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Deconvolution suport, part 1.

This commit is contained in:
Alexey Kamenev 2016-05-04 18:46:56 -07:00
Родитель 4132eec7f8
Коммит 112dd21785
6 изменённых файлов: 71 добавлений и 52 удалений
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4
Examples/Image/MNIST/Config/Macros.ndl
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@ -48,6 +48,10 @@ ConvND(w, inp, kW, kH, inMap, outMap, hStride, vStride) = [
c = Convolution(w, inp, {kW, kH, inMap}, mapCount=outMap, stride={hStride, vStride, inMap}, sharing={true, true, true}, autoPadding={true, true, false}, lowerPad=0, upperPad=0, imageLayout=$imageLayout$)
]
DeConv(w, inp, kW, kH, inMap, outMap, hStride, vStride) = [
c = Convolution(w, inp, {kW, kH, inMap}, mapCount=outMap, stride={hStride, vStride, inMap}, sharing={true, true, true}, autoPadding={true, true, false}, lowerPad=0, upperPad=0, transpose=1, imageLayout=$imageLayout$)
]
Conv2DReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue) = [
w = ConvW(outMap, inWCount, wScale)
b = ConvB(outMap, bValue)

7
Source/ActionsLib/NDLNetworkBuilder.cpp
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@ -299,10 +299,10 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
if (cnNodeType == OperationNameOf(ConvolutionNode))
{
RuntimeError("%ls: unexpected parameter count. %ls supports 2 modes: \n"
"1. 2D convolution which takes 7 fixed parameters [weightNodeName, inputValueNodeName, kernelWidth, kernelHeight, outputChannels,horizontalSubsample, verticalSubsample] \n"
"1. 2D convolution which takes 7 fixed parameters [weightNodeName, inputValueNodeName, kernelWidth, kernelHeight, outputChannels, horizontalSubsample, verticalSubsample] \n"
"and two optional parameters [zeroPadding = [false|yourvalue], maxTempMemSizeInSamples = [0|yourvalue], imageLayout = \"HWC\"|\"cudnn\"]. \n"
"2. ND convolution which takes 3 fixed parameters [weightNodeName, inputValueNodeName, kernelShape] and \n"
"9 optional parameters [mapCount = [1|yourvalue], stride = [1|yourvalue], sharing = [true|yourvalue], autoPadding = [true|yourvalue], lowerPad = [0|yourvalue], upperPad = [0|yourvalue], maxTempMemSizeInSamples = [0|yourvalue], imageLayout = \"cudnn\"|\"HWC\"]. \n"
"10 optional parameters [mapCount = [1|yourvalue], stride = [1|yourvalue], sharing = [true|yourvalue], autoPadding = [true|yourvalue], lowerPad = [0|yourvalue], upperPad = [0|yourvalue], bool transpose = [false|yourvalue], maxTempMemSizeInSamples = [0|yourvalue], imageLayout = \"cudnn\"|\"HWC\"]. \n"
"For ND convolution, parameters kernelShape, mapCount, stride, sharing, autoPadding, lowerPad, upperPad can be arrays, e.g. kernelShape={5, 5, 3}",
cnNodeType.c_str(), cnNodeType.c_str());
}
@ -387,8 +387,9 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
if (pool == PoolKind::None)
{
bool transpose = node->GetOptionalParameter("transpose", "false");
nodePtr = builder.Convolution(NULL, NULL, kernelShape, mapCount, stride, sharing,
autoPad, lowerPad, upperPad, imageLayout, maxTempMemSizeInSamples, name);
autoPad, lowerPad, upperPad, transpose, imageLayout, maxTempMemSizeInSamples, name);
}
else
{

8
Source/ComputationNetworkLib/ComputationNetworkBuilder.cpp
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@ -249,12 +249,12 @@ template <class ElemType>
shared_ptr<ComputationNode<ElemType>> ComputationNetworkBuilder<ElemType>::CreateConvolutionNode(const std::wstring& nodeName, const TensorShape& kernelShape, const TensorShape& mapCount,
const TensorShape& strideShape, const std::vector<bool>& sharing,
const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
{
return net.AddNodeToNetWithElemType(New<ConvolutionNode<ElemType>>(net.GetDeviceId(), nodeName,
kernelShape, mapCount, strideShape,
sharing, autoPadding, lowerPad, upperPad,
imageLayout, maxTempMemSizeInSamples));
transpose, imageLayout, maxTempMemSizeInSamples));
}
template <class ElemType>
@ -314,13 +314,13 @@ shared_ptr<ComputationNode<ElemType>> ComputationNetworkBuilder<ElemType>::Convo
const TensorShape& kernelShape, const TensorShape& mapCount,
const TensorShape& strideShape, const std::vector<bool>& sharing,
const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples,
bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples,
const std::wstring nodeName)
{
return net.AddNodeToNetAndAttachInputs(New<ConvolutionNode<ElemType>>(net.GetDeviceId(), nodeName,
kernelShape, mapCount, strideShape,
sharing, autoPadding, lowerPad, upperPad,
imageLayout, maxTempMemSizeInSamples),
transpose, imageLayout, maxTempMemSizeInSamples),
{ weight, inputValues });
}

