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bug fix: Pooling nodes initialized wrong conv-engine factory class

This commit is contained in:
Frank Seide 2016-01-01 19:14:10 -08:00
Родитель 5bfc54b8a8
Коммит 7ee40e6cc8
3 изменённых файлов: 21 добавлений и 17 удалений
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33
Source/ComputationNetworkLib/ConvolutionalNodes.h
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@ -82,7 +82,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
m_imageLayoutKind(imageLayoutKind)
{
SetDims(ImageDimensions::AsTensorShape(1, 1, m_outputChannels, m_imageLayoutKind), 0); // TODO: necessary?
m_factory = ConvolutionEngineFactory<ElemType>::Create(deviceId, ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
m_factory = ConvolutionEngineFactory<ElemType>::Create(GetDeviceId(), ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
}
ConvolutionNode(const ScriptableObjects::IConfigRecordPtr configp) :
ConvolutionNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"kernelWidth"), configp->Get(L"kernelHeight"), configp->Get(L"outputChannels"),
@ -267,16 +267,17 @@ namespace Microsoft { namespace MSR { namespace CNTK {
{
Base::DumpNodeInfo(printValues, fstream);
auto inputSampleLayout = GetInputSampleLayout(1);
auto inDims = ImageDimensions(GetInputSampleLayout(1), m_imageLayoutKind);
auto outDims = ImageDimensions(m_sampleLayout, m_imageLayoutKind);
char str[4096];
sprintf(str, "Input[Width:%lu, Height:%lu, Channels:%lu] \n", inputSampleLayout[1], inputSampleLayout[2], inputSampleLayout[0]);
sprintf(str, "Input[Width:%lu, Height:%lu, Channels:%lu] \n", inDims.m_width, inDims.m_height, inDims.m_numChannels);
fstream << string(str);
sprintf(str, "Kernel[Width:%lu, Height:%lu] SubSample[Horizontal:%lu, Vertical:%lu]\n", m_kernelWidth, m_kernelHeight, m_horizontalSubsample, m_verticalSubsample);
fstream << string(str);
sprintf(str, "Output[Width:%lu, Height:%lu, Channels:%lu] \n", m_sampleLayout[1], m_sampleLayout[2], m_sampleLayout[0]);
sprintf(str, "Output[Width:%lu, Height:%lu, Channels:%lu] \n", outDims.m_width, outDims.m_height, outDims.m_numChannels);
fstream << string(str);
sprintf(str, "ZeroPadding=%ls maxTempMemSizeInSamples=%lu\n", m_zeroPadding? L"true" : L"false", m_maxTempMemSizeInSamples);
sprintf(str, "zeroPadding=%ls maxTempMemSizeInSamples=%lu\n", m_zeroPadding? L"true" : L"false", m_maxTempMemSizeInSamples);
fstream << string(str);
}
@ -328,8 +329,6 @@ namespace Microsoft { namespace MSR { namespace CNTK {
// PoolingNodeBase (input)
// -----------------------------------------------------------------------
// Max/Average Pooling: support multi channel
// Each sample is stored as a column-major matrix (height, width) of float[numChannels] (r00, g00, b00, r10, g10, b10, r01, g01, b01, r11, g11, b11).
template<class ElemType>
class PoolingNodeBase : public ComputationNode<ElemType>, public NumInputs<1>
{
@ -347,7 +346,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
m_horizontalSubsample(horizontalSubsample), m_verticalSubsample(verticalSubsample),
m_imageLayoutKind(imageLayoutKind)
{
m_factory = ConvolutionEngineFactory<ElemType>::Create(deviceId, ConvolutionEngineFactory<ElemType>::EngineType::Auto, ImageLayoutKind::HWC/*m_imageLayoutKind*/);
m_factory = ConvolutionEngineFactory<ElemType>::Create(GetDeviceId(), ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
}
PoolingNodeBase(const ScriptableObjects::IConfigRecordPtr configp) :
