COMPMID-2310: CLGenerateProposalsLayer: support for QASYMM8

Change-Id: I48b77e09857cd43f9498d28e8f4bf346e3d7110d
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1969
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 18 insertions, 3 deletions

diff --git a/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
index 79e364caf7..16d0e86d7d 100644
--- a/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
+++ b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
@@ -44,7 +44,7 @@ Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anc
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(anchors, all_anchors);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(anchors);
     ARM_COMPUTE_RETURN_ERROR_ON(anchors->dimension(0) != info.values_per_roi());
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(anchors, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(anchors, DataType::QSYMM16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(anchors->num_dimensions() > 2);
     if(all_anchors->total_size() > 0)
     {
@@ -55,6 +55,11 @@ Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anc
         ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->num_dimensions() > 2);
         ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(0) != info.values_per_roi());
         ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(1) != feature_height * feature_width * num_anchors);
+
+        if(is_data_type_quantized(anchors->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(anchors, all_anchors);
+        }
     }
     return Status{};
 }
@@ -78,12 +83,14 @@ void CLComputeAllAnchorsKernel::configure(const ICLTensor *anchors, ICLTensor *a
 
     // Initialize the output if empty
     const TensorShape output_shape(info.values_per_roi(), width * height * num_anchors);
-    auto_init_if_empty(*all_anchors->info(), output_shape, 1, data_type);
+    auto_init_if_empty(*all_anchors->info(), TensorInfo(output_shape, 1, data_type, anchors->info()->quantization_info()));
 
     // Set instance variables
     _anchors     = anchors;
     _all_anchors = all_anchors;
 
+    const bool is_quantized = is_data_type_quantized(anchors->info()->data_type());
+
     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
@@ -93,8 +100,16 @@ void CLComputeAllAnchorsKernel::configure(const ICLTensor *anchors, ICLTensor *a
     build_opts.add_option("-DNUM_ANCHORS=" + support::cpp11::to_string(num_anchors));
     build_opts.add_option("-DNUM_ROI_FIELDS=" + support::cpp11::to_string(info.values_per_roi()));
 
+    if(is_quantized)
+    {
+        const UniformQuantizationInfo qinfo = anchors->info()->quantization_info().uniform();
+        build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(qinfo.scale));
+        build_opts.add_option("-DOFFSET=" + float_to_string_with_full_precision(qinfo.offset));
+    }
+
     // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("generate_proposals_compute_all_anchors", build_opts.options()));
+    const std::string kernel_name = (is_quantized) ? "generate_proposals_compute_all_anchors_quantized" : "generate_proposals_compute_all_anchors";
+    _kernel                       = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // The tensor all_anchors can be interpreted as an array of structs (each structs has values_per_roi fields).
     // This means we don't need to pad on the X dimension, as we know in advance how many fields