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extract_feature.py
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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_data', "*.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
print idx
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()