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gd_mae.yaml
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gd_mae.yaml
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CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
POINT_CLOUD_RANGE: [-74.88, -74.88, -2, 74.88, 74.88, 4.0]
DATA_SPLIT: {
'train': train,
'test': val
}
SAMPLED_INTERVAL: {
'train': 1,
'test': 1
}
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
BACKEND:
NAME: HardDiskBackend
USE_ROAD_PLANE: False
DB_INFO_PATH:
- waymo_processed_data_waymo_dbinfos_train_sampled_1.pkl
USE_SHARED_MEMORY: False # set it to True to speed up (it costs about 15GB shared memory)
DB_DATA_PATH:
- waymo_processed_data_gt_database_train_sampled_1_global.npy
PREPARE: {
filter_by_min_points: ['Vehicle:5', 'Pedestrian:10', 'Cyclist:10'],
filter_by_difficulty: [-1],
}
SAMPLE_GROUPS: ['Vehicle:15', 'Pedestrian:10', 'Cyclist:10']
NUM_POINT_FEATURES: 5
REMOVE_POINTS: True
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
PROBABILITY: 0.5
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
PROBABILITY: 1.0
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
PROBABILITY: 1.0
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: calculate_grid_size
VOXEL_SIZE: [0.32, 0.32, 6.0]
MODEL:
NAME: CenterPoint
VFE:
NAME: DynVFE
TYPE: mean
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_CLUSTER_XYZ: True
MLPS: [[64, 128]]
BACKBONE_3D:
NAME: SPTBackbone
SST_BLOCK_LIST:
- NAME: sst_block_x1
PREPROCESS:
WINDOW_SHAPE: [8, 8, 1]
DROP_INFO: {
'train': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
},
'test': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
}
}
SHUFFLE_VOXELS: False
POS_TEMPERATURE: 1000
NORMALIZE_POS: False
ENCODER:
NUM_BLOCKS: 2
STRIDE: 1
D_MODEL: 128
NHEAD: 8
DIM_FEEDFORWARD: 256
DROPOUT: 0.0
ACTIVATION: "gelu"
LAYER_CFG: {
'cosine': True,
'tau_min': 0.01
}
- NAME: sst_block_x2
PREPROCESS:
WINDOW_SHAPE: [8, 8, 1]
DROP_INFO: {
'train': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
},
'test': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
}
}
SHUFFLE_VOXELS: False
POS_TEMPERATURE: 1000
NORMALIZE_POS: False
ENCODER:
NUM_BLOCKS: 2
STRIDE: 2
D_MODEL: 256
NHEAD: 8
DIM_FEEDFORWARD: 512
DROPOUT: 0.0
ACTIVATION: "gelu"
LAYER_CFG: {
'cosine': True,
'tau_min': 0.01
}
- NAME: sst_block_x4
PREPROCESS:
WINDOW_SHAPE: [8, 8, 1]
DROP_INFO: {
'train': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
},
'test': {
'0': {'max_tokens': 16, 'drop_range': [0, 16]},
'1': {'max_tokens': 32, 'drop_range': [16, 32]},
'2': {'max_tokens': 64, 'drop_range': [32, 100000]}
}
}
SHUFFLE_VOXELS: False
POS_TEMPERATURE: 1000
NORMALIZE_POS: False
ENCODER:
NUM_BLOCKS: 2
STRIDE: 2
D_MODEL: 256
NHEAD: 8
DIM_FEEDFORWARD: 512
DROPOUT: 0.0
ACTIVATION: "gelu"
LAYER_CFG: {
'cosine': True,
'tau_min': 0.01
}
FEATURES_SOURCE: ['x_conv1', 'x_conv2', 'x_conv3']
FUSE_LAYER:
x_conv1:
UPSAMPLE_STRIDE: 1
NUM_FILTER: 128
NUM_UPSAMPLE_FILTER: 128
x_conv2:
UPSAMPLE_STRIDE: 2
NUM_FILTER: 256
NUM_UPSAMPLE_FILTER: 128
x_conv3:
UPSAMPLE_STRIDE: 4
NUM_FILTER: 256
NUM_UPSAMPLE_FILTER: 128
BACKBONE_2D:
NAME: SSTBEVBackbone
NUM_FILTER: 128
CONV_KWARGS: [
{'out_channels': 128, 'kernel_size': 3, 'dilation': 1, 'padding': 1, 'stride': 1},
{'out_channels': 128, 'kernel_size': 3, 'dilation': 1, 'padding': 1, 'stride': 1},
{'out_channels': 128, 'kernel_size': 3, 'dilation': 2, 'padding': 2, 'stride': 1},
{'out_channels': 128, 'kernel_size': 3, 'dilation': 1, 'padding': 1, 'stride': 1},
]
CONV_SHORTCUT: [0, 1, 2]
DENSE_HEAD:
NAME: CenterHead
CLASS_AGNOSTIC: False
CLASS_NAMES_EACH_HEAD: [
['Vehicle', 'Pedestrian', 'Cyclist']
]
SHARED_CONV_CHANNEL: 64
USE_BIAS_BEFORE_NORM: True
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'center_z', 'dim', 'rot']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'center_z': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 1
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
SCORE_THRESH: 0.1
POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
EVAL_METRIC: waymo_custom
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 8
NUM_EPOCHS: 30
OPTIMIZER: adam_onecycle
LR: 0.003
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