diff --git a/pcdet/utils/loss_utils.py b/pcdet/utils/loss_utils.py
new file mode 100644
index 0000000..bd114ba
--- /dev/null
+++ b/pcdet/utils/loss_utils.py
@@ -0,0 +1,649 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import box_utils
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+
+
+class SigmoidFocalClassificationLoss(nn.Module):
+    """
+    Sigmoid focal cross entropy loss.
+    """
+
+    def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
+        """
+        Args:
+            gamma: Weighting parameter to balance loss for hard and easy examples.
+            alpha: Weighting parameter to balance loss for positive and negative examples.
+        """
+        super(SigmoidFocalClassificationLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    @staticmethod
+    def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
+        """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
+            max(x, 0) - x * z + log(1 + exp(-abs(x))) in
+            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+
+        Returns:
+            loss: (B, #anchors, #classes) float tensor.
+                Sigmoid cross entropy loss without reduction
+        """
+        loss = torch.clamp(input, min=0) - input * target + \
+               torch.log1p(torch.exp(-torch.abs(input)))
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            weighted_loss: (B, #anchors, #classes) float tensor after weighting.
+        """
+        pred_sigmoid = torch.sigmoid(input)
+        alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha)
+        pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid
+        focal_weight = alpha_weight * torch.pow(pt, self.gamma)
+
+        bce_loss = self.sigmoid_cross_entropy_with_logits(input, target)
+
+        loss = focal_weight * bce_loss
+
+        if weights.shape.__len__() == 2 or \
+                (weights.shape.__len__() == 1 and target.shape.__len__() == 2):
+            weights = weights.unsqueeze(-1)
+
+        assert weights.shape.__len__() == loss.shape.__len__()
+
+        return loss * weights
+
+
+class WeightedSmoothL1Loss(nn.Module):
+    """
+    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
+    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
+                  | 0.5 * x ** 2 / beta   if abs(x) < beta
+    smoothl1(x) = |
+                  | abs(x) - 0.5 * beta   otherwise,
+    where x = input - target.
+    """
+    def __init__(self, beta: float = 1.0 / 9.0, code_weights: list = None):
+        """
+        Args:
+            beta: Scalar float.
+                L1 to L2 change point.
+                For beta values < 1e-5, L1 loss is computed.
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedSmoothL1Loss, self).__init__()
+        self.beta = beta
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @staticmethod
+    def smooth_l1_loss(diff, beta):
+        if beta < 1e-5:
+            loss = torch.abs(diff)
+        else:
+            n = torch.abs(diff)
+            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
+
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        target = torch.where(torch.isnan(target), input, target)  # ignore nan targets
+
+        diff = input - target
+        # code-wise weighting
+        if self.code_weights is not None:
+            diff = diff * self.code_weights.view(1, 1, -1)
+
+        loss = self.smooth_l1_loss(diff, self.beta)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class WeightedL1Loss(nn.Module):
+    def __init__(self, code_weights: list = None):
+        """
+        Args:
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedL1Loss, self).__init__()
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.float16)
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        target = torch.where(torch.isnan(target), input, target)  # ignore nan targets
+
+        diff = input - target
+        # code-wise weighting
+        if self.code_weights is not None:
+            diff = diff * self.code_weights.view(1, 1, -1)
+
+        loss = torch.abs(diff)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class WeightedCrossEntropyLoss(nn.Module):
+    """
+    Transform input to fit the fomation of PyTorch offical cross entropy loss
+    with anchor-wise weighting.
+    """
+    def __init__(self):
+        super(WeightedCrossEntropyLoss, self).__init__()
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predited logits for each class.
+            target: (B, #anchors, #classes) float tensor.
+                One-hot classification targets.
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted cross entropy loss without reduction
+        """
+        input = input.permute(0, 2, 1)
+        target = target.argmax(dim=-1)
+        loss = F.cross_entropy(input, target, reduction='none') * weights
+        return loss
+
+
+def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
+    """
+    Args:
+        pred_bbox3d: (N, 7) float Tensor.
