import tensorflow as tf
import numpy as np
from datetime import datetime
import math
import time
# import readdata

batch_size = 32
num_batchs = 100
# class_num = 10
# max_step = 10000
#
# model_path = '../../model/Inception/'
# model_name = 'model.ckpt'
#
# keep_prob = tf.placeholder(tf.float32, None, name='keep_prob')
# x = tf.placeholder(tf.float32, [None, 224, 224, 3], name='x')
# y_ = tf.placeholder(tf.float32, [None, class_num], name='y_')

slim = tf.contrib.slim

def trunc_normal(stddtv):
    return tf.truncated_normal_initializer(0.0,stddtv)

def inception_v3_arg_scope(weight_decay = 0.00004,
                           stddev = 0.1,
                           batch_norm_var_clooection = 'moving_vars'):
    batch_norm_params = {
        'decay':0.9997,
        'epsilon':0.001,
        'updates_collections':tf.GraphKeys.UPDATE_OPS,
        'variables_collections':{
            'beta':None,
            'gamma':None,
            'moving_mean':[batch_norm_var_clooection],
            'moving_variance':[batch_norm_var_clooection],
        }
    }

    with slim.arg_scope([slim.conv2d,slim.fully_connected],
                        weights_regularizer = slim.l2_regularizer(weight_decay)):
        with slim.arg_scope(
            [slim.conv2d],
            weights_initializer = tf.truncated_normal_initializer(stddev=stddev),
            activation_fn = tf.nn.relu,
            normalizer_fn =slim.batch_norm,
            normalizer_params = batch_norm_params) as sc:
            return sc

def inception_v3_base(inputs,scope = None):
    end_points = {}

    with tf.variable_scope(scope,'InceptionV3',[inputs]):
        with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],
                            stride = 1,padding = 'VALID'):
            net = slim.conv2d(inputs,32,[3,3],stride=2,scope = 'Conv2d_1a_3x3')
            net = slim.conv2d(net,32,[3,3],scope = 'Convd_2a_3x3')
            net = slim.conv2d(net,64,[3,3],padding='SAME',scope = 'Convd_2b_3x3')
            net = slim.max_pool2d(net,[3,3],stride = 2,scope = 'MaxPool_3a_3x3')
            net = slim.conv2d(net, 80, [1,1], scope='Convd_3b_1x1')
            net = slim.conv2d(net, 192, [3, 3], scope='Convd_4a_3x3')
            net = slim.max_pool2d(net,[3, 3], stride=2, scope='MaxPool_5a_3x3')

        with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],
                            stride = 1,padding = 'SAME'):
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,48,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,64,[5,5],scope = 'Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,32,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_5c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,48,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,64,[5,5],scope = 'Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,64,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_5d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,48,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,64,[5,5],scope = 'Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2,96,[3,3],scope = 'Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,64,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_6a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,384,[3,3],stride = 2,
                                           padding = 'VALID',scope = 'Conv2d_0a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,64,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,96,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_1 = slim.conv2d(branch_1,96,[3,3],stride = 2,
                                           padding = 'VALID',scope = 'Conv2d_0c_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net,[3,3],stride = 2,
                                               padding = 'VALID',scope = 'MaxPool_0d_3x3')
                net = tf.concat([branch_0,branch_1,branch_2],3)

            with tf.variable_scope('Mixed_6b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,128,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,128,[1,7],scope = 'Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1,192, [7,1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,128,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,128,[7,1],scope = 'Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2,128, [1,7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_6c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,160,[1,7],scope = 'Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1,192, [7,1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,160,[7,1],scope = 'Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2,160, [1,7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_6d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,160,[1,7],scope = 'Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1,192, [7,1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,160,[7,1],scope = 'Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2,160, [1,7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_6e'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,160,[1,7],scope = 'Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1,192, [7,1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,160,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,160,[7,1],scope = 'Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2,160, [1,7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            end_points['Mixed_6e'] = net

            with tf.variable_scope('Mixed_7a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 320, [3,3],stride = 2,
                                           padding = 'VALID',scope='Conv2d_0b_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,192,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1,192,[1,7],scope = 'Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1,192,[7,1],scope = 'Conv2d_0c_7x1')
                    branch_1 = slim.conv2d(branch_1, 192, [3,3], stride=2,
                                           padding='VALID', scope='Conv2d_0d_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net,[3,3],stride = 2,
                                               padding = 'VALID',scope = 'MaxPool_0a_3x3')
                net = tf.concat([branch_0,branch_1,branch_2],3)

