Added Qasymm8 datatype support to CLROIPoolingLayer with Tests
Also fixes RoiPoolingLayer not matching reference with Float32 datatype Issue
Tests added to check ROIPooling Layer against reference with both Float32 and Qasymm8 input.
Resolves : COMPMID-2320
Change-Id: Ib86d2e6b3803e74f922a545ea573da02c28e54cc
Signed-off-by: Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5332
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
diff --git a/src/core/CL/cl_kernels/roi_pooling_layer.cl b/src/core/CL/cl_kernels/roi_pooling_layer.cl
index ac193e8..6899b95 100644
--- a/src/core/CL/cl_kernels/roi_pooling_layer.cl
+++ b/src/core/CL/cl_kernels/roi_pooling_layer.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2019 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "helpers.h"
+#include "helpers_asymm.h"
#if DATA_SIZE == 32
#define VEC_SIZE 4
@@ -29,24 +30,41 @@
#elif DATA_SIZE == 16
#define VEC_SIZE 8
#define VEC_MAX vec8_max
-#else /* DATA_SIZE not equals 32 or 16 */
+#elif DATA_SIZE == 8
+#define VEC_SIZE 16
+#define VEC_MAX vec16_max
+#else /* DATA_SIZE not equals 8, 16, 32 */
#error "Unsupported data size"
#endif /* DATA_SIZE == 32 */
+// Define whether to use max (Quantized datatype) or fmax (Float) functions
+#if defined(OFFSET_OUT) && defined(SCALE_OUT)
+#define MAX(x, y) max(x, y)
+#else // !(defined(OFFSET_OUT) && defined(SCALE_OUT)
+#define MAX(x, y) fmax(x, y)
+#endif // defined(OFFSET_OUT) && defined(SCALE_OUT)
+
inline DATA_TYPE vec4_max(VEC_DATA_TYPE(DATA_TYPE, 4) vec)
{
VEC_DATA_TYPE(DATA_TYPE, 2)
- temp = fmax(vec.lo, vec.hi);
- return fmax(temp.x, temp.y);
+ temp = MAX(vec.lo, vec.hi);
+ return MAX(temp.x, temp.y);
}
inline DATA_TYPE vec8_max(VEC_DATA_TYPE(DATA_TYPE, 8) vec)
{
VEC_DATA_TYPE(DATA_TYPE, 4)
- temp = fmax(vec.lo, vec.hi);
+ temp = MAX(vec.lo, vec.hi);
return vec4_max(temp);
}
+inline DATA_TYPE vec16_max(VEC_DATA_TYPE(DATA_TYPE, 16) vec)
+{
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ temp = MAX(vec.lo, vec.hi);
+ return vec8_max(temp);
+}
+
/** Performs a roi pooling on a single output pixel.
*
* @param[in] input Pointer to input Tensor3D struct.
@@ -69,7 +87,8 @@
{
int num_iter = (int)((region_end_x - region_start_x) / VEC_SIZE);
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
- curr_max = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(-FLT_MAX);
+ curr_max = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(MIN_VALUE);
+
for(int j = region_start_y; j < region_end_y; ++j)
{
int i = region_start_x;
@@ -77,27 +96,34 @@
{
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor3D_offset(input, i, j, pz));
- curr_max = fmax(val, curr_max);
+ curr_max = MAX(val, curr_max);
}
for(; i < region_end_x; ++i)
{
DATA_TYPE val = *(__global DATA_TYPE *)tensor3D_offset(input, i, j, pz);
- curr_max = fmax(curr_max, val);
+ curr_max = MAX(curr_max, val);
}
}
- return (DATA_TYPE)VEC_MAX(curr_max);
+
+ const DATA_TYPE temp = (DATA_TYPE)VEC_MAX(curr_max);
+
+#if defined(OFFSET_OUT) && defined(SCALE_OUT)
+ return QUANTIZE(temp, OFFSET_OUT, SCALE_OUT, DATA_TYPE, 1);
+#endif /* if quantized, requantize and return */
+
+ return temp;
}
}
/** Performs a roi pooling function.
