, 5, 11 } })));
- topology.add(reorder("input_confidence_padded", "input_confidence", input_location.get_layout().with_padding({ { 0, 0, 2, 7 },{ 0, 0, 13, 1 } })));
+TYPED_TEST(detection_output_test, test_forward_no_share_location_gpu)
+{
+ this->forward_no_share_location(true);
+}
- topology.add(detection_output("detection_output", "input_location_padded", "input_confidence_padded", "input_prior_box", this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k));
- network network(engine, topology);
- 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);
+TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k)
+{
+ this->forward_no_share_location_top_k(false);
+}
- auto outputs = network.execute();
+TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_gpu)
+{
+ this->forward_no_share_location_top_k(true);
+}
- EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0)
+{
+ this->forward_no_share_location_neg_0(false);
+}
- 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);
+TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0_gpu)
+{
+ this->forward_no_share_location_neg_0(true);
+}
- auto output_prim = outputs.begin()->second.get_memory();
+TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0_top_k)
+{
+ this->forward_no_share_location_neg_0_top_k(false);
+}
- this->check_results(output_prim, 0, "0 0 0.6 0.55 0.55 0.85 0.85");
- this->check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+TYPED_TEST(detection_output_test, test_forward_no_share_location_neg_0_top_k_gpu)
+{
+ this->forward_no_share_location_neg_0_top_k(true);
+}
+
+TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_input_padding)
+{
+ this->forward_no_share_location_top_k_input_padding(false);
+}
+
+TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_input_padding_gpu)
+{
+ this->forward_no_share_location_top_k_input_padding(true);
}
TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_faster_rcnn_case)
{
+ this->test_forward_no_share_location_top_k_faster_rcnn_case(false);
+}
+
+TYPED_TEST(detection_output_test, test_forward_no_share_location_top_k_faster_rcnn_case_gpu)
+{
+ this->test_forward_no_share_location_top_k_faster_rcnn_case(true);
+}
+
+TYPED_TEST(detection_output_test, test_detection_output_sort_gpu)
+{
const bool share_location = false;
const int num_loc_classes = share_location ? 1 : this->num_classes;
- const int keep_top_k = 4;
+ const int keep_top_k = 10;
const int background_label_id = -1;
- const int top_k = 2;
- const float eta = 1.0f;
- const prior_box_code_type code_type = prior_box_code_type::corner;
- const bool variance_encoded_in_target = true;
- const float confidence_threshold = -std::numeric_limits<float>::max();
- const int32_t prior_info_size = 5;
- const int32_t prior_coordinates_offset = 1;
- const bool prior_is_normalized = true;
-
- cldnn::engine engine;
- cldnn::memory input_location = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * num_loc_classes * 4, 1, 1 } });
- cldnn::memory input_confidence = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ this->num_of_images, this->num_priors * this->num_classes, 1, 1 } });
- cldnn::memory input_prior_box = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 1, 1, this->num_priors * prior_info_size } });
-
- this->init_buffers(input_prior_box, input_confidence, input_location, share_location, variance_encoded_in_target,
- prior_info_size, prior_coordinates_offset, prior_is_normalized);
+ const int top_k = -1;
+
+ const unsigned out_row_size = 7;
+ const unsigned score_space = ((this->num_of_images + 15) / 16) * 16;
+ int input_size = this->num_of_images * num_loc_classes * this->num_priors * out_row_size + score_space;
+
+ const auto& engine = get_test_engine();
+ cldnn::memory input_buff = memory::allocate(engine, { type_to_data_type<TypeParam>::value, format::bfyx,{ 1, 1, 1, input_size } });
+
+ this->init_buffer_sort(input_buff);
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(reorder("input_location_padded", "input_location", input_location.get_layout().with_padding({ { 0, 0, 12, 3 },{ 0, 0, 5, 11 } })));
- topology.add(reorder("input_confidence_padded", "input_confidence", input_location.get_layout().with_padding({ { 0, 0, 2, 7 },{ 0, 0, 13, 1 } })));
-
- topology.add(detection_output("detection_output", "input_location_padded", "input_confidence_padded", "input_prior_box",
- this->num_classes, keep_top_k, share_location, background_label_id, this->nms_threshold, top_k,
- eta, code_type, variance_encoded_in_target, confidence_threshold, prior_info_size, prior_coordinates_offset,
- prior_is_normalized, this->img_size, this->img_size
- ));
+ topology.add(input_layout("input_location", input_buff.get_layout()));
+
+ topology.add(detection_output_sort("detection_output_sort", "input_location", this->num_of_images, this->num_classes, keep_top_k, share_location, top_k, background_label_id));
network network(engine, topology);
- 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);
+ network.set_input_data("input_location", input_buff);
auto outputs = network.execute();
EXPECT_EQ(outputs.size(), size_t(1));
- EXPECT_EQ(outputs.begin()->first, "detection_output");
+ EXPECT_EQ(outputs.begin()->first, "detection_output_sort");
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);
this->check_results(output_prim, 0, "0 0 0.6 0.55 0.55 0.85 0.85");
this->check_results(output_prim, 1, "0 0 0.4 0.15 0.55 0.45 0.85");
- this->check_results(output_prim, 2, "0 1 1.0 0.20 0.20 0.50 0.50");
- this->check_results(output_prim, 3, "0 1 0.8 0.50 0.20 0.80 0.50");
- this->check_results(output_prim, 4, "1 0 1.0 0.25 0.25 0.55 0.55");
- this->check_results(output_prim, 5, "1 1 0.6 0.40 0.40 0.70 0.70");
- this->check_results(output_prim, 6, "-1 0 0 0 0 0 0");
- this->check_results(output_prim, 7, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 2, "0 0 0.2 0.55 0.15 0.85 0.45");
+ this->check_results(output_prim, 3, "0 0 0.0 0.15 0.15 0.45 0.45");
+ this->check_results(output_prim, 4, "0 1 1.0 0.20 0.20 0.50 0.50");
+ this->check_results(output_prim, 5, "0 1 0.8 0.50 0.20 0.80 0.50");
+ this->check_results(output_prim, 6, "0 1 0.6 0.20 0.50 0.50 0.80");
+ this->check_results(output_prim, 7, "0 1 0.4 0.50 0.50 0.80 0.80");
+ this->check_results(output_prim, 8, "1 0 1.0 0.25 0.25 0.55 0.55");
+ this->check_results(output_prim, 9, "1 0 0.4 0.45 0.45 0.75 0.75");
+ this->check_results(output_prim, 10, "1 1 0.6 0.40 0.40 0.70 0.70");
+ this->check_results(output_prim, 11, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 12, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 13, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 14, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 15, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 16, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 17, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 18, "-1 0 0 0 0 0 0");
+ this->check_results(output_prim, 19, "-1 0 0 0 0 0 0");
}
projects / platform / upstream / dldt.git / blobdiff