Merge pull request #20 from kartikdutt18/DarknetModel

Add Darknet model.

.gitignore

@@ -13,5 +13,6 @@ data/*
 *.jpg
 *.png
 *.txt
+*.bin
 .travis/configs.hpp
 Testing/*

CMakeLists.txt

@@ -215,7 +215,9 @@ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin/)
 # Recurse into each model.
 set(DIRS
   utils/
+  ensmallen_utils/
   dataloader/
+  models/
   tests/
 )
 

dataloader/dataloader.hpp

@@ -20,6 +20,7 @@
 #include <boost/foreach.hpp>
 #include <mlpack/core.hpp>
 #include <utils/utils.hpp>
+#include <set>
 
 
 #ifndef MODELS_DATALOADER_HPP

dataloader/dataloader_impl.hpp

@@ -383,7 +383,7 @@ template<
 
   // We use to endls here as one of them will be replaced by print
   // command below.
-  Log::Info << "Found " << imagesDirectory.size() << " belonging to " <<
+  mlpack::Log::Info << "Found " << imagesDirectory.size() << " belonging to " <<
       label << " class." << std::endl << std::endl;
 
   size_t loadedImages = 0;
@@ -395,7 +395,6 @@ template<
     {
       continue;
     }
-
     mlpack::data::ImageInfo imageInfo(imageWidth, imageHeight, imageDepth);
 
     // Load the image.
@@ -406,12 +405,14 @@ template<
     mlpack::data::Load(imageName.string(), image, imageInfo);
 
     // Add object to training set.
-    dataset.insert_cols(0, image);
-    labels.insert_cols(0, arma::vec(1).fill(label));
-
-    loadedImages++;
-    mlpack::Log::Info << "Loaded " << loadedImages << " out of " <<
-        imagesDirectory.size() << "\r" << std::endl;
+    if (image.n_rows == dataset.n_rows || dataset.n_elem == 0)
+    {
+      labels.insert_cols(0, arma::vec(1).fill(label));
+      dataset.insert_cols(0, image);
+      loadedImages++;
+      mlpack::Log::Info << "Loaded " << loadedImages << " out of " <<
+          imagesDirectory.size() << "\r" << std::endl;
+    }
   }
 }
 
@@ -485,6 +486,7 @@ template<
       validationData.n_rows - 1);
 
   augmentations.Transform(trainFeatures, imageWidth, imageHeight, imageDepth);
+  augmentations.Transform(validFeatures, imageWidth, imageHeight, imageDepth);
 
   mlpack::Log::Info << "Found " << totalClasses << " classes." << std::endl;
 

dataloader/preprocessor.hpp

@@ -55,6 +55,44 @@ class PreProcessor
   {
     // Nothing to do here. Added to match the rest of the codebase.
   }
+
+  /**
+   * Converts image to channel first format used in PyTorch. Performs the same function
+   * as torch.transforms.ToTensor().
+   *
+   * @param trainFeatures Input features that will be converted into channel first format.
+   * @param imageWidth Width of the image in dataset.
+   * @param imageHeight Height of the image in dataset.
+   * @param imageDepth Depth / Number of channels of the image in dataset.
+   */
+  static void ChannelFirstImages(DatasetX& trainFeatures,
+      const size_t imageWidth,
+      const size_t imageHeight,
+      const size_t imageDepth,
+      bool normalize = true)
+  {
+    for (size_t idx = 0; idx < trainFeatures.n_cols; idx++)
+    {
+        // Create a copy of the current image so that the image isn't affected.
+        arma::cube inputTemp(trainFeatures.col(idx).memptr(), 3, 224, 224);
+
+        size_t currentOffset = 0;
+        for (size_t i = 0; i < inputTemp.n_slices; i++)
+        {
+          trainFeatures.col(idx)(arma::span(currentOffset, currentOffset +
+              inputTemp.slice(i).n_elem - 1), arma::span()) =
+              arma::vectorise(inputTemp.slice(i).t());
+          currentOffset += inputTemp.slice(i).n_elem;
+        }
+    }
+
+    if (normalize)
+    {
+      // Convert each element to uint8 first and then divide by 255.
+      for (size_t i = 0; i < trainFeatures.n_elem; i++)
+          trainFeatures(i) = ((uint8_t)(trainFeatures(i)) / 255.0);
+    }
+  }
 };
 
