Homework3_李冠毅、李季紘

LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2016 Taehoon Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -20,8 +20,25 @@ Just for your convenience, here is a compilation of images from those clasees:<b
 <a>https://dl.dropboxusercontent.com/u/26848284/indoor.tgz</a><br>
 The link will be removed after homework due date.
 
-# Important dates
-* The generated images must be submitted before 6pm, ~~December 21~~ December 26.
-* Evaluation dataset will be avaialbe after 10pm, ~~December 21~~ December 26.
-* The prediction results must be submitted before 10am, ~~December 22~~ December 27.
-* Your written report is due 10pm, ~~December 22~~ December 27.
+# About our trained network
+Our network is trained based on indoor dataset and the codes within this repository(with orginate from <a href="https://github.com/carpedm20/DCGAN-tensorflow">carpedm20</a>). The presented result here is trained for 40 epochs and 64 batch_size. We have several other results, among them, we consider this one looks like good one.
+
+# Charts
+#### d_loss
+![d_loss](Charts/d_loss.png "d_loss")
+#### g_loss
+![g_loss](Charts/g_loss.png "g_loss")
+
+# Our report of this assignment
+We present our report as a <a href="HW3_Report.pdf">pdf file</a>.
+
+# Generated Samples
+Our generated 500 images from indoor dataset:
+Link: <a>https://drive.google.com/open?id=0BxFvBc0crSHbRksyOVUzLTdVeEU</a><br>
+
+# Member
+- 姓名：<a href="https://github.com/Timforce">李冠毅</a>　學號：104064510 <br>
+負責內容：Network evaluation，實驗討論，架構整理，報告彙整
+
+- 姓名：<a href="https://github.com/gjlnnv">李季紘</a>　學號：(交大)0556083 <br>
+負責內容：Network training，實驗討論，架構整理，程式修訂

download.py

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+"""
+Modification of https://github.com/stanfordnlp/treelstm/blob/master/scripts/download.py
+
+Downloads the following:
+- Celeb-A dataset
+- LSUN dataset
+- MNIST dataset
+"""
+
+from __future__ import print_function
+import os
+import sys
+import gzip
+import json
+import shutil
+import zipfile
+import argparse
+import subprocess
+from six.moves import urllib
+
+parser = argparse.ArgumentParser(description='Download dataset for DCGAN.')
+parser.add_argument('datasets', metavar='N', type=str, nargs='+', choices=['celebA', 'lsun', 'mnist'],
+                   help='name of dataset to download [celebA, lsun, mnist]')
+
+def download(url, dirpath):
+    filename = url.split('/')[-1]
+    filepath = os.path.join(dirpath, filename)
+    u = urllib.request.urlopen(url)
+    f = open(filepath, 'wb')
+    filesize = int(u.headers["Content-Length"])
+    print("Downloading: %s Bytes: %s" % (filename, filesize))
+
+    downloaded = 0
+    block_sz = 8192
+    status_width = 70
+    while True:
+        buf = u.read(block_sz)
+        if not buf:
+            print('')
+            break
+        else:
+            print('', end='\r')
+        downloaded += len(buf)
+        f.write(buf)
+        status = (("[%-" + str(status_width + 1) + "s] %3.2f%%") %
+            ('=' * int(float(downloaded) / filesize * status_width) + '>', downloaded * 100. / filesize))
+        print(status, end='')
+        sys.stdout.flush()
+    f.close()
+    return filepath
+
+def unzip(filepath):
+    print("Extracting: " + filepath)
+    dirpath = os.path.dirname(filepath)
+    with zipfile.ZipFile(filepath) as zf:
+        zf.extractall(dirpath)
+    os.remove(filepath)
+
+def download_celeb_a(dirpath):
+    data_dir = 'celebA'
+    if os.path.exists(os.path.join(dirpath, data_dir)):
+        print('Found Celeb-A - skip')
+        return
+    url = 'https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AADIKlz8PR9zr6Y20qbkunrba/Img/img_align_celeba.zip?dl=1&pv=1'
+    filepath = download(url, dirpath)
+    zip_dir = ''
+    with zipfile.ZipFile(filepath) as zf:
+        zip_dir = zf.namelist()[0]
+        zf.extractall(dirpath)
+    os.remove(filepath)
+    os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, data_dir))
+
+def _list_categories(tag):
+    url = 'http://lsun.cs.princeton.edu/htbin/list.cgi?tag=' + tag
+    f = urllib.request.urlopen(url)
+    return json.loads(f.read())
+
+def _download_lsun(out_dir, category, set_name, tag):
+    url = 'http://lsun.cs.princeton.edu/htbin/download.cgi?tag={tag}' \
+          '&category={category}&set={set_name}'.format(**locals())
+    print(url)
+    if set_name == 'test':
+        out_name = 'test_lmdb.zip'
+    else:
+        out_name = '{category}_{set_name}_lmdb.zip'.format(**locals())
+    out_path = os.path.join(out_dir, out_name)
+    cmd = ['curl', url, '-o', out_path]
+    print('Downloading', category, set_name, 'set')
+    subprocess.call(cmd)
+
+def download_lsun(dirpath):
+    data_dir = os.path.join(dirpath, 'lsun')
+    if os.path.exists(data_dir):
+        print('Found LSUN - skip')
+        return
+    else:
+        os.mkdir(data_dir)
+
+    tag = 'latest'
+    #categories = _list_categories(tag)
+    categories = ['bedroom']
+
+    for category in categories:
+        _download_lsun(data_dir, category, 'train', tag)
+        _download_lsun(data_dir, category, 'val', tag)
+    _download_lsun(data_dir, '', 'test', tag)
+
+def download_mnist(dirpath):
+    data_dir = os.path.join(dirpath, 'mnist')
+    if os.path.exists(data_dir):
+        print('Found MNIST - skip')
+        return
+    else:
+        os.mkdir(data_dir)
+    url_base = 'http://yann.lecun.com/exdb/mnist/'
+    file_names = ['train-images-idx3-ubyte.gz','train-labels-idx1-ubyte.gz','t10k-images-idx3-ubyte.gz','t10k-labels-idx1-ubyte.gz']
+    for file_name in file_names:
+        url = (url_base+file_name).format(**locals())
+        print(url)
+        out_path = os.path.join(data_dir,file_name)
+        cmd = ['curl', url, '-o', out_path]
+        print('Downloading ', file_name)
+        subprocess.call(cmd)
+        cmd = ['gzip', '-d', out_path]
+        print('Decompressing ', file_name)
+        subprocess.call(cmd)
+
+def prepare_data_dir(path = './data'):
+    if not os.path.exists(path):
+        os.mkdir(path)
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    prepare_data_dir()
+
+    if 'celebA' in args.datasets:
+        download_celeb_a('./data')
+    if 'lsun' in args.datasets:
+        download_lsun('./data')
+    if 'mnist' in args.datasets:
+        download_mnist('./data')