*
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32, QASYMM8;
* @note Datasize must be passed using -DDATA_SIZE e.g. -DDATA_SIZE=32;
* @note Input dimensions must be passed using -DMAX_DIM_X, -DMAX_DIM_Y and -DMAX_DIM_Z;
* @note Pooled region dimensions must be passed using -DPOOLED_DIM_X and -DPOOLED_DIM_Y;
* @note Spatial scale must be passed using -DSPATIAL_SCALE;
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32, QASYMM8
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
@@ -111,7 +137,7 @@
* @param[in] rois_stride_y Stride of the ROIs tensor in Y dimension (in bytes)
* @param[in] rois_step_y Step of the ROIs tensor in Y dimension (in bytes)
* @param[in] rois_offset_first_element_in_bytes The offset of the first element in the ROIs tensor
- * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32
+ * @param[out] output_ptr Pointer to the destination image. Supported data types: same as input
* @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes)
@@ -139,9 +165,9 @@
// Load roi parameters
// roi is laid out as follows { batch_index, x1, y1, x2, y2 }
- const ushort roi_batch = (ushort) * ((__global DATA_TYPE *)offset(&rois, 0, pw));
- const VEC_DATA_TYPE(DATA_TYPE, 4)
- roi = vload4(0, (__global DATA_TYPE *)offset(&rois, 1, pw));
+ const ushort roi_batch = (ushort) * ((__global ushort *)offset(&rois, 0, pw));
+ const VEC_DATA_TYPE(ushort, 4)
+ roi = vload4(0, (__global ushort *)offset(&rois, 1, pw));
const int2 roi_anchor = convert_int2_sat(round(convert_float2(roi.s01) * (float)SPATIAL_SCALE));
const int2 roi_dims = convert_int2_sat(fmax(round(convert_float2(roi.s23 - roi.s01) * (float)SPATIAL_SCALE), 1.f));
diff --git a/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp b/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
index 5867cde..2deb8fa 100644
--- a/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
@@ -36,6 +36,7 @@
#include "src/core/helpers/WindowHelpers.h"
#include "support/StringSupport.h"
+#include <float.h>
#include <cmath>
#include <set>
#include <string>
@@ -44,13 +45,13 @@
{
namespace
{
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output auto initialization if not yet initialized
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->dimension(2), rois->dimension(1));
- auto_init_if_empty((*output), output_shape, 1, input->data_type());
+ auto_init_if_empty((*output), output_shape, 1, input->data_type(), output->quantization_info());
// Configure kernel window
constexpr unsigned int num_elems_processed_per_iteration = 1;
@@ -70,31 +71,38 @@
{
}
+Status CLROIPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output);
+
+ //Validate arguments
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rois, 1, DataType::U16);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(0) != 5);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(0) != pool_info.pooled_width()) || (output->dimension(1) != pool_info.pooled_height()));
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(1) != output->dimension(3));
+ }
+
+ return Status{};
+}
+
void CLROIPoolingLayerKernel::configure(const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input, rois, output, pool_info);
}
-void CLROIPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
+void CLROIPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, const ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, rois, output);
-
- //Validate arguments
- ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), rois->info(), output->info());
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rois, 1, DataType::U16);
- ARM_COMPUTE_ERROR_ON(rois->info()->dimension(0) != 5);
- ARM_COMPUTE_ERROR_ON(rois->info()->num_dimensions() > 2);
- ARM_COMPUTE_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
- ARM_COMPUTE_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
-
- if(output->info()->total_size() != 0)
- {
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pool_info.pooled_width()) || (output->info()->dimension(1) != pool_info.pooled_height()));
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(2));
- ARM_COMPUTE_ERROR_ON(rois->info()->dimension(1) != output->info()->dimension(3));
- }
+ ARM_COMPUTE_ERROR_THROW_ON(CLROIPoolingLayerKernel::validate(input->info(), rois->info(), output->info(), pool_info));
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), rois->info(), output->info(), pool_info);
@@ -106,20 +114,39 @@
_output = output;
_pool_info = pool_info;
+ const DataType data_type = input->info()->data_type();
