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"""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.""" 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: self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(roidb) print 'done'
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'): 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()
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})
if not os.path.exists(self.output_dir): os.makedirs(self.output_dir)
infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX if cfg.TRAIN.SNAPSHOT_INFIX != '' else '') filename = (cfg.TRAIN.SNAPSHOT_PREFIX + infix + '_iter_{:d}'.format(iter + 1) + '.ckpt') filename = os.path.join(self.output_dir, filename)
self.saver.save(sess, filename) print 'Wrote snapshot to: {:s}'.format(filename)
if cfg.TRAIN.BBOX_REG and net.layers.has_key('bbox_pred'): with tf.variable_scope('bbox_pred', reuse=True): 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})
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 """ sigma2 = sigma * sigma
inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets)) smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32) smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2) smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2) 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))))
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_cls_score = tf.reshape(self.net.get_output('rpn_cls_score_reshape'), [-1, 2]) rpn_label = tf.reshape(self.net.get_output('rpn-data')[0], [-1]) rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, tf.where(tf.not_equal(rpn_label, -1))), [-1, 2]) rpn_label = tf.reshape(tf.gather(rpn_label, tf.where(tf.not_equal(rpn_label, -1))), [-1]) rpn_cross_entropy = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))
rpn_bbox_pred = self.net.get_output('rpn_bbox_pred') 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])
rpn_smooth_l1 = self._modified_smooth_l1(3.0, rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights) rpn_loss_box = tf.reduce_mean(tf.reduce_sum(rpn_smooth_l1, reduction_indices=[1, 2, 3]))
cls_score = self.net.get_output('cls_score') label = tf.reshape(self.net.get_output('roi-data')[1], [-1]) cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label))
bbox_pred = self.net.get_output('bbox_pred') 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]
smooth_l1 = self._modified_smooth_l1(1.0, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights) loss_box = tf.reduce_mean(tf.reduce_sum(smooth_l1, reduction_indices=[1]))
loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box
global_step = tf.Variable(0, trainable=False) lr = tf.train.exponential_decay(cfg.TRAIN.LEARNING_RATE, global_step, cfg.TRAIN.STEPSIZE, 0.1, staircase=True) momentum = cfg.TRAIN.MOMENTUM train_op = tf.train.MomentumOptimizer(lr, momentum).minimize(loss, global_step=global_step)
sess.run(tf.global_variables_initializer()) 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 timer = Timer() for iter in range(max_iters): blobs = data_layer.forward()
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']}
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()
timer.tic()
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)
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()
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)
if (iter + 1) % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = iter self.snapshot(sess, iter)
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): overlaps = entry['max_overlaps'] fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0] bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) & (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] 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 """ roidb = filter_roidb(roidb) saver = tf.train.Saver(max_to_keep=100) with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: sw = SolverWrapper(sess, saver, network, imdb, roidb, output_dir, pretrained_model=pretrained_model) print 'Solving...' sw.train_model(sess, max_iters) print 'done solving'
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