| /* |
| * Copyright (c) 2016-2018 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/NEPixelWiseMultiplicationKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <arm_neon.h> |
| #include <climits> |
| #include <cmath> |
| #include <cstdint> |
| #include <cstdlib> |
| |
| #if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| #include <arm_fp16.h> // needed for float16_t |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| using namespace arm_compute; |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| const float scale255_constant = 1.f / 255.f; |
| const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant); |
| const float32x4_t positive_round_f32q = vdupq_n_f32(0.5f); |
| |
| constexpr unsigned int num_elems_processed_per_iteration = 16; |
| |
| inline Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy) |
| { |
| ARM_COMPUTE_UNUSED(overflow_policy); |
| ARM_COMPUTE_UNUSED(rounding_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::S16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8), |
| "Output can only be U8 if both inputs are U8"); |
| |
| const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| |
| if(std::abs(scale - scale255_constant) < 0.00001f) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_ZERO); |
| |
| int exponent = 0; |
| const float normalized_mantissa = std::frexp(scale, &exponent); |
| |
| // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15 |
| // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14 |
| // Moreover, it will be negative as we deal with 1/2^n |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)), "Scale value not supported (Should be 1/(2^n) or 1/255"); |
| } |
| |
| 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 ValidRegion &valid_region = broadcast_pair.second; |
| |
| // Auto initialize output if not initialized |
| { |
| set_shape_if_empty(*output, input1->tensor_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::F32 || input2->data_type() == DataType::F32) |
| { |
| set_format_if_unknown(*output, Format::F32); |
| } |
| else if(input1->data_type() == DataType::F16 || input2->data_type() == DataType::F16) |
| { |
| set_format_if_unknown(*output, Format::F16); |
| } |
| } |
| |
| // Configure kernel window |
| 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); |
| } |
| |
| /* Scales a given vector by 1/255. |
| * |
| * @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats. |
| * |
| * @param in Input vector to scale. |
| * @return Scaled output rounded to nearest (round half up). |
| */ |
| inline int32x4_t scale255_S32_S32(int32x4_t in) |
| { |
| // Scale |
| const float32x4_t tmp = vmulq_f32(vcvtq_f32_s32(in), scale255_constant_f32q); |
| // Round to nearest (round half up) |
| // Add +0.5 for all values |
| // Afterwards vcvt rounds toward zero |
| return vcvtq_s32_f32(vaddq_f32(tmp, positive_round_f32q)); |
| } |
| |
| inline uint16x8_t scale255_U16_U16(uint16x8_t in) |
| { |
| const int32x4_t tmp_s1 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(in)))); |
| const int32x4_t tmp_s2 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(in)))); |
| return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1))); |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_U8_U8_U8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n) |
| { |
| const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr); |
| const auto output = static_cast<uint8_t *__restrict>(output_ptr); |
| |
| const uint8x16_t ta1 = vld1q_u8(input1); |
| const uint8x16_t ta2 = vld1q_u8(input2); |
| |
| uint16x8_t tmp1_high = vmovl_u8(vget_high_u8(ta1)); |
| const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2)); |
| uint16x8_t tmp1_low = vmovl_u8(vget_low_u8(ta1)); |
| const uint16x8_t tmp2_low = vmovl_u8(vget_low_u8(ta2)); |
| |
| tmp1_high = vmulq_u16(tmp1_high, tmp2_high); |
| tmp1_low = vmulq_u16(tmp1_low, tmp2_low); |
| |
| if(is_scale255) |
| { |
| tmp1_high = scale255_U16_U16(tmp1_high); |
| tmp1_low = scale255_U16_U16(tmp1_low); |
| } |
| else |
| { |
| const int16x8_t vn = vdupq_n_s16(-n); |
| |
| if(is_sat) |
| { |
| tmp1_high = vqshlq_u16(tmp1_high, vn); |
| tmp1_low = vqshlq_u16(tmp1_low, vn); |
| } |
| else |
| { |
| tmp1_high = vshlq_u16(tmp1_high, vn); |
