diff options
Diffstat (limited to 'src/core')
-rw-r--r-- | src/core/CL/cl_kernels/roi_pooling_layer.cl | 56 | ||||
-rw-r--r-- | src/core/CL/kernels/CLROIPoolingLayerKernel.cpp | 89 | ||||
-rw-r--r-- | src/core/CL/kernels/CLROIPoolingLayerKernel.h | 21 |
3 files changed, 117 insertions, 49 deletions
diff --git a/src/core/CL/cl_kernels/roi_pooling_layer.cl b/src/core/CL/cl_kernels/roi_pooling_layer.cl index ac193e8fb6..6899b952e0 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 @@ inline DATA_TYPE roi_pool_1x1(const Tensor3D *input, int region_start_x, int reg { 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 @@ inline DATA_TYPE roi_pool_1x1(const Tensor3D *input, int region_start_x, int reg { 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 @@ inline DATA_TYPE roi_pool_1x1(const Tensor3D *input, int region_start_x, int reg * @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 @@ __kernel void roi_pooling_layer( // 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 5867cde3bd..2deb8fac81 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 arm_compute { 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 @@ CLROIPoolingLayerKernel::CLROIPoolingLayerKernel() { } +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 @@ void CLROIPoolingLayerKernel::configure(const CLCompileContext &compile_context, _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 124ae3f268..7b7b457632 100644 --- a/src/core/CL/kernels/CLROIPoolingLayerKernel.h +++ b/src/core/CL/kernels/CLROIPoolingLayerKernel.h @@ -63,7 +63,7 @@ public: /** 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 @@ public: * @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 |