initial commit

.gitignore

@@ -0,0 +1,128 @@
+# Data
+data/hand
+data/gaze
+data/*
+samples
+outputs
+
+# ipython checkpoints
+.ipynb_checkpoints
+
+# Log
+logs
+
+# ETC
+paper.pdf
+.DS_Store
+
+# Created by https://www.gitignore.io/api/python,vim
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+.venv/
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+
+### Vim ###
+# swap
+[._]*.s[a-v][a-z]
+[._]*.sw[a-p]
+[._]s[a-v][a-z]
+[._]sw[a-p]
+# session
+Session.vim
+# temporary
+.netrwhist
+*~
+# auto-generated tag files
+tags
+
+# End of https://www.gitignore.io/api/python,vim

README.md

@@ -0,0 +1,57 @@
+# BEGAN in Tensorflow
+
+Tensorflow implementation of [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/abs/1703.10717).
+
+![alt tag](./assets/model.png)
+
+
+## Requirements
+
+- Python 2.7
+- [Pillow](https://pillow.readthedocs.io/en/4.0.x/)
+- [tqdm](https://github.com/tqdm/tqdm)
+- [TensorFlow](https://github.com/tensorflow/tensorflow)
+- [requests](https://github.com/kennethreitz/requests) (Only used for downloading CelebA dataset)
+
+
+## Usage
+
+First download [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) datasets with:
+
+    $ apt-get install p7zip-full # ubuntu
+    $ brew install p7zip # Mac
+    $ python download.py
+
+or you can use your own dataset by placing images like:
+
+    data
+    └── YOUR_DATASET_NAME
+        ├── xxx.jpg (name doesn't matter)
+        ├── yyy.jpg
+        └── ...
+
+To train a model:
+
+    $ python main.py --dataset=CelebA --num_gpu=1
+    $ python main.py --dataset=YOUR_DATASET_NAME --num_gpu=4
+
+To test a model (use your `load_path`):
+
+    $ python main.py --dataset=CelebA --load_path=./logs/CelebA_0405_124806 --num_gpu=0 --is_train=False --split valid
+
+
+## Results
+
+- [BEGAN-tensorflow](https://github.com/carpedm20/began-tensorflow) at least can generate human faces but [BEGAN-pytorch](https://github.com/carpedm20/BEGAN-pytorch) can't.
+- Both [BEGAN-tensorflow](https://github.com/carpedm20/began-tensorflow) and [BEGAN-pytorch](https://github.com/carpedm20/BEGAN-pytorch) shows **modal collapses** and I guess this is due to a wrong scheuduling of lr (Paper mentioned that *simply reducing the lr was sufficient to avoid them*).
+
+### Reusults after 82400 step
+
+![alt tag](./assets/82400_1.png) ![alt tag](./assets/82400_2.png)
+
+(in progress)
+
+
+## Author
+
+Taehoon Kim / [@carpedm20](http://carpedm20.github.io)

