feat: archive best scoliosis checkpoints

2026-03-11 10:23:38 +08:00
parent a0150c791f
commit fbc0696dc4
10 changed files with 489 additions and 4 deletions
@@ -0,0 +1,76 @@
+# Scoliosis ScoNet-MT-ske Artifacts
+
+This directory archives the two retained best checkpoints from the practical
+`1:1:2` skeleton-map training path:
+
+- best by macro-F1
+- best by accuracy
+
+Model recipe:
+
+- split: `Scoliosis1K_112.json`
+- representation: `body-only` skeleton heatmap
+- losses: plain CE + triplet
+- finetune optimizer: `AdamW`
+- scheduler: cosine annealing
+
+Files:
+
+- `*_scalar_test_f1.pt`: retained best checkpoint by macro-F1
+- `*_scalar_test_accuracy.pt`: retained best checkpoint by accuracy
+- `train_config.yaml`: training config for the finetune run
+- `eval_best_f1_27000.yaml`: standalone eval config for the retained best-F1 checkpoint
+- `eval_best_accuracy_64000.yaml`: standalone eval config for the retained best-accuracy checkpoint
+
+## Reproduce eval
+
+Run from the repo root.
+
+Best-F1 checkpoint:
+
+```bash
+CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
+uv run python -m torch.distributed.run --nproc_per_node=1 \
+  opengait/main.py \
+  --cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_f1_27000.yaml \
+  --phase test
+```
+
+Best-accuracy checkpoint:
+
+```bash
+CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
+uv run python -m torch.distributed.run --nproc_per_node=1 \
+  opengait/main.py \
+  --cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_accuracy_64000.yaml \
+  --phase test
+```
+
+Paths to change on another machine:
+
+- `data_cfg.dataset_root`
+  - point this to your local body-only Scoliosis1K skeleton dataset root
+- `data_cfg.dataset_partition`
+  - change this only if your repo or partition file lives elsewhere
+- `evaluator_cfg.restore_hint`
+  - change this only if you move the `artifact/` folder or rename the checkpoint files
+- `evaluator_cfg.output_root`
+  - change this if you want eval outputs somewhere else
+
+## Verified results
+
+These commands were rerun from the copied `artifact/` configs on `2026-03-11`.
+
+Best-F1 checkpoint (`27000`):
+
+- Accuracy: `92.38%`
+- Macro Precision: `90.30%`
+- Macro Recall: `87.39%`
+- Macro F1: `88.70%`
+
+Best-accuracy checkpoint (`64000`):
+
+- Accuracy: `94.25%`
+- Macro Precision: `83.24%`
+- Macro Recall: `95.76%`
+- Macro F1: `87.63%`
@@ -0,0 +1,60 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
+  data_in_use:
+    - true
+    - false
+  num_workers: 1
+  remove_no_gallery: false
+  test_dataset_name: Scoliosis1K
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-64000-score-0.9425-scalar_test_accuracy.pt
+  save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_64000_1gpu
+  output_root: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval-output
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 1
+    sample_type: all_ordered
+    type: InferenceSampler
+    frames_all_limit: 720
+  metric: euc
+  transform:
+    - type: BaseSilCuttingTransform
+
+model_cfg:
+  model: ScoNet
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    in_channel: 2
+    channels:
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  SeparateFCs:
+    in_channels: 512
+    out_channels: 256
+    parts_num: 16
+  SeparateBNNecks:
+    class_num: 3
+    in_channels: 256
+    parts_num: 16
+  bin_num:
+    - 16
@@ -0,0 +1,60 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
+  data_in_use:
+    - true
+    - false
+  num_workers: 1
+  remove_no_gallery: false
+  test_dataset_name: Scoliosis1K
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-27000-score-0.8870-scalar_test_f1.pt
+  save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_27000_1gpu
+  output_root: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval-output
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 1
+    sample_type: all_ordered
+    type: InferenceSampler
+    frames_all_limit: 720
+  metric: euc
+  transform:
+    - type: BaseSilCuttingTransform
+
+model_cfg:
+  model: ScoNet
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    in_channel: 2
+    channels:
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  SeparateFCs:
+    in_channels: 512
+    out_channels: 256
+    parts_num: 16
+  SeparateBNNecks:
+    class_num: 3
+    in_channels: 256
+    parts_num: 16
+  bin_num:
+    - 16
@@ -0,0 +1,115 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
+  data_in_use:
+    - true
+    - false
+  num_workers: 1
+  remove_no_gallery: false
+  test_dataset_name: Scoliosis1K
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 80000
+  save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k
+  output_root: /mnt/hddl/data/OpenGait-output
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 1
+    sample_type: all_ordered
+    type: InferenceSampler
+    frames_all_limit: 720
+  metric: euc
+  transform:
+    - type: BaseSilCuttingTransform
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+  - loss_term_weight: 1.0
+    scale: 16
+    type: CrossEntropyLoss
+    log_prefix: softmax
+    log_accuracy: true
+
+model_cfg:
+  model: ScoNet
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    in_channel: 2
+    channels:
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  SeparateFCs:
+    in_channels: 512
+    out_channels: 256
+    parts_num: 16
+  SeparateBNNecks:
+    class_num: 3
+    in_channels: 256
+    parts_num: 16
+  bin_num:
+    - 16
+
+optimizer_cfg:
+  lr: 0.0005
+  solver: AdamW
+  weight_decay: 0.0005
+
+scheduler_cfg:
+  scheduler: CosineAnnealingLR
+  T_max: 60000
+  eta_min: 0.00001
+
+trainer_cfg:
+  enable_float16: true
+  fix_BN: false
+  with_test: true
+  log_iter: 100
+  restore_ckpt_strict: true
+  optimizer_reset: false
+  scheduler_reset: false
+  restore_hint: /mnt/hddl/data/OpenGait-output/Scoliosis1K/ScoNet/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k/checkpoints/latest.pt
+  output_root: /mnt/hddl/data/OpenGait-output
+  auto_resume_latest: true
+  resume_every_iter: 500
+  resume_keep: 6
+  best_ckpt_cfg:
+    keep_n: 3
+    metric_names:
+      - scalar/test_f1/
+      - scalar/test_accuracy/
+  eval_iter: 1000
+  save_iter: 500
+  save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k
+  sync_BN: false
+  total_iter: 80000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 8
+      - 8
+    frames_num_fixed: 30
+    sample_type: fixed_unordered
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform