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@frankseide
frankseide committed Apr 16, 2016
2 parents 0f1ce6c + e87b4d6 commit 4c9f918
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4 changes: 4 additions & 0 deletions .gitignore
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Expand Up @@ -150,6 +150,10 @@ GeneratedArtifacts/
_Pvt_Extensions/
ModelManifest.xml

# Python
*.pyc
__pychache__/

# =========================
# Windows detritus
# =========================
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62 changes: 62 additions & 0 deletions CNTK.sln
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Expand Up @@ -913,6 +913,58 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "NetworkTests", "Tests\UnitT
{EAD17188-072C-4726-B840-A769C36DAD1B} = {EAD17188-072C-4726-B840-A769C36DAD1B}
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Text", "Text", "{8656B71D-E24C-4AC2-8BE4-C07B415A3E15}"
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SequenceClassification", "SequenceClassification", "{E53E63A0-FAA9-4416-9AD1-08A8FB87FEE1}"
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{8629430A-821E-43BA-AEC5-8B2CF31A2A7A}"
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CIFAR-10", "CIFAR-10", "{0141526B-F257-4574-8CBE-99634726FFCE}"
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "01_Convolution", "01_Convolution", "{58286327-6742-44C4-A34E-D2583419E55E}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.linux.cpu.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.linux.cpu.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.linux.gpu.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.linux.gpu.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.windows.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\baseline.windows.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\run-test = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\run-test
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\testcases.yml = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\01_Convolution\testcases.yml
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "02_BatchNormConv", "02_BatchNormConv", "{AB9207B9-B134-4C57-B7ED-F3DCF7B0DC5F}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\baseline.linux.gpu.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\baseline.linux.gpu.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\baseline.windows.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\baseline.windows.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\run-test = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\run-test
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\testcases.yml = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv\testcases.yml
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "03_ResNet", "03_ResNet", "{12FB912C-43F8-40FE-BD7F-B52F589A1EBC}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\baseline.linux.gpu.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\baseline.linux.gpu.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\baseline.windows.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\baseline.windows.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\run-test = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\run-test
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\testcases.yml = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\03_ResNet\testcases.yml
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "04_ResNet", "04_ResNet", "{2BFE4D88-6F32-4701-887A-1DE3D7626DBB}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\baseline.linux.gpu.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\baseline.linux.gpu.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\baseline.windows.txt = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\baseline.windows.txt
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\run-test = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\run-test
Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\testcases.yml = Tests\EndToEndTests\Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56\testcases.yml
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{EC780385-7580-4D15-914B-1D878A295CBC}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Text\SequenceClassification\Config\seqcla.cntk = Tests\EndToEndTests\Text\SequenceClassification\Config\seqcla.cntk
EndProjectSection
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{D11F76CC-DB6D-4CB4-B3B7-AB139DE2F5FA}"
ProjectSection(SolutionItems) = preProject
Tests\EndToEndTests\Text\SequenceClassification\Data\embeddingmatrix.txt = Tests\EndToEndTests\Text\SequenceClassification\Data\embeddingmatrix.txt
Tests\EndToEndTests\Text\SequenceClassification\Data\Train.txt = Tests\EndToEndTests\Text\SequenceClassification\Data\Train.txt
EndProjectSection
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug_CpuOnly|x64 = Debug_CpuOnly|x64
Expand Down Expand Up @@ -1254,5 +1306,15 @@ Global
{48C2A9DE-FB2C-4724-9ADC-744216D79BCF} = {08A05A9A-4E45-42D5-83FA-719E99C04A30}
{2B1046A1-0140-43B7-B3DC-CF7DEEE1009E} = {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3}
{CDA96AA3-3252-4978-A0BF-2ACD670823CB} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
{8656B71D-E24C-4AC2-8BE4-C07B415A3E15} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5}
{E53E63A0-FAA9-4416-9AD1-08A8FB87FEE1} = {8656B71D-E24C-4AC2-8BE4-C07B415A3E15}
{8629430A-821E-43BA-AEC5-8B2CF31A2A7A} = {FC7E7EC7-6E6A-4518-81C6-DA60451C657A}
{0141526B-F257-4574-8CBE-99634726FFCE} = {8629430A-821E-43BA-AEC5-8B2CF31A2A7A}
{58286327-6742-44C4-A34E-D2583419E55E} = {0141526B-F257-4574-8CBE-99634726FFCE}
{AB9207B9-B134-4C57-B7ED-F3DCF7B0DC5F} = {0141526B-F257-4574-8CBE-99634726FFCE}
{12FB912C-43F8-40FE-BD7F-B52F589A1EBC} = {0141526B-F257-4574-8CBE-99634726FFCE}
{2BFE4D88-6F32-4701-887A-1DE3D7626DBB} = {0141526B-F257-4574-8CBE-99634726FFCE}
{EC780385-7580-4D15-914B-1D878A295CBC} = {E53E63A0-FAA9-4416-9AD1-08A8FB87FEE1}
{D11F76CC-DB6D-4CB4-B3B7-AB139DE2F5FA} = {E53E63A0-FAA9-4416-9AD1-08A8FB87FEE1}
EndGlobalSection
EndGlobal
1 change: 0 additions & 1 deletion Makefile
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Expand Up @@ -586,7 +586,6 @@ CNTK_SRC =\
$(SOURCEDIR)/CNTK/BrainScript/BrainScriptEvaluator.cpp \
$(SOURCEDIR)/CNTK/BrainScript/BrainScriptParser.cpp \
$(SOURCEDIR)/CNTK/BrainScript/BrainScriptTest.cpp \
$(SOURCEDIR)/CNTK/BrainScript/ExperimentalNetworkBuilder.cpp \
$(SOURCEDIR)/Common/BestGpu.cpp \
$(SOURCEDIR)/Common/MPIWrapper.cpp \
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3 changes: 2 additions & 1 deletion Source/ActionsLib/EvalActions.cpp
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Expand Up @@ -209,7 +209,8 @@ template <typename ElemType>
void DoWriteOutput(const ConfigParameters& config)
{
ConfigParameters readerConfig(config(L"reader"));
readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
// Why?
//readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
readerConfig.Insert("randomize", "None"); // we don't want randomization when output results

