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avx_docs
dangqingqing 8 years ago
parent 312326899e
commit 44e3c724f9
6 changed files with 197 additions and 160 deletions
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161
paddle/function/PadOp.cpp
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@ -24,20 +24,19 @@ void Pad<DEVICE_TYPE_CPU>(real* outputs,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
const PadConf& pad) {
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
for (int i = 0; i < num; i++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int inoff = ((i * inC + c) * inH + h) * inW;
int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
int outoff =
((i * outC + c + cstart) * outH + h + hstart) * outW + wstart;
memcpy(outputs + outoff, inputs + inoff, inW * sizeof(real));
}
}
@ -51,20 +50,19 @@ void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
const PadConf& pad) {
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
for (int i = 0; i < num; i++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int inoff = ((i * inC + c) * inH + h) * inW;
int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
int outoff =
((i * outC + c + cstart) * outH + h + hstart) * outW + wstart;
CpuVector inG = CpuVector(inW, inGrad + inoff);
CpuVector outG = CpuVector(inW, const_cast<real*>(outGrad + outoff));
inG += outG;
@ -73,22 +71,71 @@ void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
}
}
/**
* \brief Padding zeros to input according to the specify dimension.
* The struct pad_ contains the padding size in each dimension.
* The input and output is a 4D tensor. In PadFunc, we only
* pad zeros to the 2nd to 4th dimension.
*
* Argument in this Function:
* \param pad_ A struct object contains the padding size in each dimension.
* It has six integers. The channelStart and channelEnd indicates
* how many zeros to add before and after the input in channel
* dimension. And the heightStart and heightEnd indicates padding
* in height dimension. The widthStart and widthEnd indicates the
* padding in width dimension.
* \param inputs A 4D tensor, only one input.
* \param outputs A 4D tensor, the output value after padding.
*
* For example,
* Input(2,2,2,3) = [
* [ [[1,2,3], [3,4,5]],
* [[2,3,5], [1,6,7]] ],
* [ [[4,3,1], [1,8,7]],
* [[3,8,9], [2,3,5]] ]
* ] # the shape is (1,2,2,3)
*
* pad_: if channelStart = channelEnd = 1, others are 0.
* Output(2,4,2,3) = [
* [ [[0,0,0], [0,0,0]],
* [[1,2,3], [3,4,5]],
* [[2,3,5], [1,6,7]],
* [[0,0,0], [0,0,0]] ],
* [ [[0,0,0], [0,0,0]],
* [[4,3,1], [1,8,7]],
* [[3,8,9], [2,3,5]],
* [[0,0,0], [0,0,0]] ]
* ] # the shape is (2,4,2,3)
*
* pad_: if widthStart = 1, widthEnd = 2, others are 0.
* Output(2,2,2,6) = [
* [ [[0,1,2,3,0,0], [0,3,4,5,0,0]],
* [[0,2,3,5,0,0], [0,1,6,7,0,0]] ],
* [ [[0,4,3,1,0,0], [0,1,8,7,0,0]],
* [[0,3,8,9,0,0], [0,2,3,5,0,0]] ],
* ] # the shape is (2,2,2,6)
*
* pad_: if heightStart = 1, heightEnd = 1, others are 0.
* Output(2,2,4,3) = [
* [ [[0,0,0], [1,2,3], [3,4,5], [0,0,0]],
* [[0,0,0], [2,3,5], [1,6,7], [0,0,0]] ],
* [ [[0,0,0], [4,3,1], [1,8,7], [0,0,0]],
* [[0,0,0], [3,8,9], [2,3,5], [0,0,0]] ],
* ] # the shape is (2,2,4,3)
*/
template <DeviceType Device>
class PadFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {
padc0_ = config.get<int>("padc0");
padc1_ = config.get<int>("padc1");
padh0_ = config.get<int>("padh0");
padh1_ = config.get<int>("padh1");
padw0_ = config.get<int>("padw0");
padw1_ = config.get<int>("padw1");
pad_.channelStart = config.get<int>("cstart");
pad_.channelEnd = config.get<int>("cend");
pad_.heightStart = config.get<int>("hstart");
pad_.heightEnd = config.get<int>("hend");
pad_.widthStart = config.get<int>("wstart");
pad_.widthEnd = config.get<int>("wend");
}
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
@ -108,39 +155,35 @@ public:
inC,
inH,
inW,
padc0_,
padc1_,
padh0_,
padh1_,
padw0_,
padw1_);
pad_);
}
private:
int padc0_;
int padc1_;
int padh0_;
int padh1_;
int padw0_;
int padw1_;
PadConf pad_;
};
/**
* \brief The backward propagation of padding Function. Remove the elements
* in the padding positions of forward.
*
* Argument in this Function:
* \param pad_ The same meaning as it in PadFunc.
* \param inputs The gradient with respect to the output value of PadFunc.
* \param outputs The gradient with respect to the input value of PadFunc.
*/
template <DeviceType Device>
class PadGradFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {
padc0_ = config.get<int>("padc0");
padc1_ = config.get<int>("padc1");
padh0_ = config.get<int>("padh0");
padh1_ = config.get<int>("padh1");
padw0_ = config.get<int>("padw0");
padw1_ = config.get<int>("padw1");
pad_.channelStart = config.get<int>("cstart");
pad_.channelEnd = config.get<int>("cend");
pad_.heightStart = config.get<int>("hstart");
pad_.heightEnd = config.get<int>("hend");
pad_.widthStart = config.get<int>("wstart");
pad_.widthEnd = config.get<int>("wend");
}
/**
* \param inputs[0] output grad.
* \param inouts[0] input grad.
*/
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
@ -163,21 +206,11 @@ public:
inC,
inH,
inW,
padc0_,
padc1_,
padh0_,
padh1_,
padw0_,
padw1_);
pad_);
}
private:
int padc0_;
int padc1_;
int padh0_;
int padh1_;
int padw0_;
int padw1_;
PadConf pad_;
};
REGISTER_TYPED_FUNC(Pad, CPU, PadFunc);

