From d98ec63fbd87851a351a2e24cabae0ce20dc5b71 Mon Sep 17 00:00:00 2001
From: mandlm <mandlm@molez.net>
Date: Sat, 31 Oct 2015 12:28:35 +0100
Subject: [PATCH] Completed loading of MNIST Databases

---
 gui/NeuroUI/mnistloader.cpp | 36 +++++++++++++++++++-----------------
 gui/NeuroUI/mnistloader.h   | 22 ++++++++++++++++++++++
 gui/NeuroUI/netlearner.cpp  |  2 ++
 3 files changed, 43 insertions(+), 17 deletions(-)

diff --git a/gui/NeuroUI/mnistloader.cpp b/gui/NeuroUI/mnistloader.cpp
index 6c2c7dd..4eac8bb 100644
--- a/gui/NeuroUI/mnistloader.cpp
+++ b/gui/NeuroUI/mnistloader.cpp
@@ -29,7 +29,7 @@ void MnistLoader::loadDatabase(const std::string &fileName)
     }
 
     int32_t magicNumber = readInt32(databaseFile);
-    if (magicNumber != 2051)
+    if (magicNumber != DatabaseFileMagicNumber)
     {
         throw std::runtime_error("unexpected data reading MNIST database file");
     }
@@ -38,18 +38,19 @@ void MnistLoader::loadDatabase(const std::string &fileName)
     int32_t sampleWidth = readInt32(databaseFile);
     int32_t sampleHeight = readInt32(databaseFile);
 
-    std::list<std::unique_ptr<int8_t[]>> samples;
-
-    for (size_t sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
+    if (sampleWidth != SampleWidth || sampleHeight != SampleHeight)
     {
-        std::unique_ptr<int8_t[]> sampleData = std::make_unique<int8_t[]>(sampleWidth * sampleHeight);
-
-        databaseFile.read(reinterpret_cast<char *>(sampleData.get()), sampleWidth * sampleHeight);
-
-        samples.push_back(std::move(sampleData));
+        throw std::runtime_error("unexpected sample size loading MNIST database");
     }
 
-    return;
+    for (int32_t sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
+    {
+        std::unique_ptr<MnistSample> sample = std::make_unique<MnistSample>();
+
+        databaseFile.read(reinterpret_cast<char *>(sample->data), sampleWidth * sampleHeight);
+
+        samples.push_back(std::move(sample));
+    }
 }
 
 void MnistLoader::loadLabels(const std::string &fileName)
@@ -63,21 +64,22 @@ void MnistLoader::loadLabels(const std::string &fileName)
     }
 
     int32_t magicNumber = readInt32(labelFile);
-    if (magicNumber != 2049)
+    if (magicNumber != LabelFileMagicNumber)
     {
         throw std::runtime_error("unexpected data reading MNIST label file");
     }
 
     int32_t labelCount = readInt32(labelFile);
-
-    std::list<int8_t> labels;
-
-    for (size_t labelIndex = 0; labelIndex < labelCount; ++labelIndex)
+    if (labelCount != static_cast<int32_t>(samples.size()))
     {
-        labels.push_back(readInt8(labelFile));
+        throw std::runtime_error("MNIST database and label files don't match in size");
     }
 
-    return;
+    auto sampleIt = samples.begin();
+    for (int32_t labelIndex = 0; labelIndex < labelCount; ++labelIndex)
+    {
+        (*sampleIt++)->label = readInt8(labelFile);
+    }
 }
 
 int8_t MnistLoader::readInt8(std::ifstream &file)
diff --git a/gui/NeuroUI/mnistloader.h b/gui/NeuroUI/mnistloader.h
index e15081c..482931c 100644
--- a/gui/NeuroUI/mnistloader.h
+++ b/gui/NeuroUI/mnistloader.h
@@ -2,10 +2,32 @@
 #define MNISTLOADER_H
 
 #include <string>
+#include <list>
+#include <memory>
 #include <inttypes.h>
 
 class MnistLoader
 {
+private:
+    static const uint32_t DatabaseFileMagicNumber = 2051;
+    static const uint32_t LabelFileMagicNumber = 2049;
+    static const size_t SampleWidth = 28;
+    static const size_t SampleHeight = 28;
+
+public:
+    template<size_t SAMPLE_WIDTH, size_t SAMPLE_HEIGHT>
+    class Sample
+    {
+    public:
+        uint8_t label;
+        uint8_t data[SAMPLE_WIDTH * SAMPLE_HEIGHT];
+    };
+
+    using MnistSample = Sample<SampleWidth, SampleHeight>;
+
+private:
+    std::list<std::unique_ptr<MnistSample>> samples;
+
 public:
     MnistLoader();
 
diff --git a/gui/NeuroUI/netlearner.cpp b/gui/NeuroUI/netlearner.cpp
index 51b5632..0b96baf 100644
--- a/gui/NeuroUI/netlearner.cpp
+++ b/gui/NeuroUI/netlearner.cpp
@@ -21,6 +21,8 @@ void NetLearner::run()
         emit logMessage("done");
         emit progress(0.0);
 
+        return;
+
         Net digitClassifier({32*32, 16*16, 32, 1});
 
         timer.start();