Noah
110 lines
2.9 KiB
YAML
110 lines
2.9 KiB
YAML
data_cfg:
|
|
dataset_name: SUSTech1K
|
|
dataset_root: your_path # download from https://lidargait.github.io/ if you don't have dataset
|
|
dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json
|
|
num_workers: 4
|
|
data_in_use: [true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
|
|
remove_no_gallery: false # Remove probe if no gallery for it
|
|
test_dataset_name: SUSTech1K
|
|
|
|
evaluator_cfg:
|
|
enable_float16: true
|
|
restore_ckpt_strict: true
|
|
restore_hint: 40000
|
|
save_name: lidargaitv2
|
|
eval_func: evaluate_indoor_dataset
|
|
sampler:
|
|
batch_shuffle: false
|
|
points_in_use: # For point-based gait recognition using point clouds
|
|
pointcloud_index: 0
|
|
points_num: 1024 #2048
|
|
batch_size: 4
|
|
frames_num_fixed: 10 # fixed frames number for training
|
|
frames_skip_num: 0
|
|
#sample_type: fixed_ordered
|
|
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
|
|
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
|
|
metric: euc # cos
|
|
transform:
|
|
- type: PointCloudsTransform
|
|
xyz_only: true
|
|
scale_aware: true
|
|
|
|
loss_cfg:
|
|
- loss_term_weight: 1.0
|
|
margin: 0.2
|
|
type: TripletLoss
|
|
log_prefix: triplet
|
|
lazy: False
|
|
- loss_term_weight: 0.1
|
|
scale: 31
|
|
type: CrossEntropyLoss
|
|
log_prefix: softmax
|
|
log_accuracy: true
|
|
|
|
|
|
model_cfg:
|
|
model: LidarGaitPlusPlus
|
|
pool: PPP_HAP
|
|
sampling: knn
|
|
channel: 16
|
|
npoints: [512, 256, 128]
|
|
nsample: 32
|
|
scale_aware: true
|
|
normalize_dp: true
|
|
SeparateFCs:
|
|
in_channels: 256
|
|
out_channels: 256
|
|
parts_num: 31
|
|
SeparateBNNecks:
|
|
class_num: 250
|
|
in_channels: 256
|
|
parts_num: 31
|
|
scale:
|
|
- 1
|
|
- 2
|
|
- 4
|
|
- 8
|
|
- 16
|
|
|
|
|
|
optimizer_cfg:
|
|
lr: 0.1
|
|
momentum: 0.9
|
|
solver: SGD
|
|
weight_decay: 0.0005
|
|
|
|
scheduler_cfg:
|
|
T_max: 40000
|
|
eta_min: 0.0001
|
|
scheduler: CosineAnnealingLR
|
|
|
|
|
|
trainer_cfg:
|
|
enable_float16: false #true # half_percesion float for memory reduction and speedup
|
|
fix_BN: false
|
|
with_test: true
|
|
log_iter: 100
|
|
restore_ckpt_strict: true
|
|
restore_hint: 0
|
|
save_iter: 5000
|
|
save_name: lidargaitv2
|
|
sync_BN: true
|
|
total_iter: 40000
|
|
sampler:
|
|
batch_shuffle: true
|
|
batch_size:
|
|
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
|
|
- 4 # batch_size[1] indicates Samples sequqnce for each Identity
|
|
frames_num_fixed: 10 # fixed frames number for training
|
|
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
|
|
type: TripletSampler
|
|
points_in_use:
|
|
pointcloud_index: 0
|
|
points_num: 1024
|
|
transform:
|
|
- type: PointCloudsTransform
|
|
xyz_only: true
|
|
scale_aware: true
|
|
scale_prob: 1
|
|
flip_prob: 0.15 |