87
paddle/function/PadOp.h
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@ -18,29 +18,34 @@ limitations under the License. */
namespace paddle {
struct PadConf {
/// how many values to add before the data along channel dimension.
int channelStart;
/// how many values to add after the data along channel dimension.
int channelEnd;
/// how many values to add before the data along height dimension.
int heightStart;
/// how many values to add after the data along height dimension.
int heightEnd;
/// how many values to add before the data along width dimension.
int widthStart;
/// how many values to add after the data along width dimension.
int widthEnd;
};
/**
* \brief This funtion pads zeros to inputs according to the specify dimension.
* The data structure of image data is NCHW.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] padc0 how many values to add before the data in dimension of
* channel.
* \param[in] padc1 how many values to add after the data in dimension of
* channel.
* \param[in] padh0 how many values to add before the data in dimension of
* height.
* \param[in] padh1 how many values to add after the data in dimension of
* height.
* \param[in] padw0 how many values to add before the data in dimension of
* width.
* \param[in] padw1 how many values to add after the data in dimension of
* width.
* The input and output is a 4D tensor. Padding zeros from the 2nd to
* the 4th dimenstion according argument of pad.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] pad the padding config, contains the size along the
* specify dimension.
*/
template <DeviceType Device>
void Pad(real* outputs,
@ -49,36 +54,19 @@ void Pad(real* outputs,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1);
const PadConf& pad);
/**
* \brief Padding operation backward.
* The data structure of image data is NCHW.
*
* \param[out] inGrad gradients of previous layer.
* \param[in] outGrad output gradients.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] padc0 how many values to add before the data in dimension of
* channel.
* \param[in] padc1 how many values to add after the data in dimension of
* channel.
* \param[in] padh0 how many values to add before the data in dimension of
* height.
* \param[in] padh1 how many values to add after the data in dimension of
* height.
* \param[in] padw0 how many values to add before the data in dimension of
* width.
* \param[in] padw1 how many values to add after the data in dimension of
* width.
*
* \param[out] inGrad gradients of previous layer.
* \param[in] outGrad output gradients.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] pad the padding config, contains the size along the
* specify dimension.
*/
template <DeviceType Device>
void PadGrad(real* inGrad,
@ -87,10 +75,5 @@ void PadGrad(real* inGrad,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1);
const PadConf& pad);
} // namespace paddle

