[Feature] Support `CENet` in `Projects` (#2619)

* add cenet in projects

* add aux head for cenet

* fix some potential bugs

* update param_scheduler

* finish the readme

* remove redundant config

* add fps

projects/CENet/README.md

+# CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
+
+> [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://arxiv.org/abs/2207.12691)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. In this paper, we present a concise and efficient image-based semantic segmentation network, named CENet. In order to improve the descriptive power of learned features and reduce the computational as well as time complexity, our CENet integrates the convolution with larger kernel size instead of MLP, carefully-selected activation functions, and multiple auxiliary segmentation heads with corresponding loss functions into architecture. Quantitative and qualitative experiments conducted on publicly available benchmarks, SemanticKITTI and SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and inference performance compared with state-of-the-art models. The code will be available at https://github.com/huixiancheng/CENet.
+
+<div align=center>
+<img src="https://github.com/open-mmlab/mmdetection3d/assets/55445986/2c268392-0e0c-4e93-bb9d-dc3417c56dad" width="800"/>
+</div>
+
+## Introduction
+
+We implement CENet and provide the results and pretrained checkpoints on SemanticKITTI dataset.
+
+## Usage
+
+<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
+
+### Training commands
+
+In MMDetection3D's root directory, run the following command to train the model:
+
+```bash
+python tools/train.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py
+```
+
+For multi-gpu training, run:
+
+```bash
+python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py
+```
+
+### Testing commands
+
+In MMDetection3D's root directory, run the following command to test the model:
+
+```bash
+python tools/test.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py ${CHECKPOINT_PATH}
+```
+
+## Results and models
+
+### NuScenes
+
+|                        Backbone                        | Input resolution | Mem (GB) | Inf time (fps) | mIoU  |         Download         |
+| :----------------------------------------------------: | :--------------: | :------: | :------------: | :---: | :----------------------: |
+| [CENet](./configs/cenet-64x512_4xb4_semantickitti.py)  |     64\*512      |          |      41.7      | 61.10 | [model](<>) \| [log](<>) |
+| [CENet](./configs/cenet-64x1024_4xb4_semantickitti.py) |     64\*1024     |          |      26.8      | 62.20 | [model](<>) \| [log](<>) |
+| [CENet](./configs/cenet-64x2048_4xb4_semantickitti.py) |     64\*2048     |          |      14.1      | 62.64 | [model](<>) \| [log](<>) |
+
+**Note**
+
+- We report point-based mIoU instead of range-view based mIoU
+- The mIoU is the best results during inference after each epoch training, which is consistent with official code
+- If your setting is different with our settings, we strongly suggest to enable `auto_scale_lr` to achieve comparable results.
+
+## Citation
+
+```latex
+@inproceedings{cheng2022cenet,
+  title={Cenet: Toward Concise and Efficient Lidar Semantic Segmentation for Autonomous Driving},
+  author={Cheng, Hui--Xian and Han, Xian--Feng and Xiao, Guo--Qiang},
+  booktitle={2022 IEEE International Conference on Multimedia and Expo (ICME)},
+  pages={01--06},
+  year={2022},
+  organization={IEEE}
+}
+```
+
+## Checklist
+
+<!-- Here is a checklist illustrating a usual development workflow of a successful project, and also serves as an overview of this project's progress. The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
+OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
+Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.
+A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR. -->
+
+- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+  - [x] Finish the code
+
+    <!-- The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmdet3d.registry.MODELS` and configurable via a config file. -->
+
+  - [x] Basic docstrings & proper citation
+
+    <!-- Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd) -->
+
+  - [x] Test-time correctness
+
+    <!-- If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone. -->
+
+  - [x] A full README
+
+    <!-- As this template does. -->
+
+- [x] Milestone 2: Indicates a successful model implementation.
+
+  - [x] Training-time correctness
+
+    <!-- If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range. -->
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+  - [ ] Type hints and docstrings
+
+    <!-- Ideally *all* the methods should have [type hints](https://www.pythontutorial.net/python-basics/python-type-hints/) and [docstrings](https://google.github.io/styleguide/pyguide.html#381-docstrings). [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/models/detectors/fcos_mono3d.py) -->
+
+  - [ ] Unit tests
+
+    <!-- Unit tests for each module are required. [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tests/test_models/test_dense_heads/test_fcos_mono3d_head.py) -->
+
+  - [ ] Code polishing
+
+    <!-- Refactor your code according to reviewer's comment. -->
+
+  - [ ] Metafile.yml
+
+    <!-- It will be parsed by MIM and Inferencer. [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/fcos3d/metafile.yml) -->
+
+- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
+
+  <!-- In particular, you may have to refactor this README into a standard one. [Example](/configs/textdet/dbnet/README.md) -->
+
+- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.

