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Commit e9b06b36 authored by YangXiuyu's avatar YangXiuyu
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add config files and README

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pcdet/datasets/custom/README.md 0 → 100644
View file @ e9b06b36
For Custom Dataset using
## Custom Dataset
For pure point cloud dataset, which means you don't have images generated when got point cloud data from a self-defined scene. Label those raw data and make sure label files to be kitti-like:
```
Car 0 0 0 0 0 0 0 1.50 1.46 3.70 -5.12 1.85 4.13 1.56
Pedestrian 0 0 0 0 0 0 0 1.54 0.57 0.41 -7.92 1.94 15.95 1.57
DontCare 0 0 0 0 0 0 0 -1 -1 -1 -1000 -1000 -1000 -10
```
Some items (which is shown from the first zero to the seventh zero above) are not necessary because they are meaningless if no cameras. And the `image` folder, `calib` folder are both needless, which will be much more convenient for not using just official dataset. The point cloud dataset should be `.bin` format.
Place the custom dataset:
```
OpenPCDet
├── data
│ ├── custom
│ │ │── ImageSets
│ │ │── training
│ │ │ ├──velodyne & label_2
│ │ │── testing
│ │ │ ├──velodyne
├── pcdet
├── tools
```
## Calibration
Calibration rules for cameras are not need. But you need to define how to transform from KITTI coordinates to lidar coordinates. The lidar coordinates are the custom coordinates. The raw data are in lidar coordinates and the labels are in KITTI coordinates. This self-defined transform is written in `custom_dataset->get_calib (188)` which is used to get gt_boxes from labels.
## Other configurations
Possible other parameters or names that need to be check to adapt the custom scene.
- config files
```
CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] # pv_rcnn.yaml
...
'anchor_sizes': [[3.9, 1.6, 1.56]], # pv_rcnn.yaml
...
POINT_CLOUD_RANGE: [-70.4, -40, -3, 70.4, 40, 1] # custom_dataset.yaml
...
```
The train, test and pred are all the same as others.
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pcdet/datasets/custom/custom_dataset.py
View file @ e9b06b36
......@@ -103,13 +103,7 @@ class CustomDataset(DatasetTemplate):
def get_calib(self, loc):
"""
This calibration is different from the kitti dataset.
The transform formual of labelCloud: ROOT/labelCloud/io/labels/kitti.py: import labels
if self.transformed:
centroid = centroid[2], -centroid[0], centroid[1] - 2.3
dimensions = [float(v) for v in line_elements[8:11]]
if self.transformed:
dimensions = dimensions[2], dimensions[1], dimensions[0]
bbox = BBox(*centroid, *dimensions)
You should check or redefine it according to your condition.
"""
loc_lidar = np.concatenate([np.array((float(loc_obj[2]), float(-loc_obj[0]), float(loc_obj[1]-2.3)), dtype=np.float32).reshape(1,3) for loc_obj in loc])
return loc_lidar
......
tools/cfgs/custom_models/pointrcnn.yaml 0 → 100644
View file @ e9b06b36
CLASS_NAMES: ['Car']
# CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: ../dataset_configs/custom_dataset.yaml
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: sample_points
NUM_POINTS: {
'train': 16384,
'test': 16384
}
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': False
}
MODEL:
NAME: PointRCNN
BACKBONE_3D:
NAME: PointNet2MSG
SA_CONFIG:
NPOINTS: [4096, 1024, 256, 64]
RADIUS: [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
NSAMPLE: [[16, 32], [16, 32], [16, 32], [16, 32]]
MLPS: [[[16, 16, 32], [32, 32, 64]],
[[64, 64, 128], [64, 96, 128]],
[[128, 196, 256], [128, 196, 256]],
[[256, 256, 512], [256, 384, 512]]]
FP_MLPS: [[128, 128], [256, 256], [512, 512], [512, 512]]
POINT_HEAD:
NAME: PointHeadBox
CLS_FC: [256, 256]
REG_FC: [256, 256]
CLASS_AGNOSTIC: False
USE_POINT_FEATURES_BEFORE_FUSION: False
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
BOX_CODER: PointResidualCoder
BOX_CODER_CONFIG: {
'use_mean_size': True,
'mean_size': [
[3.9, 1.6, 1.56],
[0.8, 0.6, 1.73],
[1.76, 0.6, 1.73]
]
}
LOSS_CONFIG:
LOSS_REG: WeightedSmoothL1Loss
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
'point_box_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
ROI_HEAD:
NAME: PointRCNNHead
CLASS_AGNOSTIC: True
ROI_POINT_POOL:
POOL_EXTRA_WIDTH: [0.0, 0.0, 0.0]
NUM_SAMPLED_POINTS: 512
DEPTH_NORMALIZER: 70.0
XYZ_UP_LAYER: [128, 128]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.0
USE_BN: False
SA_CONFIG:
NPOINTS: [128, 32, -1]
RADIUS: [0.2, 0.4, 100]
NSAMPLE: [16, 16, 16]
MLPS: [[128, 128, 128],
[128, 128, 256],
[256, 256, 512]]
NMS_CONFIG:
TRAIN:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 512
NMS_THRESH: 0.8
TEST:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.85
TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5
SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: cls
CLS_FG_THRESH: 0.6
CLS_BG_THRESH: 0.45
CLS_BG_THRESH_LO: 0.1
HARD_BG_RATIO: 0.8
REG_FG_THRESH: 0.55
LOSS_CONFIG:
CLS_LOSS: BinaryCrossEntropy
REG_LOSS: smooth-l1
CORNER_LOSS_REGULARIZATION: True
LOSS_WEIGHTS: {
'rcnn_cls_weight': 1.0,
'rcnn_reg_weight': 1.0,
'rcnn_corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