config.py

@@ -0,0 +1,66 @@
+#-*- coding: utf-8 -*-
+import argparse
+
+def str2bool(v):
+    return v.lower() in ('true', '1')
+
+arg_lists = []
+parser = argparse.ArgumentParser()
+
+def add_argument_group(name):
+    arg = parser.add_argument_group(name)
+    arg_lists.append(arg)
+    return arg
+
+# Network
+net_arg = add_argument_group('Network')
+net_arg.add_argument('--input_scale_size', type=int, default=64,
+                     help='input image will be resized with the given value as width and height')
+net_arg.add_argument('--conv_hidden_num', type=int, default=128, help='n in the paper')
+net_arg.add_argument('--z_num', type=int, default=128)
+
+# Data
+data_arg = add_argument_group('Data')
+data_arg.add_argument('--dataset', type=str, default='CelebA')
+data_arg.add_argument('--split', type=str, default='train')
+data_arg.add_argument('--batch_size', type=int, default=16)
+data_arg.add_argument('--grayscale', type=str2bool, default=False)
+data_arg.add_argument('--num_worker', type=int, default=12)
+
+# Training / test parameters
+train_arg = add_argument_group('Training')
+train_arg.add_argument('--is_train', type=str2bool, default=True)
+train_arg.add_argument('--optimizer', type=str, default='adam')
+train_arg.add_argument('--max_step', type=int, default=250000)
+train_arg.add_argument('--lr_update_step', type=int, default=3000)
+train_arg.add_argument('--d_lr', type=float, default=0.0001)
+train_arg.add_argument('--g_lr', type=float, default=0.0001)
+train_arg.add_argument('--beta1', type=float, default=0.5)
+train_arg.add_argument('--beta2', type=float, default=0.999)
+train_arg.add_argument('--gamma', type=float, default=0.5)
+train_arg.add_argument('--lambda_k', type=float, default=0.001)
+train_arg.add_argument('--use_gpu', type=float, default=True)
+
+# Misc
+misc_arg = add_argument_group('Misc')
+misc_arg.add_argument('--load_path', type=str, default='')
+misc_arg.add_argument('--log_step', type=int, default=50)
+misc_arg.add_argument('--save_step', type=int, default=5000)
+misc_arg.add_argument('--num_log_samples', type=int, default=3)
+misc_arg.add_argument('--log_level', type=str, default='INFO', choices=['INFO', 'DEBUG', 'WARN'])
+misc_arg.add_argument('--log_dir', type=str, default='logs')
+misc_arg.add_argument('--data_dir', type=str, default='data')
+misc_arg.add_argument('--test_data_path', type=str, default=None,
+                      help='directory with images which will be used in test sample generation')
+misc_arg.add_argument('--sample_per_image', type=int, default=64,
+                      help='# of sample per image during test sample generation')
+misc_arg.add_argument('--random_seed', type=int, default=123)
+
+def get_config():
+    config, unparsed = parser.parse_known_args()
+    if config.use_gpu:
+        data_format = 'NCHW'
+    else:
+        data_format = 'NHWC'
+    setattr(config, 'data_format', data_format)
+    return config, unparsed

data_loader.py

@@ -0,0 +1,56 @@
+import os
+from PIL import Image
+from glob import glob
+import tensorflow as tf
+
+def get_loader(root, batch_size, scale, data_format, split=None, is_grayscale=False, seed=None):
+    dataset_name = os.path.basename(root)
+    if dataset_name in ['CelebA'] and split:
+        root = os.path.join(root, 'splits', split)
+
+    for ext in ["jpg", "png"]:
+        paths = glob("{}/*.{}".format(root, ext))
+
+        if ext == "jpg":
+            tf_decode = tf.image.decode_jpeg
+        elif ext == "png":
+            tf_decode = tf.image.decode_png
+
+        if len(paths) != 0:
+            break
+
+    with Image.open(paths[0]) as img:
+        w, h = img.size
+        shape = [h, w, 3]
+
+    filename_queue = tf.train.string_input_producer(list(paths), shuffle=False, seed=seed)
+    reader = tf.WholeFileReader()
+    filename, data = reader.read(filename_queue)
+    image = tf_decode(data, channels=3)
+
+    if is_grayscale:
+        image = tf.image.rgb_to_grayscale(image)
+    image.set_shape(shape)
+
+    min_after_dequeue = 5000
+    capacity = min_after_dequeue + 3 * batch_size
+
+    queue = tf.train.shuffle_batch(
+        [image], batch_size=batch_size,
+        num_threads=4, capacity=capacity,
+        min_after_dequeue=min_after_dequeue, name='synthetic_inputs')
+
+    if dataset_name in ['CelebA']:
+        queue = tf.image.resize_nearest_neighbor(queue, [78, 64])
+        queue = tf.image.crop_to_bounding_box(queue, 7, 0, 64, 64)
+    else:
+        queue = tf.image.resize_nearest_neighbor(queue, [scale_size, scale_size])
+
+    if data_format == 'NCHW':
+        queue = tf.transpose(queue, [0, 3, 1, 2])
+    elif data_format == 'NHWC':
+        pass
+    else:
+        raise Exception("[!] Unkown data_format: {}".format(data_format))
+
+    return tf.to_float(queue)