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# modify from https://github.com/mit-han-lab/bevfusion
from typing import Tuple
import torch
from torch import nn
from mmdet3d.registry import MODELS
from .ops import bev_pool
def gen_dx_bx(xbound, ybound, zbound):
dx = torch.Tensor([row[2] for row in [xbound, ybound, zbound]])
bx = torch.Tensor(
[row[0] + row[2] / 2.0 for row in [xbound, ybound, zbound]])
nx = torch.LongTensor([(row[1] - row[0]) / row[2]
for row in [xbound, ybound, zbound]])
return dx, bx, nx
class BaseViewTransform(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
image_size: Tuple[int, int],
feature_size: Tuple[int, int],
xbound: Tuple[float, float, float],
ybound: Tuple[float, float, float],
zbound: Tuple[float, float, float],
dbound: Tuple[float, float, float],
) -> None:
super().__init__()
self.in_channels = in_channels
self.image_size = image_size
self.feature_size = feature_size
self.xbound = xbound
self.ybound = ybound
self.zbound = zbound
self.dbound = dbound
dx, bx, nx = gen_dx_bx(self.xbound, self.ybound, self.zbound)
self.dx = nn.Parameter(dx, requires_grad=False)
self.bx = nn.Parameter(bx, requires_grad=False)
self.nx = nn.Parameter(nx, requires_grad=False)
self.C = out_channels
self.frustum = self.create_frustum()
self.D = self.frustum.shape[0]
self.fp16_enabled = False
def create_frustum(self):
iH, iW = self.image_size
fH, fW = self.feature_size
ds = (
torch.arange(*self.dbound,
dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW))
D, _, _ = ds.shape
xs = (
torch.linspace(0, iW - 1, fW,
dtype=torch.float).view(1, 1, fW).expand(D, fH, fW))
ys = (
torch.linspace(0, iH - 1, fH,
dtype=torch.float).view(1, fH, 1).expand(D, fH, fW))
frustum = torch.stack((xs, ys, ds), -1)
return nn.Parameter(frustum, requires_grad=False)
def get_geometry(
self,
camera2lidar_rots,
camera2lidar_trans,
intrins,
post_rots,
post_trans,
**kwargs,
):
B, N, _ = camera2lidar_trans.shape
# undo post-transformation
# B x N x D x H x W x 3
points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)
points = (
torch.inverse(post_rots).view(B, N, 1, 1, 1, 3,
3).matmul(points.unsqueeze(-1)))
# cam_to_lidar
points = torch.cat(
(
points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3],
points[:, :, :, :, :, 2:3],
),
5,
)
combine = camera2lidar_rots.matmul(torch.inverse(intrins))
points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)
points += camera2lidar_trans.view(B, N, 1, 1, 1, 3)
if 'extra_rots' in kwargs:
extra_rots = kwargs['extra_rots']
points = (
extra_rots.view(B, 1, 1, 1, 1, 3,
3).repeat(1, N, 1, 1, 1, 1, 1).matmul(
points.unsqueeze(-1)).squeeze(-1))
if 'extra_trans' in kwargs:
extra_trans = kwargs['extra_trans']
points += extra_trans.view(B, 1, 1, 1, 1,
3).repeat(1, N, 1, 1, 1, 1)
return points
def get_cam_feats(self, x):
raise NotImplementedError
def bev_pool(self, geom_feats, x):
B, N, D, H, W, C = x.shape
Nprime = B * N * D * H * W
# flatten x
x = x.reshape(Nprime, C)
# flatten indices
geom_feats = ((geom_feats - (self.bx - self.dx / 2.0)) /
self.dx).long()
geom_feats = geom_feats.view(Nprime, 3)
batch_ix = torch.cat([
torch.full([Nprime // B, 1], ix, device=x.device, dtype=torch.long)
for ix in range(B)
])
geom_feats = torch.cat((geom_feats, batch_ix), 1)
# filter out points that are outside box
kept = ((geom_feats[:, 0] >= 0)
