def _box_params_loss(self, ground_truth, ground_truth_num,
anchor_centers, offsets, proposals_num):
# ground_truth shape is M x 4, where M is count and 4 are y,x,h,w
ground_truth = tf.expand_dims(ground_truth, axis=0)
ground_truth = tf.tile(ground_truth, [proposals_num, 1, 1])
# anchor_centers shape is N x 4 where N is count and 4 are ya,xa,ha,wa
anchor_centers = tf.expand_dims(anchor_centers, axis=1)
anchor_centers = tf.tile(anchor_centers, [1, ground_truth_num, 1])
# pos_sample_mask shape is N x M, True are for positive proposals and, hence,
# for anchor centers
pos_sample_mask = tf.greater(self.iou_metric, 0.7)
# convert mask shape from N to N x 1 to make it broadcastable with pos_sample_mask
mask = tf.expand_dims(self.cross_boundary_mask, axis=1)
# convert resulting shape to align it with offsets
mask = tf.expand_dims(tf.cast(pos_sample_mask & mask, tf.float32), axis=2)
y_anchor, x_anchor, height_anchor, width_anchor = tf.unstack(anchor_centers, axis=2)
y_ground_truth, x_ground_truth, height_ground_truth, width_ground_truth = tf.unstack(
ground_truth, axis=2)
# idea is to calculate N x M tx, ty, tw, th for ground truth boxes
# for every proposal. Then we caclulate loss, multiply it with mask
# to filter out non-positive samples and sum to one
# each shape is N x M
tx_ground_truth = (x_ground_truth - x_anchor) / width_anchor
ty_ground_truth = (y_ground_truth - y_anchor) / height_anchor
tw_ground_truth = tf.log(width_ground_truth / width_anchor)
th_ground_truth = tf.log(height_ground_truth / height_anchor)
gt_params = tf.stack(
[ty_ground_truth, tx_ground_truth, th_ground_truth, tw_ground_truth], axis=2)
offsets = tf.expand_dims(offsets, axis=1)
offsets = tf.tile(offsets, [1, ground_truth_num, 1])
return huber_loss((offsets - gt_params) * mask)
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