+        gt_bbox3d: (N, 7) float Tensor.
+
+    Returns:
+        corner_loss: (N) float Tensor.
+    """
+    assert pred_bbox3d.shape[0] == gt_bbox3d.shape[0]
+
+    pred_box_corners = box_utils.boxes_to_corners_3d(pred_bbox3d)
+    gt_box_corners = box_utils.boxes_to_corners_3d(gt_bbox3d)
+
+    gt_bbox3d_flip = gt_bbox3d.clone()
+    gt_bbox3d_flip[:, 6] += np.pi
+    gt_box_corners_flip = box_utils.boxes_to_corners_3d(gt_bbox3d_flip)
+    # (N, 8)
+    corner_dist = torch.min(torch.norm(pred_box_corners - gt_box_corners, dim=2),
+                            torch.norm(pred_box_corners - gt_box_corners_flip, dim=2))
+    # (N, 8)
+    corner_loss = WeightedSmoothL1Loss.smooth_l1_loss(corner_dist, beta=1.0)
+
+    return corner_loss.mean(dim=1)
+
+
+def compute_fg_mask(gt_boxes2d, shape, downsample_factor=1, device=torch.device("cpu")):
+    """
+    Compute foreground mask for images
+    Args:
+        gt_boxes2d: (B, N, 4), 2D box labels
+        shape: torch.Size or tuple, Foreground mask desired shape
+        downsample_factor: int, Downsample factor for image
+        device: torch.device, Foreground mask desired device
+    Returns:
+        fg_mask (shape), Foreground mask
+    """
+    fg_mask = torch.zeros(shape, dtype=torch.bool, device=device)
+
+    # Set box corners
+    gt_boxes2d /= downsample_factor
+    gt_boxes2d[:, :, :2] = torch.floor(gt_boxes2d[:, :, :2])
+    gt_boxes2d[:, :, 2:] = torch.ceil(gt_boxes2d[:, :, 2:])
+    gt_boxes2d = gt_boxes2d.long()
+
+    # Set all values within each box to True
+    B, N = gt_boxes2d.shape[:2]
+    for b in range(B):
+        for n in range(N):
+            u1, v1, u2, v2 = gt_boxes2d[b, n]
+            fg_mask[b, v1:v2, u1:u2] = True
+
+    return fg_mask
+
+
+def neg_loss_cornernet(pred, gt, mask=None):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint.
+    Modified focal loss. Exactly the same as CornerNet. Runs faster and costs a little bit more memory
+    Args:
+        pred: (batch x c x h x w)
+        gt: (batch x c x h x w)
+        mask: (batch x h x w)
+    Returns:
+    """
+    pos_inds = gt.eq(1).float()
+    neg_inds = gt.lt(1).float()
+
+    neg_weights = torch.pow(1 - gt, 4)
+
+    loss = 0
+
+    pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
+    neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
+
+    if mask is not None:
+        mask = mask[:, None, :, :].float()
+        pos_loss = pos_loss * mask
+        neg_loss = neg_loss * mask
+        num_pos = (pos_inds.float() * mask).sum()
+    else:
+        num_pos = pos_inds.float().sum()
+
+    pos_loss = pos_loss.sum()
+    neg_loss = neg_loss.sum()
+
+    if num_pos == 0:
+        loss = loss - neg_loss
+    else:
+        loss = loss - (pos_loss + neg_loss) / num_pos
+    return loss
+
+
+def neg_loss_sparse(pred, gt):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint.
