| /* |
| * Copyright (C) 2018 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| #define LOG_TAG "Operations" |
| |
| #pragma clang diagnostic push |
| #pragma clang diagnostic ignored "-Wunused-parameter" |
| #include <tensorflow/lite/kernels/internal/common.h> |
| #pragma clang diagnostic pop |
| |
| #include <algorithm> |
| #include <cfloat> |
| #include <cmath> |
| #include <vector> |
| |
| #include "CpuOperationUtils.h" |
| #include "GroupedConv2D.h" |
| #include "Operations.h" |
| #include "Tracing.h" |
| |
| namespace android { |
| namespace nn { |
| |
| #define ANDROID_NN_GROUPED_CONV_PARAMETERS \ |
| uint32_t numBatches = getSizeOfDimension(inputShape, 0); \ |
| uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \ |
| uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \ |
| uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \ |
| uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ |
| uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ |
| uint32_t filterDepth = getSizeOfDimension(filterShape, 3); \ |
| uint32_t outputHeight = getSizeOfDimension(outputShape, 1); \ |
| uint32_t outputWidth = getSizeOfDimension(outputShape, 2); \ |
| uint32_t outputDepth = getSizeOfDimension(outputShape, 3); \ |
| uint32_t outputGroupDepth = outputDepth / numGroups; |
| |
| bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, |
| const Shape& filterShape, const float* biasData, const Shape& /*biasShape*/, |
| int32_t padding_left, int32_t /*padding_right*/, int32_t padding_top, |
| int32_t /*padding_bottom*/, int32_t stride_width, int32_t stride_height, |
| int32_t numGroups, int32_t activation, float* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("groupConvFloat32"); |
| ANDROID_NN_GROUPED_CONV_PARAMETERS |
| |
| float output_activation_min = 0.0f, output_activation_max = 0.0f; |
| CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); |
| |
| const float* inputBase = inputData; |
| float* outPtr = outputData; |
| for (uint32_t b = 0; b < numBatches; b++) { |
| for (uint32_t h = 0; h < outputHeight; h++) { |
| for (uint32_t w = 0; w < outputWidth; w++) { |
| const float* filterBase = filterData; |
| for (int32_t g = 0; g < numGroups; g++) { |
| for (uint32_t d = 0; d < outputGroupDepth; d++) { |
| int32_t wInputOrigin = |
| static_cast<int32_t>(w) * stride_width - padding_left; |
| int32_t hInputOrigin = |
| static_cast<int32_t>(h) * stride_height - padding_top; |
| float sum = 0.0f; |
| for (uint32_t i = 0; i < filterHeight; i++) { |
| for (uint32_t j = 0; j < filterWidth; j++) { |
| for (uint32_t k = 0; k < filterDepth; k++) { |
| int32_t hInput = hInputOrigin + static_cast<int32_t>(i); |
| int32_t wInput = wInputOrigin + static_cast<int32_t>(j); |
| uint32_t dInput = filterDepth * g + k; |
| if (hInput >= 0 && hInput < static_cast<int32_t>(inputHeight) && |
| wInput >= 0 && wInput < static_cast<int32_t>(inputWidth)) { |
| uint32_t filterIndex = |
| i * filterWidth * filterDepth + j * filterDepth + k; |
| uint32_t inputIndex = hInput * inputWidth * inputDepth + |
| wInput * inputDepth + dInput; |
| sum += filterBase[filterIndex] * inputBase[inputIndex]; |
| } |
| } |
| } |
| } |
| sum += biasData[g * outputGroupDepth + d]; |
| sum = std::max(std::min(sum, output_activation_max), output_activation_min); |
| outPtr[d] = sum; |
| filterBase += filterHeight * filterWidth * filterDepth; |
| } |
| outPtr += outputGroupDepth; |
| } |
| } |
| } |
| inputBase += inputHeight * inputWidth * inputDepth; |
| } |
| |
| return true; |
| } |
| |
| template <typename T> |
| bool groupedConvQuant8(const T* inputData, const Shape& inputShape, const T* filterData, |
| const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, |
| int32_t padding_left, int32_t /*padding_right*/, int32_t padding_top, |
| int32_t /*padding_bottom*/, int32_t stride_width, int32_t stride_height, |
| int32_t numGroups, int32_t activation, T* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("groupConvQuant8"); |
| ANDROID_NN_GROUPED_CONV_PARAMETERS |
| |
| int32_t inputOffset = -inputShape.offset; |
| int32_t filterOffset = -filterShape.offset; |
| int32_t outputOffset = outputShape.offset; |
| |
| double realMultiplier = 0.0; |
| int32_t outputMultiplier = 0; |
| int32_t outputShift = 0; |
| NN_RET_CHECK(GetQuantizedConvolutionMultiplier(inputShape, filterShape, biasShape, outputShape, |
| &realMultiplier)); |
