layout detection_output_inst::calc_output_layout(detection_output_node const& node)
{
+ assert((bool)node.get_primitive()->output_data_type == false
+ && "Output data type forcing is not supported for "
+ "detection_output_node!");
CLDNN_ERROR_NOT_EQUAL(node.id(), "Detection output layer input number", node.get_dependencies().size(), "expected number of inputs", static_cast<size_t>(3), "");
auto input_layout = node.location().get_output_layout();
// Batch size and feature size are 1.
- // Number of bounding boxes to be kept is set to keep_top_k*batch size.
- // If number of detections is lower than keep_top_k, will write dummy results at the end with image_id=-1.
+ // Number of bounding boxes to be kept is set to keep_top_k*batch size.
+ // If number of detections is lower than top_k, will write dummy results at the end with image_id=-1.
// Each row is a 7 dimension vector, which stores:
// [image_id, label, confidence, xmin, ymin, xmax, ymax]
- return{ input_layout.data_type, cldnn::format::bfyx, cldnn::tensor(1, 1, DETECTION_OUTPUT_ROW_SIZE, node.get_primitive()->keep_top_k * input_layout.size.batch[0]) };
+ int output_size = (int)input_layout.get_linear_size() / PRIOR_BOX_SIZE;
+ int num_classes = node.get_primitive()->num_classes;
+
+ if (node.get_primitive()->share_location)
+ {
+ num_classes = (node.get_primitive()->background_label_id == 0) ? node.get_primitive()->num_classes - 1 : node.get_primitive()->num_classes;
+ output_size *= num_classes;
+ }
+
+ if (node.get_primitive()->top_k != -1)
+ {
+ int top_k = node.get_primitive()->top_k * num_classes * input_layout.size.batch[0];
+ if (top_k < output_size)
+ {
+ output_size = top_k;
+ }
+ }
+
+ output_size *= DETECTION_OUTPUT_ROW_SIZE;
+ // Add space for number of output results per image - needed in the next detection output step
+ output_size += ((input_layout.size.batch[0] + 15) / 16) * 16;
+
+ if (node.get_program().get_options().get<build_option_type::detection_output_gpu>()->enabled())
+ {
+ return{ input_layout.data_type, cldnn::format::bfyx, cldnn::tensor(1, 1, 1, output_size) };
+ }
+ else
+ {
+ return{ input_layout.data_type, cldnn::format::bfyx, cldnn::tensor(1, 1, DETECTION_OUTPUT_ROW_SIZE, node.get_primitive()->keep_top_k * input_layout.size.batch[0]) };
+ }
}
std::string detection_output_inst::to_string(detection_output_node const& node)
auto variance_encoded = desc->variance_encoded_in_target ? "true" : "false";
auto prior_is_normalized = desc->prior_is_normalized ? "true" : "false";
auto decrease_label_id = desc->decrease_label_id ? "true" : "false";
- auto clip = desc->clip ? "true" : "false";
+ auto clip_before_nms = desc->clip_before_nms ? "true" : "false";
+ auto clip_after_nms = desc->clip_after_nms ? "true" : "false";
auto& input_location = node.location();
auto& input_prior_box = node.prior_box();
auto& input_confidence = node.confidence();
-
+
std::stringstream primitive_description;
std::string str_code_type;
str_code_type = "not supported code type";
break;
}
-
+
json_composite detec_out_info;
detec_out_info.add("input location id", input_location.id());
detec_out_info.add("input confidence id", input_confidence.id());
detec_out_info.add("input_width", desc->input_width);
detec_out_info.add("input_height", desc->input_height);
detec_out_info.add("decrease_label_id", decrease_label_id);
- detec_out_info.add("clip", clip);
+ detec_out_info.add("clip_before_nms", clip_before_nms);
+ detec_out_info.add("clip_after_nms", clip_after_nms);
detec_out_info.dump(primitive_description);
node_info->add("dection output info", detec_out_info);
auto desc = node.get_primitive();
int prior_feature_size = desc->variance_encoded_in_target ? 1 : 2;
tensor prior_box_size = prior_box_layout.size;
- CLDNN_ERROR_NOT_EQUAL(node.id(), "Prior box batch size", prior_box_size.batch[0], "expected value", 1, "");
