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Tip revision: 84633f442d54a2f6c3557e16fe76b74fe6f2ef17 authored by Spandan Tiwari on 21 August 2018, 21:52:16 UTC
Overhaul ConvTranpose for ONNX 1.2.2.
Overhaul ConvTranpose for ONNX 1.2.2.
Tip revision: 84633f4
DSSMReader.h
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
// DSSMReader.h - Include file for the MTK and MLF format of features and samples
//
#pragma once
#include "DataReader.h"
#include "DataWriter.h"
#include "Config.h"
#include "RandomOrdering.h"
#include <string>
#include <map>
#include <vector>
namespace Microsoft { namespace MSR { namespace CNTK {
enum LabelKind
{
labelNone = 0, // no labels to worry about
labelCategory = 1, // category labels, creates mapping tables
labelRegression = 2, // regression labels
labelOther = 3, // some other type of label
};
template <class ElemType>
class DSSM_BinaryInput
{
private:
HANDLE m_hndl;
HANDLE m_filemap;
HANDLE m_header;
HANDLE m_offsets;
HANDLE m_data;
// void* header_orig; // Don't need this since the header is at the start of the file
void* offsets_orig;
void* data_orig;
void* header_buffer;
void* offsets_buffer;
void* data_buffer;
size_t m_dim;
size_t mbSize;
size_t MAX_BUFFER = 300;
ElemType* values; // = (ElemType*)malloc(sizeof(float)* 230 * 1024);
int64_t* offsets; // = (int*)malloc(sizeof(int)* 230 * 1024);
int32_t* colIndices; // = (int*)malloc(sizeof(int)* (batchsize + 1));
int32_t* rowIndices; // = (int*)malloc(sizeof(int)* MAX_BUFFER * batchsize);
public:
int64_t numRows;
int32_t numCols;
int64_t totalNNz;
DSSM_BinaryInput();
~DSSM_BinaryInput();
void Init(std::wstring fileName, size_t dim);
bool SetupEpoch(size_t minibatchSize);
bool Next_Batch(Matrix<ElemType>& matrices, size_t cur, size_t numToRead, int* ordering);
void Dispose();
};
template <class ElemType>
class DSSMReader : public DataReaderBase
{
// public:
// typedef std::string LabelType;
// typedef unsigned LabelIdType;
private:
int* read_order; // array to shuffle to reorder the dataset
std::wstring m_featuresNameQuery;
std::wstring m_featuresNameDoc;
size_t m_featuresDimQuery;
size_t m_featuresDimDoc;
DSSM_BinaryInput<ElemType> dssm_queryInput;
DSSM_BinaryInput<ElemType> dssm_docInput;
size_t m_mbSize; // size of minibatch requested
LabelIdType m_labelIdMax; // maximum label ID we have encountered so far
LabelIdType m_labelDim; // maximum label ID we will ever see (used for array dimensions)
size_t m_mbStartSample; // starting sample # of the next minibatch
size_t m_epochSize; // size of an epoch
size_t m_epoch; // which epoch are we on
size_t m_epochStartSample; // the starting sample for the epoch
size_t m_totalSamples; // number of samples in the dataset
size_t m_randomizeRange; // randomization range
size_t m_featureCount; // feature count
size_t m_readNextSample; // next sample to read
bool m_labelFirst; // the label is the first element in a line
bool m_partialMinibatch; // a partial minibatch is allowed
LabelKind m_labelType; // labels are categories, create mapping table
RandomOrdering m_randomordering; // randomizing class
MBLayoutPtr m_pMBLayout;
std::wstring m_labelsName;
std::wstring m_featuresName;
std::wstring m_labelsCategoryName;
std::wstring m_labelsMapName;
ElemType* m_qfeaturesBuffer;
ElemType* m_dfeaturesBuffer;
ElemType* m_labelsBuffer;
LabelIdType* m_labelsIdBuffer;
std::wstring m_labelFileToWrite; // set to the path if we need to write out the label file
bool m_endReached;
int m_traceLevel;
// feature and label data are parallel arrays
std::vector<ElemType> m_featureData;
std::vector<LabelIdType> m_labelIdData;
std::vector<LabelType> m_labelData;
// map is from ElemType to LabelType
// For DSSM, we really only need an int for label data, but we have to transmit in Matrix, so use ElemType instead
std::map<LabelIdType, LabelType> m_mapIdToLabel;
std::map<LabelType, LabelIdType> m_mapLabelToId;
// caching support
DataReader* m_cachingReader;
DataWriter* m_cachingWriter;
ConfigParameters m_readerConfig;
size_t RandomizeSweep(size_t epochSample);
// bool Randomize() {return m_randomizeRange != randomizeNone;}
bool Randomize()
{
return false;
}
void SetupEpoch();
void StoreLabel(ElemType& labelStore, const LabelType& labelValue);
size_t RecordsToRead(size_t mbStartSample, bool tail = false);
void ReleaseMemory();
void WriteLabelFile();
virtual bool ReadRecord(size_t readSample);
public:
template <class ConfigRecordType>
void InitFromConfig(const ConfigRecordType&);
virtual void Init(const ConfigParameters& config) override
{
InitFromConfig(config);
}
virtual void Init(const ScriptableObjects::IConfigRecord& config) override
{
InitFromConfig(config);
}
virtual void Destroy();
DSSMReader()
: m_pMBLayout(make_shared<MBLayout>())
{
m_pMBLayout->SetUniqueAxisName(L"DSSMReader");
m_qfeaturesBuffer = NULL;
m_dfeaturesBuffer = NULL;
m_labelsBuffer = NULL;
}
virtual ~DSSMReader();
virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize);
virtual bool TryGetMinibatch(StreamMinibatchInputs& matrices);
size_t GetNumParallelSequencesForFixingBPTTMode()
{
return 1;
}
void SetNumParallelSequences(const size_t){};
void CopyMBLayoutTo(MBLayoutPtr pMBLayout)
{
pMBLayout->CopyFrom(m_pMBLayout);
NOT_IMPLEMENTED;
}
virtual const std::map<LabelIdType, LabelType>& GetLabelMapping(const std::wstring& sectionName);
virtual void SetLabelMapping(const std::wstring& sectionName, const std::map<LabelIdType, LabelType>& labelMapping);
virtual bool GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart = 0);
virtual bool DataEnd();
void SetRandomSeed(int)
{
NOT_IMPLEMENTED;
}
};
} } }