| /* |
| * Copyright (C) 2017 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" |
| |
| #include <tensorflow/lite/kernels/internal/optimized/depthwiseconv_uint8.h> |
| #include <tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h> |
| |
| #include <algorithm> |
| #include <vector> |
| |
| #include "CpuOperationUtils.h" |
| #include "OperationResolver.h" |
| #include "Operations.h" |
| #include "Tracing.h" |
| |
| namespace android { |
| namespace nn { |
| namespace depthwise_conv_2d { |
| constexpr char kOperationName[] = "DEPTHWISE_CONV_2D"; |
| |
| constexpr uint32_t kNumInputsArray[] = {8, 9, 11, 12, 14}; |
| constexpr uint32_t kInputTensor = 0; |
| constexpr uint32_t kFilterTensor = 1; |
| constexpr uint32_t kBiasTensor = 2; |
| |
| constexpr uint32_t kNumOutputs = 1; |
| constexpr uint32_t kOutputTensor = 0; |
| |
| namespace { |
| |
| struct DepthwiseConv2dParam { |
| int32_t padding_left, padding_right; |
| int32_t padding_top, padding_bottom; |
| int32_t stride_width, stride_height; |
| int32_t dilation_width_factor = 1, dilation_height_factor = 1; |
| int32_t depth_multiplier; |
| int32_t activation; |
| bool useNchw = false; |
| |
| bool initialize(const IOperationExecutionContext* context) { |
| uint32_t inCount = context->getNumInputs(); |
| int32_t padding_implicit = 0; |
| bool useImplicitPadding = false; |
| if ((inCount >= 9 && context->getInputType(8) == OperandType::BOOL) || inCount == 8) { |
| padding_implicit = context->getInputValue<int32_t>(3); |
| stride_width = context->getInputValue<int32_t>(4); |
| stride_height = context->getInputValue<int32_t>(5); |
| depth_multiplier = context->getInputValue<int32_t>(6); |
| activation = context->getInputValue<int32_t>(7); |
| if (inCount >= 9) { |
| useNchw = context->getInputValue<bool>(8); |
| } |
| if (inCount == 11) { |
| dilation_width_factor = context->getInputValue<int32_t>(9); |
| dilation_height_factor = context->getInputValue<int32_t>(10); |
| } |
| useImplicitPadding = true; |
| } else if (inCount >= 11 && context->getInputType(8) == OperandType::INT32) { |
| padding_left = context->getInputValue<int32_t>(3); |
| padding_right = context->getInputValue<int32_t>(4); |
| padding_top = context->getInputValue<int32_t>(5); |
| padding_bottom = context->getInputValue<int32_t>(6); |
| stride_width = context->getInputValue<int32_t>(7); |
| stride_height = context->getInputValue<int32_t>(8); |
| depth_multiplier = context->getInputValue<int32_t>(9); |
| activation = context->getInputValue<int32_t>(10); |
| if (inCount >= 12) { |
| useNchw = context->getInputValue<bool>(11); |
| } |
| if (inCount == 14) { |
| dilation_width_factor = context->getInputValue<int32_t>(12); |
| dilation_height_factor = context->getInputValue<int32_t>(13); |
| } |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported input spec for operation " << kOperationName; |
| } |
| if (useImplicitPadding) { |
| Shape inputShape = context->getInputShape(kInputTensor); |
| Shape filterShape = context->getInputShape(kFilterTensor); |
| int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); |
| int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); |
| int32_t filter_width = getSizeOfDimension(filterShape, 2); |
| int32_t filter_height = getSizeOfDimension(filterShape, 1); |
| calculateExplicitPadding(input_width, stride_width, dilation_width_factor, filter_width, |
| padding_implicit, &padding_left, &padding_right); |
| calculateExplicitPadding(input_height, stride_height, dilation_height_factor, |
| filter_height, padding_implicit, &padding_top, |
| &padding_bottom); |
| } |
| NN_RET_CHECK_GE(padding_left, 0); |
| NN_RET_CHECK_GE(padding_right, 0); |
| NN_RET_CHECK_GE(padding_top, 0); |
| NN_RET_CHECK_GE(padding_bottom, 0); |
| NN_RET_CHECK_GT(stride_width, 0); |
| NN_RET_CHECK_GT(stride_height, 0); |
| NN_RET_CHECK_GT(dilation_width_factor, 0); |
| NN_RET_CHECK_GT(dilation_height_factor, 0); |
| NN_RET_CHECK_GT(depth_multiplier, 0); |
| NN_RET_CHECK_GE(activation, 0); |
| return true; |
| } |
| }; |
| |
| #define ANDROID_NN_DEPTHWISE_CONV_PARAMETERS \ |
| uint32_t height = getSizeOfDimension(inputShape, 1); \ |
| uint32_t width = getSizeOfDimension(inputShape, 2); \ |
| uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ |
| uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ |
| uint32_t outHeight = getSizeOfDimension(outputShape, 1); \ |