projects/CENet/cenet/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .boundary_loss import BoundaryLoss
+from .cenet_backbone import CENet
+from .range_image_head import RangeImageHead
+from .range_image_segmentor import RangeImageSegmentor
+from .transforms_3d import SemkittiRangeView
+
+__all__ = [
+    'CENet', 'RangeImageHead', 'RangeImageSegmentor', 'SemkittiRangeView',
+    'BoundaryLoss'
+]

projects/CENet/cenet/boundary_loss.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from mmdet3d.registry import MODELS
+
+
+def one_hot(label: Tensor,
+            n_classes: int,
+            requires_grad: bool = True) -> Tensor:
+    """Return One Hot Label."""
+    device = label.device
+    one_hot_label = torch.eye(
+        n_classes, device=device, requires_grad=requires_grad)[label]
+    one_hot_label = one_hot_label.transpose(1, 3).transpose(2, 3)
+
+    return one_hot_label
+
+
+@MODELS.register_module()
+class BoundaryLoss(nn.Module):
+    """Boundary loss."""
+
+    def __init__(self, theta0=3, theta=5, loss_weight: float = 1.0) -> None:
+        super(BoundaryLoss, self).__init__()
+        self.theta0 = theta0
+        self.theta = theta
+        self.loss_weight = loss_weight
+
+    def forward(self, pred: Tensor, gt: Tensor) -> Tensor:
+        """Forward function.
+
+        Args:
+            pred (Tensor): The output from model.
+            gt (Tensor): Ground truth map.
+
+        Returns:
+            Tensor: Loss tensor.
+        """
+        pred = F.softmax(pred, dim=1)
+        n, c, _, _ = pred.shape
+
+        # one-hot vector of ground truth
+        one_hot_gt = one_hot(gt, c)
+
+        # boundary map
+        gt_b = F.max_pool2d(
+            1 - one_hot_gt,
+            kernel_size=self.theta0,
+            stride=1,
+            padding=(self.theta0 - 1) // 2)
+        gt_b -= 1 - one_hot_gt
+
+        pred_b = F.max_pool2d(
+            1 - pred,
+            kernel_size=self.theta0,
+            stride=1,
+            padding=(self.theta0 - 1) // 2)
+        pred_b -= 1 - pred
+
+        gt_b = gt_b.view(n, c, -1)
+        pred_b = pred_b.view(n, c, -1)
+
+        # Precision, Recall
+        P = torch.sum(pred_b * gt_b, dim=2) / (torch.sum(pred_b, dim=2) + 1e-7)
+        R = torch.sum(pred_b * gt_b, dim=2) / (torch.sum(gt_b, dim=2) + 1e-7)
+
+        # Boundary F1 Score
+        BF1 = 2 * P * R / (P + R + 1e-7)
+
+        # summing BF1 Score for each class and average over mini-batch
+        loss = torch.mean(1 - BF1)
+
+        return self.loss_weight * loss

