COMPMID-2246: NEBoundingBoxTransform support QASYMM16
Change-Id: I0704f71159a3caec4705779cab2ef38aeb33aaca
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1864
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/core/NEON/kernels/NEBoundingBoxTransformKernel.cpp b/src/core/NEON/kernels/NEBoundingBoxTransformKernel.cpp
index cfd3e70..5a40b99 100644
--- a/src/core/NEON/kernels/NEBoundingBoxTransformKernel.cpp
+++ b/src/core/NEON/kernels/NEBoundingBoxTransformKernel.cpp
@@ -40,21 +40,40 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(boxes, pred_boxes, deltas);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(boxes);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(boxes, DataType::F32, DataType::F16);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(deltas, DataType::F32, DataType::F16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(boxes, DataType::QASYMM16, DataType::F32, DataType::F16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(deltas, DataType::QASYMM8, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON(deltas->tensor_shape()[1] != boxes->tensor_shape()[1]);
ARM_COMPUTE_RETURN_ERROR_ON(deltas->tensor_shape()[0] % 4 != 0);
ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[0] != 4);
ARM_COMPUTE_RETURN_ERROR_ON(deltas->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(boxes->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(info.scale() <= 0);
+
+ if(boxes->data_type() == DataType::QASYMM16)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(deltas, 1, DataType::QASYMM8);
+ const UniformQuantizationInfo deltas_qinfo = deltas->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(deltas_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(deltas_qinfo.offset != 0);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(boxes, deltas);
+ }
if(pred_boxes->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(pred_boxes->tensor_shape(), deltas->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(pred_boxes, deltas);
ARM_COMPUTE_RETURN_ERROR_ON(pred_boxes->num_dimensions() > 2);
+ if(pred_boxes->data_type() == DataType::QASYMM16)
+ {
+ const UniformQuantizationInfo pred_qinfo = pred_boxes->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(pred_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(pred_qinfo.offset != 0);
+ }
}
- ARM_COMPUTE_RETURN_ERROR_ON(info.scale() <= 0);
+
return Status{};
}
@@ -62,7 +81,7 @@
{
ARM_COMPUTE_ERROR_ON_NULLPTR(boxes, pred_boxes);
- auto_init_if_empty(*pred_boxes, *deltas);
+ auto_init_if_empty(*pred_boxes, deltas->clone()->set_data_type(boxes->data_type()).set_quantization_info(boxes->quantization_info()));
const unsigned int num_boxes = boxes->dimension(1);
@@ -110,13 +129,70 @@
return Status{};
}
+template <>
+void NEBoundingBoxTransformKernel::internal_run<uint16_t>(const Window &window, const ThreadInfo &info)
+{
+ const size_t num_classes = _deltas->info()->tensor_shape()[0] >> 2;
+ const size_t deltas_width = _deltas->info()->tensor_shape()[0];
+ const int img_h = std::floor(_bbinfo.img_height() / _bbinfo.scale() + 0.5f);
+ const int img_w = std::floor(_bbinfo.img_width() / _bbinfo.scale() + 0.5f);
+
+ const auto scale_after = (_bbinfo.apply_scale() ? _bbinfo.scale() : 1.f);
+ const auto scale_before = _bbinfo.scale();
+ const auto offset = (_bbinfo.correct_transform_coords() ? 1.f : 0.f);
+
+ auto pred_ptr = reinterpret_cast<uint16_t *>(_pred_boxes->buffer() + _pred_boxes->info()->offset_first_element_in_bytes());
+ auto delta_ptr = reinterpret_cast<uint8_t *>(_deltas->buffer() + _deltas->info()->offset_first_element_in_bytes());
+
+ const auto boxes_qinfo = _boxes->info()->quantization_info().uniform();