DataReader testDataReader(readerConfig);
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11 changes: 7 additions & 4 deletions Source/ActionsLib/NDLNetworkBuilder.cpp
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Expand Up @@ -73,14 +73,16 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
size_t i = 0;
auto tensorShape = ProcessTensorShapeParameters(node, params, i, /*isImage=*/false, cnNodeType);

wstring dynamicAxis = node->GetOptionalParameter("dynamicAxis", "");
// TODO: Map dynamicAxis from name to node at this point, where that node is memoized inside NDL.
// first look for this node already existing in the network
// BUGBUG: How does this set the dimensions then?
if (m_net->NodeNameExists(name))
nodePtr = dynamic_pointer_cast<ComputationNode<ElemType>>(m_net->GetNodeFromName(name));
else if (isSparse)
nodePtr = builder.CreateSparseInputNode(name, tensorShape);
nodePtr = builder.CreateSparseInputNode(name, tensorShape, dynamicAxis);
else
nodePtr = builder.CreateInputNode(name, tensorShape);
nodePtr = builder.CreateInputNode(name, tensorShape, dynamicAxis);
}
}
else if (cnNodeType == L"ImageInput" || cnNodeType == L"SparseImageInput")
Expand All @@ -97,11 +99,12 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
size_t imageHeight = ((NDLNode<ElemType>*) params[1])->GetScalar();
size_t imageChannels = ((NDLNode<ElemType>*) params[2])->GetScalar();
ImageLayoutKind imageLayoutKind = ImageLayoutKindFrom(node->GetOptionalParameter("imageLayout", "HWC"));
wstring dynamicAxis = node->GetOptionalParameter("dynamicAxis", "");

