def _compute_targets(ex_rois, gt_rois):
"""Compute bounding-box regression targets for an image."""
# targets = bbox_transform(ex_rois, gt_rois[:, :5]).astype(
# np.float32, copy=False)
targets = encode_and_decode.encode_boxes_rotate(
unencode_boxes=gt_rois,
reference_boxes=ex_rois,
scale_factors=cfgs.ANCHOR_SCALE_FACTORS)
# targets = encode_and_decode.encode_boxes(ex_rois=ex_rois,
# gt_rois=gt_rois,
# scale_factor=None)
return targets
def _compute_targets_r(ex_rois, gt_rois_r, labels):
"""Compute bounding-box regression targets for an image.
that is : [label, tx, ty, tw, th]
"""
assert ex_rois.shape[0] == gt_rois_r.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois_r.shape[1] == 5
targets_r = encode_and_decode.encode_boxes_rotate(
unencode_boxes=gt_rois_r,
reference_boxes=ex_rois,
scale_factors=cfgs.ROI_SCALE_FACTORS)
return np.hstack((labels[:, np.newaxis], targets_r)).astype(np.float32,
copy=False)
def fast_rcnn_loss(self):
with tf.variable_scope('fast_rcnn_loss'):
minibatch_indices, minibatch_reference_boxes_mattached_gtboxes, \
minibatch_reference_boxes_mattached_gtboxes_rotate, minibatch_object_mask, \
minibatch_label_one_hot = self.fast_rcnn_minibatch(self.fast_rcnn_all_level_proposals)
minibatch_reference_boxes = tf.gather(self.fast_rcnn_all_level_proposals, minibatch_indices)
minibatch_encode_boxes = tf.gather(self.fast_rcnn_encode_boxes,
minibatch_indices) # [minibatch_size, num_classes*4]
minibatch_encode_boxes_rotate = tf.gather(self.fast_rcnn_encode_boxes_rotate,
minibatch_indices) # [minibatch_size, num_classes*5]
minibatch_scores = tf.gather(self.fast_rcnn_scores, minibatch_indices)
minibatch_scores_rotate = tf.gather(self.fast_rcnn_scores_rotate, minibatch_indices)
# encode gtboxes
minibatch_encode_gtboxes = \
encode_and_decode.encode_boxes(
unencode_boxes=minibatch_reference_boxes_mattached_gtboxes,
reference_boxes=minibatch_reference_boxes,
scale_factors=self.scale_factors
)
minibatch_encode_gtboxes_rotate = encode_and_decode.encode_boxes_rotate(
unencode_boxes=minibatch_reference_boxes_mattached_gtboxes_rotate,
reference_boxes=minibatch_reference_boxes,
scale_factors=self.scale_factors
)
# [minibatch_size, num_classes*4]
minibatch_encode_gtboxes = tf.tile(minibatch_encode_gtboxes, [1, self.num_classes])
# [minibatch_size, num_classes*5]
minibatch_encode_gtboxes_rotate = tf.tile(minibatch_encode_gtboxes_rotate, [1, self.num_classes])
class_weights_list = []
category_list = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes+1):
tmp_class_weights = tf.ones(shape=[tf.shape(minibatch_encode_boxes)[0], 4], dtype=tf.float32)
tmp_class_weights = tmp_class_weights * tf.expand_dims(category_list[i], axis=1)
class_weights_list.append(tmp_class_weights)
class_weights = tf.concat(class_weights_list, axis=1) # [minibatch_size, num_classes*4]
class_weights_list_rotate = []
category_list_rotate = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes + 1):
tmp_class_weights_rotate = tf.ones(shape=[tf.shape(minibatch_encode_boxes_rotate)[0], 5], dtype=tf.float32)
tmp_class_weights_rotate = tmp_class_weights_rotate * tf.expand_dims(category_list_rotate[i], axis=1)
class_weights_list_rotate.append(tmp_class_weights_rotate)
class_weights_rotate = tf.concat(class_weights_list_rotate, axis=1) # [minibatch_size, num_classes*5]
# loss
with tf.variable_scope('fast_rcnn_classification_loss'):
fast_rcnn_classification_loss = slim.losses.softmax_cross_entropy(logits=minibatch_scores,