| tmp1_low = vshlq_u16(tmp1_low, vn); |
| } |
| } |
| |
| if(is_sat) |
| { |
| vst1q_u8(output, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high))); |
| } |
| else |
| { |
| vst1q_u8(output, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high))); |
| } |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &input1, const int16x8_t &input2, int n) |
| { |
| int32x4_t tmp1_high = vmovl_s16(vget_high_s16(input1)); |
| const int32x4_t tmp2_high = vmovl_s16(vget_high_s16(input2)); |
| int32x4_t tmp1_low = vmovl_s16(vget_low_s16(input1)); |
| const int32x4_t tmp2_low = vmovl_s16(vget_low_s16(input2)); |
| |
| tmp1_high = vmulq_s32(tmp1_high, tmp2_high); |
| tmp1_low = vmulq_s32(tmp1_low, tmp2_low); |
| |
| if(is_scale255) |
| { |
| tmp1_high = scale255_S32_S32(tmp1_high); |
| tmp1_low = scale255_S32_S32(tmp1_low); |
| } |
| else |
| { |
| // Right shift amount |
| const int32x4_t vn = vdupq_n_s32(-n); |
| // Left shift amount |
| const int32x4_t vnl = vdupq_n_s32(n); |
| // Calculate conversion bit |
| const uint32x4_t tmp1_high_u = vreinterpretq_u32_s32(tmp1_high); |
| const uint32x4_t tmp1_low_u = vreinterpretq_u32_s32(tmp1_low); |
| const uint32x4_t sign_high = vshrq_n_u32(tmp1_high_u, 31); |
| const uint32x4_t sign_low = vshrq_n_u32(tmp1_low_u, 31); |
| const int32x4_t sign_high_s = vreinterpretq_s32_u32(sign_high); |
| const int32x4_t sign_low_s = vreinterpretq_s32_u32(sign_low); |
| const int32x4_t convert_high = vsubq_s32(vshlq_s32(sign_high_s, vnl), sign_high_s); |
| const int32x4_t convert_low = vsubq_s32(vshlq_s32(sign_low_s, vnl), sign_low_s); |
| if(is_sat) |
| { |
| tmp1_high = vqshlq_s32(vaddq_s32(tmp1_high, convert_high), vn); |
| tmp1_low = vqshlq_s32(vaddq_s32(tmp1_low, convert_low), vn); |
| } |
| else |
| { |
| tmp1_high = vshlq_s32(vaddq_s32(tmp1_high, convert_high), vn); |
| tmp1_low = vshlq_s32(vaddq_s32(tmp1_low, convert_low), vn); |
| } |
| } |
| |
| if(is_sat) |
| { |
| return vcombine_s16(vqmovn_s32(tmp1_low), vqmovn_s32(tmp1_high)); |
| } |
| else |
| { |
| return vcombine_s16(vmovn_s32(tmp1_low), vmovn_s32(tmp1_high)); |
| } |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| inline int16x8x2_t mul_S16_S16_S16_n_k(const int16x8x2_t &input1, const int16x8x2_t &input2, int n) |
| { |
| const int16x8x2_t result = |
| { |
| { |
| // First 8 elements |
| mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[0], input2.val[0], n), |
| // Second 8 elements |
| mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[1], input2.val[1], n) |
| } |
| }; |
| |
| return result; |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_S16_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n) |
| { |
| const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const int16_t *__restrict>(input2_ptr); |
| const auto output = static_cast<int16_t *__restrict>(output_ptr); |
| |
| const int16x8x2_t ta1 = vld2q_s16(input1); |
| const int16x8x2_t ta2 = vld2q_s16(input2); |
| const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n); |
| |
| vst2q_s16(output, result); |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale) |
| { |
| const auto input1 = static_cast<const float *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const float *__restrict>(input2_ptr); |
| const auto output = static_cast<float *__restrict>(output_ptr); |
| |
| const float32x4x4_t ta1 = vld4q_f32(input1); |
| const float32x4x4_t ta2 = vld4q_f32(input2); |
| const float32x4_t scale_vec = vdupq_n_f32(scale); |
| const float32x4x4_t result = |
| { |
| { |
| vmulq_f32(vmulq_f32(ta1.val[0], ta2.val[0]), scale_vec), |
| vmulq_f32(vmulq_f32(ta1.val[1], ta2.val[1]), scale_vec), |
| vmulq_f32(vmulq_f32(ta1.val[2], ta2.val[2]), scale_vec), |
| vmulq_f32(vmulq_f32(ta1.val[3], ta2.val[3]), scale_vec) |
| } |
| }; |
| vst4q_f32(output, result); |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_F16_F16_F16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const auto input1 = static_cast<const float16_t *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const float16_t *__restrict>(input2_ptr); |
| const auto output = static_cast<float16_t *__restrict>(output_ptr); |
| const float16x8x2_t ta1 = vld2q_f16(input1); |
| const float16x8x2_t ta2 = vld2q_f16(input2); |