if (isSparse)
nodePtr = builder.CreateSparseInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind));
nodePtr = builder.CreateSparseInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind), dynamicAxis);
else
nodePtr = builder.CreateInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind));
nodePtr = builder.CreateInputNode(name, ImageDimensions::AsTensorShape(imageWidth, imageHeight, imageChannels, imageLayoutKind), dynamicAxis);
}
}
else if (OperationNameOf(LearnableParameter) == cnNodeType || cnNodeType == L"ImageParameter")
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15 changes: 9 additions & 6 deletions Source/CNTK/BrainScript/CNTKCoreLib/CNTK.core.bs
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Expand Up @@ -34,10 +34,12 @@ Parameter = LearnableParameter // deprecated
# TODO: make Parameter take tensor dims?
ParameterTensor(dims, learningRateMultiplier = 1.0, init = 'uniform'/*|fixedValue|gaussian|fromFile*/, initValueScale = 1, value = 0, initFromFilePath = '', initFromLiteral = '', initOnCPUOnly=true, randomSeed=-1, tag='') = new ComputationNode [ operation = 'LearnableParameter' ; shape = new TensorShape [ /*dims*/ ] /*plus the function args*/ ]
ConstantFromString(literal, tag='') = ParameterTensor((0)/*dim, will be inferred*/, init = 'fromLiteral', initFromLiteral = literal, learningRateMultiplier = 0.0)
Input(dims, tag='feature') = new ComputationNode [ operation = 'InputValue' ; shape = new TensorShape [ /*dims*/ ] ; isImage = false /*plus the function args*/ ]
SparseInput(dims, tag='feature') = new ComputationNode [ operation = 'SparseInputValue' ; shape = new TensorShape [ /*dims*/ ] ; isImage = false /*plus the function args*/ ]
ImageInput(imageWidth, imageHeight, imageChannels, imageLayout='CHW', tag='feature') = new ComputationNode [ operation = 'InputValue' ; isImage = true /*plus the function args*/ ]
SparseImageInput(imageWidth, imageHeight, imageChannels, imageLayout='CHW', tag='feature') = new ComputationNode [ operation = 'SparseInputValue' ; isImage = true /*plus the function args*/ ]
DynamicAxis(tag='') = new ComputationNode [ operation = 'DynamicAxis' ; /*plus the function args*/ ]
Input(dims, dynamicAxis='', tag='feature') = new ComputationNode [ operation = 'InputValue' ; shape = new TensorShape [ /*dims*/ ] ; isImage = false /*plus the function args*/ ]
# TODO: change from dynamicAxis by name to dynamicAxis being an actual object
SparseInput(dims, dynamicAxis='', tag='feature') = new ComputationNode [ operation = 'SparseInputValue' ; shape = new TensorShape [ /*dims*/ ] ; isImage = false /*plus the function args*/ ]
ImageInput(imageWidth, imageHeight, imageChannels, imageLayout='CHW', dynamicAxis='', tag='feature') = new ComputationNode [ operation = 'InputValue' ; isImage = true /*plus the function args*/ ]
SparseImageInput(imageWidth, imageHeight, imageChannels, imageLayout='CHW', dynamicAxis='', tag='feature') = new ComputationNode [ operation = 'SparseInputValue' ; isImage = true /*plus the function args*/ ]
EnvironmentInput(propertyName, tag='') = new ComputationNode [ operation = 'EnvironmentInput' /*plus the function args*/ ]
ConstantTensor(val, dims, tag='') = ParameterTensor(dims, learningRateMultiplier = 0, init = 'fixedValue', value = val)
Constant(val, rows = 1, cols = 1, tag='') = Parameter(rows, cols, learningRateMultiplier = 0, init = 'fixedValue', value = val)
Expand Down Expand Up @@ -79,8 +81,9 @@ Transpose(x) = TransposeDimensions(x, 1, 2)
Times(A, B, outputRank=1, tag='') = new ComputationNode [ operation = 'Times' ; inputs = ( A : B ) /*plus the function args*/ ]
Logistic(label, probability, tag='') = new ComputationNode [ operation = 'Logistic' ; inputs = (label : probability) /*plus the function args*/ ]
WeightedLogistic(label, probability, instanceWeight, tag='') = new ComputationNode [ operation = 'Logistic' ; inputs = (label : probability : instanceWeight) /*plus the function args*/ ]
ReconcileMBLayout(dataInput, layoutInput, tag='') = new ComputationNode [ operation = 'ReconcileMBLayout' ; inputs = (dataInput : layoutInput) /*plus the function args*/ ]
CastAs (type, data) = ReconcileMBLayout (data, type) # read as CastAs<type>(data) where the cast may consist of rearranging the data w.r.t. MBLayout or broadcasting across sequence items
ReconcileDynamicAxis(dataInput, layoutInput, tag='') = new ComputationNode [ operation = 'ReconcileDynamicAxis' ; inputs = (dataInput : layoutInput) /*plus the function args*/ ]
ReconcileMBLayout = ReconcileDynamicAxis # back compat
CastAs (type, data) = ReconcileDynamicAxis (data, type) # read as CastAs<type>(data) where the cast may consist of rearranging the data w.r.t. MBLayout or broadcasting across sequence items
Convolution(weightNode, inputValueNode, kernelDims, mapDims = 1, stride = 1, sharing = true, autoPadding = true, lowerPad = 0, upperPad = 0, imageLayout='CHW', maxTempMemSizeInSamples = 0, tag='') = new ComputationNode [ operation = 'Convolution' ; inputs = (weightNode : inputValueNode); kernelShape = new TensorShape [ dims = kernelDims ] ; mapCount = new TensorShape [ dims = mapDims ] ; strideShape = new TensorShape [ dims = stride ] ; dimSharing = new BoolVector [ items = sharing ] ; dimPadding = new BoolVector [ items = autoPadding ] ; dimPadLower = new TensorShape [ dims = lowerPad ] ; dimPadUpper = new TensorShape [ dims = upperPad ] /*plus the function args*/ ]
Pooling(input, poolKind/*'max'|'average'*/, kernelDims, stride=1, autoPadding = true, lowerPad = 0, upperPad = 0, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'Pooling' ; inputs = (input); pool = poolKind ; kernelShape = new TensorShape [ dims = kernelDims ] ; strideShape = new TensorShape [ dims = stride ] ; dimPadding = new BoolVector [ items = autoPadding ] ; dimPadLower = new TensorShape [ dims = lowerPad ] ; dimPadUpper = new TensorShape [ dims = upperPad ] /*plus the function args*/ ]
MaxPooling(input, windowWidth, windowHeight, horizontalSubsample, verticalSubsample, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'MaxPooling' ; inputs = input /*plus the function args*/ ]
Expand Down
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