SSD thoroughly get to know the network structure

Still have to talk about from the chart below, until not actually clear.

SSD flow network structure as shown below:
SSD block 11 total, compared to previous VGG16 change the fourth block layer 5, the first layer is totally removed 6,7,8 convolution, increased red box, black box, yellow box, blue box.

tensorflow code is as follows:

    with tf.variable_scope(scope, 'ssd_300_vgg', [inputs], reuse=reuse):
        # Original VGG-16 blocks.
        net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
        end_points['block1'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool1')
        # Block 2.
        net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
        end_points['block2'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool2')
        # Block 3.
        net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
        end_points['block3'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool3')
        # Block 4.
        net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
        end_points['block4'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool4')
        # Block 5.
        net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
        end_points['block5'] = net
        #注意处
        net = slim.max_pool2d(net, [3, 3], stride=1, scope='pool5')

        # Additional SSD blocks.
        # Block 6: let's dilate the hell out of it!
        #注意处
        net = slim.conv2d(net, 1024, [3, 3], rate=6, scope='conv6')
        end_points['block6'] = net
        net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)
        # Block 7: 1x1 conv. Because the fuck.
        #注意处
        net = slim.conv2d(net, 1024, [1, 1], scope='conv7')
        end_points['block7'] = net
        net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)

        # Block 8/9/10/11: 1x1 and 3x3 convolutions stride 2 (except lasts).
        end_point = 'block8'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 256, [1, 1], scope='conv1x1')
            #注意点：实际上相当于下面的卷积操作进行padding了
            net = custom_layers.pad2d(net, pad=(1, 1))
            net = slim.conv2d(net, 512, [3, 3], stride=2, scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block9'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            #注意点：实际上相当于下面的卷积操作进行padding了
            net = custom_layers.pad2d(net, pad=(1, 1))
            net = slim.conv2d(net, 256, [3, 3], stride=2, scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block10'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block11'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='VALID')
        end_points[end_point] = net