feat: add drf author checkpoint compatibility bundle

fix(demo): pace gait windows before buffering
Make the OpenGait-studio demo drop unpaced frames before they grow the silhouette window. Separate source-frame gap tracking from paced-frame stride tracking so runtime scheduling matches the documented demo-window-and-stride behavior. Add regressions for paced window growth and schedule-frame stride semantics.
2026-03-14 17:12:27 +08:00 · 2026-03-14 11:31:44 +08:00
22 changed files with 1275 additions and 22 deletions
@@ -0,0 +1,62 @@
 # DRF Author Checkpoint Artifact
 This artifact bundle archives the author-provided DRF checkpoint together with the compatibility configs that make it usable in this repo.
 Files:
 - `DRF_118_unordered_iter2w_lr0.001_8830-08000.pt`
 - `author_original_config.yaml`
 - `eval_112_1gpu.yaml`
 - `eval_118_defaultroot_1gpu.yaml`
 - `eval_118_splitroot_1gpu.yaml`
 - `eval_118_aligned_1gpu.yaml`
 - `eval_118_paper_1gpu.yaml`
 Best recovered path:
 - config: `configs/drf/drf_author_eval_118_aligned_1gpu.yaml`
 - result:
  - `80.24 Acc / 76.73 Prec / 76.40 Rec / 76.56 F1`
 ## Run Commands
 Recommended eval:
 ```bash
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29693 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_aligned_1gpu.yaml \
  --phase test
 ```
 Other compatibility checks:
 ```bash
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29695 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_112_1gpu.yaml \
  --phase test
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29696 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_splitroot_1gpu.yaml \
  --phase test
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29697 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_paper_1gpu.yaml \
  --phase test
 ```
 ## Paths To Change On Another Machine
 If you move this artifact bundle or use a different dataset location, update:
 - `data_cfg.dataset_root`
 - `data_cfg.dataset_partition`
 - `evaluator_cfg.restore_hint`
 In this repo, the checkpoint is archived at:
 - `artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt`
@@ -0,0 +1,106 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /home/kj666/project_temp/opengait/datasets/Scoliosis1K/Scoliosis1K-drf-pkl
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
  num_workers: 1
  data_in_use: [True, False] # heatmap, sil
  remove_no_gallery: false # Remove probe if no gallery for it
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  eval_func: evaluate_scoliosis
  sampler:
    batch_shuffle: false
    batch_size: 1
    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: 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: DRF
  backbone_cfg:
    type: ResNet9
    block: BasicBlock
    in_channel: 2
    channels: # Layers configuration for automatically model construction
      - 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: 0.01
  momentum: 0.9
  solver: SGD
  weight_decay: 0.0005
 scheduler_cfg:
  gamma: 0.1
  milestones: # Learning Rate Reduction at each milestones
    - 10000
    - 14000
    - 18000
  scheduler: MultiStepLR
 trainer_cfg:
  find_unused_parameters: True
  enable_float16: 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: 2000
  save_name: DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  total_iter: 20000
  sampler:
    batch_shuffle: true
    batch_size:
      - 8 # TripletSampler, batch_size[0] indicates Number of Identity
      - 8 #                 batch_size[1] indicates Samples sequqnce for each Identity
    frames_num_fixed: 30 # fixed frames number for training
    frames_skip_num: 2
    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
  transform:
    - type: BaseSilCuttingTransform
@@ -0,0 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_112_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_aligned_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-paper
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_paper_1gpu
  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: BaseSilTransform
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_splitroot_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_112_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_aligned_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-paper
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_paper_1gpu
  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: BaseSilTransform
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,65 @@
 data_cfg:
  dataset_name: Scoliosis1K
  data_type: heatmap
  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118
  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
  num_workers: 1
  data_in_use: [true, true]
  remove_no_gallery: false
  test_dataset_name: Scoliosis1K
 evaluator_cfg:
  enable_float16: true
  restore_ckpt_strict: true
  restore_hint: ./artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt
  save_name: DRF_author_eval_118_splitroot_1gpu
  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
    - type: NoOperation
 model_cfg:
  model: DRF
  label_order:
    - negative
    - positive
    - neutral
  num_pairs: 8
  num_metrics: 3
  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 +1,192 @@
 # DRF Author Checkpoint Compatibility Note
 This note records what happened when evaluating the author-provided DRF bundle in this repo:
 - checkpoint: `artifact/scoliosis_drf_author_118_compat/DRF_118_unordered_iter2w_lr0.001_8830-08000.pt`
 - config: `ckpt/drf_author/drf_scoliosis1k_20000.yaml`
 The short version:
 - the weight file is real and structurally usable
 - the provided YAML is not a reliable source of truth
 - the main problem was integration-contract mismatch, not a broken checkpoint
 ## What Was Wrong
 The author bundle was internally inconsistent in several ways.
