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pointpillars_waymo.yml
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dataset:
name: Waymo
dataset_path: # path/to/your/dataset
cache_dir: ./logs/cache
steps_per_epoch_train: 4000
model:
name: PointPillars
ckpt_path: # path/to/your/checkpoint
batcher: "ignore"
point_cloud_range: [-74.88, -74.88, -2, 74.88, 74.88, 4]
classes: ['VEHICLE', 'PEDESTRIAN', 'CYCLIST']
loss:
focal:
gamma: 2.0
alpha: 0.25
loss_weight: 1.0
smooth_l1:
beta: 0.11
loss_weight: 2.0
cross_entropy:
loss_weight: 0.2
voxelize:
max_num_points: 20
voxel_size: &vsize
[0.32, 0.32, 6]
max_voxels: [32000, 32000]
voxel_encoder:
in_channels: 4
feat_channels: [64]
voxel_size: *vsize
scatter:
in_channels: 64
output_shape: [468, 468]
backbone:
in_channels: 64
out_channels: [64, 128, 256]
layer_nums: [3, 5, 5]
layer_strides: [1, 2, 2]
neck:
in_channels: [64, 128, 256]
out_channels: [128, 128, 128]
upsample_strides: [1, 2, 4]
use_conv_for_no_stride: false
head:
in_channels: 384
feat_channels: 384
nms_pre: 4096
score_thr: 0.1
ranges: [
[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345],
[-74.88, -74.88, -0.1188, 74.88, 74.88, -0.1188],
[-74.88, -74.88, 0, 74.88, 74.88, 0],
]
sizes: [
[2.08, 4.73, 1.77], # VEHICLE
[0.84, 1.81, 1.77], # CYCLIST
[0.84, 0.91, 1.74] # PEDESTRIAN
]
dir_offset: 0.7854
rotations: [0, 1.57]
iou_thr: [[0.4, 0.55], [0.3, 0.5], [0.3, 0.5]]
augment:
PointShuffle: True
ObjectRangeFilter:
point_cloud_range: [-74.88, -74.88, -2, 74.88, 74.88, 4]
ObjectSample:
min_points_dict:
VEHICLE: 5
PEDESTRIAN: 10
CYCLIST: 10
sample_dict:
VEHICLE: 15
PEDESTRIAN: 10
CYCLIST: 10
pipeline:
name: ObjectDetection
test_compute_metric: true
batch_size: 6
val_batch_size: 6
test_batch_size: 1
save_ckpt_freq: 5
max_epoch: 200
main_log_dir: ./logs
train_sum_dir: train_log
grad_clip_norm: 2
optimizer:
lr: 0.001
betas: [0.95, 0.99]
weight_decay: 0.01
# evaluation properties
overlaps: [0.5, 0.5, 0.5]
difficulties: [0, 1, 2]
summary:
record_for: []
max_pts:
use_reference: false
max_outputs: 1