COMPMID-2317: Implement CLROIAlignLayer
Change-Id: Iaa61b7a3528d3f82339d2ff8a2d77e77a1c68603
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1821
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index b938a18..4b3b37c 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -447,6 +447,7 @@
{ "RGBA8888_to_RGB888_bt709", "color_convert.cl" },
{ "RGBA8888_to_YUV444_bt709", "color_convert.cl" },
{ "roi_align_layer", "roi_align_layer.cl" },
+ { "roi_align_layer_quantized", "roi_align_layer_quantized.cl" },
{ "roi_pooling_layer", "roi_pooling_layer.cl" },
{ "scale_nearest_neighbour_nchw", "scale.cl" },
{ "scale_nearest_neighbour_nhwc", "scale.cl" },
@@ -935,6 +936,10 @@
#include "./cl_kernels/roi_align_layer.clembed"
},
{
+ "roi_align_layer_quantized.cl",
+#include "./cl_kernels/roi_align_layer_quantized.clembed"
+ },
+ {
"roi_pooling_layer.cl",
#include "./cl_kernels/roi_pooling_layer.clembed"
},
@@ -1251,4 +1256,4 @@
cl_uint CLKernelLibrary::get_num_compute_units()
{
return _device.getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>();
-}
\ No newline at end of file
+}
diff --git a/src/core/CL/cl_kernels/roi_align_layer_quantized.cl b/src/core/CL/cl_kernels/roi_align_layer_quantized.cl
new file mode 100644
index 0000000..f9360e9
--- /dev/null
+++ b/src/core/CL/cl_kernels/roi_align_layer_quantized.cl
@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "helpers.h"
+
+// This specifies the value to shift the result of roi_dims / pooled_dims before ceiling.
+// It is close to the epsilon machine (for a floating point system, x and x+EPS are the same number).
+#define EPS_GRID 0.00001f
+
+#if defined(DATA_TYPE) && defined(POOLED_DIM_X) && defined(POOLED_DIM_Y) && defined(MAX_DIM_X) && defined(MAX_DIM_Y) && defined(MAX_DIM_Z) && defined(SPATIAL_SCALE) && defined(OFFSET_IN) && defined(OFFSET_OUT) && defined(SCALE_IN) && defined(SCALE_OUT) && defined(OFFSET_ROIS) && defined(SCALE_ROIS) // Check for compile time constants
+
+#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
+#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type)
+inline float dequantize_qasymm8(uchar input, float offset, float scale)
+{
+ return ((float)input - offset) * scale;
+}
+
+inline uchar quantize_qasymm8(float input, float offset, float scale)
+{
+ float out_f32 = input / scale + offset;
+ uchar res_u8 = CONVERT_SAT(CONVERT_DOWN(out_f32, int), uchar);
+ return res_u8;
+}
+
+inline float4 dequantize_qasymm16(ushort4 input, float offset, float scale)
+{
+ float4 in_f32 = (CONVERT(input, float4) - (float4)(offset)) * (float4)(scale);
+ return in_f32;
+}
+
+/** Performs a roi align on a single output pixel.
+ *
+ * @param[in] input Pointer to input Tensor3D struct.
+ * @param[in] region_start_x Start x index projected onto the input tensor.
+ * @param[in] region_end_x End x index projected onto the input tensor.
+ * @param[in] region_start_y Start y index projected onto the input tensor.
+ * @param[in] region_end_y End y index projected onto the input tensor.
+ * @param[in] pz z index of the input tensor.
+ *
+ * @return An average pooled value from the region specified in the input tensor.