 ### 1. Split mismatch
 The DRF paper says the main experiment uses `1:1:8`, i.e. the `118` split.
 But the provided YAML pointed to:
 - `./datasets/Scoliosis1K/Scoliosis1K_112.json`
 while the checkpoint filename itself says:
 - `DRF_118_...`
 So the bundle already disagreed with itself.
 ### 2. Class-order mismatch
 The biggest hidden bug was class ordering.
 The current repo evaluator assumes:
 - `negative = 0`
 - `neutral = 1`
 - `positive = 2`
 But the author stub in `research/drf.py` uses:
 - `negative = 0`
 - `positive = 1`
 - `neutral = 2`
 That means an otherwise good checkpoint can look very bad if logits are interpreted in the wrong class order.
 ### 3. Legacy module-name mismatch
 The author checkpoint stores PGA weights under:
 - `attention_layer.*`
 The current repo uses:
 - `PGA.*`
 This is a small compatibility issue, but it must be remapped before loading.
 ### 4. Preprocessing/runtime-contract mismatch
 The author checkpoint does not line up with the stale YAML’s full runtime contract.
 Most importantly, it did **not** work well with the more paper-literal local export:
 - `Scoliosis1K-drf-pkl-118-paper`
 It worked much better with the more OpenGait-like aligned export:
 - `Scoliosis1K-drf-pkl-118-aligned`
 That strongly suggests the checkpoint was trained against a preprocessing/runtime path closer to the aligned OpenGait integration than to the later local “paper-literal” summed-heatmap ablation.
 ## What Was Added In-Tree
 The current repo now has a small compatibility layer in:
 - `opengait/modeling/models/drf.py`
 It does two things:
 - remaps legacy keys `attention_layer.* -> PGA.*`
 - supports configurable `model_cfg.label_order`
 The model also canonicalizes inference logits back into the repo’s evaluator order, so author checkpoints can be evaluated without modifying the evaluator itself.
 ## Tested Compatibility Results
 ### Best usable author-checkpoint path
 Config:
 - `configs/drf/drf_author_eval_118_aligned_1gpu.yaml`
 Dataset/runtime:
 - dataset root: `Scoliosis1K-drf-pkl-118-aligned`
 - partition: `Scoliosis1K_118.json`
 - transform: `BaseSilCuttingTransform`
 - label order:
  - `negative`
  - `positive`
  - `neutral`
 Result:
 - `80.24 Acc / 76.73 Prec / 76.40 Rec / 76.56 F1`
 This is the strongest recovered path so far.
 ### Other tested paths
 `configs/drf/drf_author_eval_118_splitroot_1gpu.yaml`
 - dataset root: `Scoliosis1K-drf-pkl-118`
 - result:
  - `77.17 Acc / 73.61 Prec / 72.59 Rec / 72.98 F1`
 `configs/drf/drf_author_eval_112_1gpu.yaml`
 - dataset root: `Scoliosis1K-drf-pkl`
 - partition: `Scoliosis1K_112.json`
 - result:
  - `85.19 Acc / 57.98 Prec / 56.65 Rec / 57.30 F1`
 `configs/drf/drf_author_eval_118_paper_1gpu.yaml`
 - dataset root: `Scoliosis1K-drf-pkl-118-paper`
 - transform: `BaseSilTransform`
 - result:
  - `27.24 Acc / 9.08 Prec / 33.33 Rec / 14.27 F1`
 ## Interpretation
 What these results mean:
 - the checkpoint is not garbage
 - the original “very bad” local eval was mostly a compatibility failure
 - the largest single hidden bug was the class-order mismatch
 - the author checkpoint is also sensitive to which local DRF dataset root is used
 What they do **not** mean:
 - we have perfectly reconstructed the author’s original training path
 - the provided YAML is trustworthy as-is
 - the paper’s full DRF claim is fully reproduced here
 The strongest recovered result:
 - `80.24 / 76.73 / 76.40 / 76.56`
 This is close to the paper’s reported `ScoNet-MT^ske` F1 and much better than our earlier broken compat evals, but it is still below the paper’s DRF headline result:
 - paper DRF: `86.0 Acc / 84.1 Prec / 79.2 Rec / 80.8 F1`
 ## Practical Recommendation
 If someone wants to use the author checkpoint in this repo today, the recommended path is:
 1. use `configs/drf/drf_author_eval_118_aligned_1gpu.yaml`
 2. keep the author label order:
   - `negative, positive, neutral`
 3. keep the legacy `attention_layer -> PGA` remap in the model
 4. do **not** assume the stale `112` YAML is the correct training/eval contract
 If someone wants to push this further, the highest-value next step is:
