本blog为github上CharlesShang/TFFRCNN版源码解析系列代码笔记
---------------个人学习笔记---------------
----------------本文作者疆--------------
------点击此处链接至博客园原文------
_DEBUG默认为False
1.SolverWrapper类
class SolverWrapper(object): # caffe中solver通过协调网络前向推理和反向梯度传播来进行模型优化，并通过权重参数更新来改善网络损失求解最优算法 # 而solver学习的任务被划分为：监督优化和参数更新，生成损失并计算梯度 # 它定义着整个模型如何运转，不管是命令行方式还是pycaffe接口方式进行网络训练或测试，都是需要一个solver配置文件的 """A simple wrapper around Caffe's solver. This wrapper gives us control over the snapshotting process, which we use to unnormalize the learned bounding-box regression weights. """
类中定义如下函数：
------------------------------------------------------__init__(...)---------------------------------------------------------
def __init__(self，sess，network，imdb，output_dir，logdir，pretrained_model=None) 构造函数
def __init__(self, sess, network, imdb, roidb, output_dir, logdir, pretrained_model=None): """Initialize the SolverWrapper.""" self.net = network self.imdb = imdb self.roidb = roidb self.output_dir = output_dir self.pretrained_model = pretrained_model print 'Computing bounding-box regression targets...' if cfg.TRAIN.BBOX_REG: # 默认cfg.TRAIN.BBOX_REG=True # 暂未知？ self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(roidb) print 'done' # For checkpoint # 模型保存与恢复 self.saver = tf.train.Saver(max_to_keep=100,write_version=saver_pb2.SaverDef.V1) # 指定一个文件保存图，其中，tf.get_default_graph()事件文件要记录的图，也就是tensorflow默认的图 self.writer = tf.summary.FileWriter(logdir=logdir, graph=tf.get_default_graph(), flush_secs=5)
其中，调用add_bbox_regression_targets(roidb)返回bbox回归目标（均值bbox_means和标准差bbox_stds）（roi_data_layer/roidb.py中）参与计算bbox_pred层weights和bias的计算规范化？
def add_bbox_regression_targets(roidb): """ Add information needed to train bounding-box regressors. For each roi find the corresponding gt box, and compute the distance. then normalize the distance into Gaussian by minus mean and divided by std """
tf.train.Saver(...)与模型保存与恢复有关、tf.summary.FileWriter(...)与保存计算图有关
------------------------------------------------------snapshot(...)---------------------------------------------------------
def snapshot(self,sess,iter)
def snapshot(self, sess, iter): """Take a snapshot of the network after unnormalizing the learned 非规范 bounding-box regression weights. This enables easy use at test-time. """ net = self.net if cfg.TRAIN.BBOX_REG and net.layers.has_key('bbox_pred') and cfg.TRAIN.BBOX_NORMALIZE_TARGETS: # net.layers字典记录网络各层输出 # 默认cfg.TRAIN.BBOX_REG=True cfg.TRAIN.BBOX_NORMALIZE_TARGETS=True 含义？ # save original values，bbox_pred为VGGnet最后一层 with tf.variable_scope('bbox_pred', reuse=True): weights = tf.get_variable("weights") biases = tf.get_variable("biases") # 暂未知eval()函数？ orig_0 = weights.eval() orig_1 = biases.eval() # scale and shift with bbox reg unnormalization; then save snapshot weights_shape = weights.get_shape().as_list() # np.tile(array,reps) 按reps指定轴用于重复array构造新的数组 # 暂未知含义？为何后面又恢复weights和biases的原值？ sess.run(weights.assign(orig_0 * np.tile(self.bbox_stds, (weights_shape[0],1)))) sess.run(biases.assign(orig_1 * self.bbox_stds + self.bbox_means)) if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) # TRAIN.SNAPSHOT_INFIX = '' infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX if cfg.TRAIN.SNAPSHOT_INFIX != '' else '') # TRAIN.SNAPSHOT_PREFIX = 'VGGnet_fast_rcnn' filename = (cfg.TRAIN.SNAPSHOT_PREFIX + infix + '_iter_{:d}'.format(iter+1) + '.ckpt') filename = os.path.join(self.output_dir, filename) #如E:\TFFRCNN\output\faster_rcnn_voc_vgg\voc_2007_trainval\VGGnet_fast_rcnn_iter_100.ckpt # 存储.ckpt模型文件 self.saver.save(sess, filename) print 'Wrote snapshot to: {:s}'.format(filename) # 这里为什么又恢复bbox_pred的原始weights和biases? if cfg.TRAIN.BBOX_REG and net.layers.has_key('bbox_pred'): # restore net to original state sess.run(weights.assign(orig_0)) sess.run(biases.assign(orig_1))
与存储.ckpt训练模型有关，这里又涉及对bbox_pred层的weights和biases值的规范化后又恢复原始值，不解其意，cfg.TRAIN.BBOX_NORMALIZE_TARGETS和eval()函数意义不明，被train_model(...)调用
------------------------------------------------------build_image_summary(...)---------------------------------------------------------
