first upload

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# 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
+.static_storage/
+.media/
+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
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

checkpoint/.gitignore

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+*
+!.gitignore

data/.gitignore

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+*
+!.gitignore

extract_feature.py

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+import os
+import sys
+import scipy.misc
+import pprint
+import numpy as np
+import time
+import tensorflow as tf
+import tensorlayer as tl
+from glob import glob
+from random import shuffle
+from tensorlayer.layers import *
+from utils import *
+from network import *
+
+pp = pprint.PrettyPrinter()
+
+"""
+TensorLayer implementation of DCGAN to generate face image.
+
+Usage : see README.md
+"""
+
+flags = tf.app.flags
+flags.DEFINE_integer("epoch", 100, "Epoch to train [25]")
+flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
+flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
+flags.DEFINE_integer("train_size", np.inf, "The size of train images [np.inf]")
+flags.DEFINE_integer("batch_size", 1, "The number of batch images [64]")
+flags.DEFINE_integer("image_size", 256, "The size of image to use (will be center cropped) [108]")
+flags.DEFINE_integer("output_size", 256, "The size of the output images to produce [64]")
+flags.DEFINE_integer("sample_size", 64, "The number of sample images [64]")
+flags.DEFINE_integer("c_dim", 3, "Dimension of image color. [3]")
+flags.DEFINE_integer("sample_step", 500, "The interval of generating sample. [500]")
+flags.DEFINE_integer("save_step", 50, "The interval of saveing checkpoints. [500]")
+flags.DEFINE_string("dataset", "uc_train_256_data", "The name of dataset [celebA, mnist, lsun]")
+flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
+flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
+flags.DEFINE_string("feature_dir", "features", "Directory name to save features")
+flags.DEFINE_boolean("is_train", False, "True for training, False for testing [False]")
+flags.DEFINE_boolean("is_crop", False, "True for training, False for testing [False]")
+flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
+FLAGS = flags.FLAGS
+
+
+
+def main(_):
+    pp.pprint(flags.FLAGS.__flags)
+
+    if not os.path.exists(FLAGS.checkpoint_dir):
+        os.makedirs(FLAGS.checkpoint_dir)
+    if not os.path.exists(FLAGS.sample_dir):
+        os.makedirs(FLAGS.sample_dir)
+
+
+    # with tf.device("/gpu:0"): # <-- if you have a GPU machine
+    real_images =  tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.output_size, FLAGS.output_size, FLAGS.c_dim], name='real_images')
+
+    # z --> generator for training
+
+
+    net_d, d_logits, features = discriminator_simplified_api(real_images, is_train=FLAGS.is_train, reuse=False)
+
+
+    sess=tf.Session()
+    tl.ops.set_gpu_fraction(sess=sess, gpu_fraction=0.88)
+    sess.run(tf.initialize_all_variables())
+
+    # load checkpoints
+    print("[*] Loading checkpoints...")
+    model_dir = "%s_%s_%s" % (FLAGS.dataset, 64, FLAGS.output_size)
+    save_dir = os.path.join(FLAGS.checkpoint_dir, model_dir)
+    #print save_dir
+    # load the latest checkpoints
+    nums = [75]
+    for num in nums:
+        net_g_name = os.path.join(save_dir, '%dnet_g.npz'%num)
+        net_d_name = os.path.join(save_dir, '%dnet_d.npz'%num)
+
+        print net_g_name, net_d_name
+
+        if not (os.path.exists(net_g_name) and os.path.exists(net_d_name)):
+            print("[!] Loading checkpoints failed!")
+        else:
+            net_d_loaded_params = tl.files.load_npz(name=net_d_name)
+            tl.files.assign_params(sess, net_d_loaded_params, net_d)
+            print("[*] Loading checkpoints SUCCESS!")
+
+        NUM_STYLE_LABELS =  21
+        style_label_file = './style_names.txt'
+        style_labels = list(np.loadtxt(style_label_file, str, delimiter='\n'))
+        if NUM_STYLE_LABELS > 0:
+            style_labels = style_labels[:NUM_STYLE_LABELS]
+
+
+        if not os.path.exists(FLAGS.feature_dir):
+            os.makedirs(FLAGS.feature_dir)
+
+        print 'extract traning feature'
+
+        data_files = glob(os.path.join("./data", 'uc_train_256_feat', "*.jpg"))
+        shuffle(data_files)
+
+
+        batch_idxs = min(len(data_files), FLAGS.train_size) // FLAGS.batch_size
+
+        lens = batch_idxs*FLAGS.batch_size
+
+
+        y = np.zeros(lens, dtype=np.uint8)
+        for i in xrange(lens):
+            for j in xrange(len(style_labels)):
+                if style_labels[j] in data_files[i]:
+                    y[i] = j
+                    break
+
+        feats = np.zeros((lens, 14336))
+
+        for idx in xrange(batch_idxs):
+            batch_files = data_files[idx*FLAGS.batch_size:(idx+1)*FLAGS.batch_size]
+            # get real images
+            batch = [get_image(batch_file, FLAGS.image_size, is_crop=FLAGS.is_crop, resize_w=FLAGS.output_size, is_grayscale = 0) for batch_file in batch_files]
+            batch_images = np.array(batch).astype(np.float32)
+            # update sample files based on shuffled data
+            #img, errG = sess.run([net_g2.outputs, g_loss], feed_dict={z : sample_seed})
+            feat = sess.run(features, feed_dict={real_images: batch_images})
+
+            #print feat.shape
+
+            begin = FLAGS.batch_size*idx
+            end = FLAGS.batch_size + begin
+            feats[begin:end, ...] = feat 
+
+        np.save('features/features%d_train.npy'%num, feats)
+        np.save('features/label%d_train.npy'%num, y)
+
+        print 'extract testing feature'
+        data_files = glob(os.path.join("./data", 'uc_test_256', "*.jpg"))
+        shuffle(data_files)
+        #data_files = data_files[0:5000]
+
+
+        batch_idxs = min(len(data_files), FLAGS.train_size) // FLAGS.batch_size
+
+        lens = batch_idxs*FLAGS.batch_size
+
+
+        y = np.zeros(lens, dtype=np.uint8)
+        for i in xrange(lens):
+            for j in xrange(len(style_labels)):
+                if style_labels[j] in data_files[i]:
+                    y[i] = j
+                    break
+
+        feats = np.zeros((lens, 14336))
+
+
+        for idx in xrange(batch_idxs):
+            batch_files = data_files[idx*FLAGS.batch_size:(idx+1)*FLAGS.batch_size]
+
+            batch = [get_image(batch_file, FLAGS.image_size, is_crop=FLAGS.is_crop, resize_w=FLAGS.output_size, is_grayscale = 0) for batch_file in batch_files]
+            batch_images = np.array(batch).astype(np.float32)
+            # update sample files based on shuffled data
+            #img, errG = sess.run([net_g2.outputs, g_loss], feed_dict={z : sample_seed})
+            feat = sess.run(features, feed_dict={real_images: batch_images})
+
+            begin = FLAGS.batch_size*idx
+            end = FLAGS.batch_size + begin
+            feats[begin:end, ...] = feat 
+
+            #print idx
+
+        np.save('features/features%d_test.npy'%num, feats)
+        np.save('features/label%d_test.npy'%num, y)
+
+
+
+if __name__ == '__main__':
+    tf.app.run()