 - finetune from the author checkpoint on the aligned `118` path instead of starting DRF from scratch
 ## How To Run
 Recommended eval:
 ```bash
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29693 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_aligned_1gpu.yaml \
  --phase test
 ```
 Other compatibility checks:
 ```bash
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29695 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_112_1gpu.yaml \
  --phase test
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29696 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_splitroot_1gpu.yaml \
  --phase test
 CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
 uv run torchrun --nproc_per_node=1 --master_port=29697 \
  opengait/main.py \
  --cfgs ./configs/drf/drf_author_eval_118_paper_1gpu.yaml \
  --phase test
 ```
 If someone wants to reproduce this on another machine, the usual paths to change are:
 - `data_cfg.dataset_root`
 - `data_cfg.dataset_partition`
 - `evaluator_cfg.restore_hint`
 The archived artifact bundle is:
 - `artifact/scoliosis_drf_author_118_compat`
@@ -7,6 +7,7 @@ Related notes:
 - [ScoNet and DRF: Status, Architecture, and Reproduction Notes](sconet-drf-status-and-training.md)
 - [Scoliosis Training Change Log](scoliosis_training_change_log.md)
 - [Scoliosis Reproducibility Audit](scoliosis_reproducibility_audit.md)
 - [DRF Author Checkpoint Compatibility Note](drf_author_checkpoint_compat.md)
 - [systemd-run Training](systemd-run-training.md)
 ## Current Best Known Result
@@ -72,6 +73,7 @@ These are the main branches already explored, so the next person does not rerun
 - `DRF`
  - implemented and tested
  - underperformed the plain skeleton baseline in the first serious practical run
  - the author-provided checkpoint is now partially recoverable on the aligned `118` path
  - still worth one final warm-start retry, but not the default winner
 ## What Not To Do First
@@ -120,7 +122,9 @@ Goal:
 Recommended setup:
 - split: `1:1:2`
 - representation: start from the same `body-only` skeleton maps
- initialization: warm-start from the strong skeleton model, not from random init
+- initialization: warm-start from either:
  - the strong practical skeleton model, or
  - the recovered author DRF checkpoint if the goal is paper-side DRF recovery
 - optimizer: small-LR `AdamW`
 - scheduler: cosine
@@ -130,9 +134,15 @@ Recommended strategy:
 3. use a smaller LR than the plain baseline finetune
 4. if needed, freeze or partially freeze the backbone for a short warmup so the PAV/PGA branch learns without immediately disturbing the already-good visual branch
 If the goal is specifically DRF recovery rather than practical best-model development, use this as the author-checkpoint starting point:
 - config: `configs/drf/drf_author_eval_118_aligned_1gpu.yaml`
 - recovered author checkpoint result:
  - `80.24 Acc / 76.73 Prec / 76.40 Rec / 76.56 F1`
 Why:
 - the earlier DRF bridge peaked early and then degraded
 - local evidence suggests the prior branch is currently weak or noisy
 - the scratch DRF path was weak, but the recovered author checkpoint is already much better than that path
 - DRF deserves one fair test from a strong starting point, not another scratch run
 Success criterion:
@@ -11,6 +11,7 @@ Related notes:
 - [ScoNet and DRF: Status, Architecture, and Reproduction Notes](sconet-drf-status-and-training.md)
 - [Scoliosis Training Change Log](scoliosis_training_change_log.md)
 - [Scoliosis: Next Experiments](scoliosis_next_experiments.md)
 - [DRF Author Checkpoint Compatibility Note](drf_author_checkpoint_compat.md)
 Primary references:
 - ScoNet paper: [arXiv-2407.05726v3-main.tex](papers/arXiv-2407.05726v3-main.tex)
@@ -155,6 +156,41 @@ Conclusion:
 - DRF currently loses to the stronger plain skeleton baseline in this repo
 - the published DRF advantage is not established locally
 ### 9a. The author-provided DRF checkpoint is partially recoverable
 This changed one important part of the audit:
 - the author checkpoint itself is not unusable
