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cleanedup ConvolutionNode vs. image interpretation of TensorShape;
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TensorOp() optimization to use SGEMM disabled for 'double' in Debug builds, so we get our code path tested once in a while;
fixed ConvolutionEngineTests.cpp w.r.t. Create();
removed unused InputIsImage() methods
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@frankseide
frankseide committed Jan 1, 2016
1 parent bb4f72c commit 8fc4b00
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Showing 8 changed files with 146 additions and 154 deletions.
6 changes: 3 additions & 3 deletions Source/CNTK/SynchronousExecutionEngine.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t numImages = parameter.size() > 3 ? ((NDLNode<ElemType>*)params[3])->GetScalar() : 1; // BUGBUG: This comes through MBLayout, and should be forbidden.
ImageLayoutKind imageLayoutKind = ImageLayoutKindFrom(node->GetOptionalParameter("imageLayout", "HWC"));

nodePtr = builder.CreateInputNode(name, ImageLayout(imageWidth, imageHeight, imageChannels, imageLayoutKind), numImages);
nodePtr = builder.CreateInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind), numImages);
}
}
else if (cnNodeType == L"SparseImageInput")
Expand All @@ -129,7 +129,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t numImages = parameter.size() > 3 ? ((NDLNode<ElemType>*)params[3])->GetScalar() : 1;
ImageLayoutKind imageLayoutKind = ImageLayoutKindFrom(node->GetOptionalParameter("imageLayout", "HWC"));

nodePtr = builder.CreateSparseInputNode(name, ImageLayout(imageWidth, imageHeight, imageChannels, imageLayoutKind), numImages);
nodePtr = builder.CreateSparseInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind), numImages);
}
}
else if (OperationNameOf(LearnableParameter) == cnNodeType)
Expand Down Expand Up @@ -325,7 +325,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t img_channels = node->GetOptionalParameter("imageChannels", "0");

bool needGradient = node->GetOptionalParameter("needGradient", "false");
nodePtr = builder.Reshape(NULL, num_rows, ImageLayoutWHC(img_width, img_height, img_channels), name); // BUGBUG: use a tensor descriptor instead
nodePtr = builder.Reshape(NULL, num_rows, ImageDimensions::AsTensorShape(img_width, img_height, img_channels, ImageLayoutKind::HWC/*legacy*/), name); // BUGBUG: use a tensor descriptor instead
nodePtr->SetParameterUpdateRequired(needGradient);
}
}
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55 changes: 37 additions & 18 deletions Source/Common/Include/DataTensor.h
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Original file line number Diff line number Diff line change
Expand Up @@ -249,12 +249,10 @@ namespace Microsoft { namespace MSR { namespace CNTK {
const SmallVector<ptrdiff_t> & GetStrides() const { return m_strides; }

// interpretation as an image tensor
size_t GetNumChannels() const { if (m_dims.empty()) return 0; else return m_dims.size() > 0 ? m_dims[0] : 1; }
size_t GetWidth() const { if (m_dims.empty()) return 0; else return m_dims.size() > 1 ? m_dims[1] : 1; }
size_t GetHeight() const { if (m_dims.empty()) return 0; else return m_dims.size() > 2 ? m_dims[2] : 1; }
// heuristics used for pretty-printing
// TODO: This will go away.
bool IsInputAnImage() const { return GetRank() == 3 && (GetWidth() != 1 || GetNumChannels() != 1); }
//size_t GetNumChannels() const { if (m_dims.empty()) return 0; else return m_dims.size() > 0 ? m_dims[0] : 1; }
//size_t GetWidth() const { if (m_dims.empty()) return 0; else return m_dims.size() > 1 ? m_dims[1] : 1; }
//size_t GetHeight() const { if (m_dims.empty()) return 0; else return m_dims.size() > 2 ? m_dims[2] : 1; }
// legacy helper function for RowSliceNode. Will go away.
bool IsVectorStoredAsImage() const { return GetRank() == 3 && m_dims[0] == 1 && m_dims[1] == 1; }

// indexing
Expand Down Expand Up @@ -468,19 +466,40 @@ namespace Microsoft { namespace MSR { namespace CNTK {
else if (s == L"HWC" || s == L"legacy") return ImageLayoutKind::HWC;
else InvalidArgument("ImageLayoutKindFrom: Unknown ImageLayoutKind '%ls', must be 'CHW' (cudnn) or 'HWC' (CNTK legacy)", s.c_str());
}
static inline TensorShape ImageLayout(size_t width, size_t height, size_t channels, ImageLayoutKind imageLayoutKind)
{
if (imageLayoutKind == ImageLayoutKind::CHW) return TensorShape(width, height, channels);
else if (imageLayoutKind == ImageLayoutKind::HWC) return TensorShape(channels, width, height);
else LogicError("ImageLayout: Invalid ImageLayoutKind");
}