| const float16x8_t scale_vec = vdupq_n_f16(scale); |
| const float16x8x2_t result = |
| { |
| { |
| vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec), |
| vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec), |
| } |
| }; |
| vst2q_f16(output, result); |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| ARM_COMPUTE_UNUSED(input1_ptr); |
| ARM_COMPUTE_UNUSED(input2_ptr); |
| ARM_COMPUTE_UNUSED(output_ptr); |
| ARM_COMPUTE_UNUSED(scale); |
| ARM_COMPUTE_ERROR("Not supported. Recompile the library with arch=arm64-v8.2-a."); |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_U8_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n) |
| { |
| const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr); |
| const auto output = static_cast<int16_t *__restrict>(output_ptr); |
| |
| const uint8x16_t bv = vld1q_u8(input2); |
| const uint8x16_t av = vld1q_u8(input1); |
| |
| uint16x8_t tmp_low = vmovl_u8(vget_low_u8(av)); |
| uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av)); |
| tmp_low = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv))); |
| tmp_high = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv))); |
| |
| if(is_scale255) |
| { |
| tmp_low = scale255_U16_U16(tmp_low); |
| tmp_high = scale255_U16_U16(tmp_high); |
| } |
| else |
| { |
| const int16x8_t vn = vdupq_n_s16(-n); |
| |
| if(is_sat) |
| { |
| tmp_low = vqshlq_u16(tmp_low, vn); |
| tmp_high = vqshlq_u16(tmp_high, vn); |
| } |
| else |
| { |
| tmp_low = vshlq_u16(tmp_low, vn); |
| tmp_high = vshlq_u16(tmp_high, vn); |
| } |
| } |
| |
| if(is_sat) |
| { |
| static const uint16x8_t max = vdupq_n_u16(SHRT_MAX); |
| |
| tmp_low = vminq_u16(tmp_low, max); |
| tmp_high = vminq_u16(tmp_high, max); |
| } |
| |
| vst1q_s16(output, vreinterpretq_s16_u16(tmp_low)); |
| vst1q_s16(output + 8, vreinterpretq_s16_u16(tmp_high)); |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_S16_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n) |
| { |
| const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr); |
| const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr); |
| const auto output = static_cast<int16_t *__restrict>(output_ptr); |
| |
| const int16x8x2_t ta1 = vld2q_s16(input1); |
| const uint8x8x2_t ta2u = vld2_u8(input2); |
| const int16x8x2_t ta2 = |
| { |
| { |
| vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])), |
| vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1])) |
| } |
| }; |
| |
| const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n); |
| |
| vst2q_s16(output, result); |
| } |
| |
| template <bool is_scale255, bool is_sat> |
| void mul_U8_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n) |
| { |
| // Simply swap the two input buffers |
| mul_S16_U8_S16_n<is_scale255, is_sat>(input2_ptr, input1_ptr, output_ptr, n); |
| } |
| } // namespace |
| |
| NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel() |
| : _func_float(nullptr), _func_int(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 } |
| { |
| } |
| |
| void NEPixelWiseMultiplicationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy) |
| { |
| ARM_COMPUTE_UNUSED(rounding_policy); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), scale, overflow_policy, rounding_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); |
| |
| _input1 = input1; |
| _input2 = input2; |
| _output = output; |
| _scale = scale; |
| _scale_exponent = 0; |
| _func_int = nullptr; |
| _func_float = nullptr; |
| |
| bool is_scale_255 = false; |
| // Check and validate scaling factor |
| if(std::abs(scale - scale255_constant) < 0.00001f) |
| { |
| is_scale_255 = true; |
| } |
| else |
| { |
| int exponent = 0; |
| |
| std::frexp(scale, &exponent); |
| |
| // Store the positive exponent. We know that we compute 1/2^n |
| // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5 |
| _scale_exponent = std::abs(exponent - 1); |
| } |
| |
| const DataType dt_input1 = input1->info()->data_type(); |
| const DataType dt_input2 = input2->info()->data_type(); |
| const DataType dt_output = output->info()->data_type(); |
| const bool is_sat = (overflow_policy == ConvertPolicy::SATURATE); |
| |
| if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::U8 == dt_output) |
