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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