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iassd_waymo.yaml
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iassd_waymo.yaml
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batch_size: 4 #on 4 gpus, total bs = 16
epochs: 30
train_dataset:
type: WaymoPCDataset
dataset_root: datasets/waymo
class_names: [ "Vehicle", "Pedestrian", "Cyclist" ]
sampled_interval: 5
transforms:
- type: SamplingDatabase
min_num_points_in_box_per_class:
Vehicle: 5
Pedestrian: 5
Cyclist: 5
max_num_samples_per_class:
Vehicle: 15
Pedestrian: 10
Cyclist: 10
ignored_difficulty: [ -1 ]
database_anno_path: datasets/waymo/waymo_train_gt_database/waymo_train_gt_database_infos.pkl
database_root: datasets/waymo
class_names: [ "Vehicle", "Pedestrian", "Cyclist" ]
- type: RandomVerticalFlip
- type: RandomHorizontalFlip
- type: GlobalRotate
min_rot: -0.78539816
max_rot: 0.78539816
- type: GlobalScale
min_scale: 0.95
max_scale: 1.05
- type: FilterBBoxOutsideRange
point_cloud_range: &point_cloud_range [-75.2, -75.2, -2, 75.2, 75.2, 4]
- type: FilterPointOutsideRange
point_cloud_range: *point_cloud_range
- type: ShufflePoint
- type: SamplePointByVoxels
voxel_size: [0.1, 0.1, 0.15]
max_points_per_voxel: 5
max_num_of_voxels: 80000
num_points: 65536
point_cloud_range: *point_cloud_range
- type: ConvertBoxFormat
mode: train
val_dataset:
type: WaymoPCDataset
dataset_root: datasets/waymo
class_names: [ "Vehicle", "Pedestrian", "Cyclist" ]
sampled_interval: 1
transforms:
- type: FilterPointOutsideRange
point_cloud_range: *point_cloud_range
- type: SamplePointByVoxels
voxel_size: [0.1, 0.1, 0.15]
max_points_per_voxel: 5
max_num_of_voxels: 90000
num_points: 65536
point_cloud_range: *point_cloud_range
mode: val
model:
type: IASSD
backbone:
type: IASSD_Backbone
npoint_list: [16384, 4096, 2048, 1024, null, 1024]
sample_method_list: &sample_method_list ["D-FPS", "D-FPS", "ctr_aware", "ctr_aware", null, null]
radius_list: [[0.2,0.8], [0.8,1.6], [1.6,4.8], [], [], [4.8, 6.4]]
nsample_list: [[16,32], [16,32], [16,32], [], [], [16, 32]]
mlps: [[[16,16,32], [32,32,64]],
[[64,64,128], [64,96,128]],
[[128,128,256], [128,256,256]],
[],
[128],
[[256,256,512], [256,512,1024]]]
layer_types: ["SA_Layer", "SA_Layer", "SA_Layer", "SA_Layer", "Vote_Layer", "SA_Layer"]
dilated_group: [False, False, False, False, False, False]
aggregation_mlps: [[64], [128], [256], [256], [], [512]]
confidence_mlps: [[], [128], [256], [], [], []]
layer_input: [0, 1, 2, 3, 4, 3]
ctr_index: [-1, -1, -1, -1, -1, 5]
max_translate_range: [3., 3., 2.]
input_channel: 5
num_classes: 3
head:
type: IASSD_Head
input_channel: 512 #last aggregation mlp
cls_fc: [256, 256]
reg_fc: [256, 256]
num_classes: 3
target_config:
gt_extra_width: [0.2, 0.2, 0.2]
extra_width: [1.0, 1.0, 1.0]
box_coder_config: {
'angle_bin_num': 12,
'use_mean_size': True,
'mean_size': [
[4.7, 2.1, 1.7],
[0.91, 0.86, 1.73],
[1.78, 0.84, 1.78]
]
}
loss_config:
loss_cls: WeightedClassificationLoss
loss_reg: WeightedSmoothL1Loss
loss_ins: WeightedClassificationLoss
sample_method_list: *sample_method_list
corner_loss_regularization: True
centerness_regularization: True
centerness_regularization_sa: True
loss_weight: {
'ins_aware_weight': [0, 1.0, 1.0],
'vote_weight': 1.0,
'point_cls_weight': 1.0,
'point_box_weight': 1.0,
'corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'dir_weight': 0.2
}
post_process_cfg:
score_thresh: 0.1
nms_config:
nms_thresh: 0.1
nms_pre_maxsize: 4096
nms_post_maxsize: 500
optimizer:
type: AdamWOnecycle
clip_grad_by_norm: 10.0
learning_rate: 0.01
beta1: 0.9
beta2: 0.99
weight_decay: 0.01
lr_scheduler:
type: OneCycle
total_step: 59280 #change to your correspondent total iters
lr_max: 0.01
moms: [0.95, 0.85]
div_factor: 10
pct_start: 0.4