| int exponent; |
| NN_RET_CHECK(QuantizeMultiplier(realMultiplier, &outputMultiplier, &exponent)); |
| outputShift = -exponent; |
| |
| int32_t output_activation_min = 0, output_activation_max = 0; |
| CalculateActivationRange<T>(activation, outputShape, &output_activation_min, |
| &output_activation_max); |
| |
| const T* inputBase = inputData; |
| T* outPtr = outputData; |
| for (uint32_t b = 0; b < numBatches; b++) { |
| for (uint32_t h = 0; h < outputHeight; h++) { |
| for (uint32_t w = 0; w < outputWidth; w++) { |
| const T* filterBase = filterData; |
| for (int32_t g = 0; g < numGroups; g++) { |
| for (uint32_t d = 0; d < outputGroupDepth; d++) { |
| int32_t wInputOrigin = |
| static_cast<int32_t>(w) * stride_width - padding_left; |
| int32_t hInputOrigin = |
| static_cast<int32_t>(h) * stride_height - padding_top; |
| int32_t sum = 0.0f; |
| for (uint32_t i = 0; i < filterHeight; i++) { |
| for (uint32_t j = 0; j < filterWidth; j++) { |
| for (uint32_t k = 0; k < filterDepth; k++) { |
| int32_t hInput = hInputOrigin + static_cast<int32_t>(i); |
| int32_t wInput = wInputOrigin + static_cast<int32_t>(j); |
| uint32_t dInput = filterDepth * g + k; |
| if (hInput >= 0 && hInput < static_cast<int32_t>(inputHeight) && |
| wInput >= 0 && wInput < static_cast<int32_t>(inputWidth)) { |
| uint32_t filterIndex = |
| i * filterWidth * filterDepth + j * filterDepth + k; |
| uint32_t inputIndex = hInput * inputWidth * inputDepth + |
| wInput * inputDepth + dInput; |
| sum += (static_cast<int32_t>(filterBase[filterIndex]) + |
| filterOffset) * |
| (static_cast<int32_t>(inputBase[inputIndex]) + |
| inputOffset); |
| } |
| } |
| } |
| } |
| sum += biasData[g * outputGroupDepth + d]; |
| sum = tflite::MultiplyByQuantizedMultiplier(sum, outputMultiplier, |
| -outputShift); |
| sum += outputOffset; |
| sum = std::max(std::min(sum, output_activation_max), output_activation_min); |
| outPtr[d] = static_cast<T>(sum); |
| filterBase += filterHeight * filterWidth * filterDepth; |
| } |
| outPtr += outputGroupDepth; |
| } |
| } |
| } |
| inputBase += inputHeight * inputWidth * inputDepth; |
| } |
| |
| return true; |
| } |
| |
| template bool groupedConvQuant8<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| const int8_t* filterData, const Shape& filterShape, |
| const int32_t* biasData, const Shape& biasShape, |
| int32_t padding_left, int32_t padding_right, |
| int32_t padding_top, int32_t padding_bottom, |
| int32_t stride_width, int32_t stride_height, |
| int32_t numGroups, int32_t activation, int8_t* outputData, |
| const Shape& outputShape); |
| |
| template bool groupedConvQuant8<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| const uint8_t* filterData, const Shape& filterShape, |
| const int32_t* biasData, const Shape& biasShape, |
| int32_t padding_left, int32_t padding_right, |
| int32_t padding_top, int32_t padding_bottom, |
| int32_t stride_width, int32_t stride_height, |
| int32_t numGroups, int32_t activation, uint8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool groupedConvQuant8PerChannel(const T* inputData, const Shape& inputShape, |
| const int8_t* filterData, const Shape& filterShape, |
| const float* filterScales, const int32_t* biasData, |
| const Shape& biasShape, int32_t padding_left, |
| int32_t /*padding_right*/, int32_t padding_top, |
| int32_t /*padding_bottom*/, int32_t stride_width, |
| int32_t stride_height, int32_t numGroups, int32_t activation, |
| T* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("groupConvQuant8"); |
| ANDROID_NN_GROUPED_CONV_PARAMETERS |
| |
| int32_t inputOffset = -inputShape.offset; |
| int32_t outputOffset = outputShape.offset; |
| |
| auto realMultiplier = std::vector<double>(outputDepth, .0f); |
| auto outputMultiplier = std::vector<int32_t>(outputDepth, 0); |
| auto outputShift = std::vector<int32_t>(outputDepth, 0); |
| |
| for (uint32_t i = 0; i < outputDepth; ++i) { |
| Shape filterChannelShape = filterShape; |
| filterChannelShape.scale = filterScales[i]; |
| Shape biasChannelShape = biasShape; |
| biasChannelShape.scale = filterScales[i] * inputShape.scale; |
| |
| NN_RET_CHECK(GetQuantizedConvolutionMultiplier( |
| inputShape, filterChannelShape, biasChannelShape, outputShape, &realMultiplier[i])); |
| int exponent; |
| NN_RET_CHECK(QuantizeMultiplier(realMultiplier[i], &outputMultiplier[i], &exponent)); |
| outputShift[i] = -exponent; |