projects/CENet/cenet/cenet_backbone.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional, Sequence, Tuple
+
+import torch
+from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
+                      build_norm_layer)
+from mmengine.model import BaseModule
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptConfigType, OptMultiConfig
+
+
+class BasicBlock(BaseModule):
+
+    def __init__(self,
+                 inplanes: int,
+                 planes: int,
+                 stride: int = 1,
+                 dilation: int = 1,
+                 downsample: Optional[nn.Module] = None,
+                 conv_cfg: OptConfigType = None,
+                 norm_cfg: ConfigType = dict(type='BN'),
+                 act_cfg: ConfigType = dict(type='LeakyReLU'),
+                 init_cfg: OptMultiConfig = None) -> None:
+        super(BasicBlock, self).__init__(init_cfg)
+
+        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
+
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            inplanes,
+            planes,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            conv_cfg, planes, planes, 3, padding=1, bias=False)
+        self.add_module(self.norm2_name, norm2)
+        self.relu = build_activation_layer(act_cfg)
+        self.downsample = downsample
+
+    @property
+    def norm1(self) -> nn.Module:
+        """nn.Module: normalization layer after the first convolution layer."""
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self) -> nn.Module:
+        """nn.Module: normalization layer after the second convolution layer.
+        """
+        return getattr(self, self.norm2_name)
+
+    def forward(self, x: Tensor) -> Tensor:
+        identity = x
+
+        out = self.conv1(x)
+        out = self.norm1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.norm2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+        return out
+
+
+@MODELS.register_module()
+class CENet(BaseModule):
+
+    def __init__(self,
+                 in_channels: int = 5,
+                 stem_channels: int = 128,
+                 num_stages: int = 4,
+                 stage_blocks: Sequence[int] = (3, 4, 6, 3),
+                 out_channels: Sequence[int] = (128, 128, 128, 128),
+                 strides: Sequence[int] = (1, 2, 2, 2),
+                 dilations: Sequence[int] = (1, 1, 1, 1),
+                 fuse_channels: Sequence[int] = (256, 128),
+                 conv_cfg: OptConfigType = None,
+                 norm_cfg: ConfigType = dict(type='BN'),
+                 act_cfg: ConfigType = dict(type='LeakyReLU'),
+                 init_cfg=None) -> None:
+        super(CENet, self).__init__(init_cfg)
+
+        assert len(stage_blocks) == len(out_channels) == len(strides) == len(
+            dilations) == num_stages, \
+            'The length of stage_blocks, out_channels, strides and ' \
+            'dilations should be equal to num_stages'
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self._make_stem_layer(in_channels, stem_channels)
+
+        inplanes = stem_channels
+        self.res_layers = []
+        for i, num_blocks in enumerate(stage_blocks):
+            stride = strides[i]
+            dilation = dilations[i]
+            planes = out_channels[i]
+            res_layer = self.make_res_layer(
+                inplanes=inplanes,
+                planes=planes,
+                num_blocks=num_blocks,
+                stride=stride,
+                dilation=dilation,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            inplanes = planes
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        in_channels = stem_channels + sum(out_channels)
+        self.fuse_layers = []
+        for i, fuse_channel in enumerate(fuse_channels):
+            fuse_layer = ConvModule(
+                in_channels,
+                fuse_channel,
+                kernel_size=3,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            in_channels = fuse_channel
+            layer_name = f'fuse_layer{i + 1}'
+            self.add_module(layer_name, fuse_layer)
+            self.fuse_layers.append(layer_name)
+
+    def _make_stem_layer(self, in_channels: int, out_channels: int) -> None:
+        self.stem = nn.Sequential(
+            build_conv_layer(
+                self.conv_cfg,
+                in_channels,
+                out_channels // 2,
+                kernel_size=3,
+                padding=1,
+                bias=False),
+            build_norm_layer(self.norm_cfg, out_channels // 2)[1],
+            build_activation_layer(self.act_cfg),
+            build_conv_layer(
+                self.conv_cfg,
+                out_channels // 2,
+                out_channels,
+                kernel_size=3,
+                padding=1,
+                bias=False),
+            build_norm_layer(self.norm_cfg, out_channels)[1],
+            build_activation_layer(self.act_cfg),
+            build_conv_layer(
+                self.conv_cfg,
+                out_channels,
+                out_channels,
+                kernel_size=3,
+                padding=1,
+                bias=False),
+            build_norm_layer(self.norm_cfg, out_channels)[1],
+            build_activation_layer(self.act_cfg))
+
+    def make_res_layer(
+        self,
+        inplanes: int,
+        planes: int,
+        num_blocks: int,
+        stride: int,
+        dilation: int,
+        conv_cfg: OptConfigType = None,
+        norm_cfg: ConfigType = dict(type='BN'),
+        act_cfg: ConfigType = dict(type='LeakyReLU')
+    ) -> nn.Sequential:
+        downsample = None
+        if stride != 1 or inplanes != planes:
+            downsample = nn.Sequential(
+                build_conv_layer(
+                    conv_cfg,
+                    inplanes,
+                    planes,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False),
+                build_norm_layer(norm_cfg, planes)[1])
+
+        layers = []
+        layers.append(
+            BasicBlock(
+                inplanes=inplanes,
+                planes=planes,
+                stride=stride,
+                dilation=dilation,
+                downsample=downsample,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg))
+        inplanes = planes
+        for _ in range(1, num_blocks):
+            layers.append(
+                BasicBlock(
+                    inplanes=inplanes,
+                    planes=planes,
+                    stride=1,
+                    dilation=dilation,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+        return nn.Sequential(*layers)
+
+    def forward(self, x: Tensor) -> Tuple[Tensor]:
+        x = self.stem(x)
+        outs = [x]
+        for layer_name in self.res_layers:
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            outs.append(x)
+
+        # TODO: move the following operation into neck.
+        for i in range(len(outs)):
+            if outs[i].shape != outs[0].shape:
+                outs[i] = F.interpolate(
+                    outs[i],
+                    size=outs[0].size()[2:],
+                    mode='bilinear',
+                    align_corners=True)
+
+        outs[0] = torch.cat(outs, dim=1)
+
+        for layer_name in self.fuse_layers:
+            fuse_layer = getattr(self, layer_name)
+            outs[0] = fuse_layer(outs[0])
+        return tuple(outs)