4
Source/ComputationNetworkLib/ComputationNetworkBuilder.h
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@ -54,7 +54,7 @@ public:
ComputationNodePtr CreateSparseInputNode(const std::wstring& inputName, const TensorShape& sampleLayout, const wstring& dynamicAxisName = L"");
ComputationNodePtr CreateConvolutionNode(const std::wstring& nodeName, const TensorShape& kernelShape, const TensorShape& mapCount, const TensorShape& strideShape,
const std::vector<bool>& sharing, const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples);
bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples);
ComputationNodePtr CreateConvolutionNode(const std::wstring& nodeName, const size_t kernelWidth, const size_t kernelHeight, const size_t outputChannels,
const size_t horizontalSubsample, const size_t verticalSubsample,
ImageLayoutKind imageLayoutKind, const bool zeroPadding = false, const size_t maxTempMemSizeInSamples = 0);
@ -81,7 +81,7 @@ public:
const ComputationNodePtr inputValues,
const TensorShape& kernelShape, const TensorShape& mapCount, const TensorShape& strideShape,
const std::vector<bool>& sharing, const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples,
bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples,
const std::wstring nodeName = L"");
ComputationNodePtr Pooling(const ComputationNodePtr inputValues,
PoolKind poolKind, const TensorShape& kernelShape, const TensorShape& strideShape,

3
Source/ComputationNetworkLib/ComputationNode.h
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@ -37,7 +37,8 @@
#define CNTK_MODEL_VERSION_6 6 // Batch norm blending
#define CNTK_MODEL_VERSION_7 7 // ElemType tag in model file
#define CNTK_MODEL_VERSION_8 8 // DynamicAxis for inputs
#define CURRENT_CNTK_MODEL_VERSION CNTK_MODEL_VERSION_8
#define CNTK_MODEL_VERSION_9 9 // Deconvolution and unpooling
#define CURRENT_CNTK_MODEL_VERSION CNTK_MODEL_VERSION_9
extern bool g_shareNodeValueMatrices;