+ const bool is_qasymm = is_data_type_quantized_asymmetric(data_type);
+
// Set build options
- std::set<std::string> build_opts;
- build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
- build_opts.emplace(("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type())));
- build_opts.emplace(("-DMAX_DIM_X=" + support::cpp11::to_string(_input->info()->dimension(Window::DimX))));
- build_opts.emplace(("-DMAX_DIM_Y=" + support::cpp11::to_string(_input->info()->dimension(Window::DimY))));
- build_opts.emplace(("-DMAX_DIM_Z=" + support::cpp11::to_string(_input->info()->dimension(Window::DimZ))));
- build_opts.emplace(("-DPOOLED_DIM_X=" + support::cpp11::to_string(pool_info.pooled_width())));
- build_opts.emplace(("-DPOOLED_DIM_Y=" + support::cpp11::to_string(pool_info.pooled_height())));
- build_opts.emplace(("-DSPATIAL_SCALE=" + support::cpp11::to_string(pool_info.spatial_scale())));
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+ build_opts.add_option("-DDATA_SIZE=" + get_data_size_from_data_type(data_type));
+ build_opts.add_option("-DMAX_DIM_X=" + support::cpp11::to_string(_input->info()->dimension(Window::DimX)));
+ build_opts.add_option("-DMAX_DIM_Y=" + support::cpp11::to_string(_input->info()->dimension(Window::DimY)));
+ build_opts.add_option("-DMAX_DIM_Z=" + support::cpp11::to_string(_input->info()->dimension(Window::DimZ)));
+ build_opts.add_option("-DPOOLED_DIM_X=" + support::cpp11::to_string(pool_info.pooled_width()));
+ build_opts.add_option("-DPOOLED_DIM_Y=" + support::cpp11::to_string(pool_info.pooled_height()));
+ build_opts.add_option("-DSPATIAL_SCALE=" + support::cpp11::to_string(pool_info.spatial_scale()));
+
+ if(is_qasymm)
+ {
+ // Determine quantization info scale, offset
+ UniformQuantizationInfo uqinfo = UniformQuantizationInfo();
+ uqinfo = compute_requantization_scale_offset(_input->info()->quantization_info().uniform(), _output->info()->quantization_info().uniform());
+ build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(uqinfo.offset));
+ build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(uqinfo.scale));
+
+ // Specify minimum possible value of datatype
+ build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(0));
+ }
+ else{
+ // Specify min value of F32 datatype
+ build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(-FLT_MAX));
+ }
// Create kernel
std::string kernel_name = "roi_pooling_layer";
- _kernel = create_kernel(compile_context, kernel_name, build_opts);
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
// Set static kernel arguments
unsigned int idx = 2 * num_arguments_per_3D_tensor() + num_arguments_per_1D_array();
diff --git a/src/core/CL/kernels/CLROIPoolingLayerKernel.h b/src/core/CL/kernels/CLROIPoolingLayerKernel.h
index 124ae3f..7b7b457 100644
--- a/src/core/CL/kernels/CLROIPoolingLayerKernel.h
+++ b/src/core/CL/kernels/CLROIPoolingLayerKernel.h
@@ -63,7 +63,7 @@
/** Set the input and output tensors.
*
* @param[in] compile_context The compile context to be used.
- * @param[in] input Source tensor. Data types supported: F16/F32.
+ * @param[in] input Source tensor. Data types supported: F16/F32/QASYMM8
* @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
* as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: U16
* @param[out] output Destination tensor. Data types supported: Same as @p input.
@@ -74,15 +74,30 @@
* @note The z dimensions of @p output tensor and @p input tensor must be the same.
* @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
*/
- void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
+ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *rois, const ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
+ /** Static Validate function to check inputs will lead to valid configuration of @ref CLROIPoolingLayer
+ *
+ * @param[in] input Source tensor. Data types supported: F16/F32/QASYMM8
+ * @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
+ * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: U16
+ * @param[out] output Destination tensor. Data types supported: Same as @p input.
+ * @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
+ *
+ * @note The x and y dimensions of @p output tensor must be the same as @p pool_info 's pooled
+ * width and pooled height.
+ * @note The z dimensions of @p output tensor and @p input tensor must be the same.
+ * @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
+
private:
const ICLTensor *_input;
const ICLTensor *_rois;
- ICLTensor *_output;
+ const ICLTensor *_output;
ROIPoolingLayerInfo _pool_info;
};
} // namespace arm_compute