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from copy import copy, deepcopy
import torch
import torch.nn as nn
from dust3r.inference import get_pred_pts3d, find_opt_scaling
from dust3r.utils.geometry import (
inv,
geotrf,
normalize_pointcloud,
normalize_pointcloud_group,
)
from dust3r.utils.geometry import (
get_group_pointcloud_depth,
get_group_pointcloud_center_scale,
weighted_procrustes,
)
# from gsplat import rasterization
import numpy as np
import lpips
from dust3r.utils.camera import (
pose_encoding_to_camera,
camera_to_pose_encoding,
relative_pose_absT_quatR,
)
def Sum(*losses_and_masks):
loss, mask = losses_and_masks[0]
if loss.ndim > 0:
# we are actually returning the loss for every pixels
return losses_and_masks
else:
# we are returning the global loss
for loss2, mask2 in losses_and_masks[1:]:
loss = loss + loss2
return loss
class BaseCriterion(nn.Module):
def __init__(self, reduction="mean"):
super().__init__()
self.reduction = reduction
class LLoss(BaseCriterion):
"""L-norm loss"""
def forward(self, a, b):
assert (
a.shape == b.shape and a.ndim >= 2 and 1 <= a.shape[-1] <= 3
), f"Bad shape = {a.shape}"
dist = self.distance(a, b)
if self.reduction == "none":
return dist
if self.reduction == "sum":
return dist.sum()
if self.reduction == "mean":
return dist.mean() if dist.numel() > 0 else dist.new_zeros(())
raise ValueError(f"bad {self.reduction=} mode")
def distance(self, a, b):
raise NotImplementedError()
class L21Loss(LLoss):
"""Euclidean distance between 3d points"""
def distance(self, a, b):
return torch.norm(a - b, dim=-1) # normalized L2 distance
L21 = L21Loss()
class MSELoss(LLoss):
def distance(self, a, b):
return (a - b) ** 2
MSE = MSELoss()
class Criterion(nn.Module):
def __init__(self, criterion=None):
super().__init__()
assert isinstance(
criterion, BaseCriterion
), f"{criterion} is not a proper criterion!"
self.criterion = copy(criterion)
def get_name(self):
return f"{type(self).__name__}({self.criterion})"
def with_reduction(self, mode="none"):
res = loss = deepcopy(self)
while loss is not None:
assert isinstance(loss, Criterion)
loss.criterion.reduction = mode # make it return the loss for each sample
loss = loss._loss2 # we assume loss is a Multiloss
return res
class MultiLoss(nn.Module):
"""Easily combinable losses (also keep track of individual loss values):
loss = MyLoss1() + 0.1*MyLoss2()
Usage:
Inherit from this class and override get_name() and compute_loss()
"""
def __init__(self):
super().__init__()
self._alpha = 1
self._loss2 = None
def compute_loss(self, *args, **kwargs):
raise NotImplementedError()
def get_name(self):
raise NotImplementedError()
def __mul__(self, alpha):
assert isinstance(alpha, (int, float))
res = copy(self)
res._alpha = alpha
return res
__rmul__ = __mul__ # same
def __add__(self, loss2):
assert isinstance(loss2, MultiLoss)
res = cur = copy(self)
# find the end of the chain
while cur._loss2 is not None:
cur = cur._loss2
cur._loss2 = loss2
return res
def __repr__(self):
name = self.get_name()
if self._alpha != 1:
name = f"{self._alpha:g}*{name}"
if self._loss2:
name = f"{name} + {self._loss2}"
return name
def forward(self, *args, **kwargs):
loss = self.compute_loss(*args, **kwargs)
if isinstance(loss, tuple):
loss, details = loss
elif loss.ndim == 0:
details = {self.get_name(): float(loss)}
else:
details = {}
loss = loss * self._alpha
if self._loss2:
loss2, details2 = self._loss2(*args, **kwargs)
loss = loss + loss2
details |= details2
return loss, details
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_pool = nn.AvgPool2d(3, 1)
self.refl = nn.ReflectionPad2d(1)
self.C1 = 0.01**2
self.C2 = 0.03**2
def forward(self, x, y):
x = self.refl(x)
y = self.refl(y)
mu_x = self.mu_x_pool(x)
mu_y = self.mu_y_pool(y)
sigma_x = self.sig_x_pool(x**2) - mu_x**2
sigma_y = self.sig_y_pool(y**2) - mu_y**2
sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
SSIM_d = (mu_x**2 + mu_y**2 + self.C1) * (sigma_x + sigma_y + self.C2)
return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)
class RGBLoss(Criterion, MultiLoss):
def __init__(self, criterion):
super().__init__(criterion)
self.ssim = SSIM()
def img_loss(self, a, b):
return self.criterion(a, b)
def compute_loss(self, gts, preds, **kw):
gt_rgbs = [gt["img"].permute(0, 2, 3, 1) for gt in gts]
pred_rgbs = [pred["rgb"] for pred in preds]
ls = [
self.img_loss(pred_rgb, gt_rgb)
for pred_rgb, gt_rgb in zip(pred_rgbs, gt_rgbs)
]
details = {}
self_name = type(self).__name__
for i, l in enumerate(ls):
details[self_name + f"_rgb/{i+1}"] = float(l)
details[f"pred_rgb_{i+1}"] = pred_rgbs[i]
rgb_loss = sum(ls) / len(ls)
return rgb_loss, details
class DepthScaleShiftInvLoss(BaseCriterion):
"""scale and shift invariant loss"""
def __init__(self, reduction="none"):
super().__init__(reduction)
def forward(self, pred, gt, mask):
assert pred.shape == gt.shape and pred.ndim == 3, f"Bad shape = {pred.shape}"
dist = self.distance(pred, gt, mask)
# assert dist.ndim == a.ndim - 1 # one dimension less
if self.reduction == "none":
return dist
if self.reduction == "sum":
return dist.sum()
if self.reduction == "mean":
return dist.mean() if dist.numel() > 0 else dist.new_zeros(())
raise ValueError(f"bad {self.reduction=} mode")
def normalize(self, x, mask):
x_valid = x[mask]
splits = mask.sum(dim=(1, 2)).tolist()
x_valid_list = torch.split(x_valid, splits)
shift = [x.mean() for x in x_valid_list]
x_valid_centered = [x - m for x, m in zip(x_valid_list, shift)]
scale = [x.abs().mean() for x in x_valid_centered]
scale = torch.stack(scale)
shift = torch.stack(shift)
x = (x - shift.view(-1, 1, 1)) / scale.view(-1, 1, 1).clamp(min=1e-6)
return x
def distance(self, pred, gt, mask):
pred = self.normalize(pred, mask)
gt = self.normalize(gt, mask)
return torch.abs((pred - gt)[mask])
class ScaleInvLoss(BaseCriterion):
"""scale invariant loss"""
def __init__(self, reduction="none"):
super().__init__(reduction)
def forward(self, pred, gt, mask):
assert pred.shape == gt.shape and pred.ndim == 4, f"Bad shape = {pred.shape}"
dist = self.distance(pred, gt, mask)
# assert dist.ndim == a.ndim - 1 # one dimension less
if self.reduction == "none":
return dist
if self.reduction == "sum":
return dist.sum()
if self.reduction == "mean":
return dist.mean() if dist.numel() > 0 else dist.new_zeros(())
raise ValueError(f"bad {self.reduction=} mode")
def distance(self, pred, gt, mask):
pred_norm_factor = (torch.norm(pred, dim=-1) * mask).sum(dim=(1, 2)) / mask.sum(
dim=(1, 2)
).clamp(min=1e-6)
gt_norm_factor = (torch.norm(gt, dim=-1) * mask).sum(dim=(1, 2)) / mask.sum(
dim=(1, 2)
).clamp(min=1e-6)
pred = pred / pred_norm_factor.view(-1, 1, 1, 1).clamp(min=1e-6)
gt = gt / gt_norm_factor.view(-1, 1, 1, 1).clamp(min=1e-6)
return torch.norm(pred - gt, dim=-1)[mask]
class Regr3DPose(Criterion, MultiLoss):
"""Ensure that all 3D points are correct.
Asymmetric loss: view1 is supposed to be the anchor.
P1 = RT1 @ D1
P2 = RT2 @ D2
loss1 = (I @ pred_D1) - (RT1^-1 @ RT1 @ D1)
loss2 = (RT21 @ pred_D2) - (RT1^-1 @ P2)
= (RT21 @ pred_D2) - (RT1^-1 @ RT2 @ D2)
"""
def __init__(
self,
criterion,
norm_mode="?avg_dis",
gt_scale=False,
sky_loss_value=2,
max_metric_scale=False,
):
super().__init__(criterion)
if norm_mode.startswith("?"):
# do no norm pts from metric scale datasets
self.norm_all = False
self.norm_mode = norm_mode[1:]
else:
self.norm_all = True
self.norm_mode = norm_mode
self.gt_scale = gt_scale
self.sky_loss_value = sky_loss_value
self.max_metric_scale = max_metric_scale
def get_norm_factor_point_cloud(
self, pts_self, pts_cross, valids, conf_self, conf_cross, norm_self_only=False
):
if norm_self_only:
norm_factor = normalize_pointcloud_group(
pts_self, self.norm_mode, valids, conf_self, ret_factor_only=True