| uint32_t outWidth = getSizeOfDimension(outputShape, 2); \ |
| \ |
| uint32_t paddingHeight = (uint32_t)paddingTop; \ |
| uint32_t paddingWidth = (uint32_t)paddingLeft; |
| |
| bool depthwiseConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, |
| const Shape& filterShape, const float* biasData, const Shape& biasShape, |
| int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop, |
| int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| int32_t depthMultiplier, int32_t activation, float* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("depthwiseConvFloat32"); |
| |
| ANDROID_NN_DEPTHWISE_CONV_PARAMETERS |
| |
| float output_activation_min, output_activation_max; |
| CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); |
| |
| tflite::DepthwiseParams params{ |
| .padding_values = {static_cast<int16>(paddingWidth), static_cast<int16>(paddingHeight), |
| 0 /*width_offset*/, 0 /*height_offset*/}, |
| .stride_width = static_cast<int16>(strideWidth), |
| .stride_height = static_cast<int16>(strideHeight), |
| .dilation_width_factor = static_cast<int16>(dilationWidthFactor), |
| .dilation_height_factor = static_cast<int16>(dilationHeightFactor), |
| .depth_multiplier = static_cast<int16>(depthMultiplier), |
| .float_activation_min = output_activation_min, |
| .float_activation_max = output_activation_max, |
| }; |
| NNTRACE_COMP_SWITCH("optimized_ops::DepthwiseConv"); |
| tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, |
| convertShapeToTflshape(filterShape), filterData, |
| convertShapeToTflshape(biasShape), biasData, |
| convertShapeToTflshape(outputShape), outputData); |
| |
| return true; |
| } |
| |
| bool depthwiseConvNhwc(const _Float16* inputData, const Shape& inputShape, |
| const _Float16* filterData, const Shape& filterShape, |
| const _Float16* biasData, const Shape& biasShape, int32_t paddingLeft, |
| int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom, |
| int32_t strideWidth, int32_t strideHeight, int32_t dilationWidthFactor, |
| int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation, |
| _Float16* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("depthwiseConvFloat16"); |
| std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); |
| convertFloat16ToFloat32(inputData, &inputDataFloat32); |
| std::vector<float> filterDataFloat32(getNumberOfElements(filterShape)); |
| convertFloat16ToFloat32(filterData, &filterDataFloat32); |
| std::vector<float> biasDataFloat32(getNumberOfElements(biasShape)); |
| convertFloat16ToFloat32(biasData, &biasDataFloat32); |
| |
| std::vector<float> outputDataFloat32(getNumberOfElements(outputShape)); |
| depthwiseConvNhwc(inputDataFloat32.data(), inputShape, filterDataFloat32.data(), filterShape, |
| biasDataFloat32.data(), biasShape, paddingLeft, paddingRight, paddingTop, |
| paddingBottom, strideWidth, strideHeight, dilationWidthFactor, |
| dilationHeightFactor, depthMultiplier, activation, outputDataFloat32.data(), |
| outputShape); |
| |
| convertFloat32ToFloat16(outputDataFloat32, outputData); |
| return true; |
| } |
| |
| bool depthwiseConvNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, |
| const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, |
| int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop, |
| int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| int32_t depthMultiplier, int32_t activation, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("depthwiseConvQuant8"); |
| |
| ANDROID_NN_DEPTHWISE_CONV_PARAMETERS |
| |
| double real_multiplier = 0.0; |
| int32_t output_multiplier = 0; |
| int32_t output_shift = 0; |
| int32_t output_activation_min = 0; |
| int32_t output_activation_max = 0; |
| |
| NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape, |
| &real_multiplier)); |
| int exponent; |
| NN_RET_CHECK(QuantizeMultiplier(real_multiplier, &output_multiplier, &exponent)); |
| output_shift = -exponent; |
| CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, |
| &output_activation_max); |
| |
| tflite::DepthwiseParams params{ |
| .padding_values = {static_cast<int16>(paddingWidth), static_cast<int16>(paddingHeight), |
| 0 /*width_offset*/, 0 /*height_offset*/}, |
| .stride_width = static_cast<int16>(strideWidth), |
| .stride_height = static_cast<int16>(strideHeight), |
| .dilation_width_factor = static_cast<int16>(dilationWidthFactor), |
