114 lines
5.3 KiB
C++
114 lines
5.3 KiB
C++
/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "Operations.h"
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#include "OperationsUtils.h"
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#include "internal/optimized/depthwiseconv_float.h"
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#include "internal/optimized/depthwiseconv_uint8.h"
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namespace android {
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namespace nn {
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#define ANDROID_NN_DEPTHWISE_CONV_PARAMETERS \
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uint32_t height = getSizeOfDimension(inputShape, 1); \
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uint32_t width = getSizeOfDimension(inputShape, 2); \
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uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \
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uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \
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uint32_t outHeight = getSizeOfDimension(outputShape, 1); \
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uint32_t outWidth = getSizeOfDimension(outputShape, 2); \
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\
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uint32_t paddingHeight = (uint32_t)padding_top; \
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uint32_t paddingWidth = (uint32_t)padding_left;
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bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape,
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const float* filterData, const Shape& filterShape,
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const float* biasData, const Shape& biasShape,
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int32_t padding_left, int32_t padding_right,
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int32_t padding_top, int32_t padding_bottom,
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int32_t stride_width, int32_t stride_height,
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int32_t depth_multiplier, int32_t activation,
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float* outputData, const Shape& outputShape) {
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ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
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#define ANDROID_NN_DEPTHWISE_CONV(activation) \
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optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \
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inputData, convertShapeToDims(inputShape), \
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filterData, convertShapeToDims(filterShape), \
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biasData, convertShapeToDims(biasShape), \
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stride_width, stride_height, \
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paddingWidth, paddingHeight, depth_multiplier, \
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outputData, convertShapeToDims(outputShape))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_DEPTHWISE_CONV)
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#undef ANDROID_NN_DEPTHWISE_CONV
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return true;
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}
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bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
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const uint8_t* filterData, const Shape& filterShape,
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const int32_t* biasData, const Shape& biasShape,
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int32_t padding_left, int32_t padding_right,
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int32_t padding_top, int32_t padding_bottom,
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int32_t stride_width, int32_t stride_height,
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int32_t depth_multiplier, int32_t activation,
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uint8_t* outputData, const Shape& outputShape) {
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ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
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float real_multiplier = 0.0;
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int32_t output_multiplier = 0;
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int32_t output_shift = 0;
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int32_t output_activation_min = 0;
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int32_t output_activation_max = 0;
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if (!GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape,
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outputShape, &real_multiplier) ||
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!QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier,
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&output_shift)) {
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return false;
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}
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CalculateActivationRangeUint8(activation, outputShape,
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&output_activation_min,
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&output_activation_max);
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uint32_t inputOffset = -inputShape.offset;
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uint32_t filterOffset = -filterShape.offset;
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uint32_t outputOffset = outputShape.offset;
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#define ANDROID_NN_DEPTHWISE_CONV(activation) \
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optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \
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inputData, convertShapeToDims(inputShape), inputOffset, \
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filterData, convertShapeToDims(filterShape), filterOffset, \
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biasData, convertShapeToDims(biasShape), \
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stride_width, stride_height, \
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paddingWidth, paddingHeight, depth_multiplier, \
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outputOffset, output_multiplier, output_shift, \
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output_activation_min, output_activation_max, \
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outputData, convertShapeToDims(outputShape))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_DEPTHWISE_CONV)
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#undef ANDROID_NN_DEPTHWISE_CONV
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return true;
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}
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#undef ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
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} // namespace nn
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} // namespace android
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