+ const auto deltas_qinfo = _deltas->info()->quantization_info().uniform();
+ const auto pred_qinfo = _pred_boxes->info()->quantization_info().uniform();
+
+ Iterator box_it(_boxes, window);
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ const auto ptr = reinterpret_cast<uint16_t *>(box_it.ptr());
+ const auto b0 = dequantize_qasymm16(*ptr, boxes_qinfo);
+ const auto b1 = dequantize_qasymm16(*(ptr + 1), boxes_qinfo);
+ const auto b2 = dequantize_qasymm16(*(ptr + 2), boxes_qinfo);
+ const auto b3 = dequantize_qasymm16(*(ptr + 3), boxes_qinfo);
+ const float width = (b2 / scale_before) - (b0 / scale_before) + 1.f;
+ const float height = (b3 / scale_before) - (b1 / scale_before) + 1.f;
+ const float ctr_x = (b0 / scale_before) + 0.5f * width;
+ const float ctr_y = (b1 / scale_before) + 0.5f * height;
+ for(size_t j = 0; j < num_classes; ++j)
+ {
+ // Extract deltas
+ const size_t delta_id = id.y() * deltas_width + 4u * j;
+ const float dx = dequantize_qasymm8(delta_ptr[delta_id], deltas_qinfo) / _bbinfo.weights()[0];
+ const float dy = dequantize_qasymm8(delta_ptr[delta_id + 1], deltas_qinfo) / _bbinfo.weights()[1];
+ float dw = dequantize_qasymm8(delta_ptr[delta_id + 2], deltas_qinfo) / _bbinfo.weights()[2];
+ float dh = dequantize_qasymm8(delta_ptr[delta_id + 3], deltas_qinfo) / _bbinfo.weights()[3];
+ // Clip dw and dh
+ dw = std::min(dw, _bbinfo.bbox_xform_clip());
+ dh = std::min(dh, _bbinfo.bbox_xform_clip());
+ // Determine the predictions
+ const float pred_ctr_x = dx * width + ctr_x;
+ const float pred_ctr_y = dy * height + ctr_y;
+ const float pred_w = std::exp(dw) * width;
+ const float pred_h = std::exp(dh) * height;
+ // Store the prediction into the output tensor
+ pred_ptr[delta_id] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_x - 0.5f * pred_w, 0.f, img_w - 1.f), pred_qinfo);
+ pred_ptr[delta_id + 1] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_y - 0.5f * pred_h, 0.f, img_h - 1.f), pred_qinfo);
+ pred_ptr[delta_id + 2] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_x + 0.5f * pred_w - offset, 0.f, img_w - 1.f), pred_qinfo);
+ pred_ptr[delta_id + 3] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_y + 0.5f * pred_h - offset, 0.f, img_h - 1.f), pred_qinfo);
+ }
+ },
+ box_it);
+}
+
template <typename T>
void NEBoundingBoxTransformKernel::internal_run(const Window &window, const ThreadInfo &info)
{
const size_t num_classes = _deltas->info()->tensor_shape()[0] >> 2;
const size_t deltas_width = _deltas->info()->tensor_shape()[0];
- const int img_h = floor(_bbinfo.img_height() / _bbinfo.scale() + 0.5f);
- const int img_w = floor(_bbinfo.img_width() / _bbinfo.scale() + 0.5f);
+ const int img_h = std::floor(_bbinfo.img_height() / _bbinfo.scale() + 0.5f);
+ const int img_w = std::floor(_bbinfo.img_width() / _bbinfo.scale() + 0.5f);
const auto scale_after = (_bbinfo.apply_scale() ? T(_bbinfo.scale()) : T(1));
const auto scale_before = T(_bbinfo.scale());
@@ -152,8 +228,8 @@
// Determine the predictions
const T pred_ctr_x = dx * width + ctr_x;
const T pred_ctr_y = dy * height + ctr_y;
- const T pred_w = T(std::exp(dw)) * width;
- const T pred_h = T(std::exp(dh)) * height;
+ const T pred_w = std::exp(dw) * width;
+ const T pred_h = std::exp(dh) * height;
// Store the prediction into the output tensor
pred_ptr[delta_id] = scale_after * utility::clamp<T>(pred_ctr_x - T(0.5f) * pred_w, T(0), T(img_w - 1));
pred_ptr[delta_id + 1] = scale_after * utility::clamp<T>(pred_ctr_y - T(0.5f) * pred_h, T(0), T(img_h - 1));
@@ -175,6 +251,11 @@
internal_run<float>(window, info);
break;
}
+ case DataType::QASYMM16:
+ {
+ internal_run<uint16_t>(window, info);
+ break;
+ }
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{