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dataset.py
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dataset.py
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# -*- coding:utf-8 -*-
# Created Time: Oct 13 Apr 2017 04:07:50 PM CST
# Author: Taihong Xiao <[email protected]>
import numpy as np
import tensorflow as tf
import glob, os, time
from scipy import misc
from functools import partial
class Config:
@property
def base_dir(self):
return os.path.abspath(os.curdir)
@property
def data_dir(self):
data_dir = os.path.join('./datasets/celebA/')
if not os.path.exists(data_dir):
raise ValueError('Please specify a data dir.')
return data_dir
@property
def exp_dir(self):
exp_dir = os.path.join('train_log')
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
return exp_dir
@property
def model_dir(self):
model_dir = os.path.join(self.exp_dir, 'model')
if not os.path.exists(model_dir):
os.makedirs(model_dir)
return model_dir
@property
def log_dir(self):
log_dir = os.path.join(self.exp_dir, 'logs')
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
@property
def sample_img_dir(self):
sample_img_dir = os.path.join(self.exp_dir, 'sample_img')
if not os.path.exists(sample_img_dir):
os.makedirs(sample_img_dir)
return sample_img_dir
def g_lr(self, init_lr=0.00005, decay_rate=1, decay_step=10000, epoch=0):
return init_lr * decay_rate ** (epoch / np.float(decay_step))
def d_lr(self, init_lr=0.00005, decay_rate=1, decay_step=10000, epoch=0):
return init_lr * decay_rate ** (epoch / np.float(decay_step))
nhwc = [64,64,64,3]
num_threads = 10
shuffle = True
buffer_size = 640
max_iter = 200000
weight_decay = 5e-5
second_ratio = 0.25
config = Config()
class Dataset(object):
def __init__(self, feature_list, data_dir=config.data_dir, nhwc=config.nhwc, num_threads=config.num_threads, shuffle=config.shuffle, buffer_size=config.buffer_size):
super(Dataset, self).__init__()
self.data_dir = data_dir
self.feature_list = feature_list
self.n_feat = len(self.feature_list)
self.batch_size, self.height, self.width, self.channel = nhwc
self.num_threads = num_threads
self.buffer_size = buffer_size
self.shuffle = shuffle
self.tfrecords_dir = os.path.join(self.data_dir, 'align_5p_tfrecords')
self.filenames = [os.path.join(self.tfrecords_dir, name) for name in sorted(os.listdir(self.tfrecords_dir)) if name.endswith('.tfrecords')]
with open(os.path.join(self.data_dir, 'list_attr_celeba.txt'), 'r') as f:
lines = f.read().strip().split('\n')
self.attribute_names = lines[1].split()
def parse_fn(self, serialized_example):
features={
'image/id_name': tf.FixedLenFeature([], tf.string),
'image/height' : tf.FixedLenFeature([], tf.int64),
'image/width' : tf.FixedLenFeature([], tf.int64),
'image/encoded': tf.FixedLenFeature([], tf.string),
}
for name in self.feature_list:
features[name] = tf.FixedLenFeature([], tf.int64)
example = tf.parse_single_example(serialized_example, features=features)
image = tf.decode_raw(example['image/encoded'], tf.uint8)
raw_height = tf.cast(example['image/height'], tf.int32)
raw_width = tf.cast(example['image/width'], tf.int32)
image = tf.reshape(image, [raw_height, raw_width, 3])
image = tf.image.resize_images(image, size=[self.height, self.width])
# from IPython import embed; embed(); exit()
feature_val_list = [tf.cast(example[name], tf.float32) for name in self.feature_list]
return image, feature_val_list
def filter_fn(self, feat_id, pos, image, feature_val):
if pos:
return tf.equal(feature_val[feat_id], tf.ones_like(feature_val[feat_id]))
else:
return tf.equal(feature_val[feat_id], -tf.ones_like(feature_val[feat_id]))
def get_filter_fn(self, feat_id, pos):
return partial(self.filter_fn, feat_id, pos)
def input(self):
datasets = [tf.contrib.data.TFRecordDataset(self.filenames) for i in range(2*self.n_feat)]
datasets = [dataset.map(self.parse_fn, num_threads=self.num_threads) for dataset in datasets]
if self.shuffle:
datasets = [dataset.shuffle(self.buffer_size) for dataset in datasets]
datasets = [datasets[2*i+j].filter(self.get_filter_fn(i, pos)).repeat().batch(self.batch_size) for i in range(self.n_feat) for j, pos in enumerate([True, False])]
iterators = [dataset.make_one_shot_iterator() for dataset in datasets]
batchs = [iterator.get_next()[0] for iterator in iterators]
labels = [iterator.get_next()[1] for iterator in iterators]
return batchs, labels
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
config = Config()
celebA = Dataset(['Bangs', 'Smiling'])
batchs, labels = celebA.input()
# batch = celebA.input1()
X1 = tf.placeholder(tf.float32, config.nhwc)
X2 = tf.placeholder(tf.float32, config.nhwc)
Y = tf.reduce_mean(X1) + tf.reduce_mean(X2)
Z1 = tf.reduce_mean(X1)
Z2 = tf.reduce_mean(X2)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
t1 = time.time()
for i in range(100):
# print(i, sess.run(Y, feed_dict={X1: sess.run(batch1), X2: sess.run(batch2)}))
print(i)
batch_images, batch_labels = sess.run([batchs, labels])
print(batch_images[0].shape, batch_images[1].shape, batch_labels[0].shape, batch_labels[1].shape)
t2 = time.time()
print(t2-t1)
coord.request_stop()
coord.join(threads)