projects/CENet/cenet/range_image_head.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List, Tuple
+
+import torch
+from torch import Tensor, nn
+
+from mmdet3d.models import Base3DDecodeHead
+from mmdet3d.registry import MODELS
+from mmdet3d.structures.det3d_data_sample import SampleList
+from mmdet3d.utils import ConfigType, OptConfigType
+
+
+@MODELS.register_module()
+class RangeImageHead(Base3DDecodeHead):
+    """RangeImage decoder head.
+
+    Args:
+        loss_ce (dict or :obj:`ConfigDict`): Config of CrossEntropy loss.
+            Defaults to dict(
+                     type='mmdet.CrossEntropyLoss',
+                     use_sigmoid=False,
+                     class_weight=None,
+                     loss_weight=1.0).
+        loss_lovasz (dict or :obj:`ConfigDict`, optional): Config of Lovasz
+            loss. Defaults to None.
+        lpss_boundary (dict or :obj:`ConfigDict`, optional): Config of boundary
+            loss. Defaults to None.
+        indices (int): The indice of features to use. Defaults to 0.
+    """
+
+    def __init__(self,
+                 loss_ce: ConfigType = dict(
+                     type='mmdet.CrossEntropyLoss',
+                     use_sigmoid=False,
+                     class_weight=None,
+                     loss_weight=1.0),
+                 loss_lovasz: OptConfigType = None,
+                 loss_boundary: OptConfigType = None,
+                 indices: int = 0,
+                 **kwargs) -> None:
+        super(RangeImageHead, self).__init__(**kwargs)
+
+        self.loss_ce = MODELS.build(loss_ce)
+        if loss_lovasz is not None:
+            self.loss_lovasz = MODELS.build(loss_lovasz)
+        else:
+            self.loss_lovasz = None
+        if loss_boundary is not None:
+            self.loss_boundary = MODELS.build(loss_boundary)
+        else:
+            self.loss_boundary = None
+
+        self.indices = indices
+
+    def build_conv_seg(self, channels: int, num_classes: int,
+                       kernel_size: int) -> nn.Module:
+        return nn.Conv2d(channels, num_classes, kernel_size=kernel_size)
+
+    def forward(self, feats: Tuple[Tensor]) -> Tensor:
+        """Forward function."""
+        seg_logit = self.cls_seg(feats[self.indices])
+        return seg_logit
+
+    def _stack_batch_gt(self, batch_data_samples: SampleList) -> Tensor:
+        gt_semantic_segs = [
+            data_sample.gt_pts_seg.semantic_seg
+            for data_sample in batch_data_samples
+        ]
+        return torch.stack(gt_semantic_segs, dim=0)
+
+    def loss_by_feat(self, seg_logit: Tensor,
+                     batch_data_samples: SampleList) -> dict:
+        """Compute semantic segmentation loss.
+
+        Args:
+            seg_logit (Tensor): Predicted  logits.
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The seg
+                data samples. It usually includes information such
+                as `metainfo` and `gt_pts_seg`.
+
+        Returns:
+            Dict[str, Tensor]: A dictionary of loss components.
+        """
+        seg_label = self._stack_batch_gt(batch_data_samples)
+        seg_label = seg_label.squeeze(dim=1)
+        loss = dict()
+        loss['loss_ce'] = self.loss_ce(
+            seg_logit, seg_label, ignore_index=self.ignore_index)
+        if self.loss_lovasz:
+            loss['loss_lovasz'] = self.loss_lovasz(
+                seg_logit, seg_label, ignore_index=self.ignore_index)
+        if self.loss_boundary:
+            loss['loss_boundary'] = self.loss_boundary(seg_logit, seg_label)
+        return loss
+
+    def predict(self, inputs: Tuple[Tensor], batch_input_metas: List[dict],
+                test_cfg: ConfigType) -> torch.Tensor:
+        """Forward function for testing.
+
+        Args:
+            inputs (Tuple[Tensor]): Features from backbone.
+            batch_input_metas (List[:obj:`Det3DDataSample`]): The det3d data
+                samples. It usually includes information such as `metainfo` and
+                `gt_pts_seg`. We use `point2voxel_map` in this function.
+            test_cfg (dict or :obj:`ConfigDict`): The testing config.
+
+        Returns:
+            List[Tensor]: List of point-wise segmentation labels.
+        """
+        seg_logits = self.forward(inputs)
+        seg_labels = seg_logits.argmax(dim=1)
+        device = seg_logits.device
+        use_knn = test_cfg.get('use_knn', False)
+        if use_knn:
+            from .utils import KNN
+            post_module = KNN(
+                test_cfg=test_cfg,
+                num_classes=self.num_classes,
+                ignore_index=self.ignore_index)
+
+        seg_label_list = []
+        for i in range(len(batch_input_metas)):
+            input_metas = batch_input_metas[i]
+            proj_x = torch.tensor(
+                input_metas['proj_x'], dtype=torch.int64, device=device)
+            proj_y = torch.tensor(
+                input_metas['proj_y'], dtype=torch.int64, device=device)
+            proj_range = torch.tensor(
+                input_metas['proj_range'], dtype=torch.float32, device=device)
+            unproj_range = torch.tensor(
+                input_metas['unproj_range'],
+                dtype=torch.float32,
+                device=device)
+
+            if use_knn:
+                seg_label_list.append(
+                    post_module(proj_range, unproj_range, seg_labels[i],
+                                proj_x, proj_y))
+            else:
+                seg_label_list.append(seg_labels[i, proj_y, proj_x])
+
+        return seg_label_list

