arm_compute v19.11
diff --git a/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
index d712a23..c9eb8ab 100644
--- a/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
+++ b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
@@ -30,26 +30,33 @@
namespace arm_compute
{
CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)),
+ : _memory_group(memory_manager),
_permute_deltas_kernel(),
_flatten_deltas_kernel(),
_permute_scores_kernel(),
_flatten_scores_kernel(),
_compute_anchors_kernel(),
_bounding_box_kernel(),
- _memset_kernel(),
- _padded_copy_kernel(),
- _cpp_nms_kernel(),
+ _pad_kernel(),
+ _dequantize_anchors(),
+ _dequantize_deltas(),
+ _quantize_all_proposals(),
+ _cpp_nms(memory_manager),
_is_nhwc(false),
+ _is_qasymm8(false),
_deltas_permuted(),
_deltas_flattened(),
+ _deltas_flattened_f32(),
_scores_permuted(),
_scores_flattened(),
_all_anchors(),
+ _all_anchors_f32(),
_all_proposals(),
+ _all_proposals_quantized(),
_keeps_nms_unused(),
_classes_nms_unused(),
_proposals_4_roi_values(),
+ _all_proposals_to_use(nullptr),
_num_valid_proposals(nullptr),
_scores_out(nullptr)
{
@@ -61,63 +68,93 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
ARM_COMPUTE_ERROR_THROW_ON(CLGenerateProposalsLayer::validate(scores->info(), deltas->info(), anchors->info(), proposals->info(), scores_out->info(), num_valid_proposals->info(), info));
- _is_nhwc = scores->info()->data_layout() == DataLayout::NHWC;
- const DataType data_type = deltas->info()->data_type();
- const int num_anchors = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL));
- const int feat_width = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH));
- const int feat_height = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT));
- const int total_num_anchors = num_anchors * feat_width * feat_height;
- const int pre_nms_topN = info.pre_nms_topN();
- const int post_nms_topN = info.post_nms_topN();
- const size_t values_per_roi = info.values_per_roi();
+ _is_nhwc = scores->info()->data_layout() == DataLayout::NHWC;
+ const DataType scores_data_type = scores->info()->data_type();
+ _is_qasymm8 = scores_data_type == DataType::QASYMM8;
+ const int num_anchors = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL));
+ const int feat_width = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH));
+ const int feat_height = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT));
+ const int total_num_anchors = num_anchors * feat_width * feat_height;
+ const int pre_nms_topN = info.pre_nms_topN();
+ const int post_nms_topN = info.post_nms_topN();
+ const size_t values_per_roi = info.values_per_roi();
+
+ const QuantizationInfo scores_qinfo = scores->info()->quantization_info();
+ const DataType rois_data_type = (_is_qasymm8) ? DataType::QASYMM16 : scores_data_type;
+ const QuantizationInfo rois_qinfo = (_is_qasymm8) ? QuantizationInfo(0.125f, 0) : scores->info()->quantization_info();
// Compute all the anchors
_memory_group.manage(&_all_anchors);
_compute_anchors_kernel.configure(anchors, &_all_anchors, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()));
const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors);
- _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, data_type));
+ _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, scores_data_type, deltas->info()->quantization_info()));
// Permute and reshape deltas
+ _memory_group.manage(&_deltas_flattened);
if(!_is_nhwc)
{
_memory_group.manage(&_deltas_permuted);
- _memory_group.manage(&_deltas_flattened);
_permute_deltas_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 });
_flatten_deltas_kernel.configure(&_deltas_permuted, &_deltas_flattened);
_deltas_permuted.allocator()->allocate();
}
else
{
- _memory_group.manage(&_deltas_flattened);
_flatten_deltas_kernel.configure(deltas, &_deltas_flattened);
}
const TensorShape flatten_shape_scores(1, total_num_anchors);
- _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, data_type));
+ _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, scores_data_type, scores_qinfo));
// Permute and reshape scores
+ _memory_group.manage(&_scores_flattened);
if(!_is_nhwc)
{
_memory_group.manage(&_scores_permuted);
- _memory_group.manage(&_scores_flattened);
_permute_scores_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 });
_flatten_scores_kernel.configure(&_scores_permuted, &_scores_flattened);
_scores_permuted.allocator()->allocate();
}
else
{
- _memory_group.manage(&_scores_flattened);