 #endif

ensmallen_utils/CMakeLists.txt

@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.1.0 FATAL_ERROR)
+project(ensmallen_utils)
+
+set(DIR_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/)
+
+set(SOURCES
+    print_metric.hpp
+    periodic_save.hpp)
+
+foreach(file ${SOURCES})
+   set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file})
+endforeach()
+
+# Append sources (with directory name) to list of all models sources (used at
+# the parent scope).
+set(DIRS ${DIRS} ${DIR_SRCS} PARENT_SCOPE)

ensmallen_utils/periodic_save.hpp

@@ -0,0 +1,102 @@
+/**
+ * @file utils.hpp
+ * @author Kartik Dutt
+ *
+ * Definition of Periodic Save utility functions.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+
+#ifndef ENSMALLEN_CALLBACKS_PERIODIC_SAVE_HPP
+#define ENSMALLEN_CALLBACKS_PERIODIC_SAVE_HPP
+
+#include <ensmallen.hpp>
+#include <mlpack/core.hpp>
+
+namespace ens {
+
+/**
+ * Saves model being trained periodically.
+ *
+ * @tparam ANNType Type of model which will be used for evaluating metric.
+ */
+template<typename AnnType>
+class PeriodicSave
+{
+ public:
+  /**
+   * Constructor for PeriodicSave class.
+   *
+   * @param network Network type which will be saved periodically.
+   * @param filePath Base path / folder where weights will be saved.
+   * @param modelPrefix Weights will be stored as
+   *    modelPrefix_epoch_loss.bin.
+   * @param period Period after which the model will be saved.
+   * @param silent Boolean to determine whether or not to print saving
+   *    of model.
+   * @param output Outputstream where output will be directed.
+   */
+  PeriodicSave(AnnType& network,
+               const std::string filePath = "./",
+               const std::string modelPrefix = "model",
+               const size_t period = 1,
+               const bool silent = false,
+               std::ostream& output = arma::get_cout_stream()) :
+               network(network),
+               filePath(filePath),
+               modelPrefix(modelPrefix),
+               period(period),
+               silent(silent),
+               output(output)
+  {
+    // Nothing to do here.
+  }
+
+  template<typename OptimizerType, typename FunctionType, typename MatType>
+  bool EndEpoch(OptimizerType& /* optimizer */,
+                FunctionType& /* function */,
+                const MatType& /* coordinates */,
+                const size_t epoch,
+                const double objective)
+  {
+    if (epoch % period == 0)
+    {
+      std::string objectiveString = std::to_string(objective);
+      std::replace(objectiveString.begin(), objectiveString.end(), '.', '_');
+      std::string modelName = modelPrefix + "_" + std::to_string(epoch) + "_" +
+          objectiveString;
+      mlpack::data::Save(filePath + modelName + ".bin", modelPrefix, network);
+      if (!silent)
+        output << "Model saved as " << modelName << std::endl;
+    }
+
+    return false;
+  }
+
+ private:
+  // Reference to the model which will be used for evaluated using the metric.
+  AnnType& network;
+
+  // Locally held string that depicts path for saving the model.
+  std::string filePath;
+
+  // Locally held string that depicts the prefix name of model being trained.
+  std::string modelPrefix;
+
+  // Period to save the model.
+  size_t period;
+
+  // Locally held boolean to determine whether to print success / failure output
+  // when model is saved.
+  bool silent;
+
+  // The output stream that all data is to be sent to; example: std::cout.
+  std::ostream& output;
+};
+
+} // namespace ens
+
+#endif

