tensorflow/sagemakercv/training/losses/frcnn_losses.py [134:166]:
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            elif loss_type == 'giou':
                box_loss = _giou_loss(y_true=box_targets, y_pred=box_outputs, weights=mask, reduction='none')
                box_loss = tf.reshape(box_loss, [-1, 512, 4]) # FIXME
            elif loss_type == 'ciou':
                box_loss = _ciou_loss(y_true=box_targets, y_pred=box_outputs, weights=mask)
            else:
                raise NotImplementedError

            oh_targets = tf.one_hot(class_targets, depth=num_classes)
            class_scores = tf.nn.softmax(class_outputs, axis=-1)
            top_class_score = class_scores * oh_targets
            pos_cls_score = top_class_score[:,:,1:] # ignore GT score
            pos_cls_score = tf.reduce_max(pos_cls_score, axis=-1)
            # carl_loss_weights = tf.pow(beta + (1. - beta) * pos_cls_score, gamma)
            # since gamma is 1.0 right now just use the linear combination term
            carl_loss_weights = beta + (1. - beta) * pos_cls_score
            # zero out bias contributions from zero pos_cls_scores (GTs)
            carl_loss_weights = tf.where(pos_cls_score > 0.0, carl_loss_weights, tf.zeros_like(carl_loss_weights))
            # normalize carl_loss_weight to make its sum equal to num positive
            num_pos = tf.math.count_nonzero(class_targets, dtype=carl_loss_weights.dtype)
            weight_ratio = tf.math.divide_no_nan(num_pos, tf.reduce_sum(carl_loss_weights))
            carl_loss_weights *= weight_ratio

            loss_carl = tf.reduce_sum(box_loss * tf.expand_dims(carl_loss_weights, -1))
            loss_bbox = tf.reduce_sum(box_loss)

            assert loss_carl.dtype == tf.float32

            regression_loss = loss_carl + loss_bbox

            if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
                regression_loss /= normalizer
            return regression_loss
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tensorflow/sagemakercv/training/losses/losses.py [361:393]:
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        elif loss_type == 'giou':
            box_loss = _giou_loss(y_true=box_targets, y_pred=box_outputs, weights=mask, reduction='none')
            box_loss = tf.reshape(box_loss, [-1, 512, 4]) # FIXME
        elif loss_type == 'ciou':
            box_loss = _ciou_loss(y_true=box_targets, y_pred=box_outputs, weights=mask)
        else:
            raise NotImplementedError

        oh_targets = tf.one_hot(class_targets, depth=num_classes)
        class_scores = tf.nn.softmax(class_outputs, axis=-1)
        top_class_score = class_scores * oh_targets
        pos_cls_score = top_class_score[:,:,1:] # ignore GT score
        pos_cls_score = tf.reduce_max(pos_cls_score, axis=-1)
        # carl_loss_weights = tf.pow(beta + (1. - beta) * pos_cls_score, gamma)
        # since gamma is 1.0 right now just use the linear combination term
        carl_loss_weights = beta + (1. - beta) * pos_cls_score
        # zero out bias contributions from zero pos_cls_scores (GTs)
        carl_loss_weights = tf.where(pos_cls_score > 0.0, carl_loss_weights, tf.zeros_like(carl_loss_weights))
        # normalize carl_loss_weight to make its sum equal to num positive
        num_pos = tf.math.count_nonzero(class_targets, dtype=carl_loss_weights.dtype)
        weight_ratio = tf.math.divide_no_nan(num_pos, tf.reduce_sum(carl_loss_weights))
        carl_loss_weights *= weight_ratio

        loss_carl = tf.reduce_sum(box_loss * tf.expand_dims(carl_loss_weights, -1))
        loss_bbox = tf.reduce_sum(box_loss)

        assert loss_carl.dtype == tf.float32

        regression_loss = loss_carl + loss_bbox

        if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
            regression_loss /= normalizer
        return regression_loss
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