| .dilation_height_factor = static_cast<int16>(dilationHeightFactor), |
| .depth_multiplier = static_cast<int16>(depthMultiplier), |
| .input_offset = -inputShape.offset, |
| .weights_offset = -filterShape.offset, |
| .output_offset = outputShape.offset, |
| .output_multiplier = output_multiplier, |
| .output_shift = -output_shift, |
| .quantized_activation_min = output_activation_min, |
| .quantized_activation_max = output_activation_max, |
| }; |
| NNTRACE_COMP_SWITCH("optimized_ops::DepthwiseConv"); |
| tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, |
| convertShapeToTflshape(filterShape), filterData, |
| convertShapeToTflshape(biasShape), biasData, |
| convertShapeToTflshape(outputShape), outputData); |
| return true; |
| } |
| |
| // Passing input, filter and output shapes by value, so that we can change the |
| // offsets without modifying the actual shapes. |
| bool depthwiseConvNhwc(const int8_t* inputData, Shape inputShape, const int8_t* filterData, |
| Shape filterShape, const int32_t* biasData, const Shape& biasShape, |
| int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop, |
| int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| int32_t depthMultiplier, int32_t activation, int8_t* outputData, |
| Shape outputShape) { |
| NNTRACE_TRANS("depthwiseConvQuant8"); |
| |
| std::vector<uint8_t> unsignedInput(getNumberOfElements(inputShape)); |
| convertInt8ToUInt8(inputData, &unsignedInput); |
| inputShape.offset += 128; |
| |
| std::vector<uint8_t> unsignedFilter(getNumberOfElements(filterShape)); |
| convertInt8ToUInt8(filterData, &unsignedFilter); |
| filterShape.offset += 128; |
| |
| std::vector<uint8_t> unsignedOutput(getNumberOfElements(outputShape)); |
| outputShape.offset += 128; |
| |
| NN_RET_CHECK(depthwiseConvNhwc(unsignedInput.data(), inputShape, unsignedFilter.data(), |
| filterShape, biasData, biasShape, paddingLeft, paddingRight, |
| paddingTop, paddingBottom, strideWidth, strideHeight, |
| dilationWidthFactor, dilationHeightFactor, depthMultiplier, |
| activation, unsignedOutput.data(), outputShape)); |
| |
| convertUInt8ToInt8(unsignedOutput, outputData); |
| |
| return true; |
| } |
| |
| template <typename T> |
| bool depthwiseConvQuant8PerChannelNhwc( |
| 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 paddingLeft, int32_t paddingRight, int32_t paddingTop, |
| int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| |
| int32_t depthMultiplier, int32_t activation, T* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("depthwiseConvQuant8"); |
| |
| uint32_t paddingHeight = (uint32_t)paddingTop; |
| uint32_t paddingWidth = (uint32_t)paddingLeft; |
| |
| 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); |
| |
| 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, .0f); |
| |
| for (int i = 0; i < outputDepth; ++i) { |
| Shape filterChannelShape = filterShape; |
| filterChannelShape.scale = filterScales[i]; |
| Shape biasChannelShape = biasShape; |
| biasChannelShape.scale = filterScales[i] * inputShape.scale; |
| NN_RET_CHECK(GetQuantizedConvolutionMultipler( |
| 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++) { |
| for (uint32_t ic = 0; ic < inputDepth; ic++) { |
| for (uint32_t m = 0; m < depthMultiplier; m++) { |
| int32_t wInputOrigin = static_cast<int32_t>(w) * strideWidth - paddingLeft; |
| int32_t hInputOrigin = static_cast<int32_t>(h) * strideHeight - paddingTop; |
| const int oc = m + ic * depthMultiplier; |
| |
| int32_t sum = 0.0f; |
| for (uint32_t i = 0; i < filterHeight; i++) { |
| for (uint32_t j = 0; j < filterWidth; j++) { |
| int32_t hInput = hInputOrigin + |
| dilationHeightFactor * static_cast<int32_t>(i); |
| int32_t wInput = wInputOrigin + |
| dilationWidthFactor * static_cast<int32_t>(j); |
| |
| 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 + oc; |
| uint32_t inputIndex = hInput * inputWidth * inputDepth + |
| wInput * inputDepth + ic; |
| sum += (static_cast<int32_t>(filterData[filterIndex])) * |
| (static_cast<int32_t>(inputBase[inputIndex]) + |
| inputOffset); |
| } |
| } |
| } |
| |
| sum += biasData[oc]; |
| sum = tflite::MultiplyByQuantizedMultiplier(sum, outputMultiplier[oc], |
| -outputShift[oc]); |
| sum += outputOffset; |
| sum = std::max(std::min(sum, output_activation_max), output_activation_min); |
| outPtr[m] = static_cast<T>(sum); |
| } |
| outPtr += depthMultiplier; |
| } |
| } |
| } |