)
else:
pts = [torch.cat([x, y], dim=2) for x, y in zip(pts_self, pts_cross)]
valids = [torch.cat([x, x], dim=2) for x in valids]
confs = [torch.cat([x, y], dim=2) for x, y in zip(conf_self, conf_cross)]
norm_factor = normalize_pointcloud_group(
pts, self.norm_mode, valids, confs, ret_factor_only=True
)
return norm_factor
def get_norm_factor_poses(self, gt_trans, pr_trans, not_metric_mask):
if self.norm_mode and not self.gt_scale:
gt_trans = [x[:, None, None, :].clone() for x in gt_trans]
valids = [torch.ones_like(x[..., 0], dtype=torch.bool) for x in gt_trans]
norm_factor_gt = (
normalize_pointcloud_group(
gt_trans,
self.norm_mode,
valids,
ret_factor_only=True,
)
.squeeze(-1)
.squeeze(-1)
)
else:
norm_factor_gt = torch.ones(
len(gt_trans), dtype=gt_trans[0].dtype, device=gt_trans[0].device
)
norm_factor_pr = norm_factor_gt.clone()
if self.norm_mode and not_metric_mask.sum() > 0 and not self.gt_scale:
pr_trans_not_metric = [
x[not_metric_mask][:, None, None, :].clone() for x in pr_trans
]
valids = [
torch.ones_like(x[..., 0], dtype=torch.bool)
for x in pr_trans_not_metric
]
norm_factor_pr_not_metric = (
normalize_pointcloud_group(
pr_trans_not_metric,
self.norm_mode,
valids,
ret_factor_only=True,
)
.squeeze(-1)
.squeeze(-1)
)
norm_factor_pr[not_metric_mask] = norm_factor_pr_not_metric
return norm_factor_gt, norm_factor_pr
def get_all_pts3d(
self,
gts,
preds,
dist_clip=None,
norm_self_only=False,
norm_pose_separately=False,
eps=1e-3,
camera1=None,
):
# everything is normalized w.r.t. camera of view1
in_camera1 = inv(gts[0]["camera_pose"]) if camera1 is None else inv(camera1)
gt_pts_self = [geotrf(inv(gt["camera_pose"]), gt["pts3d"]) for gt in gts]
gt_pts_cross = [geotrf(in_camera1, gt["pts3d"]) for gt in gts]
valids = [gt["valid_mask"].clone() for gt in gts]
camera_only = gts[0]["camera_only"]
if dist_clip is not None:
# points that are too far-away == invalid
dis = [gt_pt.norm(dim=-1) for gt_pt in gt_pts_cross]
valids = [valid & (dis <= dist_clip) for valid, dis in zip(valids, dis)]
pr_pts_self = [pred["pts3d_in_self_view"] for pred in preds]
pr_pts_cross = [pred["pts3d_in_other_view"] for pred in preds]
conf_self = [torch.log(pred["conf_self"]).detach().clip(eps) for pred in preds]
conf_cross = [torch.log(pred["conf"]).detach().clip(eps) for pred in preds]
if not self.norm_all:
if self.max_metric_scale:
B = valids[0].shape[0]
dist = [
torch.where(valid, torch.linalg.norm(gt_pt_cross, dim=-1), 0).view(
B, -1
)
for valid, gt_pt_cross in zip(valids, gt_pts_cross)
]
for d in dist:
gts[0]["is_metric"] = gts[0]["is_metric_scale"] & (
d.max(dim=-1).values < self.max_metric_scale
)
not_metric_mask = ~gts[0]["is_metric"]
else:
not_metric_mask = torch.ones_like(gts[0]["is_metric"])
# normalize 3d points
# compute the scale using only the self view point maps
if self.norm_mode and not self.gt_scale:
norm_factor_gt = self.get_norm_factor_point_cloud(
gt_pts_self,
gt_pts_cross,
valids,
conf_self,
conf_cross,
norm_self_only=norm_self_only,
)
else:
norm_factor_gt = torch.ones_like(
preds[0]["pts3d_in_other_view"][:, :1, :1, :1]
)
norm_factor_pr = norm_factor_gt.clone()
if self.norm_mode and not_metric_mask.sum() > 0 and not self.gt_scale:
norm_factor_pr_not_metric = self.get_norm_factor_point_cloud(
[pr_pt_self[not_metric_mask] for pr_pt_self in pr_pts_self],
[pr_pt_cross[not_metric_mask] for pr_pt_cross in pr_pts_cross],
[valid[not_metric_mask] for valid in valids],
[conf[not_metric_mask] for conf in conf_self],
[conf[not_metric_mask] for conf in conf_cross],
norm_self_only=norm_self_only,
)
norm_factor_pr[not_metric_mask] = norm_factor_pr_not_metric
norm_factor_gt = norm_factor_gt.clip(eps)
norm_factor_pr = norm_factor_pr.clip(eps)
gt_pts_self = [pts / norm_factor_gt for pts in gt_pts_self]
gt_pts_cross = [pts / norm_factor_gt for pts in gt_pts_cross]
pr_pts_self = [pts / norm_factor_pr for pts in pr_pts_self]
