Added Qasymm8 datatype support to NEROIPoolingLayer with Tests
Tests added to check ROIPooling Layer against reference with both Float32 and Qasymm8 input.
Resolves : COMPMID-2319
Change-Id: I867bc4dde1e3e91f9f42f4a7ce8debfe83b8db50
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/296640
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Comments-Addressed: Pablo Tello <pablo.tello@arm.com>
Signed-off-by: Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5060
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
index 9a3a757..400e829 100644
--- a/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
@@ -22,7 +22,6 @@
* SOFTWARE.
*/
#include "src/core/NEON/kernels/NEROIPoolingLayerKernel.h"
-
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
@@ -35,35 +34,101 @@
namespace arm_compute
{
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output, rois);
+
+ //Validate arguments
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(rois, 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_DATA_TYPE_NOT_IN(input, DataType::F32, 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{};
+}
+
+/** Evaluate number needing to be stored in output tensor as quantized format.
+ *
+ * @param[in] input Source tensor. Data types supported: QASYMM8
+ * @param[out] output Destination tensor. Where output value will be stored, same datatype as input
+ * @param[in] region_start_x Beginning region of x coordinate of pooling region
+ * @param[in] region_start_y Beginning region of y coordinate of pooling region
+ * @param[in] region_end_x End of pooling region, x coordinate
+ * @param[in] region_end_y End of pooling region, y coordinate
+ * @param[in] fm Channel index of coordinate in output Tensor to store value
+ * @param[in] px Width index of coodinate in output Tensor to store value
+ * @param[in] py Height index of coordinate in output Tensor to store value
+ * @param[in] roi_batch Index of image to perform Pooling on in input Tensor
+ * @param[in] roi_indx Index of image of coordinate in output Tensor to store value
+ */
+template <typename T>
+void template_eval(const ITensor *input, const ITensor *output, int region_start_x, int region_start_y,
+ int region_end_x, int region_end_y, int fm, int px, int py, int roi_batch, int roi_indx)
+{
+ if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
+ {
+ *reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = 0;
+ }
+ else
+ {
+ T curr_max = std::numeric_limits<T>::lowest(); // Min value of typename T
+ for(int j = region_start_y; j < region_end_y; ++j)
+ {
+ for(int i = region_start_x; i < region_end_x; ++i)
+ {
+ const auto val = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(i, j, fm, roi_batch)));
+ curr_max = std::max(val, curr_max);
+ }
+ }
+
+ // if quantized datatype, requantize then store in output tensor
+ if(is_data_type_quantized(input->info()->data_type()))
+ {
+ // covert qasymm to new output quantization scale and offset
+ UniformQuantizationInfo uqinfo = compute_requantization_scale_offset(input->info()->quantization_info().uniform(), output->info()->quantization_info().uniform());
+ *reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = quantize_qasymm8(curr_max, uqinfo);
+ }
+ else
+ {
+ *reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = curr_max;
+ }
+ }
+}
+} // namespace
+
NEROIPoolingLayerKernel::NEROIPoolingLayerKernel()
: _input(nullptr), _rois(nullptr), _output(nullptr), _pool_info(0, 0, 0.f)
{
}
-void NEROIPoolingLayerKernel::configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info)
+Status NEROIPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, rois, output, pool_info));
+ return Status{};
+}
+
+void NEROIPoolingLayerKernel::configure(const ITensor *input, const ITensor *rois, const ITensor *output, const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois);
//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_CPU_F16_UNSUPPORTED(input);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- 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(validate_arguments(input->info(), rois->info(), output->info(), pool_info));
// Output auto initialization if not yet initialized
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->info()->dimension(2), rois->info()->dimension(1));
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
+
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), output->info()->quantization_info());