 - the earlier very poor local eval was mostly a compatibility failure
 The recovered best author-checkpoint path is:
 - config: `configs/drf/drf_author_eval_118_aligned_1gpu.yaml`
 - result:
  - `80.24 Acc / 76.73 Prec / 76.40 Rec / 76.56 F1`
 This is still below the paper's DRF headline result:
 - `86.0 / 84.1 / 79.2 / 80.8`
 But it is far better than the earlier broken compat evals, which means:
 - the weight file is real
 - the stale author YAML is not a reliable runtime contract
 The main causes were:
 - split mismatch:
  - checkpoint name says `118`
  - provided YAML points to `112`
 - class-order mismatch:
  - author stub uses `negative=0, positive=1, neutral=2`
  - repo evaluator assumes `negative=0, neutral=1, positive=2`
 - legacy module naming mismatch:
  - `attention_layer.*` vs `PGA.*`
 - preprocessing/runtime mismatch:
  - the checkpoint aligns much better with `Scoliosis1K-drf-pkl-118-aligned`
  - it performs very badly on the local `118-paper` export
 Conclusion:
 - the author checkpoint can be made meaningfully usable in this repo
 - but the provided bundle still does not fully specify the original training/eval contract
 ### 10. The paper-level `1:1:8` skeleton story is not reproduced here
 What happened locally:
@@ -254,6 +290,7 @@ Practical bottom line:
 - keep the `body-only` skeleton baseline as the mainline path
 - keep the retained best checkpoint family as the working artifact
 - treat DRF as an optional follow-up branch, not the current winner
 - if DRF work continues, the author checkpoint on the aligned `118` path is now a much better starting point than scratch DRF
 Current strongest practical checkpoint:
 - `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1`
@@ -38,6 +38,10 @@ Use it for:
 | 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_bridge_1gpu_10k` | ScoNet-MT-ske bridge | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Same plain-CE `1:1:2` bridge, relaunched on the `5070 Ti` only per user request | complete | best proxy subset at `7000`: `88.28/69.12/74.15/68.80`; full test at `7000`: `83.16/68.24/80.02/68.47`; final proxy at `10000`: `75.00/65.00/63.41/54.55` (Acc/Prec/Rec/F1) |
 | 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_headlite_plaince_bridge_1gpu_10k` | ScoNet-MT-ske bridge | `Scoliosis1K-drf-pkl-112-sigma15-joint8-headlite` + `Scoliosis1K_112.json` | Added `head-lite` structure (nose plus shoulder links, no eyes/ears) on top of the plain-CE `1:1:2` bridge; first `3090` launch OOMed due unrelated occupancy, then relaunched on the UUID-pinned `5070 Ti` | complete | best proxy subset at `7000`: `86.72/70.15/89.00/70.44`; full test at `7000`: `78.07/65.42/80.50/62.08` (Acc/Prec/Rec/F1) |
 | 2026-03-10 | `DRF_skeleton_112_sigma15_joint8_bodyonly_plaince_bridge_1gpu_10k` | DRF bridge | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | First practical DRF run on the winning `1:1:2` skeleton recipe: `body-only`, plain CE, SGD, `10k` bridge schedule, fixed proxy subset seed `112` | complete | best proxy subset at `2000`: `88.28/61.79/60.31/60.93`; full test at `2000`: `80.21/58.92/59.23/57.84` (Acc/Prec/Rec/F1) |
 | 2026-03-14 | `DRF_author_eval_112_1gpu` | DRF author checkpoint compat | `Scoliosis1K-drf-pkl` + `Scoliosis1K_112.json` | Re-evaluated the author-provided checkpoint after adding legacy module-name remap and correcting the author class order; kept the stale `112` path to test whether the provided YAML was trustworthy | complete | `85.19/57.98/56.65/57.30` (Acc/Prec/Rec/F1) |
 | 2026-03-14 | `DRF_author_eval_118_splitroot_1gpu` | DRF author checkpoint compat | `Scoliosis1K-drf-pkl-118` + `Scoliosis1K_118.json` | Same author-checkpoint compat path, but switched to the `118` split-specific local DRF dataset root while keeping `BaseSilCuttingTransform` and author class order | complete | `77.17/73.61/72.59/72.98` (Acc/Prec/Rec/F1) |
 | 2026-03-14 | `DRF_author_eval_118_aligned_1gpu` | DRF author checkpoint compat | `Scoliosis1K-drf-pkl-118-aligned` + `Scoliosis1K_118.json` | Same author-checkpoint compat path, but evaluated on the aligned `118` DRF export; this is currently the best recovered author-checkpoint runtime contract | complete | `80.24/76.73/76.40/76.56` (Acc/Prec/Rec/F1) |
