Faster R-CNN源码阅读之三：Faster R-CNN/lib/networks/VGGnet_test.py

2019-09-04

目标检测

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一、介绍

本demo由Faster R-CNN官方提供，我只是在官方的代码上增加了注释，一方面方便我自己学习，另一方面贴出来和大家一起交流。
该文件中的函数和类的主要目的是定义Faster R-CNN中基于VGG16的网络结构。

二、代码以及注释

# -*- coding:utf-8 -*-
import tensorflow as tf
from networks.network import Network

n_classes = 21  # 类别数目（含背景）
_feat_stride = [16, ]  # 特征步长
anchor_scales = [8, 16, 32]  # anchor尺寸


# test使用的vgg网络。该类继承了Network类，因此在方法中大量使用链式方法定义网络结构
class VGGnet_test(Network):
    def __init__(self, trainable=True):
        # 上一层网络的输出
        self.inputs = []

        # 图片输入数据
        self.data = tf.placeholder(tf.float32, shape=[None, None, None, 3])

        # 图片尺寸信息
        self.im_info = tf.placeholder(tf.float32, shape=[None, 3])

        # dropout的保留概率
        self.keep_prob = tf.placeholder(tf.float32)

        # 网络中所有的层
        self.layers = dict({'data': self.data, 'im_info': self.im_info})

        # 是否可训练
        self.trainable = trainable

        # 建立网络结构
        self.setup()

    def setup(self):
        # VGG16的基础结构
        (self.feed('data')
         .conv(3, 3, 64, 1, 1, name='conv1_1', trainable=False)
         .conv(3, 3, 64, 1, 1, name='conv1_2', trainable=False)
         .max_pool(2, 2, 2, 2, padding='VALID', name='pool1')
         .conv(3, 3, 128, 1, 1, name='conv2_1', trainable=False)
         .conv(3, 3, 128, 1, 1, name='conv2_2', trainable=False)
         .max_pool(2, 2, 2, 2, padding='VALID', name='pool2')
         .conv(3, 3, 256, 1, 1, name='conv3_1')
         .conv(3, 3, 256, 1, 1, name='conv3_2')
         .conv(3, 3, 256, 1, 1, name='conv3_3')
         .max_pool(2, 2, 2, 2, padding='VALID', name='pool3')
         .conv(3, 3, 512, 1, 1, name='conv4_1')
         .conv(3, 3, 512, 1, 1, name='conv4_2')
         .conv(3, 3, 512, 1, 1, name='conv4_3')
         .max_pool(2, 2, 2, 2, padding='VALID', name='pool4')
         .conv(3, 3, 512, 1, 1, name='conv5_1')
         .conv(3, 3, 512, 1, 1, name='conv5_2')
         .conv(3, 3, 512, 1, 1, name='conv5_3'))

        # ========= RPN ============
        (self.feed('conv5_3')
         .conv(3, 3, 512, 1, 1, name='rpn_conv/3x3')
         .conv(1, 1, len(anchor_scales) * 3 * 2, 1, 1, padding='VALID', relu=False, name='rpn_cls_score'))

        (self.feed('rpn_conv/3x3')
         .conv(1, 1, len(anchor_scales) * 3 * 4, 1, 1, padding='VALID', relu=False, name='rpn_bbox_pred'))

        # ========= RoI Proposal ============
        (self.feed('rpn_cls_score')
         .reshape_layer(2, name='rpn_cls_score_reshape')
         .softmax(name='rpn_cls_prob'))

        (self.feed('rpn_cls_prob')
         .reshape_layer(len(anchor_scales) * 3 * 2, name='rpn_cls_prob_reshape'))

        (self.feed('rpn_cls_prob_reshape', 'rpn_bbox_pred', 'im_info')
         .proposal_layer(_feat_stride, anchor_scales, 'TEST', name='rois'))

        # ========= RCNN ============
        (self.feed('conv5_3', 'rois')
         .roi_pool(7, 7, 1.0 / 16, name='pool_5')
         .fc(4096, name='fc6')
         .fc(4096, name='fc7')
         .fc(n_classes, relu=False, name='cls_score')
         .softmax(name='cls_prob'))

        (self.feed('fc7')
         .fc(n_classes * 4, relu=False, name='bbox_pred'))