projects/CENet/cenet/range_image_segmentor.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, List
+
+from torch import Tensor
+
+from mmdet3d.models import EncoderDecoder3D
+from mmdet3d.registry import MODELS
+from mmdet3d.structures import PointData
+from mmdet3d.structures.det3d_data_sample import OptSampleList, SampleList
+
+
+@MODELS.register_module()
+class RangeImageSegmentor(EncoderDecoder3D):
+
+    def loss(self, batch_inputs_dict: dict,
+             batch_data_samples: SampleList) -> Dict[str, Tensor]:
+        """Calculate losses from a batch of inputs and data samples.
+
+        Args:
+            batch_inputs_dict (dict): Input sample dict which
+                includes 'points' and 'imgs' keys.
+
+                - points (List[Tensor]): Point cloud of each sample.
+                - imgs (Tensor, optional): Image tensor has shape (B, C, H, W).
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The det3d data
+                samples. It usually includes information such as `metainfo` and
+                `gt_pts_seg`.
+
+        Returns:
+            Dict[str, Tensor]: A dictionary of loss components.
+        """
+        # extract features using backbone
+        imgs = batch_inputs_dict['imgs']
+        x = self.extract_feat(imgs)
+
+        losses = dict()
+
+        loss_decode = self._decode_head_forward_train(x, batch_data_samples)
+        losses.update(loss_decode)
+
+        if self.with_auxiliary_head:
+            loss_aux = self._auxiliary_head_forward_train(
+                x, batch_data_samples)
+            losses.update(loss_aux)
+        return losses
+
+    def predict(self,
+                batch_inputs_dict: dict,
+                batch_data_samples: SampleList,
+                rescale: bool = True) -> SampleList:
+        """Simple test with single scene.
+
+        Args:
+            batch_inputs_dict (dict): Input sample dict which includes 'points'
+                and 'imgs' keys.
+
+                - points (List[Tensor]): Point cloud of each sample.
+                - imgs (Tensor, optional): Image tensor has shape (B, C, H, W).
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The det3d data
+                samples. It usually includes information such as `metainfo` and
+                `gt_pts_seg`.
+            rescale (bool): Whether transform to original number of points.
+                Will be used for voxelization based segmentors.
+                Defaults to True.
+
+        Returns:
+            List[:obj:`Det3DDataSample`]: Segmentation results of the input
+            points. Each Det3DDataSample usually contains:
+
+            - ``pred_pts_seg`` (PointData): Prediction of 3D semantic
+              segmentation.
+            - ``pts_seg_logits`` (PointData): Predicted logits of 3D semantic
+              segmentation before normalization.
+        """
+        # 3D segmentation requires per-point prediction, so it's impossible
+        # to use down-sampling to get a batch of scenes with same num_points
+        # therefore, we only support testing one scene every time
+        batch_input_metas = []
+        for data_sample in batch_data_samples:
+            batch_input_metas.append(data_sample.metainfo)
+
+        imgs = batch_inputs_dict['imgs']
+        x = self.extract_feat(imgs)
+        seg_labels_list = self.decode_head.predict(x, batch_input_metas,
+                                                   self.test_cfg)
+
+        return self.postprocess_result(seg_labels_list, batch_data_samples)
+
+    def _forward(self,
+                 batch_inputs_dict: dict,
+                 batch_data_samples: OptSampleList = None) -> Tensor:
+        """Network forward process.
+
+        Args:
+            batch_inputs_dict (dict): Input sample dict which includes 'points'
+                and 'imgs' keys.
+
+                - points (List[Tensor]): Point cloud of each sample.
+                - imgs (Tensor, optional): Image tensor has shape (B, C, H, W).
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The det3d data
+                samples. It usually includes information such as `metainfo` and
+                `gt_pts_seg`.
+
+        Returns:
+            Tensor: Forward output of model without any post-processes.
+        """
+        imgs = batch_inputs_dict['imgs']
+        x = self.extract_feat(imgs)
+        return self.decode_head.forward(x)
+
+    def postprocess_result(self, seg_labels_list: List[Tensor],
+                           batch_data_samples: SampleList) -> SampleList:
+        """Convert results list to `Det3DDataSample`.
+
+        Args:
+            seg_labels_list (List[Tensor]): List of segmentation results,
+                seg_logits from model of each input point clouds sample.
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The det3d data
+                samples. It usually includes information such as `metainfo` and
+                `gt_pts_seg`.
+
+        Returns:
+            List[:obj:`Det3DDataSample`]: Segmentation results of the input
+            points. Each Det3DDataSample usually contains:
+
+            - ``pred_pts_seg`` (PointData): Prediction of 3D semantic
+              segmentation.
+            - ``pts_seg_logits`` (PointData): Predicted logits of 3D semantic
+              segmentation before normalization.
+        """
+
+        for i, seg_pred in enumerate(seg_labels_list):
+            batch_data_samples[i].set_data(
+                {'pred_pts_seg': PointData(**{'pts_semantic_mask': seg_pred})})
+        return batch_data_samples

