Align DRF skeleton preprocessing with upstream heatmap path

configs/drf/drf_scoliosis1k.yaml

@@ -1,6 +1,6 @@
 data_cfg:
   dataset_name: Scoliosis1K
-  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-paper
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
   dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
   num_workers: 1
   remove_no_gallery: false
@@ -19,7 +19,7 @@ evaluator_cfg:
     frames_all_limit: 720
   metric: euc
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation
 
 loss_cfg:
@@ -102,5 +102,5 @@ trainer_cfg:
     sample_type: fixed_unordered
     type: TripletSampler
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation

configs/drf/drf_scoliosis1k_eval_1gpu.yaml

@@ -1,6 +1,6 @@
 data_cfg:
   dataset_name: Scoliosis1K
-  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-paper
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
   dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
   num_workers: 1
   remove_no_gallery: false
@@ -19,7 +19,7 @@ evaluator_cfg:
     frames_all_limit: 720
   metric: euc
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation
 
 loss_cfg:
@@ -102,5 +102,5 @@ trainer_cfg:
     sample_type: fixed_unordered
     type: TripletSampler
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation

configs/drf/drf_scoliosis1k_smoke.yaml

@@ -1,6 +1,6 @@
 data_cfg:
   dataset_name: Scoliosis1K
-  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-paper
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
   dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.json
   num_workers: 1
   remove_no_gallery: false
@@ -23,7 +23,7 @@ evaluator_cfg:
     frames_all_limit: 720
   metric: euc
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation
 
 loss_cfg:
@@ -109,5 +109,5 @@ trainer_cfg:
     sample_type: fixed_unordered
     type: TripletSampler
   transform:
-    - type: BaseSilTransform
+    - type: BaseSilCuttingTransform
     - type: NoOperation

configs/drf/pretreatment_heatmap_drf.yaml

@@ -9,7 +9,6 @@ norm_args:
   pose_format: coco
   use_conf: ${padkeypoints_args.use_conf}
   heatmap_image_height: 128
-  target_body_height: ${norm_args.heatmap_image_height}
 
 heatmap_generator_args:
   sigma: 8.0

configs/sconet/sconet_scoliosis1k_skeleton_118.yaml

@@ -0,0 +1,105 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-aligned
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_118.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: 20000
+  save_name: ScoNet_skeleton_118
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 2
+    sample_type: all_ordered
+    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: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.0005
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones:
+    - 10000
+    - 14000
+    - 18000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  enable_float16: true
+  fix_BN: false
+  with_test: false
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 20000
+  save_name: ScoNet_skeleton_118
+  sync_BN: true
+  total_iter: 20000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 8
+      - 8
+    frames_num_fixed: 30
+    sample_type: fixed_unordered
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform

datasets/Scoliosis1K/README.md

@@ -109,9 +109,17 @@ uv run python datasets/pretreatment_scoliosis_drf.py \
   --output_path=<path_to_drf_pkl>
 ```
 
-To reproduce the paper defaults more closely, the script now uses
+The script uses `configs/drf/pretreatment_heatmap_drf.yaml` by default.
-`configs/drf/pretreatment_heatmap_drf.yaml` by default, which enables
+That keeps the upstream OpenGait/SkeletonGait heatmap behavior from
-summed two-channel skeleton maps and a literal 128-pixel height normalization.
+commit `f754f6f3831e9f83bb28f4e2f63dd43d8bcf9dc4` for the skeleton-map
+branch while still building the DRF-specific two-channel output.
+
+If you explicitly want the more paper-literal summed heatmap ablation, add:
+
+```bash
+  --heatmap_reduction=sum
+```
+
 If you explicitly want train-only PAV min-max statistics, add:
 
 ```bash

datasets/pretreatment_heatmap.py

@@ -8,6 +8,7 @@ import pickle
 import argparse
 import numpy as np
 from glob import glob 
+from typing import Literal
 from tqdm import tqdm
 import matplotlib.cm as cm
 import torch.distributed as dist
@@ -328,7 +329,7 @@ class HeatmapToImage:
 class HeatmapReducer:
     """Reduce stacked joint/limb heatmaps to a single grayscale channel."""
 
-    def __init__(self, reduction: str = "max") -> None:
+    def __init__(self, reduction: Literal["max", "sum"] = "max") -> None:
         if reduction not in {"max", "sum"}:
             raise ValueError(f"Unsupported heatmap reduction: {reduction}")
         self.reduction = reduction
@@ -574,7 +575,7 @@ def GenerateHeatmapTransform(
     norm_args,
     heatmap_generator_args,
     align_args,
-    reduction="max",
+    reduction: Literal["upstream", "max", "sum"] = "upstream",
 ):
 
     base_transform = T.Compose([
@@ -585,17 +586,27 @@ def GenerateHeatmapTransform(
 
     heatmap_generator_args["with_limb"] = True
     heatmap_generator_args["with_kp"] = False
+    bone_image_transform = (
+        HeatmapToImage()
+        if reduction == "upstream"
+        else HeatmapReducer(reduction=reduction)
+    )
     transform_bone = T.Compose([
         GeneratePoseTarget(**heatmap_generator_args), 
-        HeatmapReducer(reduction=reduction),
+        bone_image_transform,
         HeatmapAlignment(**align_args) 
     ])
 
