https://github.com/Microsoft/CNTK
Tip revision: c48e1a4ab7b4ed309492623fd936467a9662b890 authored by Frank Seide on 29 January 2016, 20:27:10 UTC
merged with pkranen/doc
merged with pkranen/doc
Tip revision: c48e1a4
EvalActions.cpp
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
// EvalActions.cpp -- CNTK evaluation-related actions
//
#define _CRT_NONSTDC_NO_DEPRECATE // make VS accept POSIX functions without _
#include "stdafx.h"
#include "Basics.h"
#include "Actions.h"
#include "ComputationNetwork.h"
#include "ComputationNode.h"
#include "DataReader.h"
#include "DataWriter.h"
#include "Config.h"
#include "SimpleEvaluator.h"
#include "SimpleOutputWriter.h"
#include "BestGpu.h"
#include "ScriptableObjects.h"
#include "BrainScriptEvaluator.h"
#include <string>
#include <chrono>
#include <algorithm>
#include <vector>
#include <iostream>
#include <queue>
#include <set>
#include <memory>
#ifndef let
#define let const auto
#endif
using namespace std;
using namespace Microsoft::MSR;
using namespace Microsoft::MSR::CNTK;
// ===========================================================================
// DoEvalBase() - implements CNTK "eval" command
// ===========================================================================
template <typename ElemType>
static void DoEvalBase(const ConfigParameters& config, IDataReader<ElemType>& reader)
{
DEVICEID_TYPE deviceId = DeviceFromConfig(config);
ConfigArray minibatchSize = config(L"minibatchSize", "40960");
size_t epochSize = config(L"epochSize", "0");
if (epochSize == 0)
{
epochSize = requestDataSize;
}
wstring modelPath = config(L"modelPath");
intargvector mbSize = minibatchSize;
int traceLevel = config(L"traceLevel", "0");
size_t numMBsToShowResult = config(L"numMBsToShowResult", "100");
ConfigArray evalNodeNames = config(L"evalNodeNames", "");
vector<wstring> evalNodeNamesVector;
for (int i = 0; i < evalNodeNames.size(); ++i)
{
evalNodeNamesVector.push_back(evalNodeNames[i]);
}
auto net = ComputationNetwork::CreateFromFile<ElemType>(deviceId, modelPath);
SimpleEvaluator<ElemType> eval(net, numMBsToShowResult, traceLevel);
eval.Evaluate(&reader, evalNodeNamesVector, mbSize[0], epochSize);
}
template <typename ElemType>
void DoEval(const ConfigParameters& config)
{
// test
ConfigParameters readerConfig(config(L"reader"));
readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
DataReader<ElemType> testDataReader(readerConfig);
DoEvalBase(config, testDataReader);
}
template void DoEval<double>(const ConfigParameters& config);
template void DoEval<float>(const ConfigParameters& config);
// ===========================================================================
// DoCrossValidate() - implements CNTK "cv" command
// ===========================================================================
template <typename ElemType>
void DoCrossValidate(const ConfigParameters& config)
{
// test
ConfigParameters readerConfig(config(L"reader"));
readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
DEVICEID_TYPE deviceId = DeviceFromConfig(config);
ConfigArray minibatchSize = config(L"minibatchSize", "40960");
size_t epochSize = config(L"epochSize", "0");
if (epochSize == 0)
{
epochSize = requestDataSize;
}
wstring modelPath = config(L"modelPath");
intargvector mbSize = minibatchSize;
ConfigArray cvIntervalConfig = config(L"crossValidationInterval");
intargvector cvInterval = cvIntervalConfig;
size_t sleepSecondsBetweenRuns = config(L"sleepTimeBetweenRuns", "0");
int traceLevel = config(L"traceLevel", "0");
size_t numMBsToShowResult = config(L"numMBsToShowResult", "100");
ConfigArray evalNodeNames = config(L"evalNodeNames", "");
vector<wstring> evalNodeNamesVector;
for (int i = 0; i < evalNodeNames.size(); ++i)
{
evalNodeNamesVector.push_back(evalNodeNames[i]);
}
