| /* |
| * Copyright (c) 2016-2019 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 "arm_compute/core/NEON/kernels/NEArithmeticSubtractionKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEAsymm.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <algorithm> |
| #include <arm_neon.h> |
| #include <cstdint> |
| #include <map> |
| #include <string> |
| |
| using namespace arm_compute; |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| constexpr unsigned int num_elems_processed_per_iteration = 16; |
| |
| void sub_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t ta1 = vld1q_u8(input1.ptr()); |
| const uint8x16_t ta2 = vld1q_u8(input2.ptr()); |
| |
| vst1q_u8(output.ptr(), vsubq_u8(ta1, ta2)); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t ta1 = vld1q_u8(input1.ptr()); |
| const uint8x16_t ta2 = vld1q_u8(input2.ptr()); |
| |
| vst1q_u8(output.ptr(), vqsubq_u8(ta1, ta2)); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_QAYSMM8_QAYSMM8_QAYSMM8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform(); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const float32x4x4_t ta1 = vdequantize(vld1q_u8(reinterpret_cast<const qasymm8_t *>(input1.ptr())), iq1_info); |
| const float32x4x4_t ta2 = vdequantize(vld1q_u8(reinterpret_cast<const qasymm8_t *>(input2.ptr())), iq2_info); |
| |
| const float32x4x4_t ta3 = |
| { |
| { |
| vsubq_f32(ta1.val[0], ta2.val[0]), |
| vsubq_f32(ta1.val[1], ta2.val[1]), |
| vsubq_f32(ta1.val[2], ta2.val[2]), |
| vsubq_f32(ta1.val[3], ta2.val[3]), |
| } |
| }; |
| |
| const uint8x16_t result = vquantize(ta3, oq_info); |
| |
| vst1q_u8(reinterpret_cast<qasymm8_t *>(output.ptr()), result); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_wrap_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const int16x8x2_t ta1 = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr())); |
| const int16x8x2_t ta2 = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr())); |
| |
| const int16x8x2_t ta3 = |
| { |
| { |
| vsubq_s16(ta1.val[0], ta2.val[0]), |
| vsubq_s16(ta1.val[1], ta2.val[1]) |
| } |
| }; |
| |
| vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), ta3); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const int16x8x2_t ta1 = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr())); |
| const int16x8x2_t ta2 = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr())); |
| |
| const int16x8x2_t ta3 = |
| { |
| { |
| vqsubq_s16(ta1.val[0], ta2.val[0]), |
| vqsubq_s16(ta1.val[1], ta2.val[1]) |
| } |
| }; |
| |
| vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), ta3); |
| }, |
| input1, input2, output); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| inline float16x8x2_t vsub2q_f16(const float16x8x2_t &a, const float16x8x2_t &b) |
| { |
| const float16x8x2_t res = |
| { |
| { |
| vsubq_f16(a.val[0], b.val[0]), |
| vsubq_f16(a.val[1], b.val[1]) |
| } |
| }; |
| |
| return res; |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| void sub_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const float16x8x2_t a = vld2q_f16(reinterpret_cast<const float16_t *>(input1.ptr())); |
| const float16x8x2_t b = vld2q_f16(reinterpret_cast<const float16_t *>(input2.ptr())); |
| |
| vst2q_f16(reinterpret_cast<float16_t *>(output.ptr()), vsub2q_f16(a, b)); |
| }, |
| input1, input2, output); |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(in1); |
| ARM_COMPUTE_UNUSED(in2); |
| ARM_COMPUTE_UNUSED(out); |
| ARM_COMPUTE_UNUSED(window); |
| ARM_COMPUTE_ERROR("Not supported, recompile the library with arch=arm64-v8.2-a"); |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| void sub_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const float32x4x4_t ta1 = vld4q_f32(reinterpret_cast<const float *>(input1.ptr())); |
| const float32x4x4_t ta2 = vld4q_f32(reinterpret_cast<const float *>(input2.ptr())); |
| |
| const float32x4x4_t ta3 = |
| { |
| { |
| vsubq_f32(ta1.val[0], ta2.val[0]), |
| vsubq_f32(ta1.val[1], ta2.val[1]), |
| vsubq_f32(ta1.val[2], ta2.val[2]), |
| vsubq_f32(ta1.val[3], ta2.val[3]), |
| } |
| }; |
| |
| vst4q_f32(reinterpret_cast<float *>(output.ptr()), ta3); |
| }, |
| input1, input2, output); |
| } |
| void sub_wrap_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t bv_0 = vld1q_u8(input2.ptr()); |
| int16x8_t a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr())); |
| int16x8_t a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8); |
| |
| a1_0 = vsubq_s16(a1_0, vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0)))); |
| a2_0 = vsubq_s16(a2_0, vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0)))); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t bv_0 = vld1q_u8(input2.ptr()); |
| int16x8_t a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr())); |
| int16x8_t a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8); |
| |
| a1_0 = vqsubq_s16(a1_0, vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0)))); |
| a2_0 = vqsubq_s16(a2_0, vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0)))); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_wrap_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t bv_0 = vld1q_u8(input1.ptr()); |
| int16x8_t a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr())); |
| int16x8_t a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()) + 8); |
| |
| a1_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))), a1_0); |
| a2_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))), a2_0); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t bv_0 = vld1q_u8(input1.ptr()); |
| int16x8_t a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr())); |
| int16x8_t a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()) + 8); |
| |
| a1_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))), a1_0); |
| a2_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))), a2_0); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_wrap_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t av_0 = vld1q_u8(input1.ptr()); |