CLDNN_ERROR_NOT_EQUAL(node.id(), "Prior box spatial X", prior_box_size.spatial[0], "expected value", 1, "");
CLDNN_ERROR_NOT_EQUAL(node.id(), "Prior box feature size", prior_box_size.feature[0], "expected value", prior_feature_size, "");
CLDNN_ERROR_BOOL(node.id(), "Detection output layer padding", node.is_padded(), "Detection output layer doesn't support output padding.");
CLDNN_ERROR_BOOL(node.id(), "Detection output layer Prior-box input padding", node.get_dependency(2).is_padded(), "Detection output layer doesn't support input padding in Prior-Box input");
}
+
+/************************ Detection Output keep_top_k part ************************/
+
+primitive_type_id detection_output_sort_type_id()
+{
+ static primitive_type_base<detection_output_sort> instance;
+ return &instance;
+}
+
+layout detection_output_sort_inst::calc_output_layout(detection_output_sort_node const& node)
+{
+ assert((bool)node.get_primitive()->output_data_type == false
+ && "Output data type forcing is not supported for "
+ "detection_output_sort_node!");
+ CLDNN_ERROR_NOT_EQUAL(node.id(), "Detection output layer input number", node.get_dependencies().size(), "expected number of inputs", static_cast<size_t>(1), "");
+
+ auto input_layout = node.input().get_output_layout();
+ int keep_top_k = node.as<detection_output_sort>().get_primitive()->keep_top_k;
+ int num_images = node.as<detection_output_sort>().get_primitive()->num_images;
+
+ // If detection output sort is used as a second part of detection output get proper info from detection otput node
+ if (num_images == 0)
+ {
+ CLDNN_ERROR_BOOL(node.id(), "node.get_dependency(0).is_type<detection_output>()", !node.get_dependency(0).is_type<detection_output>(), "Cannot calculate output layout.");
+ input_layout = node.get_dependency(0).as<detection_output>().location().get_output_layout();
+ keep_top_k = node.get_dependency(0).as<detection_output>().get_primitive()->keep_top_k;
+ num_images = input_layout.size.batch[0];
+ }
+ // Batch size and feature size are 1.
+ // Number of bounding boxes to be kept is set to keep_top_k*batch size.
+ // If number of detections is lower than keep_top_k, will write dummy results at the end with image_id=-1.
+ // Each row is a 7 dimension vector, which stores:
+ // [image_id, label, confidence, xmin, ymin, xmax, ymax]
+ return{ input_layout.data_type, cldnn::format::bfyx, cldnn::tensor(1, 1, DETECTION_OUTPUT_ROW_SIZE, keep_top_k * num_images) };
+}
+
+std::string detection_output_sort_inst::to_string(detection_output_sort_node const& node)
+{
+ auto node_info = node.desc_to_json();
+ auto desc = node.get_primitive();
+
+ auto& input_bboxes = node.input();
+
+ std::stringstream primitive_description;
+
+ json_composite detec_out_info;
+ detec_out_info.add("input bboxes id", input_bboxes.id());
+ detec_out_info.add("num_classes:", desc->num_images);
+ detec_out_info.add("num_classes:", desc->num_classes);
+ detec_out_info.add("keep_top_k", desc->keep_top_k);
+ detec_out_info.add("share_location", desc->share_location);
+ detec_out_info.add("top_k", desc->top_k);
+ detec_out_info.dump(primitive_description);
+
+ node_info->add("dection output info", detec_out_info);
+ node_info->dump(primitive_description);
+
+ return primitive_description.str();
+}
+
+detection_output_sort_inst::typed_primitive_inst(network_impl& network, detection_output_sort_node const& node)
+ :parent(network, node)
+{
+ CLDNN_ERROR_NOT_PROPER_FORMAT(node.id(), "Input memory format", node.get_dependency(0).get_output_layout().format.value, "expected bfyx input format", format::bfyx);
+
+ CLDNN_ERROR_BOOL(node.id(), "Detecion output layer padding", node.is_padded(), "Detection output layer doesn't support output padding.");
+}
}
projects / platform / upstream / dldt.git / blobdiff