pr_pts_cross = [pts / norm_factor_pr for pts in pr_pts_cross]
# [(Bx3, BX4), (BX3, BX4), ...], 3 for translation, 4 for quaternion
gt_poses = [
camera_to_pose_encoding(in_camera1 @ gt["camera_pose"]).clone()
for gt in gts
]
pr_poses = [pred["camera_pose"].clone() for pred in preds]
pose_norm_factor_gt = norm_factor_gt.clone().squeeze(2, 3)
pose_norm_factor_pr = norm_factor_pr.clone().squeeze(2, 3)
if norm_pose_separately:
gt_trans = [gt[:, :3] for gt in gt_poses]
pr_trans = [pr[:, :3] for pr in pr_poses]
pose_norm_factor_gt, pose_norm_factor_pr = self.get_norm_factor_poses(
gt_trans, pr_trans, not_metric_mask
)
elif any(camera_only):
gt_trans = [gt[:, :3] for gt in gt_poses]
pr_trans = [pr[:, :3] for pr in pr_poses]
pose_only_norm_factor_gt, pose_only_norm_factor_pr = (
self.get_norm_factor_poses(gt_trans, pr_trans, not_metric_mask)
)
pose_norm_factor_gt = torch.where(
camera_only[:, None], pose_only_norm_factor_gt, pose_norm_factor_gt
)
pose_norm_factor_pr = torch.where(
camera_only[:, None], pose_only_norm_factor_pr, pose_norm_factor_pr
)
gt_poses = [
(gt[:, :3] / pose_norm_factor_gt.clip(eps), gt[:, 3:]) for gt in gt_poses
]
pr_poses = [
(pr[:, :3] / pose_norm_factor_pr.clip(eps), pr[:, 3:]) for pr in pr_poses
]
pose_masks = (pose_norm_factor_gt.squeeze() > eps) & (
pose_norm_factor_pr.squeeze() > eps
)
if any(camera_only):
# this is equal to a loss for camera intrinsics
gt_pts_self = [
torch.where(
camera_only[:, None, None, None],
(gt / gt[..., -1:].clip(1e-6)).clip(-2, 2),
gt,
)
for gt in gt_pts_self
]
pr_pts_self = [
torch.where(
camera_only[:, None, None, None],
(pr / pr[..., -1:].clip(1e-6)).clip(-2, 2),
pr,
)
for pr in pr_pts_self
]
# # do not add cross view loss when there is only camera supervision
skys = [gt["sky_mask"] & ~valid for gt, valid in zip(gts, valids)]
return (
gt_pts_self,
gt_pts_cross,
pr_pts_self,
pr_pts_cross,
gt_poses,
pr_poses,
valids,
skys,
pose_masks,
{},
)
def get_all_pts3d_with_scale_loss(
self,
gts,
preds,
dist_clip=None,
norm_self_only=False,
norm_pose_separately=False,
eps=1e-3,
):
# everything is normalized w.r.t. camera of view1
in_camera1 = inv(gts[0]["camera_pose"])
gt_pts_self = [geotrf(inv(gt["camera_pose"]), gt["pts3d"]) for gt in gts]
gt_pts_cross = [geotrf(in_camera1, gt["pts3d"]) for gt in gts]
valids = [gt["valid_mask"].clone() for gt in gts]
camera_only = gts[0]["camera_only"]
if dist_clip is not None:
# points that are too far-away == invalid
dis = [gt_pt.norm(dim=-1) for gt_pt in gt_pts_cross]
valids = [valid & (dis <= dist_clip) for valid, dis in zip(valids, dis)]
pr_pts_self = [pred["pts3d_in_self_view"] for pred in preds]
pr_pts_cross = [pred["pts3d_in_other_view"] for pred in preds]
conf_self = [torch.log(pred["conf_self"]).detach().clip(eps) for pred in preds]
conf_cross = [torch.log(pred["conf"]).detach().clip(eps) for pred in preds]
if not self.norm_all:
if self.max_metric_scale:
B = valids[0].shape[0]
dist = [
torch.where(valid, torch.linalg.norm(gt_pt_cross, dim=-1), 0).view(
B, -1
)
for valid, gt_pt_cross in zip(valids, gt_pts_cross)
]
for d in dist:
gts[0]["is_metric"] = gts[0]["is_metric_scale"] & (
d.max(dim=-1).values < self.max_metric_scale
)
not_metric_mask = ~gts[0]["is_metric"]
else:
not_metric_mask = torch.ones_like(gts[0]["is_metric"])
# normalize 3d points
# compute the scale using only the self view point maps
if self.norm_mode and not self.gt_scale:
norm_factor_gt = self.get_norm_factor_point_cloud(
gt_pts_self[:1],
gt_pts_cross[:1],
valids[:1],
conf_self[:1],
conf_cross[:1],
norm_self_only=norm_self_only,
)
else:
norm_factor_gt = torch.ones_like(
preds[0]["pts3d_in_other_view"][:, :1, :1, :1]
)
if self.norm_mode:
norm_factor_pr = self.get_norm_factor_point_cloud(
pr_pts_self[:1],
pr_pts_cross[:1],
valids[:1],
conf_self[:1],
conf_cross[:1],
norm_self_only=norm_self_only,
)
else:
raise NotImplementedError
# only add loss to metric scale norm factor
if (~not_metric_mask).sum() > 0:
pts_scale_loss = torch.abs(
norm_factor_pr[~not_metric_mask] - norm_factor_gt[~not_metric_mask]
).mean()
else:
pts_scale_loss = 0.0
norm_factor_gt = norm_factor_gt.clip(eps)
norm_factor_pr = norm_factor_pr.clip(eps)
gt_pts_self = [pts / norm_factor_gt for pts in gt_pts_self]
gt_pts_cross = [pts / norm_factor_gt for pts in gt_pts_cross]
pr_pts_self = [pts / norm_factor_pr for pts in pr_pts_self]
pr_pts_cross = [pts / norm_factor_pr for pts in pr_pts_cross]
# [(Bx3, BX4), (BX3, BX4), ...], 3 for translation, 4 for quaternion
gt_poses = [
camera_to_pose_encoding(in_camera1 @ gt["camera_pose"]).clone()
for gt in gts
]
pr_poses = [pred["camera_pose"].clone() for pred in preds]
pose_norm_factor_gt = norm_factor_gt.clone().squeeze(2, 3)
pose_norm_factor_pr = norm_factor_pr.clone().squeeze(2, 3)
if norm_pose_separately:
gt_trans = [gt[:, :3] for gt in gt_poses][:1]
pr_trans = [pr[:, :3] for pr in pr_poses][:1]
pose_norm_factor_gt, pose_norm_factor_pr = self.get_norm_factor_poses(
gt_trans, pr_trans, torch.ones_like(not_metric_mask)
)
elif any(camera_only):
gt_trans = [gt[:, :3] for gt in gt_poses][:1]
pr_trans = [pr[:, :3] for pr in pr_poses][:1]
pose_only_norm_factor_gt, pose_only_norm_factor_pr = (
self.get_norm_factor_poses(
gt_trans, pr_trans, torch.ones_like(not_metric_mask)
)
)
pose_norm_factor_gt = torch.where(
camera_only[:, None], pose_only_norm_factor_gt, pose_norm_factor_gt
)
pose_norm_factor_pr = torch.where(
camera_only[:, None], pose_only_norm_factor_pr, pose_norm_factor_pr
)
# only add loss to metric scale norm factor
if (~not_metric_mask).sum() > 0:
pose_scale_loss = torch.abs(
pose_norm_factor_pr[~not_metric_mask]
- pose_norm_factor_gt[~not_metric_mask]
).mean()
else:
pose_scale_loss = 0.0
gt_poses = [
(gt[:, :3] / pose_norm_factor_gt.clip(eps), gt[:, 3:]) for gt in gt_poses
]
pr_poses = [
(pr[:, :3] / pose_norm_factor_pr.clip(eps), pr[:, 3:]) for pr in pr_poses
]
pose_masks = (pose_norm_factor_gt.squeeze() > eps) & (
pose_norm_factor_pr.squeeze() > eps
)
if any(camera_only):
# this is equal to a loss for camera intrinsics
gt_pts_self = [
torch.where(
camera_only[:, None, None, None],
(gt / gt[..., -1:].clip(1e-6)).clip(-2, 2),
gt,
)
for gt in gt_pts_self
]
pr_pts_self = [
torch.where(
camera_only[:, None, None, None],
(pr / pr[..., -1:].clip(1e-6)).clip(-2, 2),
pr,
)
for pr in pr_pts_self
]
# # do not add cross view loss when there is only camera supervision
skys = [gt["sky_mask"] & ~valid for gt, valid in zip(gts, valids)]
return (
gt_pts_self,
gt_pts_cross,
pr_pts_self,
pr_pts_cross,
gt_poses,
pr_poses,
valids,
skys,
pose_masks,
{"scale_loss": pose_scale_loss + pts_scale_loss},
)
def compute_relative_pose_loss(
self, gt_trans, gt_quats, pr_trans, pr_quats, masks=None
):
if masks is None:
masks = torch.ones(len(gt_trans), dtype=torch.bool, device=gt_trans.device)
gt_trans_matrix1 = gt_trans[:, :, None, :].repeat(1, 1, gt_trans.shape[1], 1)[
masks
]
gt_trans_matrix2 = gt_trans[:, None, :, :].repeat(1, gt_trans.shape[1], 1, 1)[
masks
]
gt_quats_matrix1 = gt_quats[:, :, None, :].repeat(1, 1, gt_quats.shape[1], 1)[
masks
]
gt_quats_matrix2 = gt_quats[:, None, :, :].repeat(1, gt_quats.shape[1], 1, 1)[
masks
]
pr_trans_matrix1 = pr_trans[:, :, None, :].repeat(1, 1, pr_trans.shape[1], 1)[
masks
]
pr_trans_matrix2 = pr_trans[:, None, :, :].repeat(1, pr_trans.shape[1], 1, 1)[
masks
]
pr_quats_matrix1 = pr_quats[:, :, None, :].repeat(1, 1, pr_quats.shape[1], 1)[
masks
]
pr_quats_matrix2 = pr_quats[:, None, :, :].repeat(1, pr_quats.shape[1], 1, 1)[
masks
]
gt_rel_trans, gt_rel_quats = relative_pose_absT_quatR(
gt_trans_matrix1, gt_quats_matrix1, gt_trans_matrix2, gt_quats_matrix2
)
pr_rel_trans, pr_rel_quats = relative_pose_absT_quatR(
pr_trans_matrix1, pr_quats_matrix1, pr_trans_matrix2, pr_quats_matrix2