| inputBase += inputHeight * inputWidth * inputDepth; |
| } |
| |
| return true; |
| } |
| |
| template <typename T_Input, typename T_Filter, typename T_Bias> |
| bool depthwiseConv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData, |
| const Shape& filterShape, const T_Bias* biasData, const Shape& biasShape, |
| int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop, |
| int32_t paddingBottom, int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| int32_t depthMultiplier, int32_t activation, bool useNchw, T_Input* outputData, |
| const Shape& outputShape) { |
| InputWithLayout<T_Input> input(useNchw); |
| OutputWithLayout<T_Input> output(useNchw); |
| NN_RET_CHECK(input.initialize(inputData, inputShape)); |
| NN_RET_CHECK(output.initialize(outputData, outputShape)); |
| NN_RET_CHECK(depthwiseConvNhwc(input.getNhwcBuffer(), input.getNhwcShape(), filterData, |
| filterShape, biasData, biasShape, paddingLeft, paddingRight, |
| paddingTop, paddingBottom, strideWidth, strideHeight, |
| dilationWidthFactor, dilationHeightFactor, depthMultiplier, |
| activation, output.getNhwcBuffer(), output.getNhwcShape())); |
| NN_RET_CHECK(output.commit()); |
| return true; |
| } |
| |
| template <typename T> |
| bool depthwiseConvQuant8PerChannel(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 paddingLeft, |
| int32_t paddingRight, int32_t paddingTop, int32_t paddingBottom, |
| int32_t strideWidth, int32_t strideHeight, |
| int32_t dilationWidthFactor, int32_t dilationHeightFactor, |
| int32_t depthMultiplier, int32_t activation, bool useNchw, |
| T* outputData, const Shape& outputShape) { |
| InputWithLayout<T> input(useNchw); |
| OutputWithLayout<T> output(useNchw); |
| NN_RET_CHECK(input.initialize(inputData, inputShape)); |
| NN_RET_CHECK(output.initialize(outputData, outputShape)); |
| NN_RET_CHECK(depthwiseConvQuant8PerChannelNhwc( |
| input.getNhwcBuffer(), input.getNhwcShape(), filterData, filterShape, filterScales, |
| biasData, biasShape, paddingLeft, paddingRight, paddingTop, paddingBottom, strideWidth, |
| strideHeight, dilationWidthFactor, dilationHeightFactor, depthMultiplier, activation, |
| output.getNhwcBuffer(), output.getNhwcShape())); |
| NN_RET_CHECK(output.commit()); |
| return true; |
| } |
| |
| #undef ANDROID_NN_DEPTHWISE_CONV_PARAMETERS |
| |
| } // namespace |
| |
| Result<Version> validate(const IOperationValidationContext* context) { |
| const uint32_t numInputs = context->getNumInputs(); |
| NN_RET_CHECK( |
| std::binary_search(std::begin(kNumInputsArray), std::end(kNumInputsArray), numInputs)); |
| NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); |
| auto inputType = context->getInputType(kInputTensor); |
| auto filterType = context->getInputType(kFilterTensor); |
| std::vector<OperandType> inExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = { |
| OperandType::TENSOR_FLOAT32, OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| }; |
| } else if (inputType == OperandType::TENSOR_FLOAT16) { |
| inExpectedTypes = { |
| OperandType::TENSOR_FLOAT16, OperandType::TENSOR_FLOAT16, |
| OperandType::TENSOR_FLOAT16, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| }; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM || |
| inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| NN_RET_CHECK(filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL || |
| filterType == inputType) |
| << "Unsupported filter tensor type for operation " << kOperationName; |
| if (filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) { |
| NN_RET_CHECK_EQ(std::get<Operand::SymmPerChannelQuantParams>( |
| context->getInputExtraParams(kFilterTensor)) |
| .channelDim, |
| 3) |
| << "Unsupported filter tensor channel dimension for operation " |
| << kOperationName; |
| } |
| inExpectedTypes = { |
| inputType, filterType, OperandType::TENSOR_INT32, |
| OperandType::INT32, OperandType::INT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| }; |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported input tensor type for operation " << kOperationName; |
| } |
| |
| // NeuralNetworks.h specifies that ANEURALNETWORKS_DEPTHWISE_CONV_2D's output must |
| // meet "outputScale > inputScale * filterScale" for the operand type |
| // ANEURALNETWORKS_TENSOR_QUANT8_ASYMM before API level 29. For other |
| // operand types (e.g., ANEURALNETWORKS_TENSOR_FLOAT32), this constraint |
