def solve(global_step):
"""add solver to losses"""
# learning reate
lr = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(lr)
tf.summary.scalar('learning_rate', lr)
# compute and apply gradient
losses = tf.get_collection(tf.GraphKeys.LOSSES)
regular_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regular_loss = tf.add_n(regular_losses)
out_loss = tf.add_n(losses)
total_loss = tf.add_n(losses + regular_losses)
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('out_loss', out_loss)
tf.summary.scalar('regular_loss', regular_loss)
### add the center loss into the summary
update_ops = []
variables_to_train = _get_variables_to_train()
# update_op = optimizer.minimize(total_loss)
gradients = optimizer.compute_gradients(total_loss,
var_list=variables_to_train)
grad_updates = optimizer.apply_gradients(gradients,
global_step=global_step)
update_ops.append(grad_updates)
# update moving mean and variance
if FLAGS.update_bn:
update_bns = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_bn = tf.group(*update_bns)
update_ops.append(update_bn)
return tf.group(*update_ops)
def solve(global_step):
"""add solver to losses"""
# learning reate
lr = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(lr)
tf.summary.scalar('learning_rate', lr)
# compute and apply gradient
losses = tf.get_collection(tf.GraphKeys.LOSSES)
regular_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regular_loss = tf.add_n(regular_losses)
out_loss = tf.add_n(losses)
total_loss = tf.add_n(losses + regular_losses)
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('out_loss', out_loss)
tf.summary.scalar('regular_loss', regular_loss)
update_ops = []
variables_to_train = _get_variables_to_train()
# update_op = optimizer.minimize(total_loss)
gradients = optimizer.compute_gradients(total_loss, var_list=variables_to_train)
grad_updates = optimizer.apply_gradients(gradients,
global_step=global_step)
update_ops.append(grad_updates)
# update moving mean and variance
if FLAGS.update_bn:
update_bns = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_bn = tf.group(*update_bns)
update_ops.append(update_bn)
return tf.group(*update_ops)
def solve(global_step):
"""add solver to losses"""
# learning reate
lr = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(lr)
tf.summary.scalar('learning_rate', lr)
# compute and apply gradient
losses = tf.get_collection(tf.GraphKeys.LOSSES)
loss = tf.add_n(losses)
regular_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regular_loss = tf.add_n(regular_losses)
total_loss = loss + regular_loss
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('loss', loss)
tf.summary.scalar('regular_loss', regular_loss)
# update_ops = []
# variables_to_train = _get_variables_to_train()
# update_op = optimizer.minimize(total_loss)
# gradients = optimizer.compute_gradients(total_loss, var_list=variables_to_train)
# grad_updates = optimizer.apply_gradients(gradients,
# global_step=global_step)
# update_ops.append(grad_updates)
## update moving mean and variance
# if FLAGS.update_bn:
# update_bns = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# update_bn = tf.group(*update_bns)
# # update_ops.append(update_bn)
# total_loss = control_flow_ops.with_dependencies([update_bn], total_loss)
# train_op = slim.learning.create_train_op(total_loss, optimizer)
if FLAGS.update_bn:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = slim.learning.create_train_op(total_loss, optimizer, global_step=global_step)
else:
train_op = slim.learning.create_train_op(total_loss, optimizer, global_step=global_step)
return train_op
loss, losses, batch_info = pyramid_network.build_losses(
pyramid,
outputs,
gt_boxes,
gt_masks,
num_classes=81,
base_anchors=9,
rpn_box_lw=0.1,
rpn_cls_lw=0.2,
refined_box_lw=2.0,
refined_cls_lw=0.1,
mask_lw=0.2)
## optimization
learning_rate = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
loss = tf.get_collection(tf.GraphKeys.LOSSES)
regular_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n(loss + regular_loss)
reg_loss = tf.add_n(regular_loss)
summaries.add(tf.summary.scalar('total_loss', total_loss))
summaries.add(tf.summary.scalar('regular_loss', reg_loss))
variables_to_train = _get_variables_to_train()
update_op = optimizer.minimize(total_loss)
# gradients = optimizer.compute_gradients(total_loss, var_list=variables_to_train)
# grad_updates = optimizer.apply_gradients(gradients,
