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pcdet/models/view_transforms/depth_lss.py
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258
pcdet/models/view_transforms/depth_lss.py
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import torch
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from torch import nn
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from pcdet.ops.bev_pool import bev_pool
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def gen_dx_bx(xbound, ybound, zbound):
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dx = torch.Tensor([row[2] for row in [xbound, ybound, zbound]])
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bx = torch.Tensor([row[0] + row[2] / 2.0 for row in [xbound, ybound, zbound]])
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nx = torch.LongTensor(
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[(row[1] - row[0]) / row[2] for row in [xbound, ybound, zbound]]
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)
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return dx, bx, nx
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class DepthLSSTransform(nn.Module):
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"""
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This module implements LSS, which lists images into 3D and then splats onto bev features.
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This code is adapted from https://github.com/mit-han-lab/bevfusion/ with minimal modifications.
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"""
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def __init__(self, model_cfg):
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super().__init__()
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self.model_cfg = model_cfg
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in_channel = self.model_cfg.IN_CHANNEL
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out_channel = self.model_cfg.OUT_CHANNEL
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self.image_size = self.model_cfg.IMAGE_SIZE
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self.feature_size = self.model_cfg.FEATURE_SIZE
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xbound = self.model_cfg.XBOUND
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ybound = self.model_cfg.YBOUND
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zbound = self.model_cfg.ZBOUND
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self.dbound = self.model_cfg.DBOUND
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downsample = self.model_cfg.DOWNSAMPLE
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dx, bx, nx = gen_dx_bx(xbound, ybound, zbound)
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self.dx = nn.Parameter(dx, requires_grad=False)
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self.bx = nn.Parameter(bx, requires_grad=False)
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self.nx = nn.Parameter(nx, requires_grad=False)
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self.C = out_channel
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self.frustum = self.create_frustum()
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self.D = self.frustum.shape[0]
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self.dtransform = nn.Sequential(
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nn.Conv2d(1, 8, 1),
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nn.BatchNorm2d(8),
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nn.ReLU(True),
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nn.Conv2d(8, 32, 5, stride=4, padding=2),
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nn.BatchNorm2d(32),
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nn.ReLU(True),
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nn.Conv2d(32, 64, 5, stride=2, padding=2),
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nn.BatchNorm2d(64),
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nn.ReLU(True),
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)
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self.depthnet = nn.Sequential(
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nn.Conv2d(in_channel + 64, in_channel, 3, padding=1),
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nn.BatchNorm2d(in_channel),
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nn.ReLU(True),
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nn.Conv2d(in_channel, in_channel, 3, padding=1),
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nn.BatchNorm2d(in_channel),
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nn.ReLU(True),
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nn.Conv2d(in_channel, self.D + self.C, 1),
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)
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if downsample > 1:
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assert downsample == 2, downsample
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self.downsample = nn.Sequential(
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nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channel),
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nn.ReLU(True),
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nn.Conv2d(out_channel, out_channel, 3, stride=downsample, padding=1, bias=False),
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nn.BatchNorm2d(out_channel),
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nn.ReLU(True),
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nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channel),
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nn.ReLU(True),
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)
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else:
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self.downsample = nn.Identity()
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def create_frustum(self):
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iH, iW = self.image_size
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fH, fW = self.feature_size
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ds = torch.arange(*self.dbound, dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW)
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D, _, _ = ds.shape
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xs = torch.linspace(0, iW - 1, fW, dtype=torch.float).view(1, 1, fW).expand(D, fH, fW)
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ys = torch.linspace(0, iH - 1, fH, dtype=torch.float).view(1, fH, 1).expand(D, fH, fW)
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frustum = torch.stack((xs, ys, ds), -1)
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return nn.Parameter(frustum, requires_grad=False)
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def get_geometry(self, camera2lidar_rots, camera2lidar_trans, intrins, post_rots, post_trans, **kwargs):
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camera2lidar_rots = camera2lidar_rots.to(torch.float)
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camera2lidar_trans = camera2lidar_trans.to(torch.float)
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intrins = intrins.to(torch.float)
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post_rots = post_rots.to(torch.float)
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post_trans = post_trans.to(torch.float)
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B, N, _ = camera2lidar_trans.shape
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# undo post-transformation
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# B x N x D x H x W x 3
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points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)
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points = torch.inverse(post_rots).view(B, N, 1, 1, 1, 3, 3).matmul(points.unsqueeze(-1))
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# cam_to_lidar
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points = torch.cat((points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3], points[:, :, :, :, :, 2:3]), 5)
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combine = camera2lidar_rots.matmul(torch.inverse(intrins))
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points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)
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points += camera2lidar_trans.view(B, N, 1, 1, 1, 3)
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if "extra_rots" in kwargs:
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extra_rots = kwargs["extra_rots"]
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points = extra_rots.view(B, 1, 1, 1, 1, 3, 3).repeat(1, N, 1, 1, 1, 1, 1) \
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.matmul(points.unsqueeze(-1)).squeeze(-1)
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if "extra_trans" in kwargs:
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extra_trans = kwargs["extra_trans"]
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points += extra_trans.view(B, 1, 1, 1, 1, 3).repeat(1, N, 1, 1, 1, 1)
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return points
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def bev_pool(self, geom_feats, x):
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geom_feats = geom_feats.to(torch.float)
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x = x.to(torch.float)
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B, N, D, H, W, C = x.shape