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: 10
tools/cfgs/custom_models/pv_rcnn.yaml 0 → 100644
View file @ e9b06b36
CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: ../dataset_configs/custom_dataset.yaml
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: False
DB_INFO_PATH:
- custom_dbinfos_train.pkl
PREPARE: {
filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'],
filter_by_difficulty: [-1],
}
SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10']
NUM_POINT_FEATURES: 4
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: False
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.95, 1.05]
MODEL:
NAME: PVRCNN
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelBackBone8x
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]
DENSE_HEAD:
NAME: AnchorHeadSingle
CLASS_AGNOSTIC: False
USE_DIRECTION_CLASSIFIER: True
DIR_OFFSET: 0.78539
DIR_LIMIT_OFFSET: 0.0
NUM_DIR_BINS: 2
ANCHOR_GENERATOR_CONFIG: [
{
'class_name': 'Car',
'anchor_sizes': [[3.9, 1.6, 1.56]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [-0.1],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.6,
'unmatched_threshold': 0.45
},
{
'class_name': 'Pedestrian',
'anchor_sizes': [[0.05, 0.03, 0.1]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [-0.03],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
},
{
'class_name': 'Cyclist',
'anchor_sizes': [[0.1, 0.03, 0.1]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [-0.03],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
]
TARGET_ASSIGNER_CONFIG:
NAME: AxisAlignedTargetAssigner
POS_FRACTION: -1.0
SAMPLE_SIZE: 512
NORM_BY_NUM_EXAMPLES: False
MATCH_HEIGHT: False
BOX_CODER: ResidualCoder
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'dir_weight': 0.2,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
PFE:
NAME: VoxelSetAbstraction
POINT_SOURCE: raw_points
NUM_KEYPOINTS: 2048
NUM_OUTPUT_FEATURES: 128
SAMPLE_METHOD: FPS
FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points']
SA_LAYER:
raw_points:
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv1:
DOWNSAMPLE_FACTOR: 1
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv2:
DOWNSAMPLE_FACTOR: 2
MLPS: [[32, 32], [32, 32]]
POOL_RADIUS: [0.8, 1.2]
NSAMPLE: [16, 32]
x_conv3:
DOWNSAMPLE_FACTOR: 4
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [1.2, 2.4]
NSAMPLE: [16, 32]
x_conv4:
DOWNSAMPLE_FACTOR: 8
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [2.4, 4.8]
NSAMPLE: [16, 32]
POINT_HEAD:
NAME: PointHeadSimple
CLS_FC: [256, 256]
CLASS_AGNOSTIC: True
USE_POINT_FEATURES_BEFORE_FUSION: True
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
LOSS_CONFIG:
LOSS_REG: smooth-l1
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
}
ROI_HEAD:
NAME: PVRCNNHead
CLASS_AGNOSTIC: True
SHARED_FC: [256, 256]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.3
NMS_CONFIG:
TRAIN:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 512
NMS_THRESH: 0.8
TEST:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 1024
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.7
ROI_GRID_POOL:
GRID_SIZE: 6
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [0.8, 1.6]
NSAMPLE: [16, 16]
POOL_METHOD: max_pool
TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5
SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: roi_iou
CLS_FG_THRESH: 0.75
CLS_BG_THRESH: 0.25
CLS_BG_THRESH_LO: 0.1
HARD_BG_RATIO: 0.8
REG_FG_THRESH: 0.55
LOSS_CONFIG:
CLS_LOSS: BinaryCrossEntropy
REG_LOSS: smooth-l1
CORNER_LOSS_REGULARIZATION: True
LOSS_WEIGHTS: {
'rcnn_cls_weight': 1.0,
'rcnn_reg_weight': 1.0,
'rcnn_corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
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: 10
tools/cfgs/dataset_configs/custom_dataset.yaml 0 → 100644
View file @ e9b06b36
DATASET: 'CustomDataset'
DATA_PATH: '../data/custom'
# If this config file is modified then pcdet/models/detectors/detector3d_template.py:
# Detector3DTemplate::build_networks:model_info_dict needs to be modified.
POINT_CLOUD_RANGE: [-70.4, -40, -3, 70.4, 40, 1] # x=[-70.4, 70.4], y=[-40,40], z=[-3,1]
DATA_SPLIT: {
'train': train,
'test': val
}
INFO_PATH: {
'train': [custom_infos_train.pkl],
'test': [custom_infos_val.pkl],
}
GET_ITEM_LIST: ["points"]
FOV_POINTS_ONLY: True
POINT_FEATURE_ENCODING: {
encoding_type: absolute_coordinates_encoding,
used_feature_list: ['x', 'y', 'z', 'intensity'],
src_feature_list: ['x', 'y', 'z', 'intensity'],
}
# Same to pv_rcnn[DATA_AUGMENTOR]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: False
DB_INFO_PATH:
- custom_dbinfos_train.pkl
PREPARE: {
filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'],
filter_by_difficulty: [-1],
}
SAMPLE_GROUPS: ['Car:20','Pedestrian:15', 'Cyclist:15']
NUM_POINT_FEATURES: 4
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']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.95, 1.05]
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': False
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.05, 0.05, 0.1]
MAX_POINTS_PER_VOXEL: 5
MAX_NUMBER_OF_VOXELS: {
'train': 16000,
'test': 40000
}
\ No newline at end of file
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