            with tf.variable_scope('Mixed_7b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,320,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,384,[1,3],scope = 'Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1,384,[1,3],scope = 'Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3,1], scope='Conv2d_0b_3x1')],3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,448,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,384,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)

            with tf.variable_scope('Mixed_7c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net,320,[1,1],scope = 'Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net,384,[1,3],scope = 'Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1,384,[1,3],scope = 'Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3,1], scope='Conv2d_0b_3x1')],3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net,448,[1,1],scope = 'Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2,384,[3,3],scope = 'Conv2d_0b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net,[3,3],scope = 'AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3,192,[1,1],scope = 'Conv2d_0b_1x1')
                net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
            return net,end_points

def inception_v3(inputs,
                 num_classes = 1000,
                 is_training = True,
                 dropout_keep_prob = 0.8,
                 prediction_fn = slim.softmax,
                 spatial_squeeze = True,
                 reuse = None,
                 scope = 'InceptionV3'):
    with tf.variable_scope(scope,scope,[inputs,num_classes],
                           reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm,slim.dropout],
                            is_training = is_training):
            net,end_points = inception_v3_base(inputs,scope = scope)
            with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],
                                stride = 1,padding = 'SAME'):
                aux_logits = end_points['Mixed_6e']
                with tf.variable_scope('AuxLogits'):
                    aux_logits = slim.avg_pool2d(
                        aux_logits,[5,5],stride = 3,padding = 'VALID',
                        scope = 'AvgPool_0a_5x5')
                    aux_logits = slim.conv2d(aux_logits,128,[1,1],
                                         scope = 'Conv2d_0b_1x1')
                    aux_logits = slim.conv2d(aux_logits,768,[5,5],
                                             weights_initializer = trunc_normal(0.01),
                                             padding = 'VALID',scope = 'Conv2d_0c_5x5')
                    aux_logits = slim.conv2d(aux_logits,num_classes,[1,1],activation_fn = None,
                                             normalizer_fn = None,
                                             weights_initializer = trunc_normal(0.001),
                                             scope = 'Conv2d_0d_1x1')
                    if spatial_squeeze:
                        aux_logits = tf.squeeze(aux_logits,[1,2],name='SpatialSqueeze')
                    end_points['AuxLogits'] = aux_logits
                with tf.variable_scope('Logits'):
                    net = slim.avg_pool2d(net,[8,8],padding = 'VALID',
                                          scope = 'AvgPool_0a_8x8')
                    net = slim.dropout(net,keep_prob = dropout_keep_prob,
                                       scope = 'Dropout_0b')
                    end_points['PreLogits'] = net
                    logits = slim.conv2d(net,num_classes,[1,1],activation_fn = None,
                                         normalizer_fn = None,scope = 'Conbv2d_0c_1x1')
                    if spatial_squeeze:
                        logits = tf.squeeze(logits,[1,2],name = 'SpatialSqueeze')
                    end_points['Logits'] = logits
                    end_points['Predictions'] = prediction_fn(logits,scope = 'Predictions')
                return logits,end_points





def time_tensorflow_run(session, target,info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    toatl_duration_squared = 0.0

    for i in range(num_batchs + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d, duration = %.3f' %
                      (datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            toatl_duration_squared += duration * duration
    mn = total_duration / num_batchs
    vr = toatl_duration_squared / num_batchs - mn * mn
    sd = math.sqrt(vr)
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
          (datetime.now(), info_string, num_batchs, mn, sd))

def run_benchmark():
    with tf.Graph().as_default():
        image_size = 299
        batch_size = 32
        images = tf.Variable(tf.random_normal([batch_size,
                                               image_size,
                                               image_size,3],
                                              dtype=tf.float32,
                                              stddev=0.1))
        with slim.arg_scope(inception_v3_arg_scope()):
            logits,end_points = inception_v3(images,is_training=False)


        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)

        time_tensorflow_run(sess,logits,'Forward')

run_benchmark()