36
paddle/function/PadOpGpu.cu
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@ -40,20 +40,18 @@ void Pad<DEVICE_TYPE_GPU>(real* outputs,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
const PadConf& pad) {
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, padc0, padh0, padw0,
(outputs, inputs, inC, inH, inW, cstart, hstart, wstart,
outC, outH, outW, nth);
CHECK_SYNC("Pad");
}
@ -81,20 +79,18 @@ void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
const PadConf& pad) {
int nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
int cstart = pad.channelStart, cend = pad.channelEnd;
int hstart = pad.heightStart, hend = pad.heightEnd;
int wstart = pad.widthStart, wend = pad.widthEnd;
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
KePadDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0,
(inGrad, outGrad, inC, inH, inW, cstart, hstart, wstart,
outC, outH, outW, nth);
CHECK_SYNC("PadGrad");
}

24
paddle/function/PadOpTest.cpp
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@ -27,12 +27,12 @@ TEST(Pad, real) {
FunctionCompare compare("Pad",
FuncConfig()
.set("padc0", 2)
.set("padc1", 3)
.set("padh0", 1)
.set("padh1", 2)
.set("padw0", 3)
.set("padw1", 2));
.set("cstart", 2)
.set("cend", 3)
.set("hstart", 1)
.set("hend", 2)
.set("wstart", 3)
.set("wend", 2));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{
numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
@ -54,12 +54,12 @@ TEST(PadGrad, real) {
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
FunctionCompare compare("PadGrad",
FuncConfig()
.set("padc0", 2)
.set("padc1", 3)
.set("padh0", 1)
.set("padh1", 2)
.set("padw0", 3)
.set("padw1", 2));
.set("cstart", 2)
.set("cend", 3)
.set("hstart", 1)
.set("hend", 2)
.set("wstart", 3)
.set("wend", 2));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{
numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};

24
paddle/gserver/layers/PadLayer.cpp
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@ -49,21 +49,21 @@ bool PadLayer::init(const LayerMap& layerMap,
createFunction(forward_,
"Pad",
FuncConfig()
.set("padc0", padc_[0])
.set("padc1", padc_[1])
.set("padh0", padh_[0])
.set("padh1", padh_[1])
.set("padw0", padw_[0])
.set("padw1", padw_[1]));
.set("cstart", padc_[0])
.set("cend", padc_[1])
.set("hstart", padh_[0])
.set("hend", padh_[1])
.set("wstart", padw_[0])
.set("wend", padw_[1]));
createFunction(backward_,
"PadGrad",
FuncConfig()
.set("padc0", padc_[0])
.set("padc1", padc_[1])
.set("padh0", padh_[0])
.set("padh1", padh_[1])
.set("padw0", padw_[0])
.set("padw1", padw_[1]));
.set("cstart", padc_[0])
.set("cend", padc_[1])
.set("hstart", padh_[0])
.set("hend", padh_[1])
.set("wstart", padw_[0])
.set("wend", padw_[1]));
return true;
}

25
python/paddle/trainer_config_helpers/layers.py
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@ -3617,6 +3617,31 @@ def pad_layer(input,
input data and 3 zeros after the input data in channel dimension.
pad_h means padding zeros in height dimension. pad_w means padding zeros
in width dimension.
For example,
.. code-block::
input(2,2,2,3) = [
[ [[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]] ],
[ [[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]] ]
]
pad_c=[1,1], pad_h=[0,0], pad_w=[0,0]
output(2,4,2,3) = [
[ [[0,0,0], [0,0,0]],
[[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]],
[[0,0,0], [0,0,0]] ],
[ [[0,0,0], [0,0,0]],
[[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]],
[[0,0,0], [0,0,0]] ]
]
The simply usage is:
.. code-block:: python

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