onehot_labels=minibatch_label_one_hot)
with tf.variable_scope('fast_rcnn_location_loss'):
fast_rcnn_location_loss = losses.l1_smooth_losses(predict_boxes=minibatch_encode_boxes,
gtboxes=minibatch_encode_gtboxes,
object_weights=minibatch_object_mask,
classes_weights=class_weights)
slim.losses.add_loss(fast_rcnn_location_loss)
with tf.variable_scope('fast_rcnn_classification_rotate_loss'):
fast_rcnn_classification_rotate_loss = slim.losses.softmax_cross_entropy(logits=minibatch_scores_rotate,
onehot_labels=minibatch_label_one_hot)
with tf.variable_scope('fast_rcnn_location_rotate_loss'):
fast_rcnn_location_rotate_loss = losses.l1_smooth_losses(predict_boxes=minibatch_encode_boxes_rotate,
gtboxes=minibatch_encode_gtboxes_rotate,
object_weights=minibatch_object_mask,
classes_weights=class_weights_rotate)
slim.losses.add_loss(fast_rcnn_location_rotate_loss)
return fast_rcnn_location_loss, fast_rcnn_classification_loss, \
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss
def fast_rcnn_loss(self):
with tf.variable_scope('fast_rcnn_loss'):
minibatch_indices, minibatch_reference_boxes_mattached_gtboxes, \
minibatch_reference_boxes_mattached_gtboxes_rotate, \
minibatch_reference_boxes_mattached_head_quadrant, minibatch_object_mask, \
minibatch_label_one_hot = self.fast_rcnn_minibatch(self.fast_rcnn_all_level_proposals)
minibatch_reference_boxes = tf.gather(
self.fast_rcnn_all_level_proposals, minibatch_indices)
minibatch_encode_boxes = tf.gather(
self.fast_rcnn_encode_boxes,
minibatch_indices) # [minibatch_size, num_classes*4]
minibatch_encode_boxes_rotate = tf.gather(
self.fast_rcnn_encode_boxes_rotate,
minibatch_indices) # [minibatch_size, num_classes*5]
minibatch_head_quadrant = tf.gather(self.fast_rcnn_head_quadrant,
minibatch_indices)
minibatch_scores = tf.gather(self.fast_rcnn_scores,
minibatch_indices)
minibatch_scores_rotate = tf.gather(self.fast_rcnn_scores_rotate,
minibatch_indices)
# encode gtboxes
minibatch_encode_gtboxes = \
encode_and_decode.encode_boxes(
unencode_boxes=minibatch_reference_boxes_mattached_gtboxes,
reference_boxes=minibatch_reference_boxes,
scale_factors=self.scale_factors
)
minibatch_encode_gtboxes_rotate = encode_and_decode.encode_boxes_rotate(
unencode_boxes=
minibatch_reference_boxes_mattached_gtboxes_rotate,
reference_boxes=minibatch_reference_boxes,
scale_factors=self.scale_factors)
############### Class-agnostic Without tile
# [minibatch_size, num_classes*4]
# minibatch_encode_gtboxes = tf.tile(minibatch_encode_gtboxes, [1, self.num_classes])
############### Class-agnostic Without tile
# [minibatch_size, num_classes*5]
# minibatch_encode_gtboxes_rotate = tf.tile(minibatch_encode_gtboxes_rotate, [1, self.num_classes])
############### Class-agnostic Without tile
# minibatch_gt_head_quadrant = tf.tile(minibatch_reference_boxes_mattached_head_quadrant, [1, self.num_classes])
minibatch_gt_head_quadrant = minibatch_reference_boxes_mattached_head_quadrant
class_weights_list = []
category_list = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes + 1):
tmp_class_weights = tf.ones(
shape=[tf.shape(minibatch_encode_boxes)[0], 4],
dtype=tf.float32)
tmp_class_weights = tmp_class_weights * tf.expand_dims(
category_list[i], axis=1)
class_weights_list.append(tmp_class_weights)
class_weights = tf.concat(
class_weights_list, axis=1) # [minibatch_size, num_classes*4]
class_weights_list_rotate = []
category_list_rotate = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes + 1):
