def build_cls_loss(self, batch_size, pos_mask, neg_mask, pos_mask_flatten, neg_mask_flatten, n_pos, do_summary, pixel_cls_loss_weight_lambda):
from OHEM import OHNM_batch
from losses import loss_with_binary_dice
with tf.name_scope('pixel_cls_loss'):
def no_pos():
return tf.constant(.0)
def has_pos():
print('the pixel_cls_logits_flatten is ', self.pixel_cls_logits_flatten)
print('the pos_mask is ', pos_mask_flatten)
pixel_cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.pixel_cls_logits_flatten,
labels=tf.cast(pos_mask_flatten, dtype=tf.int32))
# pixel_neg_scores = self.pixel_cls_scores_flatten[:, :, 0]
# selected_neg_pixel_mask = OHNM_batch(batch_size, pixel_neg_scores, pos_mask_flatten, neg_mask_flatten)
#
# cur_pixel_cls_weights = tf.cast(selected_neg_pixel_mask, tf.float32)
# n_neg = tf.cast(tf.reduce_sum(selected_neg_pixel_mask), tf.float32)
# loss = tf.reduce_sum(pixel_cls_loss * cur_pixel_cls_weights) / (n_neg + n_pos)
# return loss
return tf.reduce_mean(pixel_cls_loss)
pixel_cls_loss = has_pos()
pixel_cls_dice_loss, pixel_cls_dice = loss_with_binary_dice(self.pixel_cls_scores, pos_mask, axis=[1, 2])
tf.add_to_collection(tf.GraphKeys.LOSSES, pixel_cls_loss * pixel_cls_loss_weight_lambda)
tf.add_to_collection(tf.GraphKeys.LOSSES, pixel_cls_dice_loss * pixel_cls_loss_weight_lambda * dice_coff)
return pixel_cls_loss, pixel_cls_dice, pixel_cls_dice_loss
def build_cls_loss(self, batch_size, pos_mask, neg_mask, pos_mask_flatten,
neg_mask_flatten, n_pos, do_summary,
pixel_cls_loss_weight_lambda):
from losses import loss_with_binary_dice
with tf.name_scope('pixel_cls_loss'):
pixel_cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.pixel_cls_logits_flatten,
labels=tf.cast(pos_mask_flatten, dtype=tf.int32)))
pixel_cls_dice_loss, pixel_cls_dice = loss_with_binary_dice(
self.pixel_cls_scores, pos_mask, axis=[1, 2])
tf.add_to_collection(tf.GraphKeys.LOSSES,
pixel_cls_loss * pixel_cls_loss_weight_lambda)
tf.add_to_collection(
tf.GraphKeys.LOSSES,
pixel_cls_dice_loss * pixel_cls_loss_weight_lambda * dice_coff)
return pixel_cls_loss, pixel_cls_dice, pixel_cls_dice_loss
def build_cls_loss(self, batch_size, pos_mask, neg_mask, pos_mask_flatten,
neg_mask_flatten, n_pos, do_summary,
pixel_cls_loss_weight_lambda):
from losses import loss_with_binary_dice
pixel_cls_weight_flatten = tf.reshape(self.pixel_cls_weight,
[batch_size, -1])
with tf.name_scope('pixel_cls_loss'):
def no_pos():
return tf.constant(.0)
def has_pos():
print('the pixel_cls_logits_flatten is ',
self.pixel_cls_logits_flatten)
print('the pos_mask is ', pos_mask_flatten)
pixel_cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.pixel_cls_logits_flatten,
labels=tf.cast(pos_mask_flatten, dtype=tf.int32))
# return tf.reduce_mean(pixel_cls_loss)
pixel_neg_scores = self.pixel_cls_scores_flatten[:, :, 0]
shape = pos_mask.get_shape().as_list()
selected_neg_pixel_mask = OHNM_batch(batch_size,
pixel_neg_scores,
pos_mask_flatten,
neg_mask_flatten)
tf.summary.image('selected_neg_mask_cls',
tf.expand_dims(tf.cast(
tf.reshape(selected_neg_pixel_mask,
tf.shape(pos_mask)),
tf.float32),
axis=3),
max_outputs=1)
n_neg = tf.cast(tf.reduce_sum(selected_neg_pixel_mask),
tf.float32)
selected_neg_pixel_mask = tf.cond(
tf.equal(n_neg, 0), lambda: neg_mask_flatten,
lambda: tf.cast(selected_neg_pixel_mask, tf.bool))
n_neg = tf.cast(
tf.reduce_sum(tf.cast(selected_neg_pixel_mask,
tf.float32)), tf.float32)
cur_pixel_cls_weights = tf.cast(pos_mask_flatten, tf.float32) + \
tf.cast(selected_neg_pixel_mask, tf.float32)
# instance balanced weight
# cur_pixel_cls_weights = tf.cast(pixel_cls_weight_flatten, tf.float32) + \
# tf.cast(selected_neg_pixel_mask, tf.float32)
loss = tf.reduce_sum(
pixel_cls_loss * cur_pixel_cls_weights) / (n_neg + n_pos)
# shape = pos_mask.get_shape().as_list()
# pixel_neg_scores = tf.abs(self.pixel_cls_scores_flatten[:, :, 0] - 0.5)
# pixel_neg_loss = tf.cast(pixel_cls_loss * tf.cast(neg_mask_flatten, tf.float32), tf.float32)
# selected_neg_pixel_mask = OHNM_batch(batch_size, pixel_neg_loss, pos_mask_flatten, neg_mask_flatten)
# tf.summary.image('selected_neg_mask_cls',
# tf.cast(tf.reshape(selected_neg_pixel_mask, [shape[0], shape[1], shape[2], 1]),
# tf.float32), max_outputs=1)
# cur_pixel_cls_weights = tf.cast(selected_neg_pixel_mask, tf.float32) + tf.cast(pos_mask_flatten,
# tf.float32)
# # 排除可能有零的情况
# loss = tf.cond(tf.equal(tf.reduce_sum(cur_pixel_cls_weights), 0), lambda: 0.0,
# lambda: tf.reduce_sum(pixel_cls_loss * cur_pixel_cls_weights) / tf.reduce_sum(
# cur_pixel_cls_weights))
return loss
pixel_cls_loss = has_pos()
pixel_cls_dice_loss, pixel_cls_dice = loss_with_binary_dice(
self.pixel_cls_scores, pos_mask, axis=[1, 2])
tf.add_to_collection(tf.GraphKeys.LOSSES,
pixel_cls_loss * pixel_cls_loss_weight_lambda)
tf.add_to_collection(
tf.GraphKeys.LOSSES,
pixel_cls_dice_loss * pixel_cls_loss_weight_lambda * dice_coff)
return pixel_cls_loss, pixel_cls_dice, pixel_cls_dice_loss