def call(self, y_true, box_outputs):
anchor_boxes = tf.tile(
self.input_anchors.boxes,
[box_outputs.shape[0] // self.input_anchors.boxes.shape[0], 1])
num_positives, box_targets = y_true
box_outputs = anchors.decode_box_outputs(box_outputs, anchor_boxes)
box_targets = anchors.decode_box_outputs(box_targets, anchor_boxes)
normalizer = num_positives * 4.0
box_iou_loss = iou_utils.iou_loss(box_outputs, box_targets,
self.iou_loss_type)
box_iou_loss = tf.reduce_sum(box_iou_loss) / normalizer
return box_iou_loss
def pre_nms(params, cls_outputs, box_outputs, topk=True):
"""Detection post processing before nms.
It takes the multi-level class and box predictions from network, merge them
into unified tensors, and compute boxes, scores, and classes.
Args:
params: a dict of parameters.
cls_outputs: a list of tensors for classes, each tensor denotes a level of
logits with shape [N, H, W, num_class * num_anchors].
box_outputs: a list of tensors for boxes, each tensor ddenotes a level of
boxes with shape [N, H, W, 4 * num_anchors].
topk: if True, select topk before nms (mainly to speed up nms).
Returns:
A tuple of (boxes, scores, classes).
"""
# get boxes by apply bounding box regression to anchors.
eval_anchors = anchors.Anchors(params['min_level'], params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
cls_outputs, box_outputs = merge_class_box_level_outputs(
params, cls_outputs, box_outputs)
if topk:
# select topK purely based on scores before NMS, in order to speed up nms.
cls_outputs, box_outputs, classes, indices = topk_class_boxes(
params, cls_outputs, box_outputs)
anchor_boxes = tf.gather(eval_anchors.boxes, indices)
else:
anchor_boxes = eval_anchors.boxes
classes = None
boxes = anchors.decode_box_outputs(box_outputs, anchor_boxes)
# convert logits to scores.
scores = tf.math.sigmoid(cls_outputs)
return boxes, scores, classes
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.
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
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'])
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), tf.float32)
cls_losses.append(tf.clip_by_value(tf.reduce_sum(cls_loss), 0.0, 2.0))
if params['box_loss_weight']:
box_losses.append(
_box_loss(box_outputs[level],
box_targets_at_level,
num_positives_sum,
delta=params['delta']))
if params['iou_loss_type']:
input_anchors = anchors.Anchors(params['min_level'],
params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
box_output_list = [tf.reshape(box_outputs[i], [-1, 4]) for i in levels]
box_outputs = tf.concat(box_output_list, axis=0)
box_target_list = [
tf.reshape(labels['box_targets_%d' % level], [-1, 4])
for level in levels
]
box_targets = tf.concat(box_target_list, axis=0)
anchor_boxes = tf.tile(input_anchors.boxes, [params['batch_size'], 1])
box_outputs = anchors.decode_box_outputs(box_outputs, anchor_boxes)
box_targets = anchors.decode_box_outputs(box_targets, anchor_boxes)
box_iou_loss = _box_iou_loss(box_outputs, box_targets,
num_positives_sum,
params['iou_loss_type'])
else:
box_iou_loss = 0
# 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 0
total_loss = (cls_loss + params['box_loss_weight'] * box_loss +
params['iou_loss_weight'] * box_iou_loss)
return total_loss, cls_loss, box_loss, box_iou_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.
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
num_positives_sum = tf.reduce_sum(labels['mean_num_positives']) + 1.0
levels = cls_outputs.keys()
cls_losses = []
box_losses = []
sumrule = {}
if params.get('sumrule'):
sumrule = params['sumrule']
# because of cls_targets -= 1 (so that bg class becomes -1, actual class then starts from 0)
# we need to subtract 1 from sumrule as well.
_sumrule = {}
for k, v in sumrule.items():
_sumrule[k - 1] = [vv - 1 for vv in v]
sumrule = _sumrule
def table_lookup(values, old_onehot, cls_targets_at_level):
for val in values:
if sumrule.get(val):
new_val = sumrule[val]
#prob = 1.0/len(new_val)
prob = 0.5 # try sigmoid cross entropy first so set this to 0.5, if we use softmax we should set this to 1.0/len(new_val)
if len(new_val) == 1:
# leaf node, prob = 1.0
prob = 1.0
_matching_onehot = old_onehot[np.where(
cls_targets_at_level == val)]
_matching_onehot[:, new_val] = prob
_matching_onehot[:, val] = 0
old_onehot[np.where(
cls_targets_at_level == val)] = _matching_onehot
return old_onehot
for level in levels:
# Onehot encoding for classification labels.
_cls_targets_at_level = tf.one_hot(labels['cls_targets_%d' % level],
params['num_classes'])
if params.get('sumrule'):
unique_labels, _ = tf.unique(
tf.reshape(labels['cls_targets_%d' % level], [-1]))
# refine one-hot labels so that we map each label to it's finest leaves
cls_targets_at_level = tf.numpy_function(
table_lookup, [
unique_labels, _cls_targets_at_level,
labels['cls_targets_%d' % level]
], _cls_targets_at_level.dtype)
cls_targets_at_level = tf.reshape(cls_targets_at_level,
_cls_targets_at_level.shape)
else:
cls_targets_at_level = _cls_targets_at_level
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), tf.float32)
cls_losses.append(tf.reduce_sum(cls_loss))
if params['box_loss_weight']:
box_losses.append(
_box_loss(box_outputs[level],
box_targets_at_level,
num_positives_sum,
delta=params['delta']))
if params['iou_loss_type']:
input_anchors = anchors.Anchors(params['min_level'],
params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
box_output_list = [tf.reshape(box_outputs[i], [-1, 4]) for i in levels]
box_outputs = tf.concat(box_output_list, axis=0)
box_target_list = [
tf.reshape(labels['box_targets_%d' % level], [-1, 4])
for level in levels
]
box_targets = tf.concat(box_target_list, axis=0)
anchor_boxes = tf.tile(input_anchors.boxes, [params['batch_size'], 1])
box_outputs = anchors.decode_box_outputs(box_outputs, anchor_boxes)
box_targets = anchors.decode_box_outputs(box_targets, anchor_boxes)
box_iou_loss = _box_iou_loss(box_outputs, box_targets,
num_positives_sum,
params['iou_loss_type'])
else:
box_iou_loss = 0
# 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 0
total_loss = (cls_loss + params['box_loss_weight'] * box_loss +
params['iou_loss_weight'] * box_iou_loss)
return total_loss, cls_loss, box_loss, box_iou_loss