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/*
* 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 "Operations.h"
#include "OperationsUtils.h"
#include "internal/optimized/depthwiseconv_float.h"
#include "internal/optimized/depthwiseconv_uint8.h"
namespace android {
namespace nn {
#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)padding_top; \
uint32_t paddingWidth = (uint32_t)padding_left;
bool depthwiseConvFloat32(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 depth_multiplier, int32_t activation,
float* outputData, const Shape& outputShape) {
ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
#define ANDROID_NN_DEPTHWISE_CONV(activation) \
optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \
inputData, convertShapeToDims(inputShape), \
filterData, convertShapeToDims(filterShape), \
biasData, convertShapeToDims(biasShape), \
stride_width, paddingWidth, paddingHeight, depth_multiplier, \
outputData, convertShapeToDims(outputShape))
ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_DEPTHWISE_CONV)
#undef ANDROID_NN_DEPTHWISE_CONV
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 padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
int32_t depth_multiplier, int32_t activation,
uint8_t* outputData, const Shape& outputShape) {
ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
float 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;
if (!GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape,
outputShape, &real_multiplier) ||
!QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier,
&output_shift)) {
return false;
}
CalculateActivationRangeUint8(activation, outputShape,
&output_activation_min,
&output_activation_max);
uint32_t inputOffset = -inputShape.offset;
uint32_t filterOffset = -filterShape.offset;
uint32_t outputOffset = outputShape.offset;
#define ANDROID_NN_DEPTHWISE_CONV(activation) \
optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \
inputData, convertShapeToDims(inputShape), inputOffset, \
filterData, convertShapeToDims(filterShape), filterOffset, \
biasData, convertShapeToDims(biasShape), \
stride_width, paddingWidth, paddingHeight, depth_multiplier, \
outputOffset, output_multiplier, output_shift, \
output_activation_min, output_activation_max, \
outputData, convertShapeToDims(outputShape))
ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_DEPTHWISE_CONV)
#undef ANDROID_NN_DEPTHWISE_CONV
return true;
}
#undef ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
} // namespace nn
} // namespace android