| { |
| if(is_scale_255) |
| { |
| _func_int = is_sat ? &mul_U8_U8_U8_n<true, true> : &mul_U8_U8_U8_n<true, false>; |
| } |
| else |
| { |
| _func_int = is_sat ? &mul_U8_U8_U8_n<false, true> : &mul_U8_U8_U8_n<false, false>; |
| } |
| } |
| else if(DataType::S16 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output) |
| { |
| if(is_scale_255) |
| { |
| _func_int = is_sat ? &mul_S16_S16_S16_n<true, true> : &mul_S16_S16_S16_n<true, false>; |
| } |
| else |
| { |
| _func_int = is_sat ? &mul_S16_S16_S16_n<false, true> : &mul_S16_S16_S16_n<false, false>; |
| } |
| } |
| else if(DataType::S16 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output) |
| { |
| if(is_scale_255) |
| { |
| _func_int = is_sat ? &mul_S16_U8_S16_n<true, true> : &mul_S16_U8_S16_n<true, false>; |
| } |
| else |
| { |
| _func_int = is_sat ? &mul_S16_U8_S16_n<false, true> : &mul_S16_U8_S16_n<false, false>; |
| } |
| } |
| else if(DataType::U8 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output) |
| { |
| if(is_scale_255) |
| { |
| _func_int = is_sat ? &mul_U8_S16_S16_n<true, true> : &mul_U8_S16_S16_n<true, false>; |
| } |
| else |
| { |
| _func_int = is_sat ? &mul_U8_S16_S16_n<false, true> : &mul_U8_S16_S16_n<false, false>; |
| } |
| } |
| else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output) |
| { |
| if(is_scale_255) |
| { |
| _func_int = is_sat ? &mul_U8_U8_S16_n<true, true> : &mul_U8_U8_S16_n<true, false>; |
| } |
| else |
| { |
| _func_int = is_sat ? &mul_U8_U8_S16_n<false, true> : &mul_U8_U8_S16_n<false, false>; |
| } |
| } |
| else if(DataType::F16 == dt_input1 && DataType::F16 == dt_input2 && DataType::F16 == dt_output) |
| { |
| _func_float = &mul_F16_F16_F16_n<false, false>; |
| _func_int = nullptr; |
| } |
| else if(DataType::F32 == dt_input1 && DataType::F32 == dt_input2 && DataType::F32 == dt_output) |
| { |
| _func_float = &mul_F32_F32_F32_n<false, false>; |
| _func_int = nullptr; |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("You called with the wrong img formats"); |
| } |
| |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, |
| RoundingPolicy rounding_policy) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first); |
| |
| return Status{}; |
| } |
| |
| void NEPixelWiseMultiplicationKernel::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); |
| |
| const TensorShape &in_shape1 = _input1->info()->tensor_shape(); |
| const TensorShape &in_shape2 = _input2->info()->tensor_shape(); |
| const TensorShape &out_shape = _output->info()->tensor_shape(); |
| |
| bool can_collapse = true; |
| if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) |
| { |
| can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); |
| for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d) |
| { |
| can_collapse = (in_shape1[d] == in_shape2[d]); |
| } |
| } |
| |
| bool has_collapsed = false; |
| Window collapsed = can_collapse ? window.collapse_if_possible(INEKernel::window(), Window::DimZ, &has_collapsed) : window; |
| |
| const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; |
| const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; |
| |
| Window slice = collapsed.first_slice_window_3D(); |
| Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); |
| Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); |
| |
| Iterator input1(_input1, slice_input1); |
| Iterator input2(_input2, slice_input2); |
| Iterator output(_output, slice); |
| |
| if(_func_int != nullptr) |
| { |
| execute_window_loop(collapsed, [&](const Coordinates & id) |
| { |
| (*_func_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent); |
| collapsed.slide_window_slice_3D(slice_input1); |
| collapsed.slide_window_slice_3D(slice_input2); |
| }, |
| input1, input2, output); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON(_func_float == nullptr); |
| execute_window_loop(collapsed, [&](const Coordinates & id) |
| { |
| (*_func_float)(input1.ptr(), input2.ptr(), output.ptr(), _scale); |
| collapsed.slide_window_slice_3D(slice_input1); |
| collapsed.slide_window_slice_3D(slice_input2); |
| }, |
| input1, input2, output); |
| } |
| } |
| |
| BorderSize NEPixelWiseMultiplicationKernel::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); |
| } |