_flatten_scores_kernel.configure(scores, &_scores_flattened);
}
+ CLTensor *anchors_to_use = &_all_anchors;
+ CLTensor *deltas_to_use = &_deltas_flattened;
+ if(_is_qasymm8)
+ {
+ _all_anchors_f32.allocator()->init(TensorInfo(_all_anchors.info()->tensor_shape(), 1, DataType::F32));
+ _deltas_flattened_f32.allocator()->init(TensorInfo(_deltas_flattened.info()->tensor_shape(), 1, DataType::F32));
+ _memory_group.manage(&_all_anchors_f32);
+ _memory_group.manage(&_deltas_flattened_f32);
+ // Dequantize anchors to float
+ _dequantize_anchors.configure(&_all_anchors, &_all_anchors_f32);
+ _all_anchors.allocator()->allocate();
+ anchors_to_use = &_all_anchors_f32;
+ // Dequantize deltas to float
+ _dequantize_deltas.configure(&_deltas_flattened, &_deltas_flattened_f32);
+ _deltas_flattened.allocator()->allocate();
+ deltas_to_use = &_deltas_flattened_f32;
+ }
// Bounding box transform
_memory_group.manage(&_all_proposals);
BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
- _bounding_box_kernel.configure(&_all_anchors, &_all_proposals, &_deltas_flattened, bbox_info);
- _deltas_flattened.allocator()->allocate();
- _all_anchors.allocator()->allocate();
+ _bounding_box_kernel.configure(anchors_to_use, &_all_proposals, deltas_to_use, bbox_info);
+ deltas_to_use->allocator()->allocate();
+ anchors_to_use->allocator()->allocate();
+ _all_proposals_to_use = &_all_proposals;
+ if(_is_qasymm8)
+ {
+ _memory_group.manage(&_all_proposals_quantized);
+ // Requantize all_proposals to QASYMM16 with 0.125 scale and 0 offset
+ _all_proposals_quantized.allocator()->init(TensorInfo(_all_proposals.info()->tensor_shape(), 1, DataType::QASYMM16, QuantizationInfo(0.125f, 0)));
+ _quantize_all_proposals.configure(&_all_proposals, &_all_proposals_quantized);
+ _all_proposals.allocator()->allocate();
+ _all_proposals_to_use = &_all_proposals_quantized;
+ }
// The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort)
// that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation.
// Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input)
@@ -128,12 +165,12 @@
_memory_group.manage(&_keeps_nms_unused);
// Note that NMS needs outputs preinitialized.
- auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, data_type);
- auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type);
+ auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo);
+ auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, rois_data_type, rois_qinfo);
auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);
// Initialize temporaries (unused) outputs
- _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(1, 1), 1, data_type));
+ _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo));
_keeps_nms_unused.allocator()->init(*scores_out->info());
// Save the output (to map and unmap them at run)
@@ -141,26 +178,26 @@
_num_valid_proposals = num_valid_proposals;
_memory_group.manage(&_proposals_4_roi_values);
- _cpp_nms_kernel.configure(&_scores_flattened, &_all_proposals, nullptr, scores_out, &_proposals_4_roi_values, &_classes_nms_unused, nullptr, &_keeps_nms_unused, num_valid_proposals,
- BoxNMSLimitInfo(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f, true, min_size_scaled, info.im_width(), info.im_height()));
+ _cpp_nms.configure(&_scores_flattened, _all_proposals_to_use, nullptr, scores_out, &_proposals_4_roi_values, &_classes_nms_unused, nullptr, &_keeps_nms_unused, num_valid_proposals,
+ BoxNMSLimitInfo(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f, true, min_size_scaled, info.im_width(), info.im_height()));
_keeps_nms_unused.allocator()->allocate();
_classes_nms_unused.allocator()->allocate();
- _all_proposals.allocator()->allocate();
+ _all_proposals_to_use->allocator()->allocate();
_scores_flattened.allocator()->allocate();
// Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images
- _padded_copy_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
+ _pad_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
_proposals_4_roi_values.allocator()->allocate();
-
- _memset_kernel.configure(proposals, PixelValue());
}
Status CLGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out,
const ITensorInfo *num_valid_proposals, const GenerateProposalsInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(scores, deltas);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores, deltas);
const int num_anchors = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::CHANNEL));
const int feat_width = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::WIDTH));
