Faster R-CNN源码阅读之十：Faster R-CNN/lib/fast_rcnn/train.py

1. Faster R-CNN源码阅读之零：写在前面
2. Faster R-CNN源码阅读之一：Faster R-CNN/lib/networks/network.py
3. Faster R-CNN源码阅读之二：Faster R-CNN/lib/networks/factory.py
4. Faster R-CNN源码阅读之三：Faster R-CNN/lib/networks/VGGnet_test.py
5. Faster R-CNN源码阅读之四：Faster R-CNN/lib/rpn_msr/generate_anchors.py
6. Faster R-CNN源码阅读之五：Faster R-CNN/lib/rpn_msr/proposal_layer_tf.py
7. Faster R-CNN源码阅读之六：Faster R-CNN/lib/fast_rcnn/bbox_transform.py
8. Faster R-CNN源码阅读之七：Faster R-CNN/lib/rpn_msr/anchor_target_layer_tf.py
9. Faster R-CNN源码阅读之八：Faster R-CNN/lib/rpn_msr/proposal_target_layer_tf.py
10. Faster R-CNN源码阅读之九：Faster R-CNN/tools/train_net.py
11. Faster R-CNN源码阅读之十：Faster R-CNN/lib/fast_rcnn/train.py
12. Faster R-CNN源码阅读之十一：Faster R-CNN预测demo代码补完
13. Faster R-CNN源码阅读之十二：写在最后

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。
   该文件中的函数的主要目的是训练整个Faster R-CNN网络。
二、代码以及注释

# coding=utf-8
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Train a Fast R-CNN network."""

from fast_rcnn.config import cfg
import gt_data_layer.roidb as gdl_roidb
import roi_data_layer.roidb as rdl_roidb
from roi_data_layer.layer import RoIDataLayer
from utils.timer import Timer
import numpy as np
import os
import tensorflow as tf
import sys
from tensorflow.python.client import timeline
import time


class SolverWrapper(object):
    """
    A simple wrapper around Caffe's solver.
    This wrapper gives us control over the snapshot process, which we
    use to unnormalize the learned bounding-box regression weights.

    对Caffe的Solver进行了简单的封装。
    这个封装可以让我们控制snapshot过程，在snapshot过程中，我们对学习得到的bbox回归权重进行了去规范化（unnormalize）。
    """

    def __init__(self, sess, saver, network, imdb, roidb, output_dir, pretrained_model=None):
        """Initialize the SolverWrapper."""
        # 使用的Faster RCNN网络结构
        self.net = network
        # 图片数据集
        self.imdb = imdb
        # rois数据集
        self.roidb = roidb
        # 网络结构和权重保存输出目录
        self.output_dir = output_dir
        # 预训练文件模型路径
        self.pretrained_model = pretrained_model

        print 'Computing bounding-box regression targets...'
        # cfg.TRAIN.BBOX_REG默认为True
        if cfg.TRAIN.BBOX_REG:
            # 不同类的均值与方差，返回格式means.ravel(), stds.ravel()
            self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(roidb)
        print 'done'

        # For checkpoint
        self.saver = saver

    def snapshot(self, sess, iter):
        """
        Take a snapshot of the network after unnormalizing the learned
        bounding-box regression weights. This enables easy use at test-time.
        在对学习的边界框回归权重进行非标准化(unnormalize)后获取网络snapshot。
        这样可以在测试使用时比较方便
        """
        net = self.net

        if cfg.TRAIN.BBOX_REG and net.layers.has_key('bbox_pred'):
            # save original values
            # 将原来的值保存下来
            with tf.variable_scope('bbox_pred', reuse=True):
                weights = tf.get_variable("weights")
                biases = tf.get_variable("biases")

            orig_0 = weights.eval()
            orig_1 = biases.eval()

            # scale and shift with bbox reg unnormalization; then save snapshot
            # 更新weights和bias
            weights_shape = weights.get_shape().as_list()
            sess.run(net.bbox_weights_assign,
                     feed_dict={net.bbox_weights: orig_0 * np.tile(self.bbox_stds, (weights_shape[0], 1))})
            sess.run(net.bbox_bias_assign,
                     feed_dict={net.bbox_biases: orig_1 * self.bbox_stds + self.bbox_means})