// When constructing an image tensor with the usual W, H, C format, use the following function instead.
// This will sort the three parameters into the correct order.
// BUGBUG: This only works for ImageLayoutKind::HWC. Also the naming is bad.
static inline TensorShape ImageLayoutWHC(size_t width, size_t height, size_t channels)
// interpret TensorShape as an image descriptor
// considering that we support two ways of storingimages
struct ImageDimensions
{
return TensorShape(channels, width, height);
}
size_t m_width, m_height, m_numChannels;
// interpret TensorShape as image
ImageDimensions(const TensorShape & shape, ImageLayoutKind imageLayoutKind)
{
if (shape.GetRank() != 3)
InvalidArgument("Convolution operation currently only supports 1D or 2D convolution on 3D tensors.");
if (imageLayoutKind == ImageLayoutKind::CHW)
{
m_width = shape[0];
m_height = shape[1];
m_numChannels = shape[2];
}
else if (imageLayoutKind == ImageLayoutKind::HWC)
{
m_width = shape[1];
m_height = shape[2];
m_numChannels = shape[0];
}
else LogicError("WHC: Invalid ImageLayoutKind");
}
ImageDimensions(size_t width, size_t height, size_t numChannels) : m_width(width), m_height(height), m_numChannels(numChannels) {}
// intepret image as TensorShape
static TensorShape AsTensorShape(size_t width, size_t height, size_t numChannels, ImageLayoutKind imageLayoutKind/* = ImageLayoutKind::HWC*/)
{
if (imageLayoutKind == ImageLayoutKind::CHW) return TensorShape(width, height, numChannels);
else if (imageLayoutKind == ImageLayoutKind::HWC) return TensorShape(numChannels, width, height);
else LogicError("ImageLayout: Invalid ImageLayoutKind");
}
TensorShape AsTensorShape(ImageLayoutKind imageLayoutKind) { return AsTensorShape(m_width, m_height, m_numChannels, imageLayoutKind); }
};

}}}
17 changes: 5 additions & 12 deletions Source/ComputationNetworkLib/ComputationNode.h
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Original file line number Diff line number Diff line change
Expand Up @@ -503,9 +503,10 @@ namespace Microsoft { namespace MSR { namespace CNTK {
}

const char * mbSizeMark = child->m_pMBLayout ? "MBSize " : "";
if (child->m_sampleLayout.GetRank() == 3 && (child->m_sampleLayout.GetWidth() != 1 || child->m_sampleLayout.GetNumChannels() != 1)) // looks like an image: use WHC notation
if (child->m_sampleLayout.GetRank() == 3 && (child->m_sampleLayout[1] != 1 || child->m_sampleLayout[0] != 1)) // looks like an image: use WHC notation
fprintf(stderr, "%ls[%lu {W=%lu, H=%lu, C=%lu}, %s%lu]", child->NodeName().c_str(), child->GetNumRows(),
child->m_sampleLayout.GetWidth(), child->m_sampleLayout.GetHeight(), child->m_sampleLayout.GetNumChannels(), mbSizeMark, child->GetNumCols());
child->m_sampleLayout[1], child->m_sampleLayout[2], child->m_sampleLayout[0], mbSizeMark, child->GetNumCols());
//BUGBUG: This ^^ will print based on the old legacy layout, and we have no way of knowing here whether that is correct.
else if (child->m_sampleLayout.GetRank() > 1) // tensor: output the tensor dimensions --TODO: there will be no numRows in the future, only the tensor
fprintf(stderr, "%ls[%lu [%s], %s%lu]", child->NodeName().c_str(), child->GetNumRows(), string(child->m_sampleLayout).c_str(), mbSizeMark, child->GetNumCols());
else
Expand Down Expand Up @@ -540,14 +541,6 @@ namespace Microsoft { namespace MSR { namespace CNTK {
return !g_shareNodeValueMatrices || m_outputNeededDuringBackprop;
}

// TODO: Remove this.
// used from:
// - Plus/Minus/ElementTimesNode --> replace by max dim over inputs. Make this standard behavior for all binary element-wise ops.
bool IsInputAnImage(const size_t index) const
{
return m_inputs[index]->m_sampleLayout.IsInputAnImage();
}

const size_t GetNumInputs() const { return m_inputs.size(); }

virtual void SetInput(const size_t childIndex, const ComputationNodeBasePtr& node) = 0;
Expand Down Expand Up @@ -1528,7 +1521,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
#define UsingComputationNodeMembers /*without OperationName; needed to support inconsistent pattern of InputValue--TODO: This comment it out of date. */ \
protected: \
typedef shared_ptr<ComputationNode<ElemType>> ComputationNodePtr; \
using Base::m_deviceId; using Base::SetDims; using Base::SetDims1; using Base::SetNumCols; using Base::GetNumRows; using Base::GetNumCols; using Base::UpdateFunctionValuesSize; using Base::LoadValue; \
using Base::m_deviceId; using Base::GetDeviceId; using Base::SetDims; using Base::SetDims1; using Base::SetNumCols; using Base::GetNumRows; using Base::GetNumCols; using Base::UpdateFunctionValuesSize; using Base::LoadValue; \
using Base::m_pMBLayout; using Base::GetNumTimeSteps; using Base::GetNumParallelSequences; \
using Base::MaskMissingColumnsToZero; using Base::MaskMissingValueColumnsToZero; using Base::MaskMissingGradientColumnsToZero; using Base::InvalidateMissingValueColumns; using Base::InvalidateMissingGradientColumns; \
using Base::DataFor; using Base::ValueFor; using Base::Gradient; using Base::GradientFor; \
Expand All @@ -1547,7 +1540,7 @@ protected: \
using Base::DumpNodeInfo; using Base::EnumerateNodes; \
using Base::HasMBLayout; using Base::GetMBLayout; using Base::LinkToMBLayout; \
using Base::Input; using Base::SetInput; \
using Base::IsInputAnImage; using Base::IsEqualTo; using Base::IsOutputOlderThanInputs; using Base::IsLeaf; using Base::SetParameterUpdateRequired; \
using Base::IsEqualTo; using Base::IsOutputOlderThanInputs; using Base::IsLeaf; using Base::SetParameterUpdateRequired; \
using Base::Load; \
using Base::PrintNodeValuesToFile; using Base::PrintSelfBeforeValidation; \
using Base::Save; using Base::UpdateFunctionMBSize; \
Expand Down
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