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README.md
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......@@ -24,7 +24,7 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
[2023-05-xx] Added support for the multi-modal 3D object detection model [`BEVFusion`](https://arxiv.org/abs/2205.13542) on Nuscenes dataset, which fuses multi-modal information on BEV space and reaches 70.98% NDS on Nuscenes validation dataset. (see the [guideline](docs/guidelines_of_approaches/bevfusion.md) on how to train/test with BEVFusion).
* Support multi-modal Nuscenes detection (See the [GETTING_STARTED.md](docs/GETTING_STARTED.md) to process data).
* Support TransFusion-Lidar head, which ahcieves 69.43% NDS on Nuscenes validation dataset.
* Support [TransFusion-Lidar](https://arxiv.org/abs/2203.11496) head, which ahcieves 69.43% NDS on Nuscenes validation dataset.
[2023-04-02] Added support for [`VoxelNeXt`](https://github.com/dvlab-research/VoxelNeXt) on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels.
......@@ -213,8 +213,8 @@ All models are trained with 8 GPUs and are available for download. For training
| [CenterPoint (voxel_size=0.1)](tools/cfgs/nuscenes_models/cbgs_voxel01_res3d_centerpoint.yaml) | 30.11 | 25.55 | 38.28 | 21.94 | 18.87 | 56.03 | 64.54 | [model-34M](https://drive.google.com/file/d/1Cz-J1c3dw7JAWc25KRG1XQj8yCaOlexQ/view?usp=sharing) |
| [CenterPoint (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_res3d_centerpoint.yaml) | 28.80 | 25.43 | 37.27 | 21.55 | 18.24 | 59.22 | 66.48 | [model-34M](https://drive.google.com/file/d/1XOHAWm1MPkCKr1gqmc3TWi5AYZgPsgxU/view?usp=sharing) |
| [VoxelNeXt (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_voxelnext.yaml) | 30.11 | 25.23 | 40.57 | 21.69 | 18.56 | 60.53 | 66.65 | [model-31M](https://drive.google.com/file/d/1IV7e7G9X-61KXSjMGtQo579pzDNbhwvf/view?usp=share_link) |
| [TransFusion-L*](tools/cfgs/nuscenes_models/cbgs_transfusion_lidar.yaml) | 27.96 | 25.37 | 29.35 | 27.31 | 18.55 | 64.58 | 69.43 | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
| [BEVFusion](tools/cfgs/nuscenes_models/cbgs_bevfusion.yaml) | 28.03 | 25.43 | 30.19 | 26.76 | 18.48 | 67.75 | 70.98 | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
| [TransFusion-L*](tools/cfgs/nuscenes_models/transfusion_lidar.yaml) | 27.96 | 25.37 | 29.35 | 27.31 | 18.55 | 64.58 | 69.43 | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
| [BEVFusion](tools/cfgs/nuscenes_models/bevfusion.yaml) | 28.03 | 25.43 | 30.19 | 26.76 | 18.48 | 67.75 | 70.98 | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
*: Use the fade strategy, which disables data augmentations in the last several epochs during training.
......
docs/guidelines_of_approaches/bevfusion.md
View file @ ed2bb815
......@@ -11,25 +11,25 @@ Please refer to [GETTING_STARTED.md](../GETTING_STARTED.md) to process the multi
1. Train the lidar branch for BEVFusion:
```shell
bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/cbgs_transfusion_lidar.yaml \
bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/transfusion_lidar.yaml \
```
The ckpt will be saved in ../output/nuscenes_models/cbgs_transfusion_lidar/default/ckpt, or you can download pretrained checkpoint directly form [here](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link).
The ckpt will be saved in ../output/nuscenes_models/transfusion_lidar/default/ckpt, or you can download pretrained checkpoint directly form [here](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link).
1. To train BEVFusion, you need to download pretrained parameters for image backbone [here](www.google.com), and specify the path in [config](../../tools/cfgs/nuscenes_models/cbgs_bevfusion.yaml#L88). Then run the following command:
2. To train BEVFusion, you need to download pretrained parameters for image backbone [here](https://drive.google.com/file/d/1v74WCt4_5ubjO7PciA5T0xhQc9bz_jZu/view?usp=share_link), and specify the path in [config](../../tools/cfgs/nuscenes_models/bevfusion.yaml#L88). Then run the following command:
```shell
bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/cbgs_bevfusion.yaml \
bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/bevfusion.yaml \
--pretrained_model path_to_pretrained_lidar_branch_ckpt \
```
## Evaluation
* Test with a pretrained model:
```shell
bash scripts/dist_test.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/cbgs_bevfusion.yaml \
--ckpt ../output/cfgs/nuscenes_models/cbgs_bevfusion/default/ckpt/checkpoint_epoch_6.pth
bash scripts/dist_test.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/bevfusion.yaml \
--ckpt ../output/cfgs/nuscenes_models/bevfusion/default/ckpt/checkpoint_epoch_6.pth
```
## Performance
All models are trained with spconv 1.0, but you can directly load them for testing regardless of the spconv version.
| | mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download |
|----------------------------------------------------------------------------------------------------|-------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:--------------------------------------------------------------------------------------------------:|
| [TransFusion-L](../../tools/cfgs/nuscenes_models/cbgs_transfusion_lidar.yaml) | 27.96 | 25.37 | 29.35 | 27.31 | 18.55 | 64.58 | 69.43 | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
| [BEVFusion](../../tools/cfgs/nuscenes_models/cbgs_bevfusion.yaml) | 28.03 | 25.43 | 30.19 | 26.76 | 18.48 | 67.75 | 70.98 | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
| [TransFusion-L](../../tools/cfgs/nuscenes_models/transfusion_lidar.yaml) | 27.96 | 25.37 | 29.35 | 27.31 | 18.55 | 64.58 | 69.43 | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
| [BEVFusion](../../tools/cfgs/nuscenes_models/bevfusion.yaml) | 28.03 | 25.43 | 30.19 | 26.76 | 18.48 | 67.75 | 70.98 | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
tools/cfgs/nuscenes_models/bevfusion.yaml 0 → 100644
View file @ ed2bb815
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
CAMERA_CONFIG:
USE_CAMERA: True
IMAGE:
FINAL_DIM: [256,704]
RESIZE_LIM_TRAIN: [0.38, 0.55]
RESIZE_LIM_TEST: [0.48, 0.48]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
- NAME: imgaug
ROT_LIM: [-5.4, 5.4]
RAND_FLIP: True
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.075, 0.075, 0.2]
MAX_POINTS_PER_VOXEL: 10
MAX_NUMBER_OF_VOXELS: {
'train': 120000,
'test': 160000
}
- NAME: image_calibrate
- NAME: image_normalize
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
MODEL:
NAME: BevFusion
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
USE_BIAS: False
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
IMAGE_BACKBONE:
NAME: SwinTransformer
EMBED_DIMS: 96
DEPTHS: [2, 2, 6, 2]
NUM_HEADS: [3, 6, 12, 24]
WINDOW_SIZE: 7
MLP_RATIO: 4
DROP_RATE: 0.
ATTN_DROP_RATE: 0.
DROP_PATH_RATE: 0.2
PATCH_NORM: True
OUT_INDICES: [1, 2, 3]
WITH_CP: False
CONVERT_WEIGHTS: True
INIT_CFG:
type: Pretrained
checkpoint: swint-nuimages-pretrained.pth
NECK:
NAME: GeneralizedLSSFPN
IN_CHANNELS: [192, 384, 768]
OUT_CHANNELS: 256
START_LEVEL: 0
END_LEVEL: -1
NUM_OUTS: 3
VTRANSFORM:
NAME: DepthLSSTransform
IMAGE_SIZE: [256, 704]
IN_CHANNEL: 256
OUT_CHANNEL: 80
FEATURE_SIZE: [32, 88]
XBOUND: [-54.0, 54.0, 0.3]
YBOUND: [-54.0, 54.0, 0.3]
ZBOUND: [-10.0, 10.0, 20.0]
DBOUND: [1.0, 60.0, 0.5]
DOWNSAMPLE: 2
FUSER:
NAME: ConvFuser
IN_CHANNEL: 336
OUT_CHANNEL: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
USE_CONV_FOR_NO_STRIDE: True
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
USE_BIAS_BEFORE_NORM: False
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
'vel': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 8
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 3
NUM_EPOCHS: 6
OPTIMIZER: adam_cosineanneal
LR: 0.0001
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
BETAS: [0.9, 0.999]
MOMS: [0.9, 0.8052631]
PCT_START: 0.4
WARMUP_ITER: 500
DECAY_STEP_LIST: [35, 45]
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 35
LOSS_SCALE_FP16: 32
\ No newline at end of file
tools/cfgs/nuscenes_models/cbgs_bevfusion.yaml deleted 100644 → 0
View file @ 123d42e5
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
CAMERA_CONFIG:
USE_CAMERA: True
IMAGE:
FINAL_DIM: [256,704]
RESIZE_LIM_TRAIN: [0.38, 0.55]
RESIZE_LIM_TEST: [0.48, 0.48]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
- NAME: imgaug
ROT_LIM: [-5.4, 5.4]
RAND_FLIP: True
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.075, 0.075, 0.2]
MAX_POINTS_PER_VOXEL: 10
MAX_NUMBER_OF_VOXELS: {
'train': 120000,
'test': 160000
}
- NAME: image_calibrate
- NAME: image_normalize
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
MODEL:
NAME: BevFusion
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
USE_BIAS: False
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
IMAGE_BACKBONE:
NAME: SwinTransformer
EMBED_DIMS: 96
DEPTHS: [2, 2, 6, 2]
NUM_HEADS: [3, 6, 12, 24]
WINDOW_SIZE: 7
MLP_RATIO: 4
DROP_RATE: 0.
ATTN_DROP_RATE: 0.