+    Modified focal loss. Exactly the same as CornerNet. Runs faster and costs a little bit more memory
+    Args:
+        pred: (batch x c x n)
+        gt: (batch x c x n)
+    Returns:
+    """
+    pos_inds = gt.eq(1).float()
+    neg_inds = gt.lt(1).float()
+
+    neg_weights = torch.pow(1 - gt, 4)
+
+    loss = 0
+
+    pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
+    neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
+
+    num_pos = pos_inds.float().sum()
+
+    pos_loss = pos_loss.sum()
+    neg_loss = neg_loss.sum()
+
+    if num_pos == 0:
+        loss = loss - neg_loss
+    else:
+        loss = loss - (pos_loss + neg_loss) / num_pos
+    return loss
+
+
+class FocalLossCenterNet(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+    def __init__(self):
+        super(FocalLossCenterNet, self).__init__()
+        self.neg_loss = neg_loss_cornernet
+
+    def forward(self, out, target, mask=None):
+        return self.neg_loss(out, target, mask=mask)
+
+
+def _reg_loss(regr, gt_regr, mask):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    L1 regression loss
+    Args:
+        regr (batch x max_objects x dim)
+        gt_regr (batch x max_objects x dim)
+        mask (batch x max_objects)
+    Returns:
+    """
+    num = mask.float().sum()
+    mask = mask.unsqueeze(2).expand_as(gt_regr).float()
+    isnotnan = (~ torch.isnan(gt_regr)).float()
+    mask *= isnotnan
+    regr = regr * mask
+    gt_regr = gt_regr * mask
+
+    loss = torch.abs(regr - gt_regr)
+    loss = loss.transpose(2, 0)
+
+    loss = torch.sum(loss, dim=2)
+    loss = torch.sum(loss, dim=1)
+    # else:
+    #  # D x M x B
+    #  loss = loss.reshape(loss.shape[0], -1)
+
+    # loss = loss / (num + 1e-4)
+    loss = loss / torch.clamp_min(num, min=1.0)
+    # import pdb; pdb.set_trace()
+    return loss
+
+
+def _gather_feat(feat, ind, mask=None):
+    dim  = feat.size(2)
+    ind  = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
+    feat = feat.gather(1, ind)
+    if mask is not None:
+        mask = mask.unsqueeze(2).expand_as(feat)
+        feat = feat[mask]
+        feat = feat.view(-1, dim)
+    return feat
+
+
+def _transpose_and_gather_feat(feat, ind):
+    feat = feat.permute(0, 2, 3, 1).contiguous()
+    feat = feat.view(feat.size(0), -1, feat.size(3))
+    feat = _gather_feat(feat, ind)
+    return feat
+
+
+class RegLossCenterNet(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+
+    def __init__(self):
+        super(RegLossCenterNet, self).__init__()
+
+    def forward(self, output, mask, ind=None, target=None):
+        """
+        Args:
+            output: (batch x dim x h x w) or (batch x max_objects)
+            mask: (batch x max_objects)
+            ind: (batch x max_objects)
+            target: (batch x max_objects x dim)
+        Returns:
+        """
+        if ind is None:
+            pred = output
+        else:
+            pred = _transpose_and_gather_feat(output, ind)
+        loss = _reg_loss(pred, target, mask)
+        return loss
+
+
+class FocalLossSparse(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+    def __init__(self):
+        super(FocalLossSparse, self).__init__()
+        self.neg_loss = neg_loss_sparse
+
+    def forward(self, out, target):
+        return self.neg_loss(out, target)
+
+
+class RegLossSparse(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+
+    def __init__(self):
+        super(RegLossSparse, self).__init__()
+
+    def forward(self, output, mask, ind=None, target=None, batch_index=None):
+        """
+        Args:
+            output: (N x dim)
+            mask: (batch x max_objects)