 | 2026-03-14 | `DRF_author_eval_118_paper_1gpu` | DRF author checkpoint compat | `Scoliosis1K-drf-pkl-118-paper` + `Scoliosis1K_118.json` | Tested the author checkpoint against the local paper-literal summed-heatmap export with `BaseSilTransform` to see whether it matched the later paper-style preprocessing branch | complete | `27.24/9.08/33.33/14.27` (Acc/Prec/Rec/F1) |
 | 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_main_1gpu_20k` | ScoNet-MT-ske mainline | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Promoted the winning practical skeleton recipe to a longer `20k` run with full `TEST_SET` eval and checkpoint save every `1000`; no proxy subset, same plain CE + SGD setup | interrupted | superseded by the true-resume continuation below |
 | 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_resume_1gpu_20k` | ScoNet-MT-ske mainline | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | True continuation of the earlier plain-CE `1:1:2` `10k` bridge from its `latest.pt`, extended to `20k` with full `TEST_SET` eval and checkpoint save every `1000` | interrupted | superseded by the AdamW finetune branch below |
 | 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_finetune_1gpu_20k` | ScoNet-MT-ske finetune | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | AdamW finetune from the earlier plain-CE `1:1:2` `10k` checkpoint; restores model weights only, resets optimizer/scheduler state, keeps full `TEST_SET` eval and checkpoint save every `1000` | interrupted | superseded by the longer overnight 40k finetune below |
@@ -69,4 +73,5 @@ Current best `ScoNet-MT-ske`-style result:
 - A later full-test rerun of the retained `body-only + plain CE` `7000` checkpoint reproduced the same `83.16 / 68.24 / 80.02 / 68.47` result exactly, so that number is now explicitly reconfirmed rather than just carried forward from the original run log.
 - `Head-lite` looked stronger than `body-only` on the fixed 128-sequence proxy subset at `7000`, but it did not transfer to the full test set: `78.07 Acc / 65.42 Prec / 80.50 Rec / 62.08 F1`, which is clearly below the `body-only + plain CE` full-test result.
 - The first practical DRF bridge on the winning `1:1:2` recipe did not beat the plain skeleton baseline. Its best retained checkpoint (`2000`) reached only `80.21 Acc / 58.92 Prec / 59.23 Rec / 57.84 F1` on the full test set, versus `83.16 / 68.24 / 80.02 / 68.47` for `body-only + plain CE` at `7000`. The working local interpretation is that the added PAV/PGA path is currently injecting a weak or noisy prior rather than a useful complementary signal.
 - The author-provided DRF checkpoint turned out to be partially recoverable once the local integration contract was corrected. The main hidden bug was class-order mismatch: the author stub uses `negative=0, positive=1, neutral=2`, while the repo evaluator assumes `negative=0, neutral=1, positive=2`. After adding label-order compatibility and the legacy `attention_layer -> PGA` remap, the checkpoint became meaningfully usable. The best recovered path so far is the aligned `118` export, which reached `80.24 Acc / 76.73 Prec / 76.40 Rec / 76.56 F1`. The stale author YAML is therefore not a trustworthy source of the original runtime contract; the checkpoint aligns much better with an `118` aligned/OpenGait-like path than with the later local `118-paper` export.
 - Extending the practical `1:1:2` body-only plain-CE baseline with an `AdamW` cosine finetune changed the picture substantially. The final `80000` checkpoint still evaluated well (`90.64 / 72.87 / 93.19 / 75.74`), but the real win was an earlier retained checkpoint: `27000` reproduced exactly at `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1` on a standalone full-test rerun. So for this practical path, the best result is no longer the original SGD bridge checkpoint but the retained `AdamW` cosine finetune checkpoint.