DROP_PATH_RATE: 0.2
PATCH_NORM: True
OUT_INDICES: [1, 2, 3]
WITH_CP: False
CONVERT_WEIGHTS: True
INIT_CFG:
type: Pretrained
# checkpoint: swint-nuimages-pretrained.pth
checkpoint: /slurm_data/shichen/package/tomopc_cu111/OpenPCDet/tmp/pretrained/swint-nuimages-pretrained.pth
NECK:
NAME: GeneralizedLSSFPN
IN_CHANNELS: [192, 384, 768]
OUT_CHANNELS: 256
START_LEVEL: 0
END_LEVEL: -1
NUM_OUTS: 3
VTRANSFORM:
NAME: DepthLSSTransform
IMAGE_SIZE: [256, 704]
IN_CHANNEL: 256
OUT_CHANNEL: 80
FEATURE_SIZE: [32, 88]
XBOUND: [-54.0, 54.0, 0.3]
YBOUND: [-54.0, 54.0, 0.3]
ZBOUND: [-10.0, 10.0, 20.0]
DBOUND: [1.0, 60.0, 0.5]
DOWNSAMPLE: 2
FUSER:
NAME: ConvFuser
IN_CHANNEL: 336
OUT_CHANNEL: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
USE_CONV_FOR_NO_STRIDE: True
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
USE_BIAS_BEFORE_NORM: False
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
'vel': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 8
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 3
NUM_EPOCHS: 6
OPTIMIZER: adam_cosineanneal
LR: 0.0001
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
BETAS: [0.9, 0.999]
MOMS: [0.9, 0.8052631]
PCT_START: 0.4
WARMUP_ITER: 500
DECAY_STEP_LIST: [35, 45]
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 35
LOSS_SCALE_FP16: 32
\ No newline at end of file
tools/cfgs/nuscenes_models/cbgs_transfusion_lidar.yaml deleted 100644 → 0
View file @ 123d42e5
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
DB_INFO_PATH:
- nuscenes_dbinfos_10sweeps_withvelo.pkl
PREPARE: {
filter_by_min_points: [
'car:5','truck:5', 'construction_vehicle:5', 'bus:5', 'trailer:5',
'barrier:5', 'motorcycle:5', 'bicycle:5', 'pedestrian:5', 'traffic_cone:5'
],
}
SAMPLE_GROUPS: [
'car:2','truck:3', 'construction_vehicle:7', 'bus:4', 'trailer:6',
'barrier:2', 'motorcycle:6', 'bicycle:6', 'pedestrian:2', 'traffic_cone:2'
]
NUM_POINT_FEATURES: 5
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.075, 0.075, 0.2]
MAX_POINTS_PER_VOXEL: 10
MAX_NUMBER_OF_VOXELS: {
'train': 120000,
'test': 160000
}
MODEL:
NAME: TransFusion
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
USE_BIAS: False
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
USE_CONV_FOR_NO_STRIDE: True
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
USE_BIAS_BEFORE_NORM: False
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
'vel': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 8
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 4
NUM_EPOCHS: 20
OPTIMIZER: adam_onecycle
LR: 0.001
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
BETAS: [0.9, 0.999]
MOMS: [0.9, 0.8052631]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 35
HOOK:
DisableAugmentationHook:
DISABLE_AUG_LIST: ['gt_sampling']
NUM_LAST_EPOCHS: 5
\ No newline at end of file
tools/cfgs/nuscenes_models/transfusion_lidar.yaml 0 → 100644
View file @ ed2bb815
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
DB_INFO_PATH:
- nuscenes_dbinfos_10sweeps_withvelo.pkl
PREPARE: {
filter_by_min_points: [
'car:5','truck:5', 'construction_vehicle:5', 'bus:5', 'trailer:5',
'barrier:5', 'motorcycle:5', 'bicycle:5', 'pedestrian:5', 'traffic_cone:5'
],
}
SAMPLE_GROUPS: [
'car:2','truck:3', 'construction_vehicle:7', 'bus:4', 'trailer:6',
'barrier:2', 'motorcycle:6', 'bicycle:6', 'pedestrian:2', 'traffic_cone:2'
]
NUM_POINT_FEATURES: 5
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.075, 0.075, 0.2]
MAX_POINTS_PER_VOXEL: 10
MAX_NUMBER_OF_VOXELS: {
'train': 120000,
'test': 160000
}
MODEL:
NAME: TransFusion
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
USE_BIAS: False
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
USE_CONV_FOR_NO_STRIDE: True
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
USE_BIAS_BEFORE_NORM: False
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
'vel': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 8
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 4
NUM_EPOCHS: 20
OPTIMIZER: adam_onecycle
LR: 0.001
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
BETAS: [0.9, 0.999]
MOMS: [0.9, 0.8052631]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 35
HOOK:
DisableAugmentationHook:
DISABLE_AUG_LIST: ['gt_sampling']
NUM_LAST_EPOCHS: 5
\ No newline at end of file
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