        # 如果网络保存的目录不存在则重新创建一个
        if not os.path.exists(self.output_dir):
            os.makedirs(self.output_dir)

        # 中缀
        infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
                 if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
        # 文件名的创建
        filename = (cfg.TRAIN.SNAPSHOT_PREFIX + infix +
                    '_iter_{:d}'.format(iter + 1) + '.ckpt')
        filename = os.path.join(self.output_dir, filename)

        # 保存网络
        self.saver.save(sess, filename)
        print 'Wrote snapshot to: {:s}'.format(filename)

        # 恢复原始的状态
        if cfg.TRAIN.BBOX_REG and net.layers.has_key('bbox_pred'):
            with tf.variable_scope('bbox_pred', reuse=True):
                # restore net to original state
                sess.run(net.bbox_weights_assign, feed_dict={net.bbox_weights: orig_0})
                sess.run(net.bbox_bias_assign, feed_dict={net.bbox_biases: orig_1})

    # smooth l1方法
    def _modified_smooth_l1(self, sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
        """
            ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
            SmoothL1(x) = 0.5 * (sigma * x)^2,    if |x| < 1 / sigma^2
                          |x| - 0.5 / sigma^2,    otherwise
        """
        # 计算sigma^2
        sigma2 = sigma * sigma

        # 计算所需要处理的x的矩阵，这里利用了之前返回的inside weights。
        inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets))
        # 获取inside mul矩阵中小于 1 / sigma ^ 2的信息，在每个位置设置为True 或者False。然后转换为1.0或者0.0。
        smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32)
        # 计算上面公式中的第一个式子，这里并没有关注到后面的判断条件。
        smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2)
        # 计算第二个式子。
        smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2)
        # 这里根据上面产生的smooth l1 sign条件产生最后的结果，就是在这里才综合考虑后面的判断条件
        smooth_l1_result = tf.add(tf.multiply(smooth_l1_option1, smooth_l1_sign),
                                  tf.multiply(smooth_l1_option2, tf.abs(tf.subtract(smooth_l1_sign, 1.0))))

        # 和outside weights相乘并返回最后的结果。
        outside_mul = tf.multiply(bbox_outside_weights, smooth_l1_result)

        return outside_mul

    def train_model(self, sess, max_iters):
        """Network training loop."""

        data_layer = get_data_layer(self.roidb, self.imdb.num_classes)

        # RPN
        # classification loss
        # rpn-data数据都是在anchor target layer中产生
        # 将'rpn_cls_score_reshape'层的输出（1, n，n，18）reshape为（-1, 2）, 其中2为前景与背景的多分类得分（）
        rpn_cls_score = tf.reshape(self.net.get_output('rpn_cls_score_reshape'), [-1, 2])
        # 将labels展开成1维
        rpn_label = tf.reshape(self.net.get_output('rpn-data')[0], [-1])
        # 把rpn_label不等于-1对应引索的rpn_cls_score取出，重新组合成rpn_cls_score
        rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, tf.where(tf.not_equal(rpn_label, -1))), [-1, 2])
        # 把rpn_label不等于 - 1对应引索的rpn_label取出，重新组合成rpn_label
        rpn_label = tf.reshape(tf.gather(rpn_label, tf.where(tf.not_equal(rpn_label, -1))), [-1])
        # labels的交叉熵损失。
        # tf.nn.sparse_softmax_cross_entropy_with_logits返回的是一个向量，最后需要通过规约操作生成损失数值。
        rpn_cross_entropy = tf.reduce_mean(
            tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))