+            ind: (batch x max_objects)
+            target: (batch x max_objects x dim)
+        Returns:
+        """
+
+        pred = []
+        batch_size = mask.shape[0]
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred.append(output[batch_inds][ind[bs_idx]])
+        pred = torch.stack(pred)
+
+        loss = _reg_loss(pred, target, mask)
+        return loss
+
+
+class IouLossSparse(nn.Module):
+    '''IouLoss loss for an output tensor
+    Arguments:
+        output (batch x dim x h x w)
+        mask (batch x max_objects)
+        ind (batch x max_objects)
+        target (batch x max_objects x dim)
+    '''
+
+    def __init__(self):
+        super(IouLossSparse, self).__init__()
+
+    def forward(self, iou_pred, mask, ind, box_pred, box_gt, batch_index):
+        if mask.sum() == 0:
+            return iou_pred.new_zeros((1))
+        batch_size = mask.shape[0]
+        mask = mask.bool()
+
+        loss = 0
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred = iou_pred[batch_inds][ind[bs_idx]][mask[bs_idx]]
+            pred_box = box_pred[batch_inds][ind[bs_idx]][mask[bs_idx]]
+            target = iou3d_nms_utils.boxes_aligned_iou3d_gpu(pred_box, box_gt[bs_idx])
+            target = 2 * target - 1
+            loss += F.l1_loss(pred, target, reduction='sum')
+
+        loss = loss / (mask.sum() + 1e-4)
+        return loss
+
+class IouRegLossSparse(nn.Module):
+    '''Distance IoU loss for output boxes
+        Arguments:
+            output (batch x dim x h x w)
+            mask (batch x max_objects)
+            ind (batch x max_objects)
+            target (batch x max_objects x dim)
+    '''
+
+    def __init__(self, type="DIoU"):
+        super(IouRegLossSparse, self).__init__()
+
+    def center_to_corner2d(self, center, dim):
+        corners_norm = torch.tensor([[-0.5, -0.5], [-0.5, 0.5], [0.5, 0.5], [0.5, -0.5]],
+                                    dtype=torch.float32, device=dim.device)
+        corners = dim.view([-1, 1, 2]) * corners_norm.view([1, 4, 2])
+        corners = corners + center.view(-1, 1, 2)
+        return corners
+
+    def bbox3d_iou_func(self, pred_boxes, gt_boxes):
+        assert pred_boxes.shape[0] == gt_boxes.shape[0]
+
+        qcorners = self.center_to_corner2d(pred_boxes[:, :2], pred_boxes[:, 3:5])
+        gcorners = self.center_to_corner2d(gt_boxes[:, :2], gt_boxes[:, 3:5])
+
+        inter_max_xy = torch.minimum(qcorners[:, 2], gcorners[:, 2])
+        inter_min_xy = torch.maximum(qcorners[:, 0], gcorners[:, 0])
+        out_max_xy = torch.maximum(qcorners[:, 2], gcorners[:, 2])
+        out_min_xy = torch.minimum(qcorners[:, 0], gcorners[:, 0])
+
+        # calculate area
+        volume_pred_boxes = pred_boxes[:, 3] * pred_boxes[:, 4] * pred_boxes[:, 5]
+        volume_gt_boxes = gt_boxes[:, 3] * gt_boxes[:, 4] * gt_boxes[:, 5]
+
+        inter_h = torch.minimum(pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5]) - \
+                torch.maximum(pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5])
+        inter_h = torch.clamp(inter_h, min=0)
+
+        inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
+        volume_inter = inter[:, 0] * inter[:, 1] * inter_h
+        volume_union = volume_gt_boxes + volume_pred_boxes - volume_inter
+
+        # boxes_iou3d_gpu(pred_boxes, gt_boxes)
+        inter_diag = torch.pow(gt_boxes[:, 0:3] - pred_boxes[:, 0:3], 2).sum(-1)
+
+        outer_h = torch.maximum(gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5]) - \
+                torch.minimum(gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5])
+        outer_h = torch.clamp(outer_h, min=0)