     heatmap_generator_args["with_limb"] = False
     heatmap_generator_args["with_kp"] = True
+    joint_image_transform = (
+        HeatmapToImage()
+        if reduction == "upstream"
+        else HeatmapReducer(reduction=reduction)
+    )
     transform_joint = T.Compose([
         GeneratePoseTarget(**heatmap_generator_args), 
-        HeatmapReducer(reduction=reduction),
+        joint_image_transform,
         HeatmapAlignment(**align_args) 
     ])
 

datasets/pretreatment_scoliosis_drf.py

@@ -7,7 +7,7 @@ import pickle
 import sys
 from glob import glob
 from pathlib import Path
-from typing import Any, TypedDict, cast
+from typing import Any, Literal, TypedDict, cast
 
 import numpy as np
 import yaml
@@ -34,6 +34,7 @@ JOINT_PAIRS = (
 )
 EPS = 1e-6
 FloatArray = NDArray[np.float32]
+HeatmapReduction = Literal["upstream", "max", "sum"]
 
 
 class SequenceRecord(TypedDict):
@@ -66,6 +67,16 @@ def get_args() -> argparse.Namespace:
         default="configs/drf/pretreatment_heatmap_drf.yaml",
         help="Heatmap preprocessing config used to build the skeleton map branch.",
     )
+    _ = parser.add_argument(
+        "--heatmap_reduction",
+        type=str,
+        choices=["upstream", "max", "sum"],
+        default="upstream",
+        help=(
+            "How to collapse joint/limb heatmaps into one channel. "
+            "'upstream' matches OpenGait at f754f6f..., while 'sum' keeps the paper-literal ablation."
+        ),
+    )
     _ = parser.add_argument(
         "--stats_partition",
         type=str,
@@ -180,7 +191,7 @@ def main() -> None:
         norm_args=heatmap_cfg["norm_args"],
         heatmap_generator_args=heatmap_cfg["heatmap_generator_args"],
         align_args=heatmap_cfg["align_args"],
-        reduction="sum",
+        reduction=cast(HeatmapReduction, args.heatmap_reduction),
     )
 
     pose_paths = iter_pose_paths(args.pose_data_path)

opengait/modeling/base_model_body.py

@@ -47,7 +47,9 @@ class BaseModelBody(BaseModel):
         labs = list2var(labs_batch).long()
         seqL = np2var(seqL_batch).int() if seqL_batch is not None else None
 
-        body_features = aggregate_body_features(body_seq, seqL)
+        # Preserve a singleton modality axis so DRF can mirror the author stub's
+        # `squeeze(1)` behavior while still accepting the same sequence-level prior.
+        body_features = aggregate_body_features(body_seq, seqL).unsqueeze(1)
 
         if seqL is not None:
             seqL_sum = int(seqL.sum().data.cpu().numpy())
@@ -80,3 +82,7 @@ def aggregate_body_features(
         aggregated.append(flattened[start:end].mean(dim=0))
         start = end
     return torch.stack(aggregated, dim=0)
+
+
+# Match the symbol name used by the author-provided DRF stub.
+BaseModel = BaseModelBody

opengait/modeling/models/drf.py

@@ -43,7 +43,7 @@ class DRF(BaseModelBody):
             list[str],
             list[str],
             Int[torch.Tensor, "1 batch"] | None,
-            Float[torch.Tensor, "batch pairs metrics"],
+            Float[torch.Tensor, "batch _ pairs metrics"] | Float[torch.Tensor, "batch pairs metrics"],
         ],
     ) -> dict[str, dict[str, Any]]:
         ipts, pids, labels, _, seqL, key_features = inputs
@@ -64,6 +64,7 @@ class DRF(BaseModelBody):
 
         feat = self.HPP(outs)
         embed_1 = self.FCs(feat)
+        key_features = canonicalize_pav(key_features)
         embed_1 = self.PGA(embed_1, key_features)
 
         embed_2, logits = self.BNNecks(embed_1)
@@ -123,3 +124,15 @@ LABEL_MAP: dict[str, int] = {
     "neutral": 1,
     "positive": 2,
 }
+
+
+def canonicalize_pav(
+    pav: Float[torch.Tensor, "batch _ pairs metrics"] | Float[torch.Tensor, "batch pairs metrics"],
+) -> Float[torch.Tensor, "batch pairs metrics"]:
+    if pav.ndim == 4:
+        if pav.shape[1] != 1:
+            raise ValueError(f"Expected singleton PAV axis, got shape {tuple(pav.shape)}")
+        return pav.squeeze(1)
+    if pav.ndim != 3:
+        raise ValueError(f"Expected PAV with 3 or 4 dims, got shape {tuple(pav.shape)}")
+    return pav