        # bounding box regression L1 loss
        # 获取RPN网络产生的bbox回归目标
        rpn_bbox_pred = self.net.get_output('rpn_bbox_pred')
        # 获取rpn-data层产生的bbox回归目标和inside weights和outside weights，并将通道顺序更改为[N, H, W, C]
        rpn_bbox_targets = tf.transpose(self.net.get_output('rpn-data')[1], [0, 2, 3, 1])
        rpn_bbox_inside_weights = tf.transpose(self.net.get_output('rpn-data')[2], [0, 2, 3, 1])
        rpn_bbox_outside_weights = tf.transpose(self.net.get_output('rpn-data')[3], [0, 2, 3, 1])

        # 计算smooth l1的结果
        rpn_smooth_l1 = self._modified_smooth_l1(3.0, rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
                                                 rpn_bbox_outside_weights)
        # 对smooth l1的结果进行归约操作，因为smooth l1返回的结果是一个矩阵。
        rpn_loss_box = tf.reduce_mean(tf.reduce_sum(rpn_smooth_l1, reduction_indices=[1, 2, 3]))

        # R-CNN
        # classification loss
        # roi-data由proposal target layer产生
        # 获取每个roi的预测的分类概率分布
        cls_score = self.net.get_output('cls_score')
        # 获取每个roi的实际label，并展开成一维数组
        label = tf.reshape(self.net.get_output('roi-data')[1], [-1])
        # 计算rois分类的交叉熵损失
        cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label))

        # bounding box regression L1 loss
        # 获取Fast RCNN网络产生的预测的bbox回归目标
        bbox_pred = self.net.get_output('bbox_pred')
        # 获取roi-data层bbox的回归目标以及inside weights和outside weights。
        bbox_targets = self.net.get_output('roi-data')[2]
        bbox_inside_weights = self.net.get_output('roi-data')[3]
        bbox_outside_weights = self.net.get_output('roi-data')[4]

        # 计算smooth l1的结果
        smooth_l1 = self._modified_smooth_l1(1.0, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights)
        # 归约smooth l1的计算结果。
        loss_box = tf.reduce_mean(tf.reduce_sum(smooth_l1, reduction_indices=[1]))

        # final loss
        # 网络的总损失函数是上述四个损失值的相加
        loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box

        # optimizer and learning rate
        # 全局的步数
        global_step = tf.Variable(0, trainable=False)
        # 学习率设置
        lr = tf.train.exponential_decay(cfg.TRAIN.LEARNING_RATE, global_step,
                                        cfg.TRAIN.STEPSIZE, 0.1, staircase=True)
        # momentum设置，默认值为0.9
        momentum = cfg.TRAIN.MOMENTUM
        # 优化器设置
        train_op = tf.train.MomentumOptimizer(lr, momentum).minimize(loss, global_step=global_step)

        # iintialize variables
        # 初始化所有变量
        sess.run(tf.global_variables_initializer())
        # 如果提供了预训练模型，则加载预训练模型
        if self.pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(self.pretrained_model)
            self.net.load(self.pretrained_model, sess, self.saver, True)

        last_snapshot_iter = -1
        # 计时器
        timer = Timer()
        # 进入循环迭代训练
        for iter in range(max_iters):
            # get one batch
            # 获取一个batch信息
            blobs = data_layer.forward()

            # Make one SGD update
            # 准备feed进网络中的数据
            feed_dict = {self.net.data: blobs['data'],
                         self.net.im_info: blobs['im_info'],
                         self.net.keep_prob: 0.5,
                         self.net.gt_boxes: blobs['gt_boxes']}

            # cfg.TRAIN.DEBUG_TIMELINE默认为False。不建议设置为True，否则可能会出错。下同。
            run_options = None
            run_metadata = None
            if cfg.TRAIN.DEBUG_TIMELINE:
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                run_metadata = tf.RunMetadata()

            # 记录开始时间戳
            timer.tic()

            # 进行一次训练
            rpn_loss_cls_value, rpn_loss_box_value, loss_cls_value, loss_box_value, _ = sess.run(
                [rpn_cross_entropy, rpn_loss_box, cross_entropy, loss_box, train_op],
                feed_dict=feed_dict,
                options=run_options,
                run_metadata=run_metadata)

