def get_learning_rate(global_step):
"""
学习率
:param global_step:
:return:
"""
if train_args.use_warm_up:
learning_rate = tf.cond(
tf.less(global_step,
train_args.train_batch_num * train_args.warm_up_epoch),
lambda: train_args.learning_rate_init * global_step /
(train_args.train_batch_num * train_args.warm_up_epoch),
lambda: config_learning_rate(
train_args, global_step - train_args.train_batch_num *
train_args.warm_up_epoch))
else:
learning_rate = config_learning_rate(train_args, global_step)
return learning_rate
################
# register the gpu nms operation here for the following evaluation scheme
pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])
pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])
gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, class_num)
################
global_step = tf.Variable(
0, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
if config_common.model_set["use_warm_up"]:
learning_rate = tf.cond(
tf.less(
global_step, train_batch_num *
config_common.model_set["warm_up_epoch"]),
lambda: config_common.model_set["warm_up_lr"],
lambda: config_learning_rate(
config_common, global_step - train_batch_num *
config_common.model_set["warm_up_epoch"
], lr_decay_freq))
else:
learning_rate = config_learning_rate(config_common,
global_step,
lr_decay_freq)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = config_optimizer(
config_common.model_set["optimizer_name"], learning_rate)
#add my clip
optimizer = tf.contrib.estimator.clip_gradients_by_norm(
optimizer, 3.0)
with tf.device(gpu_device[0]):
tower_grads = []
update_vars = tf.contrib.framework.get_variables_to_restore(
include=args.update_part)
tf.summary.scalar('train_batch_statistics/total_loss', loss[0])
tf.summary.scalar('train_batch_statistics/loss_xy', loss[1])
tf.summary.scalar('train_batch_statistics/loss_wh', loss[2])
tf.summary.scalar('train_batch_statistics/loss_conf', loss[3])
tf.summary.scalar('train_batch_statistics/loss_class', loss[4])
global_step = tf.Variable(0,
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
if args.use_warm_up:
learning_rate = tf.cond(
tf.less(global_step, args.train_batch_num * args.warm_up_epoch),
lambda: args.warm_up_lr, lambda: config_learning_rate(
args, global_step - args.train_batch_num * args.warm_up_epoch))
else:
learning_rate = config_learning_rate(args, global_step)
tf.summary.scalar('learning_rate', learning_rate)
if not args.save_optimizer:
saver_to_save = tf.train.Saver()
optimizer = config_optimizer(args.optimizer_name, learning_rate)
if args.save_optimizer:
saver_to_save = tf.train.Saver()
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])
pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])
gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num)
################
saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=args.restore_part))
update_vars = tf.contrib.framework.get_variables_to_restore(include=args.update_part)
tf.summary.scalar('train_batch_statistics/total_loss', loss[0])
tf.summary.scalar('train_batch_statistics/loss_xy', loss[1])
tf.summary.scalar('train_batch_statistics/loss_wh', loss[2])
tf.summary.scalar('train_batch_statistics/loss_conf', loss[3])
tf.summary.scalar('train_batch_statistics/loss_class', loss[4])
global_step = tf.Variable(0, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
learning_rate = config_learning_rate(args, global_step)
tf.summary.scalar('learning_rate', learning_rate)
if not args.save_optimizer:
saver_to_save = tf.train.Saver()
optimizer = config_optimizer(args.optimizer_name, learning_rate)
if args.save_optimizer:
saver_to_save = tf.train.Saver()
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss[0], var_list=update_vars, global_step=global_step)
tf.summary.scalar('train_batch_statistics/loss_l2', l2_loss_fintune)
tf.summary.scalar('train_batch_statistics/loss_ratio',
l2_loss_fintune / loss_fintune[0])
global_step_finetune = tf.Variable(
float(args.global_step),
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
if args.use_warm_up:
learning_rate_finetune = tf.cond(
tf.less(global_step_finetune,
args.train_batch_num * args.warm_up_epoch),
lambda: args.learning_rate_init * global_step_finetune /
(args.train_batch_num * args.warm_up_epoch),
lambda: config_learning_rate(
args, global_step_finetune - args.train_batch_num * args.
warm_up_epoch))
else:
learning_rate_finetune = config_learning_rate(args,
global_step_finetune)
tf.summary.scalar('learning_rate', learning_rate_finetune)
if not args.save_optimizer:
saver_best_finetune = tf.train.Saver()
print("[INFO] save_best_finetune construct _not ")
optimizer_finetune = config_optimizer(args.optimizer_name,
learning_rate_finetune)
update_vars_finetune = tf.contrib.framework.get_variables_to_restore(
include=prune_model._update_part)
if args.save_optimizer:
update_vars = tf.contrib.framework.get_variables_to_restore(
include=flag.update_part)
tf.summary.scalar('train_batch_statistics/total_loss', loss[0])
tf.summary.scalar('train_batch_statistics/loss_xy', loss[1])
tf.summary.scalar('train_batch_statistics/loss_wh', loss[2])
tf.summary.scalar('train_batch_statistics/loss_conf', loss[3])
tf.summary.scalar('train_batch_statistics/loss_class', loss[4])
global_step = tf.Variable(0,
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
if flag.use_warm_up:
learning_rate = tf.cond(
tf.less(global_step, flag.train_batch_num * flag.warm_up_epoch),
lambda: flag.warm_up_lr, lambda: config_learning_rate(
flag, global_step - flag.train_batch_num * flag.warm_up_epoch))
else:
learning_rate = config_learning_rate(flag, global_step)
tf.summary.scalar('learning_rate', learning_rate)
if not flag.save_optimizer:
saver_to_save = tf.train.Saver()
optimizer = config_optimizer(flag.optimizer_name, learning_rate)
if flag.save_optimizer:
saver_to_save = tf.train.Saver()
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