@@ -169,8 +206,17 @@
const int total_num_anchors = num_anchors * feat_width * feat_height;
const int values_per_roi = info.values_per_roi();
+ const bool is_qasymm8 = scores->data_type() == DataType::QASYMM8;
+
ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);
+ if(is_qasymm8)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(anchors, 1, DataType::QSYMM16);
+ const UniformQuantizationInfo anchors_qinfo = anchors->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(anchors_qinfo.scale != 0.125f);
+ }
+
TensorInfo all_anchors_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
ARM_COMPUTE_RETURN_ON_ERROR(CLComputeAllAnchorsKernel::validate(anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())));
@@ -190,15 +236,36 @@
TensorInfo deltas_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info));
- TensorInfo scores_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
+ TensorInfo scores_flattened_info(scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
TensorInfo proposals_4_roi_values(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info));
- ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info, BoundingBoxTransformInfo(info.im_width(), info.im_height(),
- 1.f)));
- ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } }));
- ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(proposals, PixelValue()));
+ TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values;
+ TensorInfo proposals_4_roi_values_quantized(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
+ proposals_4_roi_values_quantized.set_data_type(DataType::QASYMM16).set_quantization_info(QuantizationInfo(0.125f, 0));
+ if(is_qasymm8)
+ {
+ TensorInfo all_anchors_f32_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDequantizationLayerKernel::validate(&all_anchors_info, &all_anchors_f32_info));
+
+ TensorInfo deltas_flattened_f32_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDequantizationLayerKernel::validate(&deltas_flattened_info, &deltas_flattened_f32_info));
+
+ TensorInfo proposals_4_roi_values_f32(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(&all_anchors_f32_info, &proposals_4_roi_values_f32, &deltas_flattened_f32_info,
+ BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLQuantizationLayerKernel::validate(&proposals_4_roi_values_f32, &proposals_4_roi_values_quantized));
+ proposals_4_roi_values_to_use = &proposals_4_roi_values_quantized;
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info,
+ BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPadLayerKernel::validate(proposals_4_roi_values_to_use, proposals, PaddingList{ { 1, 0 } }));
if(num_valid_proposals->total_size() > 0)
{
@@ -212,7 +279,17 @@
ARM_COMPUTE_RETURN_ERROR_ON(proposals->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1);
ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors));
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, deltas);
+ if(is_qasymm8)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(proposals, 1, DataType::QASYMM16);
+ const UniformQuantizationInfo proposals_qinfo = proposals->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.offset != 0);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, scores);
+ }
}
if(scores_out->total_size() > 0)
@@ -229,7 +306,7 @@
{
// Map inputs
_scores_flattened.map(true);
- _all_proposals.map(true);
+ _all_proposals_to_use->map(true);
// Map outputs
_scores_out->map(CLScheduler::get().queue(), true);
@@ -239,7 +316,7 @@
_classes_nms_unused.map(true);
// Run nms
- CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX);
+ _cpp_nms.run();
// Unmap outputs
_keeps_nms_unused.unmap();
@@ -250,7 +327,7 @@
// Unmap inputs
_scores_flattened.unmap();
- _all_proposals.unmap();
+ _all_proposals_to_use->unmap();
}
void CLGenerateProposalsLayer::run()
@@ -270,12 +347,23 @@
CLScheduler::get().enqueue(_flatten_deltas_kernel, false);
CLScheduler::get().enqueue(_flatten_scores_kernel, false);
+ if(_is_qasymm8)
+ {
+ CLScheduler::get().enqueue(_dequantize_anchors, false);
+ CLScheduler::get().enqueue(_dequantize_deltas, false);
+ }
+
// Build the boxes
CLScheduler::get().enqueue(_bounding_box_kernel, false);
+
+ if(_is_qasymm8)
+ {
+ CLScheduler::get().enqueue(_quantize_all_proposals, false);
+ }
+
// Non maxima suppression
run_cpp_nms_kernel();
// Add dummy batch indexes
- CLScheduler::get().enqueue(_memset_kernel, true);
- CLScheduler::get().enqueue(_padded_copy_kernel, true);
+ CLScheduler::get().enqueue(_pad_kernel, true);
}
} // namespace arm_compute