@@ -148,6 +148,7 @@ class ScoliosisPipeline:
    _visualizer: OpenCVVisualizer | None
    _last_viz_payload: _VizPayload | None
    _frame_pacer: _FramePacer | None
    _paced_frame_idx: int
    _visualizer_accepts_pose_data: bool | None
    _visualizer_signature_owner: object | None
@@ -209,6 +210,7 @@ class ScoliosisPipeline:
            self._visualizer = None
        self._last_viz_payload = None
        self._frame_pacer = _FramePacer(target_fps) if target_fps is not None else None
        self._paced_frame_idx = -1
        self._visualizer_accepts_pose_data = None
        self._visualizer_signature_owner = None
@@ -393,8 +395,6 @@ class ScoliosisPipeline:
                "confidence": None,
                "pose": pose_data,
            }
        self._window.push(silhouette, frame_idx=frame_idx, track_id=track_id)
        if self._frame_pacer is not None and not self._frame_pacer.should_emit(
            timestamp_ns
        ):
@@ -409,6 +409,13 @@ class ScoliosisPipeline:
                "confidence": None,
                "pose": pose_data,
            }
        self._paced_frame_idx += 1
        self._window.push(
            silhouette,
            frame_idx=frame_idx,
            track_id=track_id,
            schedule_frame_idx=self._paced_frame_idx,
        )
        segmentation_input = self._window.buffered_silhouettes
        if not self._window.should_classify():
@@ -67,7 +67,8 @@ class SilhouetteWindow:
    stride: int
    gap_threshold: int
    _buffer: deque[Float[ndarray, "64 44"]]
-    _frame_indices: deque[int]
+    _source_frame_indices: deque[int]
    _schedule_frame_indices: deque[int]
    _track_id: int | None
    _last_classified_frame: int
    _frame_count: int
@@ -91,12 +92,20 @@ class SilhouetteWindow:
        # Bounded storage via deque
        self._buffer = deque(maxlen=window_size)
-        self._frame_indices = deque(maxlen=window_size)
+        self._source_frame_indices = deque(maxlen=window_size)
        self._schedule_frame_indices = deque(maxlen=window_size)
        self._track_id = None
        self._last_classified_frame = -1
        self._frame_count = 0
-    def push(self, sil: np.ndarray, frame_idx: int, track_id: int) -> None:
+    def push(
        self,
        sil: np.ndarray,
        frame_idx: int,
        track_id: int,
        *,
        schedule_frame_idx: int | None = None,
    ) -> None:
        """Push a new silhouette into the window.
        Automatically resets buffer on track ID change or frame gap
@@ -112,8 +121,8 @@ class SilhouetteWindow:
            self.reset()
        # Check for frame gap
-        if self._frame_indices:
+        if self._source_frame_indices:
-            last_frame = self._frame_indices[-1]
+            last_frame = self._source_frame_indices[-1]
            gap = frame_idx - last_frame
            if gap > self.gap_threshold:
                self.reset()
@@ -129,7 +138,10 @@ class SilhouetteWindow:
            )
        self._buffer.append(sil_array)
-        self._frame_indices.append(frame_idx)
+        self._source_frame_indices.append(frame_idx)
        self._schedule_frame_indices.append(
            frame_idx if schedule_frame_idx is None else schedule_frame_idx
        )
        self._frame_count += 1
    def is_ready(self) -> bool:
@@ -152,7 +164,7 @@ class SilhouetteWindow:
        if self._last_classified_frame < 0:
            return True
-        current_frame = self._frame_indices[-1]
+        current_frame = self._schedule_frame_indices[-1]
        frames_since = current_frame - self._last_classified_frame
        return frames_since >= self.stride
@@ -185,15 +197,16 @@ class SilhouetteWindow:
    def reset(self) -> None:
        """Reset the window, clearing all buffers and counters."""
        self._buffer.clear()
-        self._frame_indices.clear()
+        self._source_frame_indices.clear()
        self._schedule_frame_indices.clear()
        self._track_id = None
        self._last_classified_frame = -1
        self._frame_count = 0
    def mark_classified(self) -> None:
        """Mark current frame as classified, updating stride tracking."""
-        if self._frame_indices:
+        if self._schedule_frame_indices:
-            self._last_classified_frame = self._frame_indices[-1]
+            self._last_classified_frame = self._schedule_frame_indices[-1]
    @property
    def current_track_id(self) -> int | None:
@@ -212,9 +225,9 @@ class SilhouetteWindow:
    @property
    def window_start_frame(self) -> int:
-        if not self._frame_indices:
+        if not self._source_frame_indices:
            raise ValueError("Window is empty")
-        return int(self._frame_indices[0])
+        return int(self._source_frame_indices[0])
    @property
    def buffered_silhouettes(self) -> Float[ndarray, "n 64 44"]:
@@ -1,5 +1,7 @@
 from __future__ import annotations
 from collections import OrderedDict
 from collections.abc import Mapping
 from typing import Any
 import torch
@@ -21,6 +23,11 @@ from ..modules import (
 class DRF(BaseModelBody):
    """Dual Representation Framework from arXiv:2509.00872v1."""