)
rel_trans_err = torch.norm(gt_rel_trans - pr_rel_trans, dim=-1)
rel_quats_err = torch.norm(gt_rel_quats - pr_rel_quats, dim=-1)
return rel_trans_err.mean() + rel_quats_err.mean()
def compute_pose_loss(self, gt_poses, pred_poses, masks=None):
"""
gt_pose: list of (Bx3, Bx4)
pred_pose: list of (Bx3, Bx4)
masks: None, or B
"""
gt_trans = torch.stack([gt[0] for gt in gt_poses], dim=1) # BxNx3
gt_quats = torch.stack([gt[1] for gt in gt_poses], dim=1) # BXNX3
pred_trans = torch.stack([pr[0] for pr in pred_poses], dim=1) # BxNx4
pred_quats = torch.stack([pr[1] for pr in pred_poses], dim=1) # BxNx4
if masks == None:
pose_loss = (
torch.norm(pred_trans - gt_trans, dim=-1).mean()
+ torch.norm(pred_quats - gt_quats, dim=-1).mean()
)
else:
if not any(masks):
return torch.tensor(0.0)
pose_loss = (
torch.norm(pred_trans - gt_trans, dim=-1)[masks].mean()
+ torch.norm(pred_quats - gt_quats, dim=-1)[masks].mean()
)
return pose_loss
def compute_loss(self, gts, preds, **kw):
(
gt_pts_self,
gt_pts_cross,
pred_pts_self,
pred_pts_cross,
gt_poses,
pr_poses,
masks,
skys,
pose_masks,
monitoring,
) = self.get_all_pts3d(gts, preds, **kw)
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
masks = [mask | sky for mask, sky in zip(masks, skys)]
# self view loss and details
if "Quantile" in self.criterion.__class__.__name__:
# masks are overwritten taking into account self view losses
ls_self, masks = self.criterion(
pred_pts_self, gt_pts_self, masks, gts[0]["quantile"]
)
else:
ls_self = [
self.criterion(pred_pt[mask], gt_pt[mask])
for pred_pt, gt_pt, mask in zip(pred_pts_self, gt_pts_self, masks)
]
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
for i, l in enumerate(ls_self):
ls_self[i] = torch.where(skys[i][masks[i]], self.sky_loss_value, l)
self_name = type(self).__name__
details = {}
for i in range(len(ls_self)):
details[self_name + f"_self_pts3d/{i+1}"] = float(ls_self[i].mean())
details[f"gt_img{i+1}"] = gts[i]["img"].permute(0, 2, 3, 1).detach()
details[f"self_conf_{i+1}"] = preds[i]["conf_self"].detach()
details[f"valid_mask_{i+1}"] = masks[i].detach()
if "img_mask" in gts[i] and "ray_mask" in gts[i]:
details[f"img_mask_{i+1}"] = gts[i]["img_mask"].detach()
details[f"ray_mask_{i+1}"] = gts[i]["ray_mask"].detach()
if "desc" in preds[i]:
details[f"desc_{i+1}"] = preds[i]["desc"].detach()
# cross view loss and details
camera_only = gts[0]["camera_only"]
pred_pts_cross = [pred_pts[~camera_only] for pred_pts in pred_pts_cross]
gt_pts_cross = [gt_pts[~camera_only] for gt_pts in gt_pts_cross]
masks_cross = [mask[~camera_only] for mask in masks]
skys_cross = [sky[~camera_only] for sky in skys]
if "Quantile" in self.criterion.__class__.__name__:
# quantile masks have already been determined by self view losses, here pass in None as quantile
ls_cross, _ = self.criterion(
pred_pts_cross, gt_pts_cross, masks_cross, None
)
else:
ls_cross = [
self.criterion(pred_pt[mask], gt_pt[mask])
for pred_pt, gt_pt, mask in zip(
pred_pts_cross, gt_pts_cross, masks_cross
)
]
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
for i, l in enumerate(ls_cross):
ls_cross[i] = torch.where(
skys_cross[i][masks_cross[i]], self.sky_loss_value, l
)
for i in range(len(ls_cross)):
details[self_name + f"_pts3d/{i+1}"] = float(
ls_cross[i].mean() if ls_cross[i].numel() > 0 else 0
)
details[f"conf_{i+1}"] = preds[i]["conf"].detach()
ls = ls_self + ls_cross
masks = masks + masks_cross
details["is_self"] = [True] * len(ls_self) + [False] * len(ls_cross)
details["img_ids"] = (
np.arange(len(ls_self)).tolist() + np.arange(len(ls_cross)).tolist()
)
details["pose_loss"] = self.compute_pose_loss(gt_poses, pr_poses, pose_masks)
return Sum(*list(zip(ls, masks))), (details | monitoring)
class Regr3DPoseBatchList(Regr3DPose):
"""Ensure that all 3D points are correct.
Asymmetric loss: view1 is supposed to be the anchor.
