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support inputchannels != outputchannels of depthwiseconv

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cblas_new
xzl 8 years ago
parent 02e04b4441
commit 11588b3670
4 changed files with 85 additions and 57 deletions
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13
paddle/function/DepthwiseConvOp.cpp
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@ -30,6 +30,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -53,6 +54,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -75,6 +77,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -122,6 +125,7 @@ public:
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
@ -137,6 +141,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),
@ -183,6 +188,7 @@ public:
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
@ -198,6 +204,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),
@ -243,13 +250,14 @@ public:
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>();
int size =
inputChannels * filterHeight * filterWidth * outputHeight * outputWidth;
int size = outputChannels * filterHeight * filterWidth * outputHeight *
outputWidth;
resizeBuffer<Device>(size);
real* colData = reinterpret_cast<real*>(memory_->getBuf());
@ -264,6 +272,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH(),

10
paddle/function/DepthwiseConvOp.h
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@ -32,6 +32,7 @@ namespace paddle {
* \param[in] inputChannels channels of inputData.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData..
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
@ -53,6 +54,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -74,7 +76,8 @@ public:
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of input data.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData..
* \param[in] inputWidth width of inputData.
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
@ -96,6 +99,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -116,7 +120,8 @@ public:
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of input data.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData..
* \param[in] inputWidth width of inputData.
* \param[in] filterMultiplier equals to outputChannels/groups_.
* \param[in] filterHeight height of filter.
* \param[in] filterWidth widht of filter.
* \param[in] strideH stride size in height direction.
@ -140,6 +145,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,