+        outer = torch.clamp((out_max_xy - out_min_xy), min=0)
+        outer_diag = outer[:, 0] ** 2 + outer[:, 1] ** 2 + outer_h ** 2
+
+        dious = volume_inter / volume_union - inter_diag / outer_diag
+        dious = torch.clamp(dious, min=-1.0, max=1.0)
+
+        return dious
+
+    def forward(self, box_pred, mask, ind, box_gt, batch_index):
+        if mask.sum() == 0:
+            return box_pred.new_zeros((1))
+        mask = mask.bool()
+        batch_size = mask.shape[0]
+
+        loss = 0
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred_box = box_pred[batch_inds][ind[bs_idx]]
+            iou = self.bbox3d_iou_func(pred_box[mask[bs_idx]], box_gt[bs_idx])
+            loss += (1. - iou).sum()
+
+        loss =  loss / (mask.sum() + 1e-4)
+        return loss
+
+class L1Loss(nn.Module):
+    def __init__(self):
+        super(L1Loss, self).__init__()
+       
+    def forward(self, pred, target):
+        if target.numel() == 0:
+            return pred.sum() * 0
+        assert pred.size() == target.size()
+        loss = torch.abs(pred - target)
+        return loss
+
+
+class GaussianFocalLoss(nn.Module):
+    """GaussianFocalLoss is a variant of focal loss.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_
+    Code is modified from `kp_utils.py
+    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
+    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
+    not 0/1 binary target.
+
+    Args:
+        alpha (float): Power of prediction.
+        gamma (float): Power of target for negative samples.
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self,
+                 alpha=2.0,
+                 gamma=4.0):
+        super(GaussianFocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, pred, target):
+        eps = 1e-12
+        pos_weights = target.eq(1)
+        neg_weights = (1 - target).pow(self.gamma)
+        pos_loss = -(pred + eps).log() * (1 - pred).pow(self.alpha) * pos_weights
+        neg_loss = -(1 - pred + eps).log() * pred.pow(self.alpha) * neg_weights
+
+        return pos_loss + neg_loss
+
+
+def calculate_iou_loss_centerhead(iou_preds, batch_box_preds, mask, ind, gt_boxes):
+    """
+    Args:
+        iou_preds: (batch x 1 x h x w)
+        batch_box_preds: (batch x (7 or 9) x h x w)
+        mask: (batch x max_objects)
+        ind: (batch x max_objects)
+        gt_boxes: (batch x N, 7 or 9)
+    Returns:
+    """
+    if mask.sum() == 0:
+        return iou_preds.new_zeros((1))
+
+    mask = mask.bool()
+    selected_iou_preds = _transpose_and_gather_feat(iou_preds, ind)[mask]
+
+    selected_box_preds = _transpose_and_gather_feat(batch_box_preds, ind)[mask]
+    iou_target = iou3d_nms_utils.paired_boxes_iou3d_gpu(selected_box_preds[:, 0:7], gt_boxes[mask][:, 0:7])
+    # iou_target = iou3d_nms_utils.boxes_iou3d_gpu(selected_box_preds[:, 0:7].clone(), gt_boxes[mask][:, 0:7].clone()).diag()
+    iou_target = iou_target * 2 - 1  # [0, 1] ==> [-1, 1]
+
+    # print(selected_iou_preds.view(-1), iou_target)
+    loss = F.l1_loss(selected_iou_preds.view(-1), iou_target, reduction='sum')
+    loss = loss / torch.clamp(mask.sum(), min=1e-4)
+    return loss
+
+
+def calculate_iou_reg_loss_centerhead(batch_box_preds, mask, ind, gt_boxes):
+    if mask.sum() == 0:
+        return batch_box_preds.new_zeros((1))
+
+    mask = mask.bool()
+
+    selected_box_preds = _transpose_and_gather_feat(batch_box_preds, ind)
+
+    iou = box_utils.bbox3d_overlaps_diou(selected_box_preds[mask][:, 0:7], gt_boxes[mask][:, 0:7])
+
+    loss = (1.0 - iou).sum() / torch.clamp(mask.sum(), min=1e-4)
+    return loss