  
import tensorflow as tf
from tflearn.layers.conv import global_avg_pool
import tensorflow.contrib as tf_contrib
from tensorflow.contrib.layers import batch_norm, flatten
from tensorflow.contrib.framework import arg_scope
from cifar10 import *
import numpy as np

weight_decay = 0.0005
momentum = 0.9

init_learning_rate = 0.1
reduction_ratio = 4

batch_size = 64
iteration = 391
# 128 * 391 ~ 50,000

test_iteration = 10

total_epochs = 100

def conv_layer(input, filter, kernel, stride=1, padding='SAME', layer_name="conv"):
    with tf.name_scope(layer_name):
        network = tf.layers.conv2d(inputs=input, use_bias=True, filters=filter, kernel_size=kernel, strides=stride, padding=padding)
        network = Relu(network)
        return network

def Fully_connected(x, units=class_num, layer_name='fully_connected') :
    with tf.name_scope(layer_name) :
        return tf.layers.dense(inputs=x, use_bias=True, units=units)

def Relu(x):
    return tf.nn.relu(x)

def Sigmoid(x):
    return tf.nn.sigmoid(x)

def Global_Average_Pooling(x):
    return global_avg_pool(x, name='Global_avg_pooling')

def Max_pooling(x, pool_size=[3,3], stride=2, padding='VALID') :
    return tf.layers.max_pooling2d(inputs=x, pool_size=pool_size, strides=stride, padding=padding)

def Avg_pooling(x, pool_size=[3,3], stride=1, padding='SAME') :
    return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride, padding=padding)

def Batch_Normalization(x, training, scope):
    with arg_scope([batch_norm],
                   scope=scope,
                   updates_collections=None,
                   decay=0.9,
                   center=True,
                   scale=True,
                   zero_debias_moving_mean=True) :
        return tf.cond(training,
                       lambda : batch_norm(inputs=x, is_training=training, reuse=None),
                       lambda : batch_norm(inputs=x, is_training=training, reuse=True))

def Concatenation(layers) :
    return tf.concat(layers, axis=3)

def Dropout(x, rate, training) :
    return tf.layers.dropout(inputs=x, rate=rate, training=training)

def Evaluate(sess):
    test_acc = 0.0
    test_loss = 0.0
    test_pre_index = 0
    add = 1000

    for it in range(test_iteration):
        test_batch_x = test_x[test_pre_index: test_pre_index + add]
        test_batch_y = test_y[test_pre_index: test_pre_index + add]
        test_pre_index = test_pre_index + add

        test_feed_dict = {
            x: test_batch_x,
            label: test_batch_y,
            learning_rate: epoch_learning_rate,
            training_flag: False
        }

        loss_, acc_ = sess.run([cost, accuracy], feed_dict=test_feed_dict)

        test_loss += loss_
        test_acc += acc_

    test_loss /= test_iteration # average loss
    test_acc /= test_iteration # average accuracy

    summary = tf.Summary(value=[tf.Summary.Value(tag='test_loss', simple_value=test_loss),
                                tf.Summary.Value(tag='test_accuracy', simple_value=test_acc)])

    return test_acc, test_loss, summary

class SE_Inception_v4():
    def __init__(self, x, training):
        self.training = training
        self.model = self.Build_SEnet(x)

    def Stem(self, x, scope):
        with tf.name_scope(scope) :
            x = conv_layer(x, filter=32, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_conv1')
            x = conv_layer(x, filter=32, kernel=[3,3], padding='VALID', layer_name=scope+'_conv2')
            block_1 = conv_layer(x, filter=64, kernel=[3,3], layer_name=scope+'_conv3')

            split_max_x = Max_pooling(block_1)
            split_conv_x = conv_layer(block_1, filter=96, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv1')
            x = Concatenation([split_max_x,split_conv_x])

            split_conv_x1 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv2')
            split_conv_x1 = conv_layer(split_conv_x1, filter=96, kernel=[3,3], padding='VALID', layer_name=scope+'_split_conv3')

            split_conv_x2 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv4')
            split_conv_x2 = conv_layer(split_conv_x2, filter=64, kernel=[7,1], layer_name=scope+'_split_conv5')
            split_conv_x2 = conv_layer(split_conv_x2, filter=64, kernel=[1,7], layer_name=scope+'_split_conv6')
            split_conv_x2 = conv_layer(split_conv_x2, filter=96, kernel=[3,3], padding='VALID', layer_name=scope+'_split_conv7')

            x = Concatenation([split_conv_x1,split_conv_x2])

            split_conv_x = conv_layer(x, filter=192, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv8')
            split_max_x = Max_pooling(x)

            x = Concatenation([split_conv_x, split_max_x])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Inception_A(self, x, scope):
        with tf.name_scope(scope) :
            split_conv_x1 = Avg_pooling(x)
            split_conv_x1 = conv_layer(split_conv_x1, filter=96, kernel=[1,1], layer_name=scope+'_split_conv1')