tmp_class_weights_rotate = tf.ones(
shape=[tf.shape(minibatch_encode_boxes_rotate)[0], 5],
dtype=tf.float32)
tmp_class_weights_rotate = tmp_class_weights_rotate * tf.expand_dims(
category_list_rotate[i], axis=1)
class_weights_list_rotate.append(tmp_class_weights_rotate)
class_weights_rotate = tf.concat(
class_weights_list_rotate,
axis=1) # [minibatch_size, num_classes*5]
class_weights_list_head = []
category_list_head = tf.unstack(minibatch_label_one_hot, axis=1)
for i in range(1, self.num_classes + 1):
tmp_class_weights_head = tf.ones(
shape=[tf.shape(minibatch_head_quadrant)[0], 4],
dtype=tf.float32)
tmp_class_weights_head = tmp_class_weights_head * tf.expand_dims(
category_list_head[i], axis=1)
class_weights_list_head.append(tmp_class_weights_head)
class_weights_head = tf.concat(class_weights_list_head, axis=1)
# loss
with tf.variable_scope('fast_rcnn_classification_loss'):
# fast_rcnn_classification_loss = slim.losses.softmax_cross_entropy(logits=minibatch_scores,
# onehot_labels=minibatch_label_one_hot)
fast_rcnn_classification_loss = losses.focal_loss(
prediction_tensor=minibatch_scores,
target_tensor=minibatch_label_one_hot)
slim.losses.add_loss(fast_rcnn_classification_loss)
with tf.variable_scope('fast_rcnn_location_loss'):
# fast_rcnn_location_loss = losses.l1_smooth_losses(predict_boxes=minibatch_encode_boxes,
# gtboxes=minibatch_encode_gtboxes,
# object_weights=minibatch_object_mask,
# classes_weights=class_weights)
# Class-agnostic regression
fast_rcnn_location_loss = losses.l1_smooth_losses(
predict_boxes=minibatch_encode_boxes,
gtboxes=minibatch_encode_gtboxes,
object_weights=minibatch_object_mask,
classes_weights=None)
slim.losses.add_loss(fast_rcnn_location_loss)
with tf.variable_scope('fast_rcnn_classification_rotate_loss'):
# fast_rcnn_classification_rotate_loss = slim.losses.softmax_cross_entropy(logits=minibatch_scores_rotate,
# onehot_labels=minibatch_label_one_hot)
fast_rcnn_classification_rotate_loss = losses.focal_loss(
prediction_tensor=minibatch_scores_rotate,
target_tensor=minibatch_label_one_hot)
slim.losses.add_loss(fast_rcnn_classification_rotate_loss)
with tf.variable_scope('fast_rcnn_location_rotate_loss'):
# fast_rcnn_location_rotate_loss = losses.l1_smooth_losses(predict_boxes=minibatch_encode_boxes_rotate,
# gtboxes=minibatch_encode_gtboxes_rotate,
# object_weights=minibatch_object_mask,
# classes_weights=class_weights_rotate)
# Class-agnostic regression
fast_rcnn_location_rotate_loss = losses.l1_smooth_losses(
predict_boxes=minibatch_encode_boxes_rotate,
gtboxes=minibatch_encode_gtboxes_rotate,
object_weights=minibatch_object_mask,
classes_weights=None)
slim.losses.add_loss(fast_rcnn_location_rotate_loss)
with tf.variable_scope('fast_rcnn_head_quadrant_loss'):
# fast_rcnn_head_quadrant_loss = losses.l1_smooth_losses(predict_boxes=minibatch_head_quadrant,
# gtboxes=minibatch_gt_head_quadrant,
# object_weights=minibatch_object_mask,
# classes_weights=class_weights_head)
# Class-agnostic regression
fast_rcnn_head_quadrant_loss = losses.l1_smooth_losses(
predict_boxes=minibatch_head_quadrant,
gtboxes=minibatch_gt_head_quadrant,
object_weights=minibatch_object_mask,
classes_weights=None)
slim.losses.add_loss(
fast_rcnn_head_quadrant_loss *
10) # More importance by the bigger weight
return fast_rcnn_location_loss, fast_rcnn_classification_loss, \
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss, fast_rcnn_head_quadrant_loss * 10
