hedaoyuan
8 years ago
parent
371147003c
commit
3b65bc7a26
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "ConvFunc.h"
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namespace paddle {
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/*
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* The three arguments are stored in memory in row major order.
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* inputData = [batchSize, inputChannels, inputHeight, inputWidth]
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* filterData = [outputChannels, inputChannels, filterHeight, filterWidth]
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* outputData = [batchSize, outputChannels, outputHeight, outputWidth]
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*/
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template <class T>
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class NaiveConvFunctor {
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public:
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void operator()(const T* inputData,
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size_t batchSize,
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size_t inputChannels,
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size_t inputHeight,
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size_t inputWidth,
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const T* filterData,
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size_t filterHeight,
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size_t filterWidth,
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T* outputData,
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size_t outputChannels,
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size_t outputHeight,
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size_t outputWidth,
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size_t padding,
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size_t stride) {
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for (size_t batch = 0; batch < batchSize; batch++) {
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for (size_t outC = 0; outC < outputChannels; outC++) {
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for (size_t outH = 0; outH < outputHeight; outH++) {
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for (size_t outW = 0; outW < outputWidth; outW++) {
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const int inStartH = (outH * stride) - padding;
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const int inStartW = (outW * stride) - padding;
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T outValue = (T)0;
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for (size_t inC = 0; inC < inputChannels; inC++) {
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for (size_t fH = 0; fH < filterHeight; fH++) {
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for (size_t fW = 0; fW < filterWidth; fW++) {
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T inValue;
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const int inH = inStartH + fH;
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const int inW = inStartW + fW;
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if ((inH >= 0 && inH < inputHeight) &&
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(inW >= 0 && inW < inputWidth)) {
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size_t offsetInput =
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batch * inputChannels * inputHeight * inputWidth +
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inC * inputHeight * inputWidth + inH * inputWidth + inW;
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inValue = inputData[offsetInput];
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} else {
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inValue = (T)0;
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}
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size_t offsetFilter =
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outC * inputChannels * filterHeight * filterWidth +
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inC * filterHeight * filterWidth + fH * filterWidth + fW;
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T filterValue = filterData[offsetFilter];
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outValue += (inValue * filterValue);
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}
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}
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}
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size_t offset =
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batch * outputChannels * outputHeight * outputWidth +
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outC * outputHeight * outputWidth + outH * outputWidth + outW;
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outputData[offset] = outValue;
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}
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}
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}
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}
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}
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};
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template <DeviceType Device>
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class NaiveConvFunction : public ConvFunctionBase {
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public:
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void init(const FuncConfig& config) override {
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ConvFunctionBase::init(config);
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}
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void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
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check(inputs, outputs);
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CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
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size_t batchSize = inputs[0].shape()[0];
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size_t inputChannels = inputs[0].shape()[1];
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size_t inputHeight = inputs[0].shape()[2];
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size_t inputWidth = inputs[0].shape()[3];
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size_t filterHeight = inputs[1].shape()[2];
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size_t filterWidth = inputs[1].shape()[2];
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size_t outputChannels = outputs[0].shape()[1];
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size_t outputHeight = outputs[0].shape()[2];
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size_t outputWidth = outputs[0].shape()[3];
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float* inputData = inputs[0].data<float>();
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float* filterData = inputs[1].data<float>();
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float* outputData = outputs[0].data<float>();
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NaiveConvFunctor<float> conv;
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conv(inputData,
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batchSize,
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inputChannels,
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inputHeight,
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inputWidth,
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filterData,
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filterHeight,
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filterWidth,
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outputData,
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outputChannels,
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outputHeight,
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outputWidth,
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padding_,
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stride_);
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}
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};
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REGISTER_TYPED_FUNC(NaiveConv, CPU, NaiveConvFunction);
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} // namespace paddle
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@ -0,0 +1,67 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "Function.h"
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namespace paddle {
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/*
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* Function Arguments:
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*
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* \param inputs[0] Input image data, is NCHW format, where N is batch size,
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* C is the number of channels, H and W is the height and
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* width of input image.
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* \param inputs[1] Filter data, is MCHW, where M is the number of output
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* channels, C is the number of input channels, H and W
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* is height and width of filter.
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* \param outputs[0] Output image data, is NCHW format, where N is batch size,
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* C is the number of channels, H and W is the height and
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* width of output image.
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*
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* \note Implemented based on the ConvFunctionBase class only supports
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* input data in the NCHW format.
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*/
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class ConvFunctionBase : public FunctionBase {
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public:
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void init(const FuncConfig& config) override {
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// function arguments
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stride_ = config.get<size_t>("stride");
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padding_ = config.get<size_t>("padding");
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// number of inputs and outputs
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numInputs_ = 2;
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numOutputs_ = 1;
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}
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virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {}
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void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
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CHECK_EQ(numInputs_, inputs.size());
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CHECK_EQ(numOutputs_, outputs.size());
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CHECK_EQ(inputs[0].shape().ndims(), (size_t)4);
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CHECK_EQ(inputs[1].shape().ndims(), (size_t)4);
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CHECK_EQ(outputs[0].shape().ndims(), (size_t)4);
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CHECK(inputs[0].shape()[0] == outputs[0].shape()[0]);
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CHECK(inputs[0].shape()[1] == inputs[1].shape()[1]);
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CHECK(outputs[0].shape()[1] == inputs[1].shape()[0]);
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}
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protected:
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size_t padding_;
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size_t stride_;
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};
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} // namespace paddle
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