P1 = RT1 @ D1
P2 = RT2 @ D2
loss1 = (I @ pred_D1) - (RT1^-1 @ RT1 @ D1)
loss2 = (RT21 @ pred_D2) - (RT1^-1 @ P2)
= (RT21 @ pred_D2) - (RT1^-1 @ RT2 @ D2)
"""
def __init__(
self,
criterion,
norm_mode="?avg_dis",
gt_scale=False,
sky_loss_value=2,
max_metric_scale=False,
):
super().__init__(
criterion, norm_mode, gt_scale, sky_loss_value, max_metric_scale
)
self.depth_only_criterion = DepthScaleShiftInvLoss()
self.single_view_criterion = ScaleInvLoss()
def reorg(self, ls_b, masks_b):
ids_split = [mask.sum(dim=(1, 2)) for mask in masks_b]
ls = [[] for _ in range(len(masks_b[0]))]
for i in range(len(ls_b)):
ls_splitted_i = torch.split(ls_b[i], ids_split[i].tolist())
for j in range(len(masks_b[0])):
ls[j].append(ls_splitted_i[j])
ls = [torch.cat(l) for l in ls]
return ls
def compute_loss(self, gts, preds, **kw):
(
gt_pts_self,
gt_pts_cross,
pred_pts_self,
pred_pts_cross,
gt_poses,
pr_poses,
masks,
skys,
pose_masks,
monitoring,
) = self.get_all_pts3d(gts, preds, **kw)
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
masks = [mask | sky for mask, sky in zip(masks, skys)]
camera_only = gts[0]["camera_only"]
depth_only = gts[0]["depth_only"]
single_view = gts[0]["single_view"]
is_metric = gts[0]["is_metric"]
# self view loss and details
if "Quantile" in self.criterion.__class__.__name__:
raise NotImplementedError
else:
# list [(B, h, w, 3)] x num_views -> list [num_views, h, w, 3] x B
gt_pts_self_b = torch.unbind(torch.stack(gt_pts_self, dim=1), dim=0)
pred_pts_self_b = torch.unbind(torch.stack(pred_pts_self, dim=1), dim=0)
masks_b = torch.unbind(torch.stack(masks, dim=1), dim=0)
ls_self_b = []
for i in range(len(gt_pts_self_b)):
if depth_only[
i
]: # if only have relative depth, no intrinsics or anything
ls_self_b.append(
self.depth_only_criterion(
pred_pts_self_b[i][..., -1],
gt_pts_self_b[i][..., -1],
masks_b[i],
)
)
elif (
single_view[i] and not is_metric[i]
): # if single view, with intrinsics and not metric
ls_self_b.append(
self.single_view_criterion(
pred_pts_self_b[i], gt_pts_self_b[i], masks_b[i]
)
)
else: # if multiple view, or metric single view
ls_self_b.append(
self.criterion(
pred_pts_self_b[i][masks_b[i]], gt_pts_self_b[i][masks_b[i]]
)
)
ls_self = self.reorg(ls_self_b, masks_b)
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
for i, l in enumerate(ls_self):
ls_self[i] = torch.where(skys[i][masks[i]], self.sky_loss_value, l)
self_name = type(self).__name__
details = {}
for i in range(len(ls_self)):
details[self_name + f"_self_pts3d/{i+1}"] = float(ls_self[i].mean())
details[f"self_conf_{i+1}"] = preds[i]["conf_self"].detach()
details[f"gt_img{i+1}"] = gts[i]["img"].permute(0, 2, 3, 1).detach()
details[f"valid_mask_{i+1}"] = masks[i].detach()
if "img_mask" in gts[i] and "ray_mask" in gts[i]:
details[f"img_mask_{i+1}"] = gts[i]["img_mask"].detach()
details[f"ray_mask_{i+1}"] = gts[i]["ray_mask"].detach()
if "desc" in preds[i]:
details[f"desc_{i+1}"] = preds[i]["desc"].detach()
if "Quantile" in self.criterion.__class__.__name__:
# quantile masks have already been determined by self view losses, here pass in None as quantile
raise NotImplementedError
else:
gt_pts_cross_b = torch.unbind(
torch.stack(gt_pts_cross, dim=1)[~camera_only], dim=0
)
pred_pts_cross_b = torch.unbind(
torch.stack(pred_pts_cross, dim=1)[~camera_only], dim=0
)
masks_cross_b = torch.unbind(torch.stack(masks, dim=1)[~camera_only], dim=0)
ls_cross_b = []
for i in range(len(gt_pts_cross_b)):
if depth_only[~camera_only][i]:
ls_cross_b.append(
self.depth_only_criterion(
pred_pts_cross_b[i][..., -1],
gt_pts_cross_b[i][..., -1],
masks_cross_b[i],
)
)
elif single_view[~camera_only][i] and not is_metric[~camera_only][i]:
ls_cross_b.append(
self.single_view_criterion(
pred_pts_cross_b[i], gt_pts_cross_b[i], masks_cross_b[i]
)
)
else:
ls_cross_b.append(
self.criterion(
pred_pts_cross_b[i][masks_cross_b[i]],
gt_pts_cross_b[i][masks_cross_b[i]],
)
)
ls_cross = self.reorg(ls_cross_b, masks_cross_b)
if self.sky_loss_value > 0:
assert (
self.criterion.reduction == "none"
), "sky_loss_value should be 0 if no conf loss"
masks_cross = [mask[~camera_only] for mask in masks]
skys_cross = [sky[~camera_only] for sky in skys]
for i, l in enumerate(ls_cross):
ls_cross[i] = torch.where(
skys_cross[i][masks_cross[i]], self.sky_loss_value, l
)
for i in range(len(ls_cross)):
details[self_name + f"_pts3d/{i+1}"] = float(
ls_cross[i].mean() if ls_cross[i].numel() > 0 else 0
)
details[f"conf_{i+1}"] = preds[i]["conf"].detach()
ls = ls_self + ls_cross
masks = masks + masks_cross
details["is_self"] = [True] * len(ls_self) + [False] * len(ls_cross)
details["img_ids"] = (
np.arange(len(ls_self)).tolist() + np.arange(len(ls_cross)).tolist()
)
pose_masks = pose_masks * gts[i]["img_mask"]
details["pose_loss"] = self.compute_pose_loss(gt_poses, pr_poses, pose_masks)
return Sum(*list(zip(ls, masks))), (details | monitoring)
class ConfLoss(MultiLoss):
"""Weighted regression by learned confidence.