| // does not apply, so by default the constraint is met. |
| bool meetsQuantizedScaleConstraintBeforeV1_2 = true; |
| if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| const float inputScale = context->getInputShape(kInputTensor).scale; |
| const float filterScale = context->getInputShape(kFilterTensor).scale; |
| const float outputScale = context->getInputShape(kOutputTensor).scale; |
| meetsQuantizedScaleConstraintBeforeV1_2 = (outputScale > inputScale * filterScale); |
| } |
| |
| bool withExplicitPadding = false; |
| bool withLayout = false; |
| bool withDilation = false; |
| if (numInputs >= 9) { |
| if (context->getInputType(8) == OperandType::INT32 && numInputs >= 11) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| withExplicitPadding = true; |
| } |
| int inputOffset = withExplicitPadding ? 3 : 0; |
| if (numInputs >= 9 + inputOffset) { |
| inExpectedTypes.push_back(OperandType::BOOL); |
| withLayout = true; |
| } |
| NN_RET_CHECK_NE(numInputs, 10 + inputOffset) |
| << "Provided only one dilation factor value, two values are required for operation " |
| << kOperationName; |
| if (numInputs == 11 + inputOffset) { |
| inExpectedTypes.push_back(OperandType::INT32); |
| inExpectedTypes.push_back(OperandType::INT32); |
| withDilation = true; |
| } |
| } |
| |
| auto minSupportedVersion = Version::ANDROID_OC_MR1; |
| if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| minSupportedVersion = Version::ANDROID_R; |
| } else if (inputType == OperandType::TENSOR_FLOAT16 || |
| filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL || withLayout || |
| withDilation || !meetsQuantizedScaleConstraintBeforeV1_2) { |
| minSupportedVersion = Version::ANDROID_Q; |
| } else { |
| minSupportedVersion = Version::ANDROID_OC_MR1; |
| } |
| NN_RET_CHECK(validateInputTypes(context, inExpectedTypes)); |
| NN_RET_CHECK(validateOutputTypes(context, {inputType})); |
| return minSupportedVersion; |
| } |
| |
| bool prepare(IOperationExecutionContext* context) { |
| Shape input = context->getInputShape(kInputTensor); |
| Shape filter = context->getInputShape(kFilterTensor); |
| Shape bias = context->getInputShape(kBiasTensor); |
| |
| if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) { |
| NN_RET_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM || |
| input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED); |
| } else { |
| NN_RET_CHECK(input.type == filter.type); |
| } |
| if (input.type == OperandType::TENSOR_QUANT8_ASYMM || |
| input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| NN_RET_CHECK(bias.type == OperandType::TENSOR_INT32); |
| } else { |
| NN_RET_CHECK(input.type == bias.type); |
| } |
| NN_RET_CHECK_EQ(getNumberOfDimensions(input), 4); |
| NN_RET_CHECK_EQ(getNumberOfDimensions(filter), 4); |
| NN_RET_CHECK_EQ(getNumberOfDimensions(bias), 1); |
| NN_RET_CHECK_EQ(getSizeOfDimension(filter, 0), 1); |
| NN_RET_CHECK_EQ(getSizeOfDimension(filter, 3), getSizeOfDimension(bias, 0)); |
| |
| DepthwiseConv2dParam param; |
| NN_RET_CHECK(param.initialize(context)); |
| |
| uint32_t batches = getSizeOfDimension(input, 0); |
| uint32_t height = getSizeOfDimension(input, param.useNchw ? 2 : 1); |
| uint32_t width = getSizeOfDimension(input, param.useNchw ? 3 : 2); |
| uint32_t channels_in = getSizeOfDimension(input, param.useNchw ? 1 : 3); |
| uint32_t channels_out = getSizeOfDimension(filter, 3); |
| uint32_t filterHeight = getSizeOfDimension(filter, 1); |
| uint32_t filterWidth = getSizeOfDimension(filter, 2); |
| |
| NN_OPS_CHECK(param.depth_multiplier * channels_in == channels_out); |
| int32_t effectiveFilterWidth = (filterWidth - 1) * param.dilation_width_factor + 1; |
| int32_t effectiveFilterHeight = (filterHeight - 1) * param.dilation_height_factor + 1; |
| NN_RET_CHECK_GT(effectiveFilterWidth, param.padding_left); |
| NN_RET_CHECK_GT(effectiveFilterWidth, param.padding_right); |
| NN_RET_CHECK_GT(effectiveFilterHeight, param.padding_top); |
| NN_RET_CHECK_GT(effectiveFilterHeight, param.padding_bottom); |
| |
| uint32_t outHeight = |
| computeOutSize(height, filterHeight, param.stride_height, param.dilation_height_factor, |
| param.padding_top, param.padding_bottom); |
| uint32_t outWidth = |
| computeOutSize(width, filterWidth, param.stride_width, param.dilation_width_factor, |
| param.padding_left, param.padding_right); |
| |
| Shape output = context->getOutputShape(kOutputTensor); |