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Nprime = B * N * D * H * W
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# flatten x
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x = x.reshape(Nprime, C)
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# flatten indices
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geom_feats = ((geom_feats - (self.bx - self.dx / 2.0)) / self.dx).long()
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geom_feats = geom_feats.view(Nprime, 3)
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batch_ix = torch.cat([torch.full([Nprime // B, 1], ix, device=x.device, dtype=torch.long) for ix in range(B)])
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geom_feats = torch.cat((geom_feats, batch_ix), 1)
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# filter out points that are outside box
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kept = (
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(geom_feats[:, 0] >= 0)
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& (geom_feats[:, 0] < self.nx[0])
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& (geom_feats[:, 1] >= 0)
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& (geom_feats[:, 1] < self.nx[1])
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& (geom_feats[:, 2] >= 0)
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& (geom_feats[:, 2] < self.nx[2])
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)
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x = x[kept]
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geom_feats = geom_feats[kept]
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x = bev_pool(x, geom_feats, B, self.nx[2], self.nx[0], self.nx[1])
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# collapse Z
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final = torch.cat(x.unbind(dim=2), 1)
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return final
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def get_cam_feats(self, x, d):
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B, N, C, fH, fW = x.shape
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d = d.view(B * N, *d.shape[2:])
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x = x.view(B * N, C, fH, fW)
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d = self.dtransform(d)
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x = torch.cat([d, x], dim=1)
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x = self.depthnet(x)
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depth = x[:, : self.D].softmax(dim=1)
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x = depth.unsqueeze(1) * x[:, self.D : (self.D + self.C)].unsqueeze(2)
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x = x.view(B, N, self.C, self.D, fH, fW)
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x = x.permute(0, 1, 3, 4, 5, 2)
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return x
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def forward(self, batch_dict):
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"""
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Args:
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batch_dict:
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image_fpn (list[tensor]): image features after image neck
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Returns:
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batch_dict:
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spatial_features_img (tensor): bev features from image modality
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"""
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x = batch_dict['image_fpn']
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x = x[0]
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BN, C, H, W = x.size()
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img = x.view(int(BN/6), 6, C, H, W)
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camera_intrinsics = batch_dict['camera_intrinsics']
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camera2lidar = batch_dict['camera2lidar']
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img_aug_matrix = batch_dict['img_aug_matrix']
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lidar_aug_matrix = batch_dict['lidar_aug_matrix']
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lidar2image = batch_dict['lidar2image']
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intrins = camera_intrinsics[..., :3, :3]
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post_rots = img_aug_matrix[..., :3, :3]
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post_trans = img_aug_matrix[..., :3, 3]
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camera2lidar_rots = camera2lidar[..., :3, :3]
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camera2lidar_trans = camera2lidar[..., :3, 3]
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points = batch_dict['points']
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batch_size = BN // 6
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depth = torch.zeros(batch_size, img.shape[1], 1, *self.image_size).to(points[0].device)
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for b in range(batch_size):
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batch_mask = points[:,0] == b
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cur_coords = points[batch_mask][:, 1:4]
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cur_img_aug_matrix = img_aug_matrix[b]
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cur_lidar_aug_matrix = lidar_aug_matrix[b]
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cur_lidar2image = lidar2image[b]
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# inverse aug
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cur_coords -= cur_lidar_aug_matrix[:3, 3]
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cur_coords = torch.inverse(cur_lidar_aug_matrix[:3, :3]).matmul(
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cur_coords.transpose(1, 0)
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)
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# lidar2image
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cur_coords = cur_lidar2image[:, :3, :3].matmul(cur_coords)
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cur_coords += cur_lidar2image[:, :3, 3].reshape(-1, 3, 1)
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# get 2d coords
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dist = cur_coords[:, 2, :]
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cur_coords[:, 2, :] = torch.clamp(cur_coords[:, 2, :], 1e-5, 1e5)
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cur_coords[:, :2, :] /= cur_coords[:, 2:3, :]
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# do image aug
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cur_coords = cur_img_aug_matrix[:, :3, :3].matmul(cur_coords)
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cur_coords += cur_img_aug_matrix[:, :3, 3].reshape(-1, 3, 1)
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cur_coords = cur_coords[:, :2, :].transpose(1, 2)
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# normalize coords for grid sample
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cur_coords = cur_coords[..., [1, 0]]
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# filter points outside of images
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on_img = (
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(cur_coords[..., 0] < self.image_size[0])
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& (cur_coords[..., 0] >= 0)
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& (cur_coords[..., 1] < self.image_size[1])
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& (cur_coords[..., 1] >= 0)
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)
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for c in range(on_img.shape[0]):
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masked_coords = cur_coords[c, on_img[c]].long()
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masked_dist = dist[c, on_img[c]]
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depth[b, c, 0, masked_coords[:, 0], masked_coords[:, 1]] = masked_dist
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extra_rots = lidar_aug_matrix[..., :3, :3]
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extra_trans = lidar_aug_matrix[..., :3, 3]
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geom = self.get_geometry(
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camera2lidar_rots, camera2lidar_trans, intrins, post_rots,
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post_trans, extra_rots=extra_rots, extra_trans=extra_trans,
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)
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# use points depth to assist the depth prediction in images
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x = self.get_cam_feats(img, depth)
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x = self.bev_pool(geom, x)
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x = self.downsample(x)
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# convert bev features from (b, c, x, y) to (b, c, y, x)
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x = x.permute(0, 1, 3, 2)
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batch_dict['spatial_features_img'] = x
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return batch_dict
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