ensmallen_utils/print_metric.hpp

@@ -0,0 +1,107 @@
+/**
+ * @file utils.hpp
+ * @author Kartik Dutt
+ *
+ * Definition of PrintMetric class.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+
+#ifndef ENSMALLEN_CALLBACKS_PRINT_METRIC_HPP
+#define ENSMALLEN_CALLBACKS_PRINT_METRIC_HPP
+
+#include <ensmallen.hpp>
+#include <functional>
+
+namespace ens {
+
+/**
+ * Prints metric on training / validation set.
+ *
+ * @tparam ANNType Type of model which will be used for evaluating metric.
+ * @tparam MetricType Metric class which must have static `Evaluate` function
+ *    that will be called at the end of the epoch.
+ * @tparam InputType Arma type of dataset features.
+ * @tparam OutputType Arma type of dataset labels.
+ */
+template<typename AnnType,
+         class MetricType,
+         typename InputType = arma::mat,
+         typename OutputType = arma::mat
+>
+class PrintMetric
+{
+ public:
+  /**
+   * Constructor for PrintMetric class.
+   * @param network Network type which will be saved periodically.
+   * @param features Input features on which model will be evaluated.
+   * @param responses Ground truth label for the mdoel.
+   * @param metricName Metric name which will be printed after each epoch.
+   * @param trainData Boolean to determine whether dataset corresponds to
+   *     training data or validation data.
+   * @param output Outputstream where output will be directed.
+   */
+  PrintMetric(AnnType &network,
+              const InputType &features,
+              const OutputType &responses,
+              const std::string metricName = "metric",
+              const bool trainData = false,
+              std::ostream &output = arma::get_cout_stream()) :
+              network(network),
+              features(features),
+              responses(responses),
+              metricName(metricName),
+              trainData(trainData),
+              output(output)
+  {
+     // Nothing to do here.
+  }
+
+  template<typename OptimizerType, typename FunctionType, typename MatType>
+  bool EndEpoch(OptimizerType& /* optimizer */,
+                FunctionType& /* function */,
+                const MatType& /* coordinates */,
+                const size_t /* epoch */,
+                const double /* objective */)
+  {
+    OutputType predictions;
+    network.Predict(features, predictions);
+    const double localObjective = MetricType::Evaluate(predictions, responses);
+    if (!std::isnan(localObjective))
+    {
+      std::string outputString = (trainData == true) ? "Train " : "Validation ";
+      outputString = outputString + metricName + " : " +
+          std::to_string(localObjective);
+      output << outputString << std::endl;
+    }
+    return false;
+  }
+
+ private:
+  // Reference to the model which will be used for evaluated using the metric.
+  AnnType& network;
+
+  // Dataset which will be used for evaluating the metric.
+  InputType features;
+
+  // Dataset labels / predictions that will be used for evaluating the dataset.
+  OutputType responses;
+
+  // Locally held string that depicts the name of the metric.
+  std::string metricName;
+
+  // Locally held boolean to determin whether evaluation is done on train data
+  // or validation data.
+  bool trainData;
+
+  // The output stream that all data is to be sent to; example: std::cout.
+  std::ostream& output;
+};
+
+} // namespace ens
+
+#endif

models/CMakeLists.txt

@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.1.0 FATAL_ERROR)
+project(models)
+
+add_subdirectory(darknet)
+
+# Add directory name to sources.
+set(DIR_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/)
+
+foreach(file ${SOURCES})
+  set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file})
+endforeach()
+
+set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/layer.hpp)
+# Append sources (with directory name) to list of all models sources (used at
+# the parent scope).
+set(DIRS ${DIRS} ${DIR_SRCS} PARENT_SCOPE)

models/darknet/CMakeLists.txt

@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.1.0 FATAL_ERROR)
+project(darknet)
+
+set(DIR_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/)
+include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../../")
+
+set(SOURCES
+  darknet.hpp
+  darknet_impl.hpp
+)
+
+foreach(file ${SOURCES})
+  set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file})
+endforeach()
+
+set(DIRS ${DIRS} ${DIR_SRCS} PARENT_SCOPE)