    LEGACY_STATE_PREFIXES: dict[str, str] = {
        "attention_layer.": "PGA.",
    }
    CANONICAL_LABEL_ORDER: tuple[str, str, str] = ("negative", "neutral", "positive")
    def build_network(self, model_cfg: dict[str, Any]) -> None:
        self.Backbone = self.get_backbone(model_cfg["backbone_cfg"])
        self.Backbone = SetBlockWrapper(self.Backbone)
@@ -34,6 +41,13 @@ class DRF(BaseModelBody):
            num_pairs=model_cfg.get("num_pairs", 8),
            num_metrics=model_cfg.get("num_metrics", 3),
        )
        self.label_order = resolve_label_order(model_cfg.get("label_order"))
        self.label_map = {label: idx for idx, label in enumerate(self.label_order)}
        self.canonical_inference_logits = bool(model_cfg.get("canonical_inference_logits", True))
        self.logit_canonical_indices = torch.tensor(
            [self.label_map[label] for label in self.CANONICAL_LABEL_ORDER],
            dtype=torch.long,
        )
    def forward(
        self,
@@ -48,7 +62,7 @@ class DRF(BaseModelBody):
    ) -> dict[str, dict[str, Any]]:
        ipts, pids, labels, _, seqL, key_features = inputs
        label_ids = torch.as_tensor(
-            [LABEL_MAP[str(label).lower()] for label in labels],
+            [self.label_map[str(label).lower()] for label in labels],
            device=pids.device,
            dtype=torch.long,
        )
@@ -69,6 +83,7 @@ class DRF(BaseModelBody):
        embed_2, logits = self.BNNecks(embed_1)
        del embed_2
        inference_logits = self.canonicalize_logits(logits)
        return {
            "training_feat": {
@@ -79,10 +94,52 @@ class DRF(BaseModelBody):
                "image/sils": rearrange(heatmaps, "n c s h w -> (n s) c h w"),
            },
            "inference_feat": {
-                "embeddings": logits,
+                "embeddings": inference_logits,
            },
        }
    def canonicalize_logits(
        self,
        logits: Float[torch.Tensor, "batch classes parts"],
    ) -> Float[torch.Tensor, "batch classes parts"]:
        if not self.canonical_inference_logits or tuple(self.label_order) == self.CANONICAL_LABEL_ORDER:
            return logits
        indices = self.logit_canonical_indices.to(device=logits.device)
        return logits.index_select(dim=1, index=indices)
    @classmethod
    def remap_legacy_state_dict(
        cls,
        state_dict: Mapping[str, torch.Tensor],
    ) -> OrderedDict[str, torch.Tensor]:
        """Map older author checkpoint names onto the current DRF module tree."""
        remapped_state = OrderedDict[str, torch.Tensor]()
        for key, value in state_dict.items():
            remapped_key = key
            for old_prefix, new_prefix in cls.LEGACY_STATE_PREFIXES.items():
                if remapped_key.startswith(old_prefix):
                    remapped_key = new_prefix + remapped_key[len(old_prefix) :]
                    break
            remapped_state[remapped_key] = value
        metadata = getattr(state_dict, "_metadata", None)
        if metadata is not None:
            setattr(remapped_state, "_metadata", metadata)
        return remapped_state
    def load_state_dict(
        self,
        state_dict: Mapping[str, torch.Tensor],
        strict: bool = True,
        assign: bool = False,
    ) -> Any:
        return super().load_state_dict(
            self.remap_legacy_state_dict(state_dict),
            strict=strict,
            assign=assign,
        )
 class PAVGuidedAttention(nn.Module):
    channel_att: nn.Sequential
@@ -119,11 +176,25 @@ class PAVGuidedAttention(nn.Module):
        return embeddings * channel_att * spatial_att
-LABEL_MAP: dict[str, int] = {
+def resolve_label_order(label_order_cfg: Any) -> tuple[str, str, str]:
-    "negative": 0,
+    if label_order_cfg is None:
-    "neutral": 1,
+        return DRF.CANONICAL_LABEL_ORDER
-    "positive": 2,
+
-}
+    if not isinstance(label_order_cfg, list | tuple):
        raise TypeError(
            "DRF model_cfg.label_order must be a list/tuple of "
            "['negative', 'neutral', 'positive'] in the desired logit order."
