def build_loss(self,
rpn_box_pred,
rpn_bbox_targets,
rpn_cls_score,
rpn_labels,
bbox_pred,
bbox_targets,
cls_score,
labels,
mask_list=None,
mask_gt_list=None):
with tf.variable_scope('build_loss'):
if cfgs.USE_SUPERVISED_MASK:
with tf.variable_scope("supervised_mask_loss"):
mask_losses = 0
print(mask_list)
print(mask_gt_list)
for i in range(len(mask_list)):
a_mask, a_mask_gt = mask_list[i], mask_gt_list[i]
# b, h, w, c = a_mask.shape
last_dim = 2 if cfgs.BINARY_MASK else cfgs.CLASS_NUM + 1
a_mask = tf.reshape(a_mask, shape=[-1, last_dim])
a_mask_gt = tf.reshape(a_mask_gt, shape=[-1])
a_mask_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=a_mask, labels=a_mask_gt))
mask_losses += a_mask_loss
self.loss_dict[
'mask_loss'] = mask_losses * cfgs.SUPERVISED_MASK_LOSS_WEIGHT / float(
len(mask_list))
with tf.variable_scope('rpn_loss'):
rpn_reg_loss = losses.smooth_l1_loss_rpn(
bbox_pred=rpn_box_pred,
bbox_targets=rpn_bbox_targets,
label=rpn_labels,
sigma=cfgs.RPN_SIGMA)
rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)),
[-1])
rpn_cls_score = tf.reshape(
tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select),
[-1])
rpn_cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_labels))
self.loss_dict[
'rpn_cls_loss'] = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
self.loss_dict[
'rpn_reg_loss'] = rpn_reg_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT
with tf.variable_scope('FastRCNN_loss'):
if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1:
reg_loss = losses.smooth_l1_loss_rcnn_r(
bbox_pred=bbox_pred,
bbox_targets=bbox_targets,
label=labels,
num_classes=cfgs.CLASS_NUM + 1,
sigma=cfgs.FASTRCNN_SIGMA)
# cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1])
# labels = tf.reshape(labels, [-1])
cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score,
labels=labels)) # beacause already sample before
else:
'''
applying OHEM here
'''
print(20 * "@@")
print("@@" + 10 * " " + "TRAIN WITH OHEM ...")
print(20 * "@@")
cls_loss, reg_loss = losses.sum_ohem_loss(
cls_score=cls_score,
label=labels,
bbox_targets=bbox_targets,
bbox_pred=bbox_pred,
num_ohem_samples=256,
num_classes=cfgs.CLASS_NUM + 1,
sigma=cfgs.FASTRCNN_SIGMA)
self.loss_dict[
'fast_cls_loss'] = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
self.loss_dict[
'fast_reg_loss'] = reg_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score,
rpn_labels, bbox_pred_h, bbox_targets_h, cls_score_h,
bbox_pred_r, bbox_targets_r, cls_score_r, labels):
'''
:param rpn_box_pred: [-1, 4]
:param rpn_bbox_targets: [-1, 4]
:param rpn_cls_score: [-1]
:param rpn_labels: [-1]
:param bbox_pred_h: [-1, 4*(cls_num+1)]
:param bbox_targets_h: [-1, 4*(cls_num+1)]
:param cls_score_h: [-1, cls_num+1]
:param bbox_pred_r: [-1, 5*(cls_num+1)]
:param bbox_targets_r: [-1, 5*(cls_num+1)]
:param cls_score_r: [-1, cls_num+1]
:param labels: [-1]
:return:
'''
with tf.variable_scope('build_loss') as sc:
with tf.variable_scope('rpn_loss'):
rpn_bbox_loss = losses.smooth_l1_loss_rpn(
bbox_pred=rpn_box_pred,
bbox_targets=rpn_bbox_targets,
label=rpn_labels,
sigma=cfgs.RPN_SIGMA)
# rpn_cls_loss:
# rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
# rpn_labels = tf.reshape(rpn_labels, [-1])
# ensure rpn_labels shape is [-1]
rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)),
[-1])
rpn_cls_score = tf.reshape(
tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select),
[-1])
rpn_cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_labels))
rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT
with tf.variable_scope('FastRCNN_loss'):
if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1:
bbox_loss_h = losses.smooth_l1_loss_rcnn_h(
bbox_pred=bbox_pred_h,
bbox_targets=bbox_targets_h,
label=labels,
num_classes=cfgs.CLASS_NUM + 1,
sigma=cfgs.FASTRCNN_SIGMA)
# cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1])
# labels = tf.reshape(labels, [-1])
cls_loss_h = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score_h,
labels=labels)) # beacause already sample before
bbox_loss_r = losses.smooth_l1_loss_rcnn_r(
bbox_pred=bbox_pred_r,
bbox_targets=bbox_targets_r,
label=labels,
num_classes=cfgs.CLASS_NUM + 1,
sigma=cfgs.FASTRCNN_SIGMA)
cls_loss_r = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score_r, labels=labels))
else:
'''
applying OHEM here
'''
print(20 * "@@")
print("@@" + 10 * " " + "TRAIN WITH OHEM ...")
print(20 * "@@")
cls_loss = bbox_loss = losses.sum_ohem_loss(
cls_score=cls_score_h,
label=labels,
bbox_targets=bbox_targets_h,
nr_ohem_sampling=128,
nr_classes=cfgs.CLASS_NUM + 1)
cls_loss_h = cls_loss_h * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
bbox_loss_h = bbox_loss_h * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
cls_loss_r = cls_loss_r * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
bbox_loss_r = bbox_loss_r * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
loss_dict = {
'rpn_cls_loss': rpn_cls_loss,
'rpn_loc_loss': rpn_bbox_loss,
'fastrcnn_cls_loss_h': cls_loss_h,
'fastrcnn_loc_loss_h': bbox_loss_h,
'fastrcnn_cls_loss_r': cls_loss_r,
'fastrcnn_loc_loss_r': bbox_loss_r,
}
return loss_dict