projects/CENet/cenet/transforms_3d.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Sequence
+
+import numpy as np
+from mmcv.transforms import BaseTransform
+
+from mmdet3d.registry import TRANSFORMS
+
+
+@TRANSFORMS.register_module()
+class SemkittiRangeView(BaseTransform):
+    """Convert Semantickitti point cloud dataset to range image."""
+
+    def __init__(self,
+                 H: int = 64,
+                 W: int = 2048,
+                 fov_up: float = 3.0,
+                 fov_down: float = -25.0,
+                 means: Sequence[float] = (11.71279, -0.1023471, 0.4952,
+                                           -1.0545, 0.2877),
+                 stds: Sequence[float] = (10.24, 12.295865, 9.4287, 0.8643,
+                                          0.1450),
+                 ignore_index: int = 19) -> None:
+        self.H = H
+        self.W = W
+        self.fov_up = fov_up / 180.0 * np.pi
+        self.fov_down = fov_down / 180.0 * np.pi
+        self.fov = abs(self.fov_down) + abs(self.fov_up)
+        self.means = np.array(means, dtype=np.float32)
+        self.stds = np.array(stds, dtype=np.float32)
+        self.ignore_index = ignore_index
+
+    def transform(self, results: dict) -> dict:
+        points_numpy = results['points'].numpy()
+
+        proj_image = np.full((self.H, self.W, 5), -1, dtype=np.float32)
+        proj_idx = np.full((self.H, self.W), -1, dtype=np.int64)
+
+        # get depth of all points
+        depth = np.linalg.norm(points_numpy[:, :3], 2, axis=1)
+
+        # get angles of all points
+        yaw = -np.arctan2(points_numpy[:, 1], points_numpy[:, 0])
+        pitch = np.arcsin(points_numpy[:, 2] / depth)
+
+        # get projection in image coords
+        proj_x = 0.5 * (yaw / np.pi + 1.0)
+        proj_y = 1.0 - (pitch + abs(self.fov_down)) / self.fov
+
+        # scale to image size using angular resolution
+        proj_x *= self.W
+        proj_y *= self.H
+
+        # round and clamp for use as index
+        proj_x = np.floor(proj_x)
+        proj_x = np.minimum(self.W - 1, proj_x)
+        proj_x = np.maximum(0, proj_x).astype(np.int64)
+
+        proj_y = np.floor(proj_y)
+        proj_y = np.minimum(self.H - 1, proj_y)
+        proj_y = np.maximum(0, proj_y).astype(np.int64)
+
+        results['proj_x'] = proj_x
+        results['proj_y'] = proj_y
+        results['unproj_range'] = depth
+
+        # order in decreasing depth
+        indices = np.arange(depth.shape[0])
+        order = np.argsort(depth)[::-1]
+        proj_idx[proj_y[order], proj_x[order]] = indices[order]
+        proj_image[proj_y[order], proj_x[order], 0] = depth[order]
+        proj_image[proj_y[order], proj_x[order], 1:] = points_numpy[order]
+        proj_mask = (proj_idx > 0).astype(np.int32)
+        results['proj_range'] = proj_image[..., 0]
+
+        proj_image = (proj_image -
+                      self.means[None, None, :]) / self.stds[None, None, :]
+        proj_image = proj_image * proj_mask[..., None].astype(np.float32)
+        results['img'] = proj_image
+
+        if 'pts_semantic_mask' in results:
+            proj_sem_label = np.full((self.H, self.W),
+                                     self.ignore_index,
+                                     dtype=np.int64)
+            proj_sem_label[proj_y[order],
+                           proj_x[order]] = results['pts_semantic_mask'][order]
+            results['gt_semantic_seg'] = proj_sem_label
+        return results

projects/CENet/cenet/utils.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from mmdet3d.utils import ConfigType
+
+
+def get_gaussian_kernel(kernel_size: int = 3, sigma: int = 2) -> Tensor:
+    # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+    x_coord = torch.arange(kernel_size)
+    x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
+    y_grid = x_grid.t()
+    xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
+
+    mean = (kernel_size - 1) / 2.
+    variance = sigma**2.
+
+    # Calculate the 2-dimensional gaussian kernel which is
+    # the product of two gaussian distributions for two different
+    # variables (in this case called x and y)
+    gaussian_kernel = (1. / (2. * math.pi * variance)) * torch.exp(-torch.sum(
+        (xy_grid - mean)**2., dim=-1) / (2 * variance))
+
+    # Make sure sum of values in gaussian kernel equals 1.