PoolingNodeBase(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"windowWidth"), configp->Get(L"windowHeight"), configp->Get(L"horizontalSubsample"), configp->Get(L"verticalSubsample"), ImageLayoutKindFrom(configp->Get(L"imageLayout")))
@ -371,6 +370,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
fstream >> imageLayoutKind >> windowWidth >> m_windowHeight >> m_horizontalSubsample >> m_verticalSubsample;
m_windowWidth = windowWidth;
m_imageLayoutKind = (ImageLayoutKind)imageLayoutKind;
m_factory = ConvolutionEngineFactory<ElemType>::Create(GetDeviceId(), ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
}
void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
@ -451,8 +451,9 @@ namespace Microsoft { namespace MSR { namespace CNTK {
// set up various engines and descriptor objects
// REVIEW alexeyk: is there a better place to create engines?
if (m_factory == nullptr)
m_factory = ConvolutionEngineFactory<ElemType>::Create(m_deviceId, ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
assert(m_factory);
//if (m_factory == nullptr)
// m_factory = ConvolutionEngineFactory<ElemType>::Create(m_deviceId, ConvolutionEngineFactory<ElemType>::EngineType::Auto, m_imageLayoutKind);
if (m_poolEng == nullptr)
m_poolEng = m_factory->CreatePoolEngine(m_deviceId);
if (m_inT == nullptr)
@ -723,7 +724,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
SetDims(Input(0));
const auto m_imageLayoutKind = ImageLayoutKind::HWC; // BUGBUG: Finish this. Must be serialized.
const auto m_imageLayoutKind = ImageLayoutKind::CHW; // BUGBUG: Finish this. Must be serialized.
auto dims = ImageDimensions(GetSampleLayout(), m_imageLayoutKind);
if (m_factory == nullptr)
@ -783,11 +784,6 @@ namespace Microsoft { namespace MSR { namespace CNTK {
VersionInfo m_version;
private:
std::unique_ptr<ConvolutionEngineFactory<ElemType>> m_factory;
std::unique_ptr<ConvolutionEngine<ElemType>> m_convEng;
std::unique_ptr<ConvolutionTensor4D> m_inT;
std::unique_ptr<ConvolutionTensor4D> m_scaleBiasT;
// Determines whether to use training or inference(evaluation) mode.
bool m_eval;
// Determines whether to use per-activation (used after non-convolutional layers like fully connected)
@ -803,6 +799,11 @@ namespace Microsoft { namespace MSR { namespace CNTK {
shared_ptr<Matrix<ElemType>> m_dScale;
// Stores bias derivatives.
shared_ptr<Matrix<ElemType>> m_dBias;
std::unique_ptr<ConvolutionEngineFactory<ElemType>> m_factory;
std::unique_ptr<ConvolutionEngine<ElemType>> m_convEng;
std::unique_ptr<ConvolutionTensor4D> m_inT;
std::unique_ptr<ConvolutionTensor4D> m_scaleBiasT;
};
template class BatchNormalizationNode<float>;

3
Source/ComputationNetworkLib/InputAndParamNodes.h
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@ -133,7 +133,8 @@ namespace Microsoft { namespace MSR { namespace CNTK {
m_value->TransferToDeviceIfNotThereAndNotAutoPlace(CPUDEVICE, true);
if (uniformInit)
{
ElemType randRange = 0.05f * initValueScale; //initValueScale/sqrt(inputSize);
// TODO: move these crazy extra factors out from here and into NDL, and make them visible in BS
ElemType randRange = 0.05f * initValueScale;
Value().SetUniformRandomValue(-randRange, randRange, randomSeed);
}
else

2
Source/ComputationNetworkLib/NonlinearityNodes.h
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@ -44,6 +44,8 @@ namespace Microsoft { namespace MSR { namespace CNTK {
virtual void /*ComputationNode::*/ForwardProp(const FrameRange & fr) override
{
static int c = 0; if (c++ == 0) { fprintf(stderr, "#NLop%d#\n", (int)opForward); }
size_t rank = DetermineElementwiseTensorRank();
auto result = ValueTensorFor(rank, fr);
auto input = Input(0)->ValueTensorFor(rank, fr);