+ */
+inline DATA_TYPE roi_align_1x1(const Tensor3D *input, float region_start_x,
+ float bin_size_x,
+ float grid_size_x,
+ float region_end_x,
+ float region_start_y,
+ float bin_size_y,
+ float grid_size_y,
+ float region_end_y,
+ int pz)
+{
+ // Iterate through the pooling region
+ float sum = 0;
+ for(int iy = 0; iy < grid_size_y; ++iy)
+ {
+ for(int ix = 0; ix < grid_size_x; ++ix)
+ {
+ // Align the window in the middle of every bin
+ const float y = region_start_y + (iy + 0.5f) * bin_size_y / (float)grid_size_y;
+ const float x = region_start_x + (ix + 0.5f) * bin_size_x / (float)grid_size_x;
+
+ // Interpolation in the unit square
+ const int y_low = (int)y;
+ const int x_low = (int)x;
+ const int y_high = y_low + 1;
+ const int x_high = x_low + 1;
+
+ const float ly = y - y_low;
+ const float lx = x - x_low;
+ const float hy = 1.f - ly;
+ const float hx = 1.f - lx;
+
+ const float w1 = hy * hx;
+ const float w2 = hy * lx;
+ const float w3 = ly * hx;
+ const float w4 = ly * lx;
+#if defined(NHWC)
+ const DATA_TYPE data1 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_low, y_low);
+ const DATA_TYPE data2 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_high, y_low);
+ const DATA_TYPE data3 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_low, y_high);
+ const DATA_TYPE data4 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_high, y_high);
+#else // !defined(NHWC)
+ const DATA_TYPE data1 = *(__global DATA_TYPE *)tensor3D_offset(input, x_low, y_low, pz);
+ const DATA_TYPE data2 = *(__global DATA_TYPE *)tensor3D_offset(input, x_high, y_low, pz);
+ const DATA_TYPE data3 = *(__global DATA_TYPE *)tensor3D_offset(input, x_low, y_high, pz);
+ const DATA_TYPE data4 = *(__global DATA_TYPE *)tensor3D_offset(input, x_high, y_high, pz);
+#endif // defined(NHWC)
+ const float data1_f32 = dequantize_qasymm8(data1, OFFSET_IN, SCALE_IN);
+ const float data2_f32 = dequantize_qasymm8(data2, OFFSET_IN, SCALE_IN);
+ const float data3_f32 = dequantize_qasymm8(data3, OFFSET_IN, SCALE_IN);
+ const float data4_f32 = dequantize_qasymm8(data4, OFFSET_IN, SCALE_IN);
+ sum += w1 * data1_f32 + w2 * data2_f32 + w3 * data3_f32 + w4 * data4_f32;
+ }
+ }
+
+ const float res_f32 = sum / (grid_size_x * grid_size_y);
+ return quantize_qasymm8(res_f32, OFFSET_OUT, SCALE_OUT);
+}
+
+/** Performs a roi align function.
+ *
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=uchar
+ * @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;
+ * @note Sampling ratio (i.e., the number of samples in each bin) may be passed using -DSAMPLING_RATIO. If not defined each roi
+ * will have a default sampling ratio of roi_dims/pooling_dims
+ *
+ * @param[in] input_ptr Pointer to the source tensor. Supported data types: QASYMM8
+ * @param[in] input_stride_x Stride of the source tensor 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 tensor in Y dimension (in bytes)
+ * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_offset_first_element_in_bytes The offset of the first element in the pooled region of the source tensor as specifed by ROI
+ * @param[in] rois_ptr Pointer to the ROIs tensor. Layout: { batch_index, x1, y1, x2, y2 }.
+ * Supported data types: QASYMM16 with 0.125f scale and 0 offset
+ * @param[in] rois_stride_x Stride of the ROIs tensor in X dimension (in bytes)
+ * @param[in] rois_step_x Step of the ROIs tensor in X dimension (in bytes)
+ * @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 tensor. Supported data types: Supported data types: same as @p input_ptr
+ * @param[in] output_stride_x Stride of the destination tensor 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 tensor in Y dimension (in bytes)
+ * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes)
+ */
+__kernel void roi_align_layer_quantized(
+ TENSOR3D_DECLARATION(input),
+ IMAGE_DECLARATION(rois),
+ TENSOR3D_DECLARATION(output),
+ unsigned int input_stride_w, unsigned int output_stride_w)
+{
+ // Get pixels pointer
+ Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input);
+ Image rois = CONVERT_TO_IMAGE_STRUCT_NO_STEP(rois);
+ Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output);
+
+#if defined(NHWC)
+ const int px = get_global_id(1);
+ const int py = get_global_id(2);
+ const int pw = get_global_id(0);
+#else // !defined(NHWC)
+ const int px = get_global_id(0);
+ const int py = get_global_id(1);
+ const int pw = get_global_id(2);
+#endif // defined(NHWC)
+
+ // Load roi parameters
+ // roi is laid out as follows { batch_index, x1, y1, x2, y2 }
+ const ushort roi_batch = *((__global ushort *)offset(&rois, 0, pw));
+ float4 roi = dequantize_qasymm16(vload4(0, (__global ushort *)offset(&rois, 1, pw)), OFFSET_ROIS, SCALE_ROIS);
+ float2 roi_anchor = roi.s01 * convert_float(SPATIAL_SCALE);
+ float2 roi_dims = fmax((roi.s23 - roi.s01) * convert_float(SPATIAL_SCALE), 1.f);
+
+ // Calculate pooled region start and end
+ float2 spatial_indx = (float2)(px, py);
+ float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y);
+ float2 max_spatial_dims = (float2)(MAX_DIM_X, MAX_DIM_Y);
+
+ float2 bin_size = (float2)((roi_dims.s0 / (float)POOLED_DIM_X), (roi_dims.s1 / (float)POOLED_DIM_Y));
+ float2 region_start = spatial_indx * bin_size + roi_anchor;
+ float2 region_end = (spatial_indx + 1) * bin_size + roi_anchor;
+
+ region_start = clamp(region_start, 0, max_spatial_dims);
+ region_end = clamp(region_end, 0, max_spatial_dims);
+
+#if defined(SAMPLING_RATIO)
+ float2 roi_bin_grid = SAMPLING_RATIO;
+#else // !defined(SAMPLING_RATIO)
+ // Note that we subtract EPS_GRID before ceiling. This is to avoid situations where 1.000001 gets ceiled to 2.