97
Source/ComputationNetworkLib/ConvolutionalNodes.h
Просмотреть файл

@ -52,15 +52,15 @@ class ConvolutionNodeBase : public ComputationNode<ElemType>
public:
ConvolutionNodeBase(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name), m_poolKind(PoolKind::None), m_maxTempMemSizeInSamples(0)
: Base(deviceId, name), m_poolKind(PoolKind::None), m_transpose(false), m_maxTempMemSizeInSamples(0)
{
}
ConvolutionNodeBase(DEVICEID_TYPE deviceId, const wstring& name, const TensorShape& kernelShape, const TensorShape& mapCount, const TensorShape& strideShape,
const std::vector<bool>& sharing, const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
PoolKind poolKind, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
: Base(deviceId, name), m_kernelShape(kernelShape), m_mapCount(mapCount), m_stride(strideShape), m_sharing(sharing),
m_autoPad(autoPadding), m_lowerPad(lowerPad), m_upperPad(upperPad), m_poolKind(poolKind),
m_imageLayout(imageLayout), m_maxTempMemSizeInSamples(maxTempMemSizeInSamples)
const std::vector<bool>& sharing, const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
PoolKind poolKind, bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
: Base(deviceId, name), m_kernelShape(kernelShape), m_mapCount(mapCount), m_stride(strideShape), m_sharing(sharing),
m_autoPad(autoPadding), m_lowerPad(lowerPad), m_upperPad(upperPad), m_poolKind(poolKind), m_transpose(transpose),
m_imageLayout(imageLayout), m_maxTempMemSizeInSamples(maxTempMemSizeInSamples)
{
}
@ -79,6 +79,7 @@ public:
fstream << (int32_t)m_poolKind;
fstream << (int32_t)m_imageLayout;
fstream << m_maxTempMemSizeInSamples;
fstream << m_transpose;
}
void Load(File& fstream, size_t modelVersion) override
@ -102,7 +103,11 @@ public:
fstream >> layout;
m_imageLayout = (ImageLayoutKind)layout;
fstream >> m_maxTempMemSizeInSamples;
}
}
if (modelVersion >= CNTK_MODEL_VERSION_9)
{
fstream >> m_transpose;
}
}
void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
@ -119,6 +124,7 @@ public:
node->m_lowerPad = m_lowerPad;
node->m_upperPad = m_upperPad;
node->m_poolKind = m_poolKind;
node->m_transpose = m_transpose;
node->m_imageLayout = m_imageLayout;
node->m_maxTempMemSizeInSamples = m_maxTempMemSizeInSamples;
}
@ -130,19 +136,22 @@ public:
if (m_poolKind == PoolKind::None)
{
if (inputIndex == 0) // derivative with respect to the weight matrix
{
auto& grad = Input(0)->GradientAsMatrix();
if (inputIndex == 0) // derivative with respect to the weight matrix
{
auto& grad = Input(0)->GradientAsMatrix();
auto sliceInput1Value = Input(1)->ValueFor(fr);
m_convEng->BackwardKernel(sliceOutputGrad, sliceInput1Value, grad, fr.IsAllFrames(), *m_tempMatrix);
}
else if (inputIndex == 1) // derivative with respect to the input feature
{
auto& input0 = Input(0)->ValueAsMatrix();
auto sliceInput1Grad = Input(1)->GradientFor(fr);
if (!m_transpose)
m_convEng->BackwardData(sliceOutputGrad, input0, sliceInput1Grad, *m_tempMatrix);
else
m_convEng->Forward(sliceOutputGrad, input0, sliceInput1Grad, *m_tempMatrix);
}
}
else if (inputIndex == 1) // derivative with respect to the input feature
{
auto& input0 = Input(0)->ValueAsMatrix();
auto sliceInput1Grad = Input(1)->GradientFor(fr);
m_convEng->BackwardData(sliceOutputGrad, input0, sliceInput1Grad, *m_tempMatrix);
}
}
else
{
Matrix<ElemType> sliceInput0Grad = Input(0)->GradientFor(fr);
@ -166,9 +175,12 @@ public:
if (m_poolKind == PoolKind::None)
{
const Matrix<ElemType>& input0 = Input(0)->ValueAsMatrix();
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
m_convEng->Forward(sliceInput1Value, input0, sliceOutputValue, *m_tempMatrix);
const Matrix<ElemType>& input0 = Input(0)->ValueAsMatrix();
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
if (!m_transpose)
m_convEng->Forward(sliceInput1Value, input0, sliceOutputValue, *m_tempMatrix);
else
m_convEng->BackwardData(sliceInput1Value, input0, sliceOutputValue, *m_tempMatrix);
}
else
{
@ -195,6 +207,7 @@ protected:
TensorShape m_lowerPad;
TensorShape m_upperPad;
PoolKind m_poolKind;
bool m_transpose;
ImageLayoutKind m_imageLayout;
size_t m_maxTempMemSizeInSamples;
@ -241,8 +254,8 @@ public:
}
ConvolutionNode(DEVICEID_TYPE deviceId, const wstring& name, const TensorShape& kernelShape, const TensorShape& mapCount, const TensorShape& strideShape,
const std::vector<bool>& sharing, const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
: Base(deviceId, name, kernelShape, mapCount, strideShape, sharing, autoPadding, lowerPad, upperPad, PoolKind::None, imageLayout, maxTempMemSizeInSamples),