| } |
| |
| int32_t output_activation_min = 0, output_activation_max = 0; |
| CalculateActivationRange<T>(activation, outputShape, &output_activation_min, |
| &output_activation_max); |
| |
| const T* inputBase = inputData; |
| T* outPtr = outputData; |
| for (uint32_t b = 0; b < numBatches; b++) { |
| for (uint32_t h = 0; h < outputHeight; h++) { |
| for (uint32_t w = 0; w < outputWidth; w++) { |
| const int8_t* filterBase = filterData; |
| for (int32_t g = 0; g < numGroups; g++) { |
| for (uint32_t d = 0; d < outputGroupDepth; d++) { |
| int32_t wInputOrigin = |
| static_cast<int32_t>(w) * stride_width - padding_left; |
| int32_t hInputOrigin = |
| static_cast<int32_t>(h) * stride_height - padding_top; |
| int32_t sum = 0.0f; |
| for (uint32_t i = 0; i < filterHeight; i++) { |
| for (uint32_t j = 0; j < filterWidth; j++) { |
| for (uint32_t k = 0; k < filterDepth; k++) { |
| int32_t hInput = hInputOrigin + static_cast<int32_t>(i); |
| int32_t wInput = wInputOrigin + static_cast<int32_t>(j); |
| uint32_t dInput = filterDepth * g + k; |
| if (hInput >= 0 && hInput < static_cast<int32_t>(inputHeight) && |
| wInput >= 0 && wInput < static_cast<int32_t>(inputWidth)) { |
| uint32_t filterIndex = |
| i * filterWidth * filterDepth + j * filterDepth + k; |
| uint32_t inputIndex = hInput * inputWidth * inputDepth + |
| wInput * inputDepth + dInput; |
| sum += (static_cast<int32_t>(filterBase[filterIndex])) * |
| (static_cast<int32_t>(inputBase[inputIndex]) + |
| inputOffset); |
| } |
| } |
| } |
| } |
| int channelIndex = g * outputGroupDepth + d; |
| sum += biasData[channelIndex]; |
| sum = tflite::MultiplyByQuantizedMultiplier( |
| sum, outputMultiplier[channelIndex], -outputShift[channelIndex]); |
| sum += outputOffset; |
| sum = std::max(std::min(sum, output_activation_max), output_activation_min); |
| outPtr[d] = static_cast<T>(sum); |
| filterBase += filterHeight * filterWidth * filterDepth; |
| } |
| outPtr += outputGroupDepth; |
| } |
| } |
| } |
| inputBase += inputHeight * inputWidth * inputDepth; |
| } |
| |
| return true; |
| } |
| |
| bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape, |
| const _Float16* filterData, const Shape& filterShape, |
| const _Float16* biasData, const Shape& biasShape, int32_t padding_left, |
| int32_t padding_right, int32_t padding_top, int32_t padding_bottom, |
| int32_t stride_width, int32_t stride_height, int32_t numGroups, |
| int32_t activation, _Float16* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("groupConvFloat16"); |
| |
| std::vector<float> inputData_float32(getNumberOfElements(inputShape)); |
| std::vector<float> filterData_float32(getNumberOfElements(filterShape)); |
| std::vector<float> biasData_float32(getNumberOfElements(biasShape)); |
| std::vector<float> outputData_float32(getNumberOfElements(outputShape)); |
| |
| convertFloat16ToFloat32(inputData, &inputData_float32); |
| convertFloat16ToFloat32(filterData, &filterData_float32); |
| convertFloat16ToFloat32(biasData, &biasData_float32); |
| |
| groupedConvFloat32(inputData_float32.data(), inputShape, filterData_float32.data(), filterShape, |
| biasData_float32.data(), biasShape, padding_left, padding_right, padding_top, |
| padding_bottom, stride_width, stride_height, numGroups, activation, |
| outputData_float32.data(), outputShape); |
| convertFloat32ToFloat16(outputData_float32, outputData); |
| |
| return true; |
| } |
| |
| template bool groupedConvQuant8PerChannel<uint8_t>( |
| const uint8_t* inputData, const Shape& inputShape, const int8_t* filterData, |
| const Shape& filterShape, const float* filterScales, const int32_t* biasData, |
| const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, |
| int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t numGroups, |
| int32_t activation, uint8_t* outputData, const Shape& outputShape); |
| |
| template bool groupedConvQuant8PerChannel<int8_t>( |
| const int8_t* inputData, const Shape& inputShape, const int8_t* filterData, |
| const Shape& filterShape, const float* filterScales, const int32_t* biasData, |
| const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, |
| int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t numGroups, |
| int32_t activation, int8_t* outputData, const Shape& outputShape); |
| |
| #undef ANDROID_NN_GROUPED_CONV_PARAMETERS |
| } // namespace nn |
| } // namespace android |