std::vector<std::vector<double>> cvErrorResults;
std::vector<std::wstring> cvModels;
DataReader<ElemType> cvDataReader(readerConfig);
bool finalModelEvaluated = false;
for (size_t i = cvInterval[0]; i <= cvInterval[2]; i += cvInterval[1])
{
wstring cvModelPath = msra::strfun::wstrprintf(L"%ls.%lld", modelPath.c_str(), i);
if (!fexists(cvModelPath))
{
fprintf(stderr, "model %ls does not exist.\n", cvModelPath.c_str());
if (finalModelEvaluated || !fexists(modelPath))
continue; // file missing
else
{
cvModelPath = modelPath;
finalModelEvaluated = true;
}
}
cvModels.push_back(cvModelPath);
auto net = ComputationNetwork::CreateFromFile<ElemType>(deviceId, cvModelPath);
SimpleEvaluator<ElemType> eval(net, numMBsToShowResult, traceLevel);
fprintf(stderr, "model %ls --> \n", cvModelPath.c_str());
auto evalErrors = eval.Evaluate(&cvDataReader, evalNodeNamesVector, mbSize[0], epochSize);
cvErrorResults.push_back(evalErrors);
::Sleep(1000 * sleepSecondsBetweenRuns);
}
// find best model
if (cvErrorResults.size() == 0)
{
LogicError("No model is evaluated.");
}
std::vector<double> minErrors;
std::vector<int> minErrIds;
std::vector<double> evalErrors = cvErrorResults[0];
for (int i = 0; i < evalErrors.size(); ++i)
{
minErrors.push_back(evalErrors[i]);
minErrIds.push_back(0);
}
for (int i = 0; i < cvErrorResults.size(); i++)
{
evalErrors = cvErrorResults[i];
for (int j = 0; j < evalErrors.size(); j++)
{
if (evalErrors[j] < minErrors[j])
{
minErrors[j] = evalErrors[j];
minErrIds[j] = i;
}
}
}
fprintf(stderr, "Best models:\n");
fprintf(stderr, "------------\n");
for (int i = 0; i < minErrors.size(); ++i)
{
fprintf(stderr, "Based on Err[%d]: Best model = %ls with min err %.8g\n", i, cvModels[minErrIds[i]].c_str(), minErrors[i]);
}
}
template void DoCrossValidate<float>(const ConfigParameters& config);
template void DoCrossValidate<double>(const ConfigParameters& config);
// ===========================================================================
// DoWriteOutput() - implements CNTK "write" command
// ===========================================================================
template <typename ElemType>
void DoWriteOutput(const ConfigParameters& config)
{
ConfigParameters readerConfig(config(L"reader"));
readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
readerConfig.Insert("randomize", "None"); // we don't want randomization when output results
DataReader<ElemType> testDataReader(readerConfig);
DEVICEID_TYPE deviceId = DeviceFromConfig(config);
ConfigArray minibatchSize = config(L"minibatchSize", "2048");
wstring modelPath = config(L"modelPath");
intargvector mbSize = minibatchSize;
size_t epochSize = config(L"epochSize", "0");
if (epochSize == 0)
{
epochSize = requestDataSize;
}
ConfigArray outputNodeNames = config(L"outputNodeNames", "");
vector<wstring> outputNodeNamesVector;
for (int i = 0; i < outputNodeNames.size(); ++i)
{
outputNodeNamesVector.push_back(outputNodeNames[i]);
}
auto net = ComputationNetwork::CreateFromFile<ElemType>(deviceId, modelPath);
SimpleOutputWriter<ElemType> writer(net, 1);
if (config.Exists("writer"))
{
ConfigParameters writerConfig(config(L"writer"));
bool bWriterUnittest = writerConfig(L"unittest", "false");
DataWriter<ElemType> testDataWriter(writerConfig);
writer.WriteOutput(testDataReader, mbSize[0], testDataWriter, outputNodeNamesVector, epochSize, bWriterUnittest);
}
else if (config.Exists("outputPath"))
{
wstring outputPath = config(L"outputPath"); // crashes if no default given?
writer.WriteOutput(testDataReader, mbSize[0], outputPath, outputNodeNamesVector, epochSize);
}
// writer.WriteOutput(testDataReader, mbSize[0], testDataWriter, outputNodeNamesVector, epochSize);
}
template void DoWriteOutput<float>(const ConfigParameters& config);
template void DoWriteOutput<double>(const ConfigParameters& config);