def _detection_loss(self, cls_outputs, box_outputs, labels, loss_vals):
"""Computes total detection loss.
Computes total detection loss including box and class loss from all levels.
Args:
cls_outputs: an OrderDict with keys representing levels and values
representing logits in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width,
num_anchors * 4].
labels: the dictionary that returned from dataloader that includes
groundtruth targets.
loss_vals: A dict of loss values.
Returns:
total_loss: an integer tensor representing total loss reducing from
class and box losses from all levels.
cls_loss: an integer tensor representing total class loss.
box_loss: an integer tensor representing total box regression loss.
box_iou_loss: an integer tensor representing total box iou loss.
"""
# Sum all positives in a batch for normalization and avoid zero
# num_positives_sum, which would lead to inf loss during training
precision = utils.get_precision(self.config.strategy,
self.config.mixed_precision)
dtype = precision.split('_')[-1]
num_positives_sum = tf.reduce_sum(labels['mean_num_positives']) + 1.0
positives_momentum = self.config.positives_momentum or 0
if positives_momentum > 0:
# normalize the num_positive_examples for training stability.
moving_normalizer_var = tf.Variable(
0.0,
name='moving_normalizer',
dtype=dtype,
synchronization=tf.VariableSynchronization.ON_READ,
trainable=False,
aggregation=tf.VariableAggregation.MEAN)
num_positives_sum = tf.keras.backend.moving_average_update(
moving_normalizer_var,
num_positives_sum,
momentum=self.config.positives_momentum)
elif positives_momentum < 0:
num_positives_sum = utils.cross_replica_mean(num_positives_sum)
num_positives_sum = tf.cast(num_positives_sum, dtype)
levels = range(len(cls_outputs))
cls_losses = []
box_losses = []
for level in levels:
# Onehot encoding for classification labels.
cls_targets_at_level = tf.one_hot(
labels['cls_targets_%d' % (level + self.config.min_level)],
self.config.num_classes,
dtype=dtype)
if self.config.data_format == 'channels_first':
bs, _, width, height, _ = cls_targets_at_level.get_shape(
).as_list()
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, -1, width, height])
else:
bs, width, height, _, _ = cls_targets_at_level.get_shape(
).as_list()
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, width, height, -1])
class_loss_layer = self.loss.get(FocalLoss.__name__, None)
if class_loss_layer:
cls_loss = class_loss_layer(
[num_positives_sum, cls_targets_at_level],
cls_outputs[level])
if self.config.data_format == 'channels_first':
cls_loss = tf.reshape(
cls_loss,
[bs, -1, width, height, self.config.num_classes])
else:
cls_loss = tf.reshape(
cls_loss,
[bs, width, height, -1, self.config.num_classes])
cls_loss *= tf.cast(
tf.expand_dims(
tf.not_equal(
labels['cls_targets_%d' %
(level + self.config.min_level)], -2), -1),
dtype)
cls_loss_sum = tf.clip_by_value(tf.reduce_sum(cls_loss), 0.0,
2.0)
cls_losses.append(tf.cast(cls_loss_sum, dtype))
if self.config.box_loss_weight and self.loss.get(
BoxLoss.__name__, None):
box_targets_at_level = (
labels['box_targets_%d' % (level + self.config.min_level)])
box_loss_layer = self.loss[BoxLoss.__name__]
box_losses.append(
box_loss_layer([num_positives_sum, box_targets_at_level],
box_outputs[level]))
if self.config.iou_loss_type:
box_outputs = tf.concat(
[tf.reshape(v, [-1, 4]) for v in box_outputs], axis=0)
box_targets = tf.concat([
tf.reshape(
labels['box_targets_%d' %
(level + self.config.min_level)], [-1, 4])
for level in levels
],
axis=0)
box_iou_loss_layer = self.loss[BoxIouLoss.__name__]
box_iou_loss = box_iou_loss_layer([num_positives_sum, box_targets],
box_outputs)
loss_vals['box_iou_loss'] = box_iou_loss
else:
box_iou_loss = 0
cls_loss = tf.add_n(cls_losses) if cls_losses else 0
box_loss = tf.add_n(box_losses) if box_losses else 0
total_loss = (cls_loss + self.config.box_loss_weight * box_loss +
self.config.iou_loss_weight * box_iou_loss)
loss_vals['det_loss'] = total_loss
loss_vals['cls_loss'] = cls_loss
loss_vals['box_loss'] = box_loss
return total_loss
def detection_loss(cls_outputs, box_outputs, labels, params):
"""Computes total detection loss.
Computes total detection loss including box and class loss from all levels.
Args:
cls_outputs: an OrderDict with keys representing levels and values
representing logits in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width,
num_anchors * 4].
labels: the dictionary that returned from dataloader that includes
groundtruth targets.
params: the dictionary including training parameters specified in
default_haprams function in this file.
Returns:
total_loss: an integer tensor representing total loss reducing from
class and box losses from all levels.
cls_loss: an integer tensor representing total class loss.
box_loss: an integer tensor representing total box regression loss.
"""
# Sum all positives in a batch for normalization and avoid zero
# num_positives_sum, which would lead to inf loss during training
num_positives_sum = tf.reduce_sum(labels['mean_num_positives']) + 1.0
positives_momentum = params.get('positives_momentum', None) or 0
if positives_momentum > 0:
# normalize the num_positive_examples for training stability.
moving_normalizer_var = tf.Variable(
0.0,
name='moving_normalizer',
dtype=tf.float32,
synchronization=tf.VariableSynchronization.ON_READ,
trainable=False,
aggregation=tf.VariableAggregation.MEAN)
num_positives_sum = tf.keras.backend.moving_average_update(
moving_normalizer_var,
num_positives_sum,
momentum=params['positives_momentum'])
elif positives_momentum < 0:
num_positives_sum = utils.cross_replica_mean(num_positives_sum)
levels = cls_outputs.keys()
cls_losses = []
box_losses = []
for level in levels:
# Onehot encoding for classification labels.
cls_targets_at_level = tf.one_hot(labels['cls_targets_%d' % level],
params['num_classes'],
dtype=cls_outputs[level].dtype)
if params['data_format'] == 'channels_first':
bs, _, width, height, _ = cls_targets_at_level.get_shape().as_list(
)
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, -1, width, height])
else:
bs, width, height, _, _ = cls_targets_at_level.get_shape().as_list(
)
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, width, height, -1])
box_targets_at_level = labels['box_targets_%d' % level]
cls_loss = focal_loss(cls_outputs[level],
cls_targets_at_level,
params['alpha'],
params['gamma'],
normalizer=num_positives_sum,
label_smoothing=params['label_smoothing'])
if params['data_format'] == 'channels_first':
cls_loss = tf.reshape(
cls_loss, [bs, -1, width, height, params['num_classes']])
else:
cls_loss = tf.reshape(
cls_loss, [bs, width, height, -1, params['num_classes']])
cls_loss *= tf.cast(
tf.expand_dims(tf.not_equal(labels['cls_targets_%d' % level], -2),
-1), cls_loss.dtype)
cls_loss_sum = tf.reduce_sum(cls_loss)
cls_losses.append(tf.cast(cls_loss_sum, tf.float32))
if params['box_loss_weight']:
box_losses.append(
_box_loss(box_outputs[level],
box_targets_at_level,
num_positives_sum,
delta=params['delta']))
# Sum per level losses to total loss.
cls_loss = tf.add_n(cls_losses)
box_loss = tf.add_n(box_losses) if box_losses else tf.constant(0.)
total_loss = (cls_loss + params['box_loss_weight'] * box_loss)
return total_loss, cls_loss, box_loss