        )
    normalized_order = tuple(str(label).lower() for label in label_order_cfg)
    expected = set(DRF.CANONICAL_LABEL_ORDER)
    if len(normalized_order) != 3 or set(normalized_order) != expected:
        raise ValueError(
            "DRF model_cfg.label_order must contain exactly "
            "negative/neutral/positive once each; "
            f"got {label_order_cfg!r}"
        )
    return normalized_order
 def canonicalize_pav(
@@ -927,6 +927,88 @@ def test_frame_pacer_emission_count_24_to_15() -> None:
    assert 60 <= emitted <= 65
 def test_pipeline_pacing_skips_window_growth_until_emitted() -> None:
    from opengait_studio.pipeline import ScoliosisPipeline
    with (
        mock.patch("opengait_studio.pipeline.YOLO") as mock_yolo,
        mock.patch("opengait_studio.pipeline.create_source") as mock_source,
        mock.patch("opengait_studio.pipeline.create_publisher") as mock_publisher,
        mock.patch("opengait_studio.pipeline.ScoNetDemo") as mock_classifier,
        mock.patch("opengait_studio.pipeline.select_person") as mock_select_person,
        mock.patch("opengait_studio.pipeline.mask_to_silhouette") as mock_mask_to_sil,
    ):
        mock_detector = mock.MagicMock()
        mock_box = mock.MagicMock()
        mock_box.xyxy = np.array([[100, 100, 200, 300]], dtype=np.float32)
        mock_box.id = np.array([1], dtype=np.int64)
        mock_mask = mock.MagicMock()
        mock_mask.data = np.random.rand(1, 480, 640).astype(np.float32)
        mock_result = mock.MagicMock()
        mock_result.boxes = mock_box
        mock_result.masks = mock_mask
        mock_detector.track.return_value = [mock_result]
        mock_yolo.return_value = mock_detector
        mock_source.return_value = []
        mock_publisher.return_value = mock.MagicMock()
        mock_model = mock.MagicMock()
        mock_model.predict.return_value = ("neutral", 0.7)
        mock_classifier.return_value = mock_model
        dummy_mask = np.random.randint(0, 256, (480, 640), dtype=np.uint8)
        dummy_bbox_mask = (100, 100, 200, 300)
        dummy_bbox_frame = (100, 100, 200, 300)
        dummy_silhouette = np.random.rand(64, 44).astype(np.float32)
        mock_select_person.return_value = (
            dummy_mask,
            dummy_bbox_mask,
            dummy_bbox_frame,
            1,
        )
        mock_mask_to_sil.return_value = dummy_silhouette
        pipeline = ScoliosisPipeline(
            source="dummy.mp4",
            checkpoint="dummy.pt",
            config=str(CONFIG_PATH) if CONFIG_PATH.exists() else "dummy.yaml",
            device="cpu",
            yolo_model="dummy.pt",
            window=2,
            stride=1,
            nats_url=None,
            nats_subject="test",
            max_frames=None,
            target_fps=15.0,
        )
        frame = np.zeros((480, 640, 3), dtype=np.uint8)
        first = pipeline.process_frame(
            frame,
            {"frame_count": 0, "timestamp_ns": 1_000_000_000},
        )
        second = pipeline.process_frame(
            frame,
            {"frame_count": 1, "timestamp_ns": 1_033_000_000},
        )
        third = pipeline.process_frame(
            frame,
            {"frame_count": 2, "timestamp_ns": 1_067_000_000},
        )
        assert first is not None
        assert second is not None
        assert third is not None
        assert first["segmentation_input"] is not None
        assert second["segmentation_input"] is not None
        assert third["segmentation_input"] is not None
        assert first["segmentation_input"].shape[0] == 1
        assert second["segmentation_input"].shape[0] == 1
        assert second["label"] is None
        assert third["segmentation_input"].shape[0] == 2
        assert third["label"] == "neutral"
 def test_frame_pacer_requires_positive_target_fps() -> None:
    from opengait_studio.pipeline import _FramePacer
@@ -144,6 +144,21 @@ class TestSilhouetteWindow:
        window.push(sil, frame_idx=7, track_id=1)
        assert window.should_classify()
    def test_should_classify_uses_schedule_frame_idx(self) -> None:
        """Stride should be measured in paced/scheduled frames, not source frames."""
        window = SilhouetteWindow(window_size=2, stride=2, gap_threshold=50)
        sil = np.ones((64, 44), dtype=np.float32)
        window.push(sil, frame_idx=0, track_id=1, schedule_frame_idx=0)
        window.push(sil, frame_idx=10, track_id=1, schedule_frame_idx=1)
        assert window.should_classify()
        window.mark_classified()
        window.push(sil, frame_idx=20, track_id=1, schedule_frame_idx=2)
        assert not window.should_classify()
    def test_should_classify_not_ready(self) -> None:
        """should_classify should return False when window not ready."""
        window = SilhouetteWindow(window_size=5, stride=1, gap_threshold=10)