/*
* 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.
*/
#include "CpuOperationUtils.h"
#include "Operations.h"
#include "tensorflow/lite/kernels/internal/optimized/depthwiseconv_float.h"
#include "tensorflow/lite/kernels/internal/optimized/depthwiseconv_uint8.h"
#include "Tracing.h"
namespace android {
namespace nn {
bool depthwiseConvFloat16(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));
depthwiseConvFloat32(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;
}
#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 depthwiseConvFloat32(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)},
.stride_width = static_cast<int16>(strideWidth),
.stride_height = static_cast<int16>(strideHeight),
.depth_multiplier = static_cast<int16>(depthMultiplier),
.float_activation_min = output_activation_min,
.float_activation_max = output_activation_max,
.dilation_width_factor = static_cast<int16>(dilationWidthFactor),
.dilation_height_factor = static_cast<int16>(dilationHeightFactor),
};
NNTRACE_COMP_SWITCH("optimized_ops::DepthwiseConv");
tflite::optimized_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData,
convertShapeToTflshape(filterShape), filterData,
convertShapeToTflshape(biasShape), biasData,
convertShapeToTflshape(outputShape), outputData);
return true;
}
bool depthwiseConvQuant8(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)},
.stride_width = static_cast<int16>(strideWidth),
.stride_height = static_cast<int16>(strideHeight),
.depth_multiplier = static_cast<int16>(depthMultiplier),
.quantized_activation_min = output_activation_min,
.quantized_activation_max = output_activation_max,
.dilation_width_factor = static_cast<int16>(dilationWidthFactor),
.dilation_height_factor = static_cast<int16>(dilationHeightFactor),
.input_offset = -inputShape.offset,
.weights_offset = -filterShape.offset,
.output_offset = outputShape.offset,
.output_shift = -output_shift,
.output_multiplier = output_multiplier,
};
NNTRACE_COMP_SWITCH("optimized_ops::DepthwiseConv");
tflite::optimized_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData,
convertShapeToTflshape(filterShape), filterData,
convertShapeToTflshape(biasShape), biasData,
convertShapeToTflshape(outputShape), outputData);
return true;
}
bool depthwiseConvQuant8PerChannel(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 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");
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;
CalculateActivationRangeUint8(activation, outputShape, &output_activation_min,
&output_activation_max);
const uint8_t* inputBase = inputData;
uint8_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<uint8_t>(sum);
}
outPtr += depthMultiplier;
}
}
}
inputBase += inputHeight * inputWidth * inputDepth;
}
return true;
}
#undef ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
} // namespace nn
} // namespace android