Files
OpenGait/configs/lidargaitv2/lidargaitv2_freegait.yaml
T
2025-06-11 14:43:19 +08:00

111 lines
2.8 KiB
YAML

data_cfg:
dataset_name: FreeGait
dataset_root: your_path
dataset_partition: ./datasets/FreeGait/FreeGait.json
num_workers: 1
data_in_use: [false, false, true, false, false]
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: FreeGait
evaluator_cfg:
enable_float16: false #true #false #true
restore_ckpt_strict: true
restore_hint: 80000
save_name: lidargaitv2
eval_func: evaluate_FreeGait
sampler:
batch_shuffle: false
points_in_use: # For point-based gait recognition using point clouds
pointcloud_index: 0
points_num: 256 #312 #256 #2048
batch_size: 8
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 #16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: LidarGaitPlusPlus
pool: PPP_HAP
sampling: knn
channel: 32
npoints: [256, 192, 128]
nsample: 16
scale_aware: true
normalize_dp: true
SeparateFCs:
in_channels: 256
out_channels: 256
parts_num: 31
SeparateBNNecks:
class_num: 500
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: 80000
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: 80000
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: 256
transform:
- type: PointCloudsTransform
xyz_only: true
scale_aware: true
scale_prob: 0.2
flip_prob: 0.1