bool transpose, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples)
: Base(deviceId, name, kernelShape, mapCount, strideShape, sharing, autoPadding, lowerPad, upperPad, PoolKind::None, transpose, imageLayout, maxTempMemSizeInSamples),
m_convolution2D(false)
{
}
@ -250,16 +263,16 @@ public:
const size_t horizontalSubsample, const size_t verticalSubsample, ImageLayoutKind imageLayout,
bool zeroPadding, size_t maxTempMemSizeInSamples)
: ConvolutionNode(deviceId, name, TensorShape(kernelWidth, kernelHeight, 1), TensorShape(1, 1, outputChannels),
TensorShape(horizontalSubsample, verticalSubsample, 1), vector<bool>{true},
TensorShape(horizontalSubsample, verticalSubsample, 1), vector<bool>{true},
vector<bool>{zeroPadding}, TensorShape(0), TensorShape(0),
imageLayout, maxTempMemSizeInSamples)
false, imageLayout, maxTempMemSizeInSamples)
{
m_convolution2D = true;
}
ConvolutionNode(const ScriptableObjects::IConfigRecordPtr configp)
: ConvolutionNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"kernelShape"), configp->Get(L"mapCount"), configp->Get(L"strideShape"),
configp->Get(L"dimSharing"), configp->Get(L"dimPadding"), configp->Get(L"dimPadLower"), configp->Get(L"dimPadUpper"),
ImageLayoutKindFrom(configp->Get(L"imageLayout")), configp->Get(L"maxTempMemSizeInSamples"))
configp->Get(L"transpose"), ImageLayoutKindFrom(configp->Get(L"imageLayout")), configp->Get(L"maxTempMemSizeInSamples"))
{
AttachInputsFromConfig(configp, GetExpectedNumInputs());
}
@ -346,12 +359,12 @@ public:
if (isFinalValidationPass && (Input(0)->GetAsMatrixNumCols() != weightCols || Input(0)->GetAsMatrixNumRows() != mapCount))
{
LogicError("Convolution weight matrix %ls should have dimension [%d, %d] which is [outputChannels, kernelWidth * kernelHeight * inputChannels]",
LogicError("Convolution weight matrix %ls should have dimension [%d, %d] which is [outputChannels, kernelWidth * kernelHeight * inputChannels]",
Input(0)->NodeName().c_str(), (int)mapCount, (int)weightCols);
}
auto outDims = ConvolveGeometry::ComputeOutputShape(inputShape, m_kernelShape, m_mapCount, m_stride,
m_sharing, m_autoPad, m_lowerPad, m_upperPad);
m_sharing, m_autoPad, m_lowerPad, m_upperPad);
// ConvolveGeometry always uses CHW.
SetDims(ImageDimensions(outDims, ImageLayoutKind::CHW).AsTensorShape(m_imageLayout), HasMBLayout());
}
@ -432,9 +445,9 @@ public:
{
}
PoolingNode(DEVICEID_TYPE deviceId, const wstring& name, PoolKind pool, const TensorShape& kernelShape, const TensorShape& strideShape,
const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout)
: Base(deviceId, name, kernelShape, TensorShape(1), strideShape, vector<bool>{true}, autoPadding, lowerPad, upperPad, pool, imageLayout, 0)
const std::vector<bool>& autoPadding, const TensorShape& lowerPad, const TensorShape& upperPad,
ImageLayoutKind imageLayout)
: Base(deviceId, name, kernelShape, TensorShape(1), strideShape, vector<bool>{true}, autoPadding, lowerPad, upperPad, pool, false, imageLayout, 0)
{
}
PoolingNode(const ScriptableObjects::IConfigRecordPtr configp)
@ -491,20 +504,20 @@ class PoolingNodeBase : public ComputationNode<ElemType>, public NumInputs<1>
public:
PoolingNodeBase(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name),
m_windowWidth(SIZE_MAX),
m_windowHeight(SIZE_MAX),
m_horizontalSubsample(SIZE_MAX),
m_verticalSubsample(SIZE_MAX),
m_imageLayoutKind(ImageLayoutKind::HWC)
m_windowWidth(SIZE_MAX),
m_windowHeight(SIZE_MAX),
m_horizontalSubsample(SIZE_MAX),
m_verticalSubsample(SIZE_MAX),
m_imageLayoutKind(ImageLayoutKind::HWC)
{
}
PoolingNodeBase(DEVICEID_TYPE deviceId, const wstring& name, const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample, ImageLayoutKind imageLayoutKind)
: Base(deviceId, name),
m_windowWidth(windowWidth),
m_windowHeight(windowHeight),
m_horizontalSubsample(horizontalSubsample),
m_verticalSubsample(verticalSubsample),
m_imageLayoutKind(imageLayoutKind)
m_windowWidth(windowWidth),
m_windowHeight(windowHeight),
m_horizontalSubsample(horizontalSubsample),
m_verticalSubsample(verticalSubsample),
m_imageLayoutKind(imageLayoutKind)
{
}
PoolingNodeBase(const ScriptableObjects::IConfigRecordPtr configp)
@ -517,8 +530,8 @@ public:
void Save(File& fstream) const override
{
Base::Save(fstream);
uint32_t imageLayoutKind = (uint32_t) m_imageLayoutKind;
uint32_t windowWidth = (uint32_t) m_windowWidth;
uint32_t imageLayoutKind = (uint32_t)m_imageLayoutKind;
uint32_t windowWidth = (uint32_t)m_windowWidth;
fstream << windowWidth << imageLayoutKind << m_windowHeight << m_horizontalSubsample << m_verticalSubsample;
}