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Commit ed2bb815 authored by chenshi3's avatar chenshi3
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Update configs

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README.md
View file @ ed2bb815
...@@ -24,7 +24,7 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18 ...@@ -24,7 +24,7 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog ## 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). [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 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. [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 ...@@ -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=0.1)](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) | | [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) | | [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) | | [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/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) | | [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. *: Use the fade strategy, which disables data augmentations in the last several epochs during training.
......
docs/guidelines_of_approaches/bevfusion.md
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...@@ -11,25 +11,25 @@ Please refer to [GETTING_STARTED.md](../GETTING_STARTED.md) to process the multi ...@@ -11,25 +11,25 @@ Please refer to [GETTING_STARTED.md](../GETTING_STARTED.md) to process the multi
1. Train the lidar branch for BEVFusion: 1. Train the lidar branch for BEVFusion:
```shell ```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 ```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 \ --pretrained_model path_to_pretrained_lidar_branch_ckpt \
``` ```
## Evaluation ## Evaluation
* Test with a pretrained model: * Test with a pretrained model:
```shell ```shell
bash scripts/dist_test.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/cbgs_bevfusion.yaml \ bash scripts/dist_test.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/bevfusion.yaml \
--ckpt ../output/cfgs/nuscenes_models/cbgs_bevfusion/default/ckpt/checkpoint_epoch_6.pth --ckpt ../output/cfgs/nuscenes_models/bevfusion/default/ckpt/checkpoint_epoch_6.pth
``` ```
## Performance ## Performance
All models are trained with spconv 1.0, but you can directly load them for testing regardless of the spconv version. 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 | | | 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) | | [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/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) | | [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
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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
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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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