& (geom_feats[:, 0] < self.nx[0])
& (geom_feats[:, 1] >= 0)
& (geom_feats[:, 1] < self.nx[1])
& (geom_feats[:, 2] >= 0)
& (geom_feats[:, 2] < self.nx[2]))
x = x[kept]
geom_feats = geom_feats[kept]
x = bev_pool(x, geom_feats, B, self.nx[2], self.nx[0], self.nx[1])
# collapse Z
final = torch.cat(x.unbind(dim=2), 1)
return final
def forward(
self,
img,
points,
lidar2image,
camera_intrinsics,
camera2lidar,
img_aug_matrix,
lidar_aug_matrix,
metas,
**kwargs,
):
intrins = camera_intrinsics[..., :3, :3]
post_rots = img_aug_matrix[..., :3, :3]
post_trans = img_aug_matrix[..., :3, 3]
camera2lidar_rots = camera2lidar[..., :3, :3]
camera2lidar_trans = camera2lidar[..., :3, 3]
extra_rots = lidar_aug_matrix[..., :3, :3]
extra_trans = lidar_aug_matrix[..., :3, 3]
geom = self.get_geometry(
camera2lidar_rots,
camera2lidar_trans,
intrins,
post_rots,
post_trans,
extra_rots=extra_rots,
extra_trans=extra_trans,
)
x = self.get_cam_feats(img)
x = self.bev_pool(geom, x)
return x
@MODELS.register_module()
class LSSTransform(BaseViewTransform):
def __init__(
self,
in_channels: int,
out_channels: int,
image_size: Tuple[int, int],
feature_size: Tuple[int, int],
xbound: Tuple[float, float, float],
ybound: Tuple[float, float, float],
zbound: Tuple[float, float, float],
dbound: Tuple[float, float, float],
downsample: int = 1,
) -> None:
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
image_size=image_size,
feature_size=feature_size,
xbound=xbound,
ybound=ybound,
zbound=zbound,
dbound=dbound,
)
self.depthnet = nn.Conv2d(in_channels, self.D + self.C, 1)
if downsample > 1:
assert downsample == 2, downsample
self.downsample = nn.Sequential(
nn.Conv2d(
out_channels, out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
nn.Conv2d(
out_channels,
out_channels,
3,
stride=downsample,
padding=1,
bias=False,
),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
nn.Conv2d(
out_channels, out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
)
else:
self.downsample = nn.Identity()
def get_cam_feats(self, x):
B, N, C, fH, fW = x.shape
x = x.view(B * N, C, fH, fW)
x = self.depthnet(x)
depth = x[:, :self.D].softmax(dim=1)
x = depth.unsqueeze(1) * x[:, self.D:(self.D + self.C)].unsqueeze(2)
x = x.view(B, N, self.C, self.D, fH, fW)
x = x.permute(0, 1, 3, 4, 5, 2)
return x
def forward(self, *args, **kwargs):
x = super().forward(*args, **kwargs)
x = self.downsample(x)
return x
class BaseDepthTransform(BaseViewTransform):
def forward(
self,
img,
points,
lidar2image,
cam_intrinsic,
camera2lidar,
img_aug_matrix,
lidar_aug_matrix,
metas,
**kwargs,
):
intrins = cam_intrinsic[..., :3, :3]
post_rots = img_aug_matrix[..., :3, :3]
post_trans = img_aug_matrix[..., :3, 3]
camera2lidar_rots = camera2lidar[..., :3, :3]
camera2lidar_trans = camera2lidar[..., :3, 3]
batch_size = len(points)
depth = torch.zeros(batch_size, img.shape[1], 1,
*self.image_size).to(points[0].device)
for b in range(batch_size):
cur_coords = points[b][:, :3]
cur_img_aug_matrix = img_aug_matrix[b]
cur_lidar_aug_matrix = lidar_aug_matrix[b]
cur_lidar2image = lidar2image[b]
# inverse aug
cur_coords -= cur_lidar_aug_matrix[:3, 3]
cur_coords = torch.inverse(cur_lidar_aug_matrix[:3, :3]).matmul(
cur_coords.transpose(1, 0))
# lidar2image
cur_coords = cur_lidar2image[:, :3, :3].matmul(cur_coords)
