| /* |
| * Copyright (c) 2017-2018 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/NEQuantizationLayerKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output, min_max); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() < 3); |
| |
| if(output->tensor_shape().total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8, 0); |
| |
| constexpr unsigned int num_elems_processed_per_iteration = 8; |
| |
| // Configure window |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| AccessWindowStatic min_max_access(min_max, 0, 0, 2, min_max->dimension(1)); |
| |
| // Update window and padding |
| bool window_changed = update_window_and_padding(win, input_access, output_access, min_max_access); |
| |
| output_access.set_valid_region(win, input->valid_region()); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_tuple(err, win); |
| } |
| } // namespace |
| |
| NEQuantizationLayerKernel::NEQuantizationLayerKernel() |
| : _input(nullptr), _output(nullptr), _min_max(nullptr) |
| { |
| } |
| |
| void NEQuantizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *min_max) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, min_max); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), min_max->info())); |
| |
| _input = input; |
| _output = output; |
| _min_max = min_max; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info(), min_max->info()); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NEQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, min_max)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), min_max->clone().get()))); |
| |
| return Status{}; |
| } |
| |
| void NEQuantizationLayerKernel::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); |
| |
| Window window_input_output(window); |
| window_input_output.set(3, Window::Dimension(0, 1, 1)); |
| |
| Window window_min_max; |
| window_min_max.use_tensor_dimensions(_min_max->info()->tensor_shape()); |
| window_min_max.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, window_input_output); |
| Iterator output(_output, window_input_output); |
| Iterator min_max(_min_max, window_min_max); |
| |
| execute_window_loop(window_min_max, [&](const Coordinates & id_batch) |
| { |
| // Get the min and max |
| float min = *(reinterpret_cast<const float *>(min_max.ptr()) + 0); |
| float max = *(reinterpret_cast<const float *>(min_max.ptr()) + 1); |
| |
| // Saturate the result if min = max |
| if(min == max) |
| { |
| min = 0.0f; |
| max = 1.0f; |
| } |
| |
| const float32x4_t vmin = vdupq_n_f32(min); |
| const float32x4_t inv_range = vdupq_n_f32(1.0f / (max - min)); |
| const float32x4_t quantization_max = vdupq_n_f32(255.0f); |
| const float32x4_t quantization_mul = vdupq_n_f32(256.0f); |
| |
| // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255] |
| execute_window_loop(window_input_output, [&](const Coordinates & id) |
| { |
| // Get the input values |
| const auto input_ptr = reinterpret_cast<const float *>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]); |
| float32x4x2_t val = vld2q_f32(input_ptr); |
| |
| // Map float values to range [0.0, 1.0] |
| val.val[0] = vsubq_f32(val.val[0], vmin); |
| val.val[1] = vsubq_f32(val.val[1], vmin); |
| val.val[0] = vmulq_f32(val.val[0], inv_range); |
| val.val[1] = vmulq_f32(val.val[1], inv_range); |
| |
| // Quantize |
| val.val[0] = vmulq_f32(val.val[0], quantization_mul); |
| val.val[1] = vmulq_f32(val.val[1], quantization_mul); |
| val.val[0] = vminq_f32(val.val[0], quantization_max); |
| val.val[1] = vminq_f32(val.val[1], quantization_max); |
| |
| const uint32x4_t val_u32_low = vcvtq_u32_f32(val.val[0]); |
| const uint32x4_t val_u32_high = vcvtq_u32_f32(val.val[1]); |
| const uint16x4x2_t val_u16 = vzip_u16(vmovn_u32(val_u32_low), vmovn_u32(val_u32_high)); |
| |
| const uint8x8_t quantized = vmovn_u16(vcombine_u16(val_u16.val[0], val_u16.val[1])); |
| |
| // Store the quantized values |
| auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr() + id_batch[1] * _output->info()->strides_in_bytes()[3]); |
| vst1_u8(output_ptr, quantized); |
| }, |
| input, output); |
| }, |
| min_max); |
| } |