            split_conv_x2 = conv_layer(x, filter=96, kernel=[1,1], layer_name=scope+'_split_conv2')

            split_conv_x3 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv3')
            split_conv_x3 = conv_layer(split_conv_x3, filter=96, kernel=[3,3], layer_name=scope+'_split_conv4')

            split_conv_x4 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv5')
            split_conv_x4 = conv_layer(split_conv_x4, filter=96, kernel=[3,3], layer_name=scope+'_split_conv6')
            split_conv_x4 = conv_layer(split_conv_x4, filter=96, kernel=[3,3], layer_name=scope+'_split_conv7')

            x = Concatenation([split_conv_x1, split_conv_x2, split_conv_x3, split_conv_x4])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Inception_B(self, x, scope):
        with tf.name_scope(scope) :
            init = x

            split_conv_x1 = Avg_pooling(x)
            split_conv_x1 = conv_layer(split_conv_x1, filter=128, kernel=[1,1], layer_name=scope+'_split_conv1')

            split_conv_x2 = conv_layer(x, filter=384, kernel=[1,1], layer_name=scope+'_split_conv2')

            split_conv_x3 = conv_layer(x, filter=192, kernel=[1,1], layer_name=scope+'_split_conv3')
            split_conv_x3 = conv_layer(split_conv_x3, filter=224, kernel=[1,7], layer_name=scope+'_split_conv4')
            split_conv_x3 = conv_layer(split_conv_x3, filter=256, kernel=[1,7], layer_name=scope+'_split_conv5')

            split_conv_x4 = conv_layer(x, filter=192, kernel=[1,1], layer_name=scope+'_split_conv6')
            split_conv_x4 = conv_layer(split_conv_x4, filter=192, kernel=[1,7], layer_name=scope+'_split_conv7')
            split_conv_x4 = conv_layer(split_conv_x4, filter=224, kernel=[7,1], layer_name=scope+'_split_conv8')
            split_conv_x4 = conv_layer(split_conv_x4, filter=224, kernel=[1,7], layer_name=scope+'_split_conv9')
            split_conv_x4 = conv_layer(split_conv_x4, filter=256, kernel=[7,1], layer_name=scope+'_split_connv10')

            x = Concatenation([split_conv_x1, split_conv_x2, split_conv_x3, split_conv_x4])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Inception_C(self, x, scope):
        with tf.name_scope(scope) :
            split_conv_x1 = Avg_pooling(x)
            split_conv_x1 = conv_layer(split_conv_x1, filter=256, kernel=[1,1], layer_name=scope+'_split_conv1')

            split_conv_x2 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv2')

            split_conv_x3 = conv_layer(x, filter=384, kernel=[1,1], layer_name=scope+'_split_conv3')
            split_conv_x3_1 = conv_layer(split_conv_x3, filter=256, kernel=[1,3], layer_name=scope+'_split_conv4')
            split_conv_x3_2 = conv_layer(split_conv_x3, filter=256, kernel=[3,1], layer_name=scope+'_split_conv5')

            split_conv_x4 = conv_layer(x, filter=384, kernel=[1,1], layer_name=scope+'_split_conv6')
            split_conv_x4 = conv_layer(split_conv_x4, filter=448, kernel=[1,3], layer_name=scope+'_split_conv7')
            split_conv_x4 = conv_layer(split_conv_x4, filter=512, kernel=[3,1], layer_name=scope+'_split_conv8')
            split_conv_x4_1 = conv_layer(split_conv_x4, filter=256, kernel=[3,1], layer_name=scope+'_split_conv9')
            split_conv_x4_2 = conv_layer(split_conv_x4, filter=256, kernel=[1,3], layer_name=scope+'_split_conv10')

            x = Concatenation([split_conv_x1, split_conv_x2, split_conv_x3_1, split_conv_x3_2, split_conv_x4_1, split_conv_x4_2])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Reduction_A(self, x, scope):
        with tf.name_scope(scope) :
            k = 256
            l = 256
            m = 384
            n = 384

            split_max_x = Max_pooling(x)

            split_conv_x1 = conv_layer(x, filter=n, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv1')

            split_conv_x2 = conv_layer(x, filter=k, kernel=[1,1], layer_name=scope+'_split_conv2')
            split_conv_x2 = conv_layer(split_conv_x2, filter=l, kernel=[3,3], layer_name=scope+'_split_conv3')
            split_conv_x2 = conv_layer(split_conv_x2, filter=m, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv4')

            x = Concatenation([split_max_x, split_conv_x1, split_conv_x2])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Reduction_B(self, x, scope):
        with tf.name_scope(scope) :
            split_max_x = Max_pooling(x)

            split_conv_x1 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv1')
            split_conv_x1 = conv_layer(split_conv_x1, filter=384, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv2')

            split_conv_x2 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv3')
            split_conv_x2 = conv_layer(split_conv_x2, filter=288, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv4')

            split_conv_x3 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv5')
            split_conv_x3 = conv_layer(split_conv_x3, filter=288, kernel=[3,3], layer_name=scope+'_split_conv6')
            split_conv_x3 = conv_layer(split_conv_x3, filter=320, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv7')

            x = Concatenation([split_max_x, split_conv_x1, split_conv_x2, split_conv_x3])