| const uint8x16_t bv_0 = vld1q_u8(input2.ptr()); |
| |
| const int16x8_t a1_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(av_0))), |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0)))); |
| const int16x8_t a2_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(av_0))), |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0)))); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| void sub_saturate_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| { |
| Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); |
| Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); |
| Iterator output(out, window); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const uint8x16_t av_0 = vld1q_u8(input1.ptr()); |
| const uint8x16_t bv_0 = vld1q_u8(input2.ptr()); |
| |
| const int16x8_t a1_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(av_0))), |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0)))); |
| const int16x8_t a2_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(av_0))), |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0)))); |
| |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0); |
| vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0); |
| }, |
| input1, input2, output); |
| } |
| |
| inline Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); |
| |
| const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32) |
| && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16), |
| "You called subtract with the wrong image formats"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && policy == ConvertPolicy::WRAP, |
| "Convert policy cannot be WRAP if datatype is QASYMM8"); |
| |
| // Validate in case of configured output |
| if(output.total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8) |
| && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && output.data_type() == DataType::QASYMM8) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) |
| && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32) |
| && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16), |
| "You called subtract with the wrong image formats"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), |
| "Wrong shape for output"); |
| } |
| return Status{}; |
| } |
| |
| inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) |
| { |
| const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); |
| const TensorShape &out_shape = broadcast_pair.first; |
| const ValidRegion &valid_region = broadcast_pair.second; |
| |
| // Auto initialize output if not initialized |
| { |
| set_shape_if_empty(output, out_shape); |
| |
| if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) |
| { |
| set_format_if_unknown(output, Format::S16); |
| } |
| else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) |
| { |
| set_format_if_unknown(output, Format::F16); |
| } |
| else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) |
| { |
| set_format_if_unknown(output, Format::F32); |
| } |
| } |
| |
| Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); |
| Window win_input1 = win.broadcast_if_dimension_le_one(input1); |
| Window win_input2 = win.broadcast_if_dimension_le_one(input2); |
| |
| AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); |
| |
| bool window_changed = update_window_and_padding(win_input1, input1_access) |
| || update_window_and_padding(win_input2, input2_access) |
| || update_window_and_padding(win, output_access); |
| |
| output_access.set_valid_region(win, valid_region); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| NEArithmeticSubtractionKernel::NEArithmeticSubtractionKernel() |
| : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr) |
| { |
| } |
| |
| void NEArithmeticSubtractionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| |
| static std::map<std::string, NEArithmeticSubtractionKernel::SubFunction *> map_function = |
| { |
| { "sub_wrap_U8_U8_U8", &sub_wrap_U8_U8_U8 }, |
| { "sub_wrap_U8_U8_S16", &sub_wrap_U8_U8_S16 }, |
| { "sub_saturate_U8_U8_U8", &sub_saturate_U8_U8_U8 }, |
| { "sub_saturate_U8_U8_S16", &sub_saturate_U8_U8_S16 }, |
| { "sub_saturate_QASYMM8_QASYMM8_QASYMM8", &sub_saturate_QAYSMM8_QAYSMM8_QAYSMM8 }, |
| { "sub_wrap_U8_S16_S16", &sub_wrap_U8_S16_S16 }, |
| { "sub_wrap_S16_U8_S16", &sub_wrap_S16_U8_S16 }, |
| { "sub_saturate_U8_S16_S16", &sub_saturate_U8_S16_S16 }, |
| { "sub_saturate_S16_U8_S16", &sub_saturate_S16_U8_S16 }, |
| { "sub_wrap_S16_S16_S16", &sub_wrap_S16_S16_S16 }, |
| { "sub_saturate_S16_S16_S16", &sub_saturate_S16_S16_S16 }, |
| { "sub_wrap_F32_F32_F32", &sub_F32_F32_F32 }, |
| { "sub_saturate_F32_F32_F32", &sub_F32_F32_F32 }, |
| { "sub_wrap_F16_F16_F16", &sub_F16_F16_F16 }, |
| { "sub_saturate_F16_F16_F16", &sub_F16_F16_F16 }, |
| }; |
| |
| _input1 = input1; |
| _input2 = input2; |
| _output = output; |
| |
| std::string function_to_call("sub_"); |
| function_to_call += policy == ConvertPolicy::WRAP ? "wrap_" : "saturate_"; |
| function_to_call += string_from_data_type(input1->info()->data_type()) + "_"; |
| function_to_call += string_from_data_type(input2->info()->data_type()) + "_"; |
| function_to_call += string_from_data_type(output->info()->data_type()); |
| |
| auto it = map_function.find(function_to_call); |
| |
| if(it != map_function.end()) |
| { |
| _func = it->second; |
| } |
| |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); |
| |
| return Status{}; |
| } |
| |
| void NEArithmeticSubtractionKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (*_func)(_input1, _input2, _output, window); |
| } |
| |
| BorderSize NEArithmeticSubtractionKernel::border_size() const |
| { |
| const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); |
| const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); |
| return BorderSize{ 0, border, 0, 0 }; |
| } |