def build_image_summary(self)
def build_image_summary(self): """ A simple graph for write image summary """ log_image_data = tf.placeholder(tf.uint8, [None, None, 3]) log_image_name = tf.placeholder(tf.string) # import tensorflow.python.ops.gen_logging_ops as logging_ops from tensorflow.python.ops import gen_logging_ops from tensorflow.python.framework import ops as _ops log_image = gen_logging_ops._image_summary(log_image_name, tf.expand_dims(log_image_data, 0), max_images=1) _ops.add_to_collection(_ops.GraphKeys.SUMMARIES, log_image) # log_image = tf.summary.image(log_image_name, tf.expand_dims(log_image_data, 0), max_outputs=1) return log_image, log_image_data, log_image_name
生成与图像、图像数据、图像名相关的摘要日志文件，tensorflow相关机制不懂，如gen_logging_ops._image_summary(...)与_ops.add_to_collection(...)，被train_model(...)调用
2.SolverWrapper类中train_model(...)函数(训练过程主要代码)代码逻辑
def train_model(self，sess，max_iters，restore=False)
调用get_data_layer(self.roidb, self.imdb.num_classes)实例化RoIDataLayer类（roi_data_layer/layer.py中）对象并返回layer---->
调用build_loss定义各种训练loss(network.py中)--->
利用tf.summary中各类方法保存训练过程，可供tensorboard，如tf.summary.scalar记录loss可用于绘制loss曲线--->
# 训练过程中，用到的tf.summary()各类方法，能够保存训练过程以及参数分布图并在tensorboard显示 # scalar summary 生成标量图，可以用于画各类loss图像 tf.summary.scalar('rpn_rgs_loss', rpn_loss_box) tf.summary.scalar('rpn_cls_loss', rpn_cross_entropy) tf.summary.scalar('cls_loss', cross_entropy) tf.summary.scalar('rgs_loss', loss_box) tf.summary.scalar('loss', loss) summary_op = tf.summary.merge_all()
调用类内函数build_image_summary()生成与图像、图像数据、图像名相关的摘要日志文件log_image, log_image_data, log_image_name--->
定义默认优化方式为opt=tf.train.MomentumOptimizer(lr,momentum)--->
# optimizer 定义优化方式 # 默认TRAIN.SOLVER = 'Momentum' TRAIN.LEARNING_RATE = 0.001 TRAIN.MOMENTUM = 0.9 if cfg.TRAIN.SOLVER == 'Adam': opt = tf.train.AdamOptimizer(cfg.TRAIN.LEARNING_RATE) elif cfg.TRAIN.SOLVER == 'RMS': opt = tf.train.RMSPropOptimizer(cfg.TRAIN.LEARNING_RATE) else: lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) # lr = tf.Variable(0.0, trainable=False) momentum = cfg.TRAIN.MOMENTUM opt = tf.train.MomentumOptimizer(lr, momentum) # Momentum优化方式，使用了超参数
定义记录全局训练步骤的单值global_step = tf.Variable(0, trainable=False)--->
训练核心代码，tf.clip_by_global_norm(tf.gradients(loss, tvars), 10.0)、opt.apply_gradients(zip(grads, tvars), global_step=global_step)，但是反向传播体现在哪？--->
with_clip = True if with_clip: # 获取所有可训练的向量 tvars = tf.trainable_variables() # tf.gradients计算向量梯度，传入参数loss和所有trainable的向量 # tf.clip_by_global_norm梯度缩放 # 传入所有trainable向量的梯度，grad为clip好的梯度，globalnorm为梯度平方和 grads, norm = tf.clip_by_global_norm(tf.gradients(loss, tvars), 10.0) # apply_gradients是tf.train.Optimizer.minimize实际操作中两步中的一步 # minizie其实是分了两步运算，第一步计算梯度tf.train.Optimizer.compute_gradients # 和第二步更新梯度tf.train.Optimizer.apply_gradients # 由于我们已经计算了梯度，所以我们只用更新梯度 train_op = opt.apply_gradients(zip(grads, tvars), global_step=global_step) else: train_op = opt.minimize(loss, global_step=global_step)
创建会话权值初始化--->定义restore_iter = 0--->
调用self.net.load(self.pretrained_model, sess, True)函数(network.py中)加载预训练模型--->
# load vgg16 if self.pretrained_model is not None and not restore: try: print ('Loading pretrained model ' 'weights from {:s}').format(self.pretrained_model) self.net.load(self.pretrained_model, sess, True) except: raise 'Check your pretrained model {:s}'.format(self.pretrained_model) # resuming a trainer # 恢复继续训练 if restore: try: ckpt = tf.train.get_checkpoint_state(self.output_dir) print 'Restoring from {}...'.format(ckpt.model_checkpoint_path), self.saver.restore(sess, ckpt.model_checkpoint_path) # basename函数去掉目录路径单独返回文件名、splitext分离文件名和扩展名 stem = os.path.splitext(os.path.basename(ckpt.model_checkpoint_path))[0] restore_iter = int(stem.split('_')[-1]) sess.run(global_step.assign(restore_iter)) # 注意global_step变量 print 'done' except: raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
View Code
本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：Tensorflow版Faster RCNN源码解析（TFFRCNN） （06） train.py - Python技术站
微信扫一扫 
支付宝扫一扫 
