import tensorflow as tf

slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)


# 生成网络常用默认参数
def inception_arg_scope(weight_decay=0.00004,
                        stddev=0.1,
                        batch_norm_var_collection='moving_vars'):
    # bs参数
    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_collection],
            'moving_variance': [batch_norm_var_collection],
        }
    }

    # 给函数参数自动赋值
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope([slim.conv2d],
                            weights_regularizer=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


# 生成网络的卷积部分
def inception_base(inputs, scope=None):
    end_points = dict()
    with tf.variable_scope(scope, 'Inception', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='VALID'):
            # 输入299 x 299 x 3
            net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')

            # 149 x 149 x 32
            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')

            # 147 x 147 x 32
            net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')

            # 147 x 147 x 64
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')

            # 73 x 73 x 64
            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_3x3')

            # 73 x 73 x 80.
            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')

            # 71 x 71 x 192.
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')


            # 35 x 35 x 192.

        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='VALID'):
            ####### group 5 #########

            # mixed: 35 x 35 x 256.
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_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_Ob_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_Ob_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_Oc_3x3')

                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_1x1')
                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_Ob_1x1')

                net = tf.concat([branch_0, branch_1, branch_3], axis=3)

            # 第四个分支的输出变为64
            # mixed_1: 35 x 35 x 288.
            with tf.variable_scope('Mixed_5c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_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_Ob_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_Ob_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_Oc_3x3')

                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_1x1')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_Ob_1x1')

                net = tf.concat([branch_0, branch_1, branch_3], 3)

            # 与上一层完全相同
            # mixed_2: 35 x 35 x 288.
            with tf.variable_scope('Mixed_5d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_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_Ob_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_Ob_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_Oc_3x3')

                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_1x1')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_Ob_1x1')

                net = tf.concat([branch_0, branch_1, branch_3], 3)

            ####### group 6 #########

            # mixed_3: 17 x 17 x 768.

            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_1a_1x1')
                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_1a_1x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_1a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])

            # mixed4: 17 x 17 x 768.
            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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])

            # mixed_5: 17 x 17 x 768.
            end_point = 'Mixed_6c'
            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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])

            # mixed_6: 17 x 17 x 768.
            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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])


                # mixed_7: 17 x 17 x 768.
            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, 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')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 192, [7, 1],
                                           scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7],
                                           scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 192, [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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])


                ######## group 7 ########

                # mixed_8: 8 x 8 x 1280.
            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_1a_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_1a_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_1a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])

            # mixed_9: 8 x 8 x 2048.
            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, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat(axis=3, values=[
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')])
                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(axis=3, values=[
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')])
                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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])

            # mixed_10: 8 x 8 x 2048.
            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, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat(axis=3, values=[
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')])
                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(axis=3, values=[
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')])
                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(axis=3, values=[branch_0, branch_1, branch_2, branch_3])

        end_points['Mixed_6e'] = net


def inception(inputs,
              num_classes=1000,
              is_training=True,
              dropout_keep_prob=0.8,
              min_depth=16,
              depth_multiplier=1.0,
              prediction_fn=slim.softmax,
              spatial_squeeze=True,
              reuse=None,
              scope='Inception'):

    with tf.variable_scope(scope, 'Inception', [inputs, num_classes],
                           reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout],
                            is_training=is_training):
            net, end_points = inception_base(inputs, scope=scope)

            # 辅助节点
            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_1a_5x5')

                    aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
                                             scope='Conv2d_1b_1x1')

                    aux_logits = slim.conv2d(aux_logits, 768, [5, 5],
                                             weights_initializer=trunc_normal(0.01),
                                             padding='VALID', scope='Conv2d_2a_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_2b_1x1')

                    if spatial_squeeze:
                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
                    end_points['AuxLogits'] = aux_logits

            # 分类预测
            with tf.variable_scope('Logits'):
                net = slim.avg_pool2d(net, [8, 8], padding='VALID',
                                      scope='AvgPool_1a_5x5')
                # 1 x 1 x 2048
                net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
                end_points['PreLogits'] = net
                # 2048
                logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                                     normalizer_fn=None, scope='Conv2d_1c_1x1')
                if spatial_squeeze:
                    logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
                    # 1000
            end_points['Logits'] = logits
            end_points['Predictions'] = prediction_fn(logits, scope='Predictions')

    return logits, end_points


batch_size = 32
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception_arg_scope()):
    logits, end_points = inception(inputs, is_training=False)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
num_batched = 100


# time_tun