# pylint: disable=missing-docstring

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import os

import re

import sys

import tarfile

from six.moves import urllib

import tensorflow as tf

import numpy as np

import tensorflow.contrib.layers as layers

import data_train

FLAGS = tf.app.flags.FLAGS

arg_scope = tf.contrib.framework.arg_scope

# Basic model parameters.

tf.app.flags.DEFINE_integer('batch_size', 6,

"""Number of images to process in a batch.""")

tf.app.flags.DEFINE_string('data_dir', '/data/train_demo',

"""Path to the Anti-Spoofing data directory.""")

tf.app.flags.DEFINE_boolean('use_fp16', False,

"""Train the model using fp16.""")

# Global constants describing the CIFAR-10 data set.

IMAGE_SIZE = data_train.IMAGE_SIZE

NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = data_train.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN

NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = data_train.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

# Constants describing the training process.

MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average.

NUM_EPOCHS_PER_DECAY = 15.0 # Epochs after which learning rate decays.

LEARNING_RATE_DECAY_FACTOR = 0.8 # Learning rate decay factor.

INITIAL_LEARNING_RATE = 0.00003 # Initial learning rate.

R_FOR_LSE = 10

TOWER_NAME = 'tower'

def _activation_summary(x):

tf.summary.histogram(tensor_name + '/activations', x)

def _variable_on_cpu(name, shape, initializer):

return var

def _variable_with_weight_decay(name, shape, stddev, wd):

return var

def distorted_inputsB(a):

return images, dmaps, labels, sizes, slabels

def inputs(testset):

return images, dmaps, labels, sizes, slabels

def inference(images, size,labels, training_nn, training_class, _reuse):

return Live_est, conv17, conv11, GT2,conv17,images,thirdPart_fft_1,LabelsWholeImage, conv11_new,conv11_new , LabelsWholeImage2, sc, sc2, conv11_fir

#

def lossSecond(dmaps, smaps, labels, slabels, sc,GT2, fftmapA, A, B,bin_labels, bin_labels2,Nsc,Lsc,sc_fake, sc_real):

return tf.add_n(tf.get_collection('losses'), name='total_loss')

def lossThird(dmaps, smaps, labels, slabels, sc,GT2, fftmapA, A, B,bin_labels, bin_labels2,Nsc,Lsc,Allsc,sc_fake, sc_real):

return tf.add_n(tf.get_collection('losses'), name='total_loss')

def lossFirst(dmaps, smaps, labels, slabels, sc,GT2, fftmapA, A, B,bin_labels, bin_labels2,Nsc,Lsc,Allsc,sc_fake, sc_real, conv11_fir):

return tf.add_n(tf.get_collection('losses'), name='total_loss')

def _add_loss_summaries(total_loss):

return loss_averages_op

def train(total_loss, global_step, varName1):

return train_op