Assuming the input pixel_loss is a pixel-level regression loss.
Principle:
high-confidence means high conf = 0.1 ==> conf_loss = x / 10 + alpha*log(10)
low confidence means low conf = 10 ==> conf_loss = x * 10 - alpha*log(10)
alpha: hyperparameter
"""
def __init__(self, pixel_loss, alpha=1):
super().__init__()
assert alpha > 0
self.alpha = alpha
self.pixel_loss = pixel_loss.with_reduction("none")
def get_name(self):
return f"ConfLoss({self.pixel_loss})"
def get_conf_log(self, x):
return x, torch.log(x)
def compute_loss(self, gts, preds, **kw):
# compute per-pixel loss
losses_and_masks, details = self.pixel_loss(gts, preds, **kw)
if "is_self" in details and "img_ids" in details:
is_self = details["is_self"]
img_ids = details["img_ids"]
else:
is_self = [False] * len(losses_and_masks)
img_ids = list(range(len(losses_and_masks)))
# weight by confidence
conf_losses = []
for i in range(len(losses_and_masks)):
pred = preds[img_ids[i]]
conf_key = "conf_self" if is_self[i] else "conf"
if not is_self[i]:
camera_only = gts[0]["camera_only"]
conf, log_conf = self.get_conf_log(
pred[conf_key][~camera_only][losses_and_masks[i][1]]
)
else:
conf, log_conf = self.get_conf_log(
pred[conf_key][losses_and_masks[i][1]]
)
conf_loss = losses_and_masks[i][0] * conf - self.alpha * log_conf
conf_loss = conf_loss.mean() if conf_loss.numel() > 0 else 0
conf_losses.append(conf_loss)
if is_self[i]:
details[self.get_name() + f"_conf_loss_self/{img_ids[i]+1}"] = float(
conf_loss
)
else:
details[self.get_name() + f"_conf_loss/{img_ids[i]+1}"] = float(
conf_loss
)
details.pop("is_self", None)
details.pop("img_ids", None)
final_loss = sum(conf_losses) / len(conf_losses) * 2.0
if "pose_loss" in details:
final_loss = (
final_loss + details["pose_loss"].clip(max=0.3) * 5.0
) # , details
if "scale_loss" in details:
final_loss = final_loss + details["scale_loss"]
return final_loss, details
class Regr3DPose_ScaleInv(Regr3DPose):
"""Same than Regr3D but invariant to depth shift.
if gt_scale == True: enforce the prediction to take the same scale than GT
"""
def get_all_pts3d(self, gts, preds):
# compute depth-normalized points
(
gt_pts_self,
gt_pts_cross,
pr_pts_self,
pr_pts_cross,
gt_poses,
pr_poses,
masks,
skys,
pose_masks,
monitoring,
) = super().get_all_pts3d(gts, preds)
# measure scene scale
_, gt_scale_self = get_group_pointcloud_center_scale(gt_pts_self, masks)
_, pred_scale_self = get_group_pointcloud_center_scale(pr_pts_self, masks)
_, gt_scale_cross = get_group_pointcloud_center_scale(gt_pts_cross, masks)
_, pred_scale_cross = get_group_pointcloud_center_scale(pr_pts_cross, masks)
# prevent predictions to be in a ridiculous range
pred_scale_self = pred_scale_self.clip(min=1e-3, max=1e3)
pred_scale_cross = pred_scale_cross.clip(min=1e-3, max=1e3)
# subtract the median depth
if self.gt_scale:
pr_pts_self = [
pr_pt_self * gt_scale_self / pred_scale_self
for pr_pt_self in pr_pts_self
]
pr_pts_cross = [
pr_pt_cross * gt_scale_cross / pred_scale_cross
for pr_pt_cross in pr_pts_cross
]
else:
gt_pts_self = [gt_pt_self / gt_scale_self for gt_pt_self in gt_pts_self]
gt_pts_cross = [
gt_pt_cross / gt_scale_cross for gt_pt_cross in gt_pts_cross
]
pr_pts_self = [pr_pt_self / pred_scale_self for pr_pt_self in pr_pts_self]
pr_pts_cross = [
pr_pt_cross / pred_scale_cross for pr_pt_cross in pr_pts_cross
]
return (
gt_pts_self,
gt_pts_cross,
pr_pts_self,
pr_pts_cross,
gt_poses,
pr_poses,
masks,
skys,
pose_masks,
monitoring,
)
|