Project for ACIT-4630: Advanced Machine Learning and Deep Learning.
Marit Øye Gjersdal, Jon-Olav Holland, Pål Anders Owren.
Linux / Windows 10
CUDA
Python 3
git clone https://github.com/owren/colorization_gan
cd colorization_gan/
pip install -r requirements.txt
Dataset is part of the repository in the data folder. The dataset can also be found at https://www.kaggle.com/puneet6060/intel-image-classification
Parameters for the dataset location, data size, enabling debugging, CUDA settings, and more, can be found in the source/config.py file.
import tensorflow as tf
import os
# DEVELOPER VARIABLES
ENABLE_CUDA = False
DEBUG_MODE = True
# DATA INFORMATION
WIDTH = 128
HEIGHT = 128
BATCH_SIZE = 16
# TRAINING INFORMATION
EPOCHS = 1000
MODEL_SAVE = 50 # How often to save the model
# TRAINING/MODEL HYPERPARAMTERS
learning_rate = 2e-4
g_optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.5)
d_optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.5)
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=False)
# DIRECTORY INFORMATION
experiment_name = "experiment_1"
path = os.path.abspath("../")
DATA_PATH = os.path.join(path, "data/seg_train/forest")
VALIDATION_PATH = os.path.join(path, "data/seg_validation/forest")
TEST_PATH = os.path.join(path, "data/seg_test/forest")
MODEL_PATH = os.path.join(path, "models/" + experiment_name + "/")
RESULT_PATH = os.path.join(path, "result/" + experiment_name + "/")
train_loss_filename = os.path.join(path, "loss/" + experiment_name + "_train.csv")
validation_loss_filename = os.path.join(path, "loss/" + experiment_name + "_validation.csv")
experiment_name is the name of the current experiment.
ENABLE_CUDA may be neccessary when running the project locally with CUDA.
MODEL_SAVE indicates how often the model is saved.
cd source
python main.py
The model is saved to models/experiment_name/. A text file is used to keep track of the most recently saved model.
cd source
python test.py
The most recently saved model is used on the test dataset. The result is saved to result/experiment_name/.
A trained model can be found at https://drive.google.com/drive/folders/1uMqtH-vjJnuymHuuT0J-nITrrBUuT7sm?usp=sharing. To use the trained model download the generator and discriminiator and put them in the models folder. Then add the filename of the generator on the first line of the models.txt file and the filename of the discriminiator to the second line.
Image-to-Image Translation with Conditional Adversarial Networks (https://arxiv.org/abs/1611.07004)
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs (https://arxiv.org/abs/1903.03519)