ataset.', default='png', choices=['jpg', 'png'])
parser.add_argument('--lfw_nrof_folds', type=int,
help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
性别、年龄识别。https://github.com/dpressel/rude-carnie 。
Adience 数据集。http://www.openu.ac.il/home/hassner/Adience/data.html#agegender 。26580张图片，2284类，年龄范围8个区段(0~2?4~6、8~13、15~20、25~32、38~43、48~53、60~)，含有噪声、姿势、光照变化。aligned # 经过剪裁对齐数据，faces # 原始数据。fold_0_data.txt至fold_4_data.txt 全部数据标记。fold_frontal_0_data.txt至fold_frontal_4_data.txt 仅用近似正面姿态面部标记。数据结构 user_id 用户Flickr帐户ID、original_image 图片文件名、face_id 人标识符、age、gender、x、y、dx、dy 人脸边框、tilt_ang 切斜角度、fiducial_yaw_angle 基准偏移角度、fiducial_score 基准分数。https://www.flickr.com/
数据预处理。脚本把数据处理成TFRecords格式。https://github.com/dpressel/rude-carnie/blob/master/preproc.py 。https://github.com/GilLevi/AgeGenderDeepLearning/tree/master/Folds文件夹，已经对训练集、测试集划分、标注。gender_train.txt、gender_val.txt 图片列表 Adience 数据集处理TFRecords文件。图片处理为大小256x256 JPEG编码RGB图像。tf.python_io.TFRecordWriter写入TFRecords文件，输出文件output_file。
构建模型。年龄、性别训练模型，Gil Levi、Tal Hassner论文《Age and Gender Classification Using Convolutional Neural Networks》http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.9654&rank=1 。模型 https://github.com/dpressel/rude-carnie/blob/master/model.py 。tenforflow.contrib.slim。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
TOWER_NAME = 'tower'
def select_model(name):
if name.startswith('inception'):
print('selected (fine-tuning) inception model')
return inception_v3
elif name == 'bn':
print('selected batch norm model')
return levi_hassner_bn
print('selected default model')
return levi_hassner
def get_checkpoint(checkpoint_path, requested_step=None, basename='checkpoint'):
if requested_step is not None:
model_checkpoint_path = '%s/%s-%s' % (checkpoint_path, basename, requested_step)
if os.path.exists(model_checkpoint_path) is None:
print('No checkpoint file found at [%s]' % checkpoint_path)
exit(-1)
print(model_checkpoint_path)
print(model_checkpoint_path)
return model_checkpoint_path, requested_step
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# Restore checkpoint as described in top of this program
print(ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
return ckpt.model_checkpoint_path, global_step
else:
print('No checkpoint file found at [%s]' % checkpoint_path)
exit(-1)
def _activation_summary(x):
tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
tf.summary.histogram(tensor_name + '/activations', x)
tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def inception_v3(nlabels, images, pkeep, is_training):
batch_norm_params = {
"is_training": is_training,
"trainable": True,
# Decay for the moving averages.
"decay"