GANs mnist

mnist_gan/.gitignore

@@ -0,0 +1,9 @@
+.ipynb_checkpoints
+saved_models
+samples_posterior
+samples_csv_files
+mnist_first250_training_4s_and_9s.arm
+mnist_gan
+mnist_gan_generate
+mnist_gan_generate.o
+mnist_gan.o

mnist_gan/Makefile

@@ -0,0 +1,36 @@
+
+TARGET := mnist_gan_generate
+SRC := mnist_gan_generate.cpp
+LIBS_NAME := armadillo mlpack
+
+CXX := g++
+CXXFLAGS += -std=c++11 -Wall -Wextra -O3 -DNDEBUG
+# Use these CXXFLAGS instead if you want to compile with debugging symbols and
+# without optimizations.
+# CXXFLAGS += -std=c++11 -Wall -Wextra -g -O0
+LDFLAGS  += -fopenmp
+LDFLAGS += -lboost_serialization
+LDFLAGS += -larmadillo
+LDFLAGS += -L /home/viole/mlpack/build/lib/ # /path/to/mlpack/library/  # if installed locally.
+# Add header directories for any includes that aren't on the
+# default compiler search path.
+INCLFLAGS := -I /home/viole/mlpac/build/include/
+CXXFLAGS  += $(INCLFLAGS)
+
+OBJS := $(SRC:.cpp=.o)
+LIBS := $(addprefix -l,$(LIBS_NAME))
+CLEAN_LIST := $(TARGET) $(OBJS)
+
+# default rule
+default: all
+
+$(TARGET): $(OBJS)
+	$(CXX) $(CXXFLAGS) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS)
+
+.PHONY: all
+all: $(TARGET)
+
+.PHONY: clean
+clean:
+	@echo CLEAN $(CLEAN_LIST)
+	@rm -f $(CLEAN_LIST)

mnist_gan/gan_utils.hpp

@@ -0,0 +1,42 @@
+/**
+ * @file gan_utils.cpp
+ * @author Roshan Swain
+ * @author Atharva Khandait
+ *
+ * Utility function necessary for  working with GAN models.
+ *
+ * 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 MODELS_GAN_UTILS_HPP
+#define MODELS_GAN_UTILS_HPP
+
+#include <mlpack/core.hpp>
+#include <mlpack/methods/ann/ffn.hpp>
+
+using namespace mlpack;
+using namespace mlpack::ann;
+
+// Sample from the output distribution and post-process the outputs(because
+// we pre-processed it before passing it to the model).
+template<typename DataType = arma::mat>
+void GetSample(DataType &input, DataType& samples, bool isBinary)
+{
+  if (isBinary)
+  {
+    samples = arma::conv_to<DataType>::from(
+        arma::randu<DataType>(input.n_rows, input.n_cols) <= input);
+    samples *= 255;
+  }
+  else
+  {
+    samples = input / 2 + 0.5;
+    samples *= 255;
+    samples = arma::clamp(samples, 0, 255);
+  }
+}
+
+#endif

mnist_gan/generate_images.py

@@ -0,0 +1,91 @@
+"""
+@file generate_images.py
+@author Atharva Khandait
+Generates jpg files from csv.
+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.
+"""
+
+from PIL import Image
+import numpy as np
+import cv2
+import os
+
+def ImagesFromCSV(filename,
+                  imgShape = (28, 28),
+                  destination = 'samples',
+                  saveIndividual = False):
+
+  # Import the data into a numpy matrix.
+  samples = np.genfromtxt(filename, delimiter = ',', dtype = np.uint8)
+
+  # Reshape and save it as an image in the destination.
+  tempImage = Image.fromarray(np.reshape(samples[:, 0], imgShape), 'L')
+  if saveIndividual:
+    tempImage.save(destination + '/sample0.jpg')
+
+  # All the images will be concatenated to this for a combined image.
+  allSamples = tempImage
+
+  for i in range(1, samples.shape[1]):
+    tempImage = np.reshape(samples[:, i], imgShape)
+
+    allSamples = np.concatenate((allSamples, tempImage), axis = 1)
+
+    tempImage = Image.fromarray(tempImage, 'L')
+    if saveIndividual:
+      tempImage.save(destination + '/sample' + str(i) + '.jpg')
+
+  tempImage = allSamples
+  allSamples = Image.fromarray(allSamples, 'L')
+  allSamples.save(destination + '/allSamples' + '.jpg')
+
+  print ('Samples saved in ' + destination + '/.')
+
+  return tempImage
+
+# Save posterior samples.
+ImagesFromCSV('./samples_csv_files/samples_posterior.csv', destination =
+    'samples_posterior')
+
+# Save prior samples with individual latent varying.
+latentSize = 10
+allLatent = ImagesFromCSV('./samples_csv_files/samples_prior_latent0.csv',
+    destination = 'samples_prior')
+
+for i in range(1, latentSize):
+  allLatent = np.concatenate((allLatent,
+      (ImagesFromCSV('./samples_csv_files/samples_prior_latent' + str(i) + '.csv',
+      destination = 'samples_prior'))), axis = 0)
+
+saved = Image.fromarray(allLatent, 'L')
+saved.save('./samples_prior/allLatent.jpg')
+
+# Save prior samples with 2d latent varying.
+nofSamples = 20
+allLatent = ImagesFromCSV('./samples_csv_files/samples_prior_latent_2d0.csv',
+    destination = 'latent')
+
+for i in range(1, nofSamples):
+  allLatent = np.concatenate((allLatent,
+      (ImagesFromCSV('./samples_csv_files/samples_prior_latent_2d' + str(i) +
+      '.csv', destination = 'samples_prior'))), axis = 0)
+
+saved = Image.fromarray(allLatent, 'L')
+saved.save('./samples_prior/2dLatent.jpg')
+
+# AVI file
+vid_fname = 'gans_celebface_training1.avi'
+sample_dir = " "
+
+files = [os.path.join(sample_dir, f) for f in os.listdir(sample_dir) if 'generated' in f]
+files.sort()
+
+out = cv2.VideoWriter(vid_fname, cv2.VideoWriter_fourcc(*'MP4V'), 1, (530, 530))
+[out.write(cv2.imread(fname)) for fname in files]
+out.release()
+
+
+###Output###