+ float2 roi_bin_grid = ceil(bin_size - EPS_GRID);
+#endif // defined(SAMPLING_RATIO)
+
+ // Move input and output pointer across the fourth dimension
+ input.ptr += roi_batch * input_stride_w;
+ output.ptr += pw * output_stride_w;
+ for(int pz = 0; pz < MAX_DIM_Z; ++pz)
+ {
+#if defined(NHWC)
+ __global DATA_TYPE *_output_ptr = (__global DATA_TYPE *)tensor3D_offset(&output, pz, px, py);
+#else // !defined(NHWC)
+ __global DATA_TYPE *_output_ptr = (__global DATA_TYPE *)tensor3D_offset(&output, px, py, pz);
+#endif // defined(NHWC)
+ *_output_ptr = (__global DATA_TYPE)roi_align_1x1(&input,
+ region_start.x,
+ bin_size.x,
+ roi_bin_grid.x,
+ region_end.x,
+ region_start.y,
+ bin_size.y,
+ roi_bin_grid.y,
+ region_end.y, pz);
+ }
+}
+#endif // Check for compile time constants
diff --git a/src/core/CL/kernels/CLROIAlignLayerKernel.cpp b/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
index 50729f2..134286b 100644
--- a/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
+++ b/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
@@ -45,11 +45,10 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois);
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);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC, DataLayout::NCHW);
ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
@@ -59,6 +58,19 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(compute_roi_align_shape(*input, *rois, pool_info), output->tensor_shape());
}
+
+ if(input->data_type() == DataType::QASYMM8)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rois, 1, DataType::QASYMM16);
+
+ const UniformQuantizationInfo rois_qinfo = rois->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois_qinfo.offset != 0);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois);
+ }
return Status{};
}
@@ -104,9 +116,12 @@
_rois = rois;
_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
CLBuildOptions build_opts;
- build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ 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(input->info()->data_type()));
build_opts.add_option("-DMAX_DIM_X=" + support::cpp11::to_string(_input->info()->dimension(get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH))));
build_opts.add_option("-DMAX_DIM_Y=" + support::cpp11::to_string(_input->info()->dimension(get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT))));
@@ -117,9 +132,23 @@
build_opts.add_option_if(input->info()->data_layout() == DataLayout::NHWC, "-DNHWC");
build_opts.add_option_if(pool_info.sampling_ratio() > 0, "-DSAMPLING_RATIO=" + support::cpp11::to_string(pool_info.sampling_ratio()));
+ if(is_qasymm)
+ {
+ const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
+ const UniformQuantizationInfo roisq_info = rois->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
+
+ build_opts.add_option("-DOFFSET_IN=" + float_to_string_with_full_precision(iq_info.offset));
+ build_opts.add_option("-DSCALE_IN=" + float_to_string_with_full_precision(iq_info.scale));
+ build_opts.add_option("-DOFFSET_ROIS=" + float_to_string_with_full_precision(roisq_info.offset));
+ build_opts.add_option("-DSCALE_ROIS=" + float_to_string_with_full_precision(roisq_info.scale));
+ build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
+ build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
+ }
+
// Create kernel
- std::string kernel_name = "roi_align_layer";
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+ const std::string kernel_name = (is_qasymm) ? "roi_align_layer_quantized" : "roi_align_layer";
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
ICLKernel::configure_internal(win_config.second);
}