            # 记录结束时间戳
            timer.toc()

            if cfg.TRAIN.DEBUG_TIMELINE:
                trace = timeline.Timeline(step_stats=run_metadata.step_stats)
                trace_file = open(str(long(time.time() * 1000)) + '-train-timeline.ctf.json', 'w')
                trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
                trace_file.close()

            # 显示训练的阶段性结果，主要为各种loss值。
            if (iter + 1) % (cfg.TRAIN.DISPLAY) == 0:
                print 'iter: %d / %d, total loss: %.4f, rpn_loss_cls: %.4f, rpn_loss_box: %.4f, loss_cls: %.4f, loss_box: %.4f, lr: %f' % \
                      (iter + 1, max_iters, rpn_loss_cls_value + rpn_loss_box_value + loss_cls_value + loss_box_value,
                       rpn_loss_cls_value, rpn_loss_box_value, loss_cls_value, loss_box_value, lr.eval())
                print 'speed: {:.3f}s / iter'.format(timer.average_time)

            # 进行网络的snapshot获取并保存整个Faster RCNN网络。
            if (iter + 1) % cfg.TRAIN.SNAPSHOT_ITERS == 0:
                last_snapshot_iter = iter
                self.snapshot(sess, iter)

        # 结束的时候再进行依次snapshot获取和网络保存
        if last_snapshot_iter != iter:
            self.snapshot(sess, iter)


def get_training_roidb(imdb):
    """
    Returns a roidb (Region of Interest database) for use in training.
    获取一个训练时使用的roidb。
    """
    if cfg.TRAIN.USE_FLIPPED:
        print 'Appending horizontally-flipped training examples...'
        imdb.append_flipped_images()
        print 'done'

    print 'Preparing training data...'
    if cfg.TRAIN.HAS_RPN:
        if cfg.IS_MULTISCALE:
            gdl_roidb.prepare_roidb(imdb)
        else:
            rdl_roidb.prepare_roidb(imdb)
    else:
        rdl_roidb.prepare_roidb(imdb)
    print 'done'

    return imdb.roidb


def get_data_layer(roidb, num_classes):
    """
    return a data layer.
    获取并返回一个一个数据层
    """
    if cfg.TRAIN.HAS_RPN:
        if cfg.IS_MULTISCALE:
            layer = GtDataLayer(roidb)
        else:
            layer = RoIDataLayer(roidb, num_classes)
    else:
        layer = RoIDataLayer(roidb, num_classes)

    return layer


def filter_roidb(roidb):
    """
    Remove roidb entries that have no usable RoIs.
    移除没有可用ROIS的roidb条目
    """

    def is_valid(entry):
        # Valid images have:
        #   (1) At least one foreground RoI OR
        #   (2) At least one background RoI
        overlaps = entry['max_overlaps']
        # find boxes with sufficient overlap
        fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
        # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
        bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
                           (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
        # image is only valid if such boxes exist
        valid = len(fg_inds) > 0 or len(bg_inds) > 0
        return valid

    num = len(roidb)
    filtered_roidb = [entry for entry in roidb if is_valid(entry)]
    num_after = len(filtered_roidb)
    print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
                                                       num, num_after)
    return filtered_roidb


def train_net(network, imdb, roidb, output_dir, pretrained_model=None, max_iters=40000):
    """
    Train a Fast R-CNN network.
    :param network: Faster RCNN训练的网络结构
    :param imdb: 图片数据集
    :param roidb: rois数据集
    :param output_dir: 网络权重文件的保存目录
    :param pretrained_model: 预训练网络权重文件路径
    :param max_iters: 最大迭代次数
    :return： None
    """
    # 筛选roidb
    roidb = filter_roidb(roidb)
    # tf网络保存器
    saver = tf.train.Saver(max_to_keep=100)
    # tf会话
    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
        # solver封装
        sw = SolverWrapper(sess, saver, network, imdb, roidb, output_dir, pretrained_model=pretrained_model)
        print 'Solving...'
        # 训练网络
        sw.train_model(sess, max_iters)
        print 'done solving'