def train():
total_timer = Timer()
train_timer = Timer()
load_timer = Timer()
max_epoch = 30
epoch_step = int(cfg.train_num//cfg.batch_size)
t = 1
for epoch in range(1, max_epoch + 1):
print('-'*25, 'epoch', epoch,'/',str(max_epoch), '-'*25)
t_loss = 0
ll_loss = 0
r_loss = 0
c_loss = 0
for step in range(1, epoch_step + 1):
t = t + 1
total_timer.tic()
load_timer.tic()
images, labels, imnm, num_boxes, imsize = data.get()
# load_timer.toc()
feed_dict = {input_: images,
get_boxes: labels[..., 1:5][:, ::-1, :],
get_classes: labels[..., 0].reshape((cfg.batch_size, -1))[:, ::-1]
}
if cfg.cnt_branch:
_, g_step_, tt_loss, cl_loss, cn_loss, re_loss, lr_ = sess.run(
[train_op,
global_step,
total_loss,
cls_loss,
cnt_loss,
reg_loss,
lr], feed_dict = feed_dict)
else:
_, g_step_, tt_loss, cl_loss, re_loss, lr_ = sess.run(
[train_op,
global_step,
total_loss,
cls_loss,
reg_loss,
lr], feed_dict = feed_dict)
total_timer.toc()
if g_step_%50 ==0:
sys.stdout.write('\r>> ' + 'iters '+str(g_step_)+str('/')+str(epoch_step*max_epoch)+' loss '+str(tt_loss) + ' ')
sys.stdout.flush()
summary_str = sess.run(summary_op, feed_dict = feed_dict)
train_total_summary = tf.Summary(value=[
tf.Summary.Value(tag="config/learning rate", simple_value=lr_),
tf.Summary.Value(tag="train/classification/focal_loss", simple_value=cfg.class_weight*cl_loss),
tf.Summary.Value(tag="train/classification/cnt_loss", simple_value=cfg.cnt_weight*cn_loss),
# tf.Summary.Value(tag="train/p_nm", simple_value=p_nm_),
tf.Summary.Value(tag="train/regress_loss", simple_value=cfg.regress_weight*re_loss),
# tf.Summary.Value(tag="train/clone_loss", simple_value=cfg.class_weight*cl_loss + cfg.regress_weight*re_loss + cfg.cnt_weight*cn_loss),
# tf.Summary.Value(tag="train/l2_loss", simple_value=l2_loss),
tf.Summary.Value(tag="train/total_loss", simple_value=tt_loss)
])
print('curent speed: ', total_timer.diff, 'remain time: ', total_timer.remain(g_step_, epoch_step*max_epoch))
summary_writer.add_summary(summary_str, g_step_)
summary_writer.add_summary(train_total_summary, g_step_)
if g_step_%10000 == 0:
print('saving checkpoint')
saver.save(sess, cfg.ckecpoint_file + '/model.ckpt', g_step_)
total_timer.toc()
sys.stdout.write('\n')
print('>> mean loss', t_loss)
print('curent speed: ', total_timer.average_time, 'remain time: ', total_timer.remain(g_step_, epoch_step*max_epoch))
print('saving checkpoint')
saver.save(sess, cfg.ckecpoint_file + '/model.ckpt', g_step_)
imnm[i], pred_b[j][0], pred_b[j][1], pred_b[j][2],
pred_b[j][3], pred_s[j], pred_l[j] + 1
])
single_gt_num = np.where(labels[i][:, 0] > 0)[0].shape[0]
box = np.hstack((labels[i][:single_gt_num, 1:],
np.reshape(labels[i][:single_gt_num, 0],
(-1, 1)))).tolist()
gt_dict[imnm[i]] = box
val_timer.toc()
sys.stdout.write('\r>> ' + 'val_nums ' + str(val_step) + str('/') +
str(cfg.test_num + 1))
sys.stdout.flush()
print('curent val speed: ', val_timer.average_time, 'val remain time: ',
val_timer.remain(val_step, cfg.test_num + 1))
if cfg.cnt_branch:
print('val mean regress loss: ', val_rloss, 'val mean class loss: ',
val_closs, 'val mean cnt loss: ', val_cnt_loss)
else:
print('val mean regress loss: ', val_rloss, 'val mean class loss: ',
val_closs)
mean_rec = 0
mean_prec = 0
mAP = 0
for classidx in range(1, cfg.class_num): #从1到21，对应[bg,...]21个类（除bg）
rec, prec, ap = voc_eval(gt_dict,
val_pred,
classidx,
iou_thres=0.5,
use_07_metric=False)