117
paddle/function/DepthwiseConvOpGpu.cu
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@ -25,7 +25,7 @@ void ConvolutionDepthwiseForward(const int nthreads,
const T* const inputData, const T* const filterData,
const int batchSize, const int outputChannels, const int outputHeight,
const int outputWidth,const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int filterMultiplier, const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const outputData) {
@ -33,23 +33,25 @@ void ConvolutionDepthwiseForward(const int nthreads,
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if(index < nthreads) {
const int n = index / outputChannels / outputHeight / outputWidth;
const int c = (index / outputHeight / outputWidth) % outputChannels;
const int h = (index / outputWidth) % outputHeight;
const int w = index % outputWidth;
const T* weight = filterData + c * filterHeight * filterWidth;
const int batch = index / outputChannels / outputHeight / outputWidth;
const int c_out = (index / outputHeight / outputWidth) % outputChannels;
const int h_out = (index / outputWidth) % outputHeight;
const int w_out = index % outputWidth;
const int c_in = c_out / filterMultiplier;
const T* weight = filterData + c_out * filterHeight * filterWidth;
T value = 0;
const int h_in_start = -paddingH + h * strideH;
const int w_in_start = -paddingW + w * strideW;
const int h_in_end = -paddingH + h * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w * strideW + filterWidth - 1;
const int h_in_start = -paddingH + h_out * strideH;
const int w_in_start = -paddingW + w_out * strideW;
const int h_in_end = -paddingH + h_out * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w_out * strideW + filterWidth - 1;
if ((h_in_start >= 0) && (h_in_end < inputHeight)
&&(w_in_start >= 0) && (w_in_end < inputWidth)) {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
const int offset = ((n * inputChannels + c) * inputHeight + h_in)
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
const int offset = ((batch * inputChannels + c_in) * inputHeight + h_in)
* inputWidth + w_in;
value += (*weight) * inputData[offset];
++weight;
@ -58,11 +60,11 @@ void ConvolutionDepthwiseForward(const int nthreads,
}else{
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int offset = ((n * outputChannels + c) * inputHeight + h_in)
const int offset = ((batch * inputChannels + c_in) * inputHeight + h_in)
* inputWidth + w_in;
value += (*weight) * inputData[offset];
}
@ -81,38 +83,42 @@ void ConvolutionDepthwiseInputBackward(const int nthreads,
const T* const top_diff, const T* const weight_data,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth,const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int filterMultiplier, const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const bottom_diff) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if(index < nthreads) {
const int n = index / inputChannels / inputHeight / inputWidth;
const int c = (index / inputHeight / inputWidth) % inputChannels;
const int h = (index / inputWidth) % inputHeight;
const int w = index % inputWidth;
const T* weight = weight_data + c * filterHeight * filterWidth;
const int batch = index / inputChannels / inputHeight / inputWidth;
const int c_in = (index / inputHeight / inputWidth) % inputChannels;
const int h_in = (index / inputWidth) % inputHeight;
const int w_in = index % inputWidth;
const int c_out_start = c_in * filterMultiplier;
T value = 0;
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_out_s = h + paddingH - kh;
const int w_out_s = w + paddingW - kw;
if (((h_out_s % strideH) == 0) && ((w_out_s % strideW) == 0)) {
const int h_out = h_out_s / strideH;
const int w_out = w_out_s / strideW;
// TODO(zhaolong) : the 'if' affect the effectiveness, it needs to optimize
if ((h_out >= 0) && (h_out < outputHeight)
&& (w_out >= 0) && (w_out < outputWidth)) {
const int offset = ((n * outputChannels + c) * outputHeight + h_out)
* outputWidth + w_out;
value += (*weight) * top_diff[offset];
}
for(int c_out = c_out_start; c_out < c_out_start + filterMultiplier; c_out ++){
//weight bixu c_out
const T* weight = weight_data + c_out * filterHeight * filterWidth;
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_out_s = h_in + paddingH - kh;
const int w_out_s = w_in + paddingW - kw;
if (((h_out_s % strideH) == 0) && ((w_out_s % strideW) == 0)) {
const int h_out = h_out_s / strideH;
const int w_out = w_out_s / strideW;
// TODO(zhaolong) : the 'if' affect the effectiveness, it needs to optimize
if ((h_out >= 0) && (h_out < outputHeight)
&& (w_out >= 0) && (w_out < outputWidth)) {
const int offset = ((batch * outputChannels + c_out) * outputHeight + h_out)
* outputWidth + w_out;
value += (*weight) * top_diff[offset];
}
}
++weight;
}
}
++weight;
}
}
bottom_diff[index] += value;
}
}
}
// CUDA kernel to compute the depthwise convolution backprop w.r.t filter.
@ -122,26 +128,27 @@ void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
const T* const top_diff, const T* const inputData,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int filterMultiplier, const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const buffer_data) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int h = (index / outputWidth) % outputHeight;
const int w = index % outputWidth;
const int h_out = (index / outputWidth) % outputHeight;
const int w_out = index % outputWidth;
const int kh = (index / filterWidth / outputHeight / outputWidth)
% filterHeight;
const int kw = (index / outputHeight / outputWidth) % filterWidth;
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int c = index / filterHeight / filterWidth / outputHeight / outputWidth;
const int n = num_i;
const int top_offset = ((n * outputChannels + c) * outputHeight + h)
* outputWidth + w;
const int bottom_offset = ((n * inputChannels + c) * inputHeight + h_in)
const int c_out = index / filterHeight / filterWidth / outputHeight / outputWidth;
const int c_in = c_out / filterMultiplier;
const int batch = num_i;
const int top_offset = ((batch * outputChannels + c_out) * outputHeight + h_out)
* outputWidth + w_out;
const int bottom_offset = ((batch * inputChannels + c_in) * inputHeight + h_in)
* inputWidth + w_in;
buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset];
} else {
@ -162,6 +169,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -190,6 +198,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
@ -212,6 +221,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -242,6 +252,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
@ -264,6 +275,7 @@ public:
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
@ -273,14 +285,14 @@ public:
T* colData,
T* filterGrad){
int colDataSize = inputChannels * filterHeight * filterWidth * outputHeight * outputWidth;
int colDataSize = outputChannels * filterHeight * filterWidth * outputHeight * outputWidth;
size_t blocks = (colDataSize + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
BaseMatrix filterGradMatrix(inputChannels * filterHeight * filterWidth, 1, filterGrad, false, true);
BaseMatrix filterGradMatrix(outputChannels * filterHeight * filterWidth, 1, filterGrad, false, true);
for(int i = 0; i < batchSize; i++) {
ConvolutionDepthwiseFilterBackward<T>
@ -296,6 +308,7 @@ public:
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
@ -304,8 +317,8 @@ public:
paddingW,
colData
);
int M = colDataSize / outputHeight / outputWidth;
int K = outputHeight * outputWidth;
int M = colDataSize / K;
BaseMatrix colMatrix(M, K, colData, false, true);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);

2
paddle/gserver/tests/test_LayerGrad.cpp
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@ -355,7 +355,7 @@ void testDepthwiseConvLayer(const string& type, bool useGpu) {
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 96});
config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192 / 2});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);

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