+    gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+    # Reshape to 2d depthwise convolutional weight
+    gaussian_kernel = gaussian_kernel.view(kernel_size, kernel_size)
+
+    return gaussian_kernel
+
+
+class KNN(nn.Module):
+
+    def __init__(self, test_cfg: ConfigType, num_classes: int,
+                 ignore_index: int) -> None:
+        super(KNN, self).__init__()
+        self.knn = test_cfg.knn
+        self.search = test_cfg.search
+        self.sigma = test_cfg.sigma
+        self.cutoff = test_cfg.cutoff
+        self.num_classes = num_classes
+        self.ignore_index = ignore_index
+
+    def forward(self, proj_range: Tensor, unproj_range: Tensor,
+                proj_argmax: Tensor, px: Tensor, py: Tensor) -> Tensor:
+
+        # sizes of projection scan
+        H, W = proj_range.shape
+
+        # number of points
+        P = unproj_range.shape
+
+        # check if size of kernel is odd and complain
+        if self.search % 2 == 0:
+            raise ValueError('Nearest neighbor kernel must be odd number')
+
+        # calculate padding
+        pad = int((self.search - 1) / 2)
+
+        # unfold neighborhood to get nearest neighbors for each pixel
+        # (range image)
+        proj_unfold_k_rang = F.unfold(
+            proj_range[None, None, ...],
+            kernel_size=(self.search, self.search),
+            padding=(pad, pad))
+
+        # index with px, py to get ALL the pcld points
+        idx_list = py * W + px
+        unproj_unfold_k_rang = proj_unfold_k_rang[:, :, idx_list]
+
+        # WARNING, THIS IS A HACK
+        # Make non valid (<0) range points extremely big so that there is no
+        # screwing up the nn self.search
+        unproj_unfold_k_rang[unproj_unfold_k_rang < 0] = float('inf')
+
+        # now the matrix is unfolded TOTALLY, replace the middle points with
+        # the actual range points
+        center = int(((self.search * self.search) - 1) / 2)
+        unproj_unfold_k_rang[:, center, :] = unproj_range
+
+        # now compare range
+        k2_distances = torch.abs(unproj_unfold_k_rang - unproj_range)
+
+        # make a kernel to weigh the ranges according to distance in (x,y)
+        # I make this 1 - kernel because I want distances that are close
+        # in (x,y) to matter more
+        inv_gauss_k = (1 - get_gaussian_kernel(self.search, self.sigma)).view(
+            1, -1, 1)
+        inv_gauss_k = inv_gauss_k.to(proj_range.device).type(proj_range.type())
+
+        # apply weighing
+        k2_distances = k2_distances * inv_gauss_k
+
+        # find nearest neighbors
+        _, knn_idx = k2_distances.topk(
+            self.knn, dim=1, largest=False, sorted=False)
+
+        # do the same unfolding with the argmax
+        proj_unfold_1_argmax = F.unfold(
+            proj_argmax[None, None, ...].float(),
+            kernel_size=(self.search, self.search),
+            padding=(pad, pad)).long()
+        unproj_unfold_1_argmax = proj_unfold_1_argmax[:, :, idx_list]
+
+        # get the top k logits from the knn at each pixel
+        knn_argmax = torch.gather(
+            input=unproj_unfold_1_argmax, dim=1, index=knn_idx)
+
+        # fake an invalid argmax of classes + 1 for all cutoff items
+        if self.cutoff > 0:
+            knn_distances = torch.gather(
+                input=k2_distances, dim=1, index=knn_idx)
+            knn_invalid_idx = knn_distances > self.cutoff
+            knn_argmax[knn_invalid_idx] = self.num_classes
+
+        # now vote
+        # argmax onehot has an extra class for objects after cutoff
+        knn_argmax_onehot = torch.zeros(
+            (1, self.num_classes + 1, P[0]),
+            device=proj_range.device).type(proj_range.type())
+        ones = torch.ones_like(knn_argmax).type(proj_range.type())
+        knn_argmax_onehot = knn_argmax_onehot.scatter_add_(1, knn_argmax, ones)
+
+        # now vote (as a sum over the onehot shit)
+        # (don't let it choose unlabeled OR invalid)
+        if self.ignore_index == self.num_classes - 1:
+            knn_argmax_out = knn_argmax_onehot[:, :-2].argmax(dim=1)
+        elif self.ignore_index == 0:
+            knn_argmax_out = knn_argmax_onehot[:, 1:-1].argmax(dim=1) + 1
+        else:
+            knn_argmax_out = knn_argmax_onehot[:, :-1].argmax(dim=1)
+
+        # reshape again
+        knn_argmax_out = knn_argmax_out.view(P)
+
+        return knn_argmax_out