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()));
@@ -99,7 +164,8 @@
const int pooled_h = _pool_info.pooled_height();
const float spatial_scale = _pool_info.spatial_scale();
- const auto *rois_ptr = reinterpret_cast<const uint16_t *>(_rois->buffer());
+ const auto *rois_ptr = reinterpret_cast<const uint16_t *>(_rois->buffer());
+ const auto data_type = _input->info()->data_type();
for(int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx)
{
@@ -133,23 +199,17 @@
region_start_y = std::min(std::max(region_start_y + roi_anchor_y, 0), height);
region_end_y = std::min(std::max(region_end_y + roi_anchor_y, 0), height);
- // Iterate through the pooling region
- if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
+ switch(data_type)
{
- *reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = 0;
- }
- else
- {
- float curr_max = -FLT_MAX;
- for(int j = region_start_y; j < region_end_y; ++j)
- {
- for(int i = region_start_x; i < region_end_x; ++i)
- {
- const auto val = *reinterpret_cast<const float *>(_input->ptr_to_element(Coordinates(i, j, fm, roi_batch)));
- curr_max = std::max(val, curr_max);
- }
- }
- *reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = curr_max;
+ case DataType::F32:
+ template_eval<float>(_input, _output, region_start_x, region_start_y, region_end_x, region_end_y, fm, px, py, roi_batch, roi_indx);
+ break;
+ case DataType::QASYMM8:
+ template_eval<qasymm8_t>(_input, _output, region_start_x, region_start_y, region_end_x, region_end_y, fm, px, py, roi_batch, roi_indx);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("DataType not Supported");
+ break;
}
}
}
diff --git a/src/core/NEON/kernels/NEROIPoolingLayerKernel.h b/src/core/NEON/kernels/NEROIPoolingLayerKernel.h
index 3642417..2fcdb81 100644
--- a/src/core/NEON/kernels/NEROIPoolingLayerKernel.h
+++ b/src/core/NEON/kernels/NEROIPoolingLayerKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -55,7 +55,7 @@
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. Data types supported: F32.
+ * @param[in] input Source tensor. Data types supported: QASYMM8/F32
* @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.
@@ -66,15 +66,31 @@
* @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 tensor.
*/
- void configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info);
+ void configure(const ITensor *input, const ITensor *rois, const ITensor *output, const ROIPoolingLayerInfo &pool_info);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
+ /** Static function to check if given info will lead to a valid configuration of @ref NEROIPoolingLayerKernel
+ *
+ * @param[in] input Source tensor info. Data types supported: QASYMM8/F32.
+ * @param[in] rois ROIs tensor info. Data types supported: U16
+ * @param[in] output Destination tensor info. 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 datatype of @p output should be the same as the datatype of @p input
+ * @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
+ *
+ * @return a Status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
+
private:
const ITensor *_input;
const ITensor *_rois;
- ITensor *_output;
+ const ITensor *_output;
ROIPoolingLayerInfo _pool_info;
};
} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEROIPoolingLayer.cpp b/src/runtime/NEON/functions/NEROIPoolingLayer.cpp
index 7ca6ecc..f943405 100644
--- a/src/runtime/NEON/functions/NEROIPoolingLayer.cpp
+++ b/src/runtime/NEON/functions/NEROIPoolingLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,7 +22,6 @@
* SOFTWARE.
*/
#include "arm_compute/runtime/NEON/functions/NEROIPoolingLayer.h"
-
#include "arm_compute/core/Helpers.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "src/core/NEON/kernels/NEROIPoolingLayerKernel.h"
@@ -36,7 +35,12 @@
{
}
-void NEROIPoolingLayer::configure(const ITensor *input, const ITensor *rois, ITensor *output, const ROIPoolingLayerInfo &pool_info)
+Status NEROIPoolingLayer::validate(const ITensorInfo *input, const ITensorInfo *rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+ return NEROIPoolingLayerKernel::validate(input, rois, output, pool_info);
+}
+
+void NEROIPoolingLayer::configure(const ITensor *input, const ITensor *rois, const ITensor *output, const ROIPoolingLayerInfo &pool_info)
{
_roi_kernel = std::make_unique<NEROIPoolingLayerKernel>();
_roi_kernel->configure(input, rois, output, pool_info);