cur_coords += cur_lidar2image[:, :3, 3].reshape(-1, 3, 1)
# get 2d coords
dist = cur_coords[:, 2, :]
cur_coords[:, 2, :] = torch.clamp(cur_coords[:, 2, :], 1e-5, 1e5)
cur_coords[:, :2, :] /= cur_coords[:, 2:3, :]
# imgaug
cur_coords = cur_img_aug_matrix[:, :3, :3].matmul(cur_coords)
cur_coords += cur_img_aug_matrix[:, :3, 3].reshape(-1, 3, 1)
cur_coords = cur_coords[:, :2, :].transpose(1, 2)
# normalize coords for grid sample
cur_coords = cur_coords[..., [1, 0]]
on_img = ((cur_coords[..., 0] < self.image_size[0])
& (cur_coords[..., 0] >= 0)
& (cur_coords[..., 1] < self.image_size[1])
& (cur_coords[..., 1] >= 0))
for c in range(on_img.shape[0]):
masked_coords = cur_coords[c, on_img[c]].long()
masked_dist = dist[c, on_img[c]]
depth = depth.to(masked_dist.dtype)
depth[b, c, 0, masked_coords[:, 0],
masked_coords[:, 1]] = masked_dist
extra_rots = lidar_aug_matrix[..., :3, :3]
extra_trans = lidar_aug_matrix[..., :3, 3]
geom = self.get_geometry(
camera2lidar_rots,
camera2lidar_trans,
intrins,
post_rots,
post_trans,
extra_rots=extra_rots,
extra_trans=extra_trans,
)
x = self.get_cam_feats(img, depth)
x = self.bev_pool(geom, x)
return x
@MODELS.register_module()
class DepthLSSTransform(BaseDepthTransform):
def __init__(
self,
in_channels: int,
out_channels: int,
image_size: Tuple[int, int],
feature_size: Tuple[int, int],
xbound: Tuple[float, float, float],
ybound: Tuple[float, float, float],
zbound: Tuple[float, float, float],
dbound: Tuple[float, float, float],
downsample: int = 1,
) -> None:
"""Compared with `LSSTransform`, `DepthLSSTransform` adds sparse depth
information from lidar points into the inputs of the `depthnet`."""
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
image_size=image_size,
feature_size=feature_size,
xbound=xbound,
ybound=ybound,
zbound=zbound,
dbound=dbound,
)
self.dtransform = nn.Sequential(
nn.Conv2d(1, 8, 1),
nn.BatchNorm2d(8),
nn.ReLU(True),
nn.Conv2d(8, 32, 5, stride=4, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(True),
nn.Conv2d(32, 64, 5, stride=2, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(True),
)
self.depthnet = nn.Sequential(
nn.Conv2d(in_channels + 64, in_channels, 3, padding=1),
nn.BatchNorm2d(in_channels),
nn.ReLU(True),
nn.Conv2d(in_channels, in_channels, 3, padding=1),
nn.BatchNorm2d(in_channels),
nn.ReLU(True),
nn.Conv2d(in_channels, self.D + self.C, 1),
)
if downsample > 1:
assert downsample == 2, downsample
self.downsample = nn.Sequential(
nn.Conv2d(
out_channels, out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
nn.Conv2d(
out_channels,
out_channels,
3,
stride=downsample,
padding=1,
bias=False,
),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
nn.Conv2d(
out_channels, out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(True),
)
else:
self.downsample = nn.Identity()
def get_cam_feats(self, x, d):
B, N, C, fH, fW = x.shape
d = d.view(B * N, *d.shape[2:])
x = x.view(B * N, C, fH, fW)
d = self.dtransform(d)
x = torch.cat([d, x], dim=1)
x = self.depthnet(x)
depth = x[:, :self.D].softmax(dim=1)
x = depth.unsqueeze(1) * x[:, self.D:(self.D + self.C)].unsqueeze(2)
x = x.view(B, N, self.C, self.D, fH, fW)
x = x.permute(0, 1, 3, 4, 5, 2)
return x
def forward(self, *args, **kwargs):
x = super().forward(*args, **kwargs)
x = self.downsample(x)
return x
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