projects/CENet/configs/cenet-64x1024_4xb4_semantickitti.py

+_base_ = ['./cenet-64x512_4xb4_semantickitti.py']
+
+backend_args = None
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(type='PointSample', num_points=0.9),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-3.1415929, 3.1415929],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0.1, 0.1, 0.1],
+    ),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=1024,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(type='Pack3DDetInputs', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=1024,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=('proj_x', 'proj_y', 'proj_range', 'unproj_range'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = val_dataloader

projects/CENet/configs/cenet-64x2048_4xb4_semantickitti.py

+_base_ = ['./cenet-64x512_4xb4_semantickitti.py']
+
+backend_args = None
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(type='PointSample', num_points=0.9),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-3.1415929, 3.1415929],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0.1, 0.1, 0.1],
+    ),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=2048,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(type='Pack3DDetInputs', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=2048,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=('proj_x', 'proj_y', 'proj_range', 'unproj_range'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = val_dataloader

projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py

+_base_ = ['../../../configs/_base_/default_runtime.py']
+custom_imports = dict(
+    imports=['projects.CENet.cenet'], allow_failed_imports=False)
+
+# For SemanticKitti we usually do 19-class segmentation.
+# For labels_map we follow the uniform format of MMDetection & MMSegmentation
+# i.e. we consider the unlabeled class as the last one, which is different
+# from the original implementation of some methods e.g. Cylinder3D.
+dataset_type = 'SemanticKittiDataset'
+data_root = 'data/semantickitti/'
+class_names = [
+    'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist',
+    'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building',
+    'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'
+]
+labels_map = {
+    0: 19,  # "unlabeled"
+    1: 19,  # "outlier" mapped to "unlabeled" --------------mapped
+    10: 0,  # "car"
+    11: 1,  # "bicycle"
+    13: 4,  # "bus" mapped to "other-vehicle" --------------mapped
+    15: 2,  # "motorcycle"
+    16: 4,  # "on-rails" mapped to "other-vehicle" ---------mapped
+    18: 3,  # "truck"
+    20: 4,  # "other-vehicle"
+    30: 5,  # "person"
+    31: 6,  # "bicyclist"
+    32: 7,  # "motorcyclist"
+    40: 8,  # "road"
+    44: 9,  # "parking"
+    48: 10,  # "sidewalk"
+    49: 11,  # "other-ground"
+    50: 12,  # "building"
+    51: 13,  # "fence"
+    52: 19,  # "other-structure" mapped to "unlabeled" ------mapped
+    60: 8,  # "lane-marking" to "road" ---------------------mapped
+    70: 14,  # "vegetation"
+    71: 15,  # "trunk"
+    72: 16,  # "terrain"
+    80: 17,  # "pole"
+    81: 18,  # "traffic-sign"
+    99: 19,  # "other-object" to "unlabeled" ----------------mapped
+    252: 0,  # "moving-car" to "car" ------------------------mapped
+    253: 6,  # "moving-bicyclist" to "bicyclist" ------------mapped
+    254: 5,  # "moving-person" to "person" ------------------mapped
+    255: 7,  # "moving-motorcyclist" to "motorcyclist" ------mapped
+    256: 4,  # "moving-on-rails" mapped to "other-vehic------mapped
+    257: 4,  # "moving-bus" mapped to "other-vehicle" -------mapped
+    258: 3,  # "moving-truck" to "truck" --------------------mapped
+    259: 4  # "moving-other"-vehicle to "other-vehicle"-----mapped
+}
+
+metainfo = dict(
+    classes=class_names, seg_label_mapping=labels_map, max_label=259)
+
+input_modality = dict(use_lidar=True, use_camera=False)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/semantickitti/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(type='PointSample', num_points=0.9),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-3.1415929, 3.1415929],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0.1, 0.1, 0.1],
+    ),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=512,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(type='Pack3DDetInputs', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='SemkittiRangeView',
+        H=64,
+        W=512,
+        fov_up=3.0,
+        fov_down=-25.0,
+        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
+        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
+        ignore_index=19),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=('proj_x', 'proj_y', 'proj_range', 'unproj_range'))
+]
+
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='semantickitti_infos_train.pkl',
+        pipeline=train_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        ignore_index=19,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='semantickitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        ignore_index=19,
+        test_mode=True,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='SegMetric')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+model = dict(
+    type='RangeImageSegmentor',
+    data_preprocessor=dict(type='Det3DDataPreprocessor'),
+    backbone=dict(
+        type='CENet',
+        in_channels=5,
+        stem_channels=128,
+        num_stages=4,
+        stage_blocks=(3, 4, 6, 3),
+        out_channels=(128, 128, 128, 128),
+        fuse_channels=(256, 128),
+        strides=(1, 2, 2, 2),
+        dilations=(1, 1, 1, 1),
+        act_cfg=dict(type='HSwish', inplace=True)),
+    decode_head=dict(
+        type='RangeImageHead',
+        channels=128,
+        num_classes=20,
+        dropout_ratio=0,
+        loss_ce=dict(
+            type='mmdet.CrossEntropyLoss',
+            use_sigmoid=False,
+            class_weight=None,
+            loss_weight=1.0),
+        loss_lovasz=dict(type='LovaszLoss', loss_weight=1.5, reduction='none'),
+        loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
+        conv_seg_kernel_size=1,
+        ignore_index=19),
+    auxiliary_head=[
+        dict(
+            type='RangeImageHead',
+            channels=128,
+            num_classes=20,
+            dropout_ratio=0,
+            loss_ce=dict(
+                type='mmdet.CrossEntropyLoss',
+                use_sigmoid=False,
+                class_weight=None,
+                loss_weight=1.0),
+            loss_lovasz=dict(
+                type='LovaszLoss', loss_weight=1.5, reduction='none'),
+            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
+            conv_seg_kernel_size=1,
+            ignore_index=19,
+            indices=2),
+        dict(
+            type='RangeImageHead',
+            channels=128,
+            num_classes=20,
+            dropout_ratio=0,
+            loss_ce=dict(
+                type='mmdet.CrossEntropyLoss',
+                use_sigmoid=False,
+                class_weight=None,
+                loss_weight=1.0),
+            loss_lovasz=dict(
+                type='LovaszLoss', loss_weight=1.5, reduction='none'),
+            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
+            conv_seg_kernel_size=1,
+            ignore_index=19,
+            indices=3),
+        dict(
+            type='RangeImageHead',
+            channels=128,
+            num_classes=20,
+            dropout_ratio=0,
+            loss_ce=dict(
+                type='mmdet.CrossEntropyLoss',
+                use_sigmoid=False,
+                class_weight=None,
+                loss_weight=1.0),
+            loss_lovasz=dict(
+                type='LovaszLoss', loss_weight=1.5, reduction='none'),
+            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
+            conv_seg_kernel_size=1,
+            ignore_index=19,
+            indices=4)
+    ],
+    train_cfg=None,
+    test_cfg=dict(use_knn=True, knn=7, search=7, sigma=1.0, cutoff=2.0))
+
+# optimizer
+# This schedule is mainly used on Semantickitti dataset in segmentation task
+optim_wrapper = dict(
+    type='AmpOptimWrapper',
+    loss_scale='dynamic',
+    optimizer=dict(
+        type='AdamW',
+        lr=0.04,
+        betas=(0.9, 0.999),
+        weight_decay=(0.01),
+        eps=0.000005))
+
+param_scheduler = [
+    dict(
+        type='OneCycleLR',
+        total_steps=50,
+        by_epoch=True,
+        eta_max=0.0025,
+        pct_start=0.2,
+        div_factor=25.0,
+        final_div_factor=100.0,
+        convert_to_iter_based=True)
+]
+
+# runtime settings
+train_cfg = dict(by_epoch=True, max_epochs=50, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (4 GPUs) x (4 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=1))

projects/DETR3D/README.md

-DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
+# DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
 
 > [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://arxiv.org/abs/2110.06922)