| output.type = input.type; |
| if (param.useNchw) { |
| output.dimensions = {batches, channels_out, outHeight, outWidth}; |
| } else { |
| output.dimensions = {batches, outHeight, outWidth, channels_out}; |
| } |
| return context->setOutputShape(kOutputTensor, output); |
| } |
| |
| bool execute(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| DepthwiseConv2dParam param; |
| NN_RET_CHECK(param.initialize(context)); |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT32: |
| return depthwiseConv(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<float>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| context->getInputBuffer<float>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, |
| param.dilation_width_factor, param.dilation_height_factor, |
| param.depth_multiplier, param.activation, param.useNchw, |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT16: |
| return depthwiseConv(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<_Float16>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| context->getInputBuffer<_Float16>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, |
| param.dilation_width_factor, param.dilation_height_factor, |
| param.depth_multiplier, param.activation, param.useNchw, |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| if (context->getInputType(kFilterTensor) == |
| OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) { |
| return depthwiseConvQuant8PerChannel( |
| context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<int8_t>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| std::get<Operand::SymmPerChannelQuantParams>( |
| context->getInputExtraParams(kFilterTensor)) |
| .scales.data(), |
| context->getInputBuffer<int32_t>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, param.dilation_width_factor, |
| param.dilation_height_factor, param.depth_multiplier, param.activation, |
| param.useNchw, context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| } else if (context->getInputType(kFilterTensor) == OperandType::TENSOR_QUANT8_ASYMM) { |
| return depthwiseConv(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<uint8_t>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| context->getInputBuffer<int32_t>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, |
| param.dilation_width_factor, param.dilation_height_factor, |
| param.depth_multiplier, param.activation, param.useNchw, |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported filter type for operation " << kOperationName; |
| } |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| if (context->getInputType(kFilterTensor) == |
| OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) { |
| return depthwiseConvQuant8PerChannel( |
| context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<int8_t>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| std::get<Operand::SymmPerChannelQuantParams>( |
| context->getInputExtraParams(kFilterTensor)) |
| .scales.data(), |
| context->getInputBuffer<int32_t>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, param.dilation_width_factor, |
| param.dilation_height_factor, param.depth_multiplier, param.activation, |
| param.useNchw, context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| } else if (context->getInputType(kFilterTensor) == |
| OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| return depthwiseConv(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getInputBuffer<int8_t>(kFilterTensor), |
| context->getInputShape(kFilterTensor), |
| context->getInputBuffer<int32_t>(kBiasTensor), |
| context->getInputShape(kBiasTensor), param.padding_left, |
| param.padding_right, param.padding_top, param.padding_bottom, |
| param.stride_width, param.stride_height, |
| param.dilation_width_factor, param.dilation_height_factor, |
| param.depth_multiplier, param.activation, param.useNchw, |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported filter type for operation " << kOperationName; |
| } |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; |
| } |
| } |
| |
| } // namespace depthwise_conv_2d |
| |
| NN_REGISTER_OPERATION(DEPTHWISE_CONV_2D, depthwise_conv_2d::kOperationName, |
| depthwise_conv_2d::validate, depthwise_conv_2d::prepare, |
| depthwise_conv_2d::execute, .allowZeroSizedInput = true); |
| |
| } // namespace nn |
| } // namespace android |