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)

            return x

    def Squeeze_excitation_layer(self, input_x, out_dim, ratio, layer_name):
        with tf.name_scope(layer_name) :
            squeeze = Global_Average_Pooling(input_x)

            excitation = Fully_connected(squeeze, units=out_dim / ratio, layer_name=layer_name+'_fully_connected1')
            excitation = Relu(excitation)
            excitation = Fully_connected(excitation, units=out_dim, layer_name=layer_name+'_fully_connected2')
            excitation = Sigmoid(excitation)

            excitation = tf.reshape(excitation, [-1,1,1,out_dim])

            scale = input_x * excitation

            return scale

    def Build_SEnet(self, input_x):
        input_x = tf.pad(input_x, [[0, 0], [32, 32], [32, 32], [0, 0]])
        # size 32 -> 96
        # only cifar10 architecture

        x = self.Stem(input_x, scope='stem')

        for i in range(4) :
            x = self.Inception_A(x, scope='Inception_A'+str(i))
            channel = int(np.shape(x)[-1])
            x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_A'+str(i))

        x = self.Reduction_A(x, scope='Reduction_A')

        for i in range(7)  :
            x = self.Inception_B(x, scope='Inception_B'+str(i))
            channel = int(np.shape(x)[-1])
            x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_B'+str(i))

        x = self.Reduction_B(x, scope='Reduction_B')

        for i in range(3) :
            x = self.Inception_C(x, scope='Inception_C'+str(i))
            channel = int(np.shape(x)[-1])
            x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_C'+str(i))

        x = Global_Average_Pooling(x)
        x = Dropout(x, rate=0.2, training=self.training)
        x = flatten(x)

        x = Fully_connected(x, layer_name='final_fully_connected')
        return x


train_x, train_y, test_x, test_y = prepare_data()
train_x, test_x = color_preprocessing(train_x, test_x)


# image_size = 32, img_channels = 3, class_num = 10 in cifar10
x = tf.placeholder(tf.float32, shape=[None, image_size, image_size, img_channels])
label = tf.placeholder(tf.float32, shape=[None, class_num])

training_flag = tf.placeholder(tf.bool)


learning_rate = tf.placeholder(tf.float32, name='learning_rate')

logits = SE_Inception_v4(x, training=training_flag).model
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=logits))

l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum, use_nesterov=True)
train = optimizer.minimize(cost + l2_loss * weight_decay)

correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(label, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(tf.global_variables())

with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)) as sess :
    ckpt = tf.train.get_checkpoint_state('./model')
    if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
        saver.restore(sess, ckpt.model_checkpoint_path)
    else:
        sess.run(tf.global_variables_initializer())

    summary_writer = tf.summary.FileWriter('./logs', sess.graph)

    epoch_learning_rate = init_learning_rate
    for epoch in range(1, total_epochs + 1):
        if epoch % 30 == 0 :
            epoch_learning_rate = epoch_learning_rate / 10

        pre_index = 0
        train_acc = 0.0
        train_loss = 0.0

        for step in range(1, iteration + 1):
            if pre_index + batch_size < 50000:
                batch_x = train_x[pre_index: pre_index + batch_size]
                batch_y = train_y[pre_index: pre_index + batch_size]
            else:
                batch_x = train_x[pre_index:]
                batch_y = train_y[pre_index:]

            batch_x = data_augmentation(batch_x)

            train_feed_dict = {
                x: batch_x,
                label: batch_y,
                learning_rate: epoch_learning_rate,
                training_flag: True
            }

            _, batch_loss = sess.run([train, cost], feed_dict=train_feed_dict)
            batch_acc = accuracy.eval(feed_dict=train_feed_dict)

            train_loss += batch_loss
            train_acc += batch_acc
            pre_index += batch_size


        train_loss /= iteration # average loss
        train_acc /= iteration # average accuracy

        train_summary = tf.Summary(value=[tf.Summary.Value(tag='train_loss', simple_value=train_loss),
                                          tf.Summary.Value(tag='train_accuracy', simple_value=train_acc)])

        test_acc, test_loss, test_summary = Evaluate(sess)

        summary_writer.add_summary(summary=train_summary, global_step=epoch)
        summary_writer.add_summary(summary=test_summary, global_step=epoch)
        summary_writer.flush()

        line = "epoch: %d/%d, train_loss: %.4f, train_acc: %.4f, test_loss: %.4f, test_acc: %.4f \n" % (
            epoch, total_epochs, train_loss, train_acc, test_loss, test_acc)
        print(line)

        with open('logs.txt', 'a') as f:
            f.write(line)

        saver.save(sess=sess, save_path='./model/Inception_v4.ckpt')