Add DRF Scoliosis1K pipeline and optional wandb logging

configs/default.yaml

@@ -7,6 +7,19 @@ data_cfg:
   cache: false
   test_dataset_name: CASIA-B
 
+logger_cfg:
+  use_tensorboard: true
+  use_wandb: false
+  wandb_project: OpenGait
+  wandb_entity: null
+  wandb_name: null
+  wandb_group: null
+  wandb_job_type: null
+  wandb_tags: []
+  wandb_mode: online
+  wandb_resume: allow
+  wandb_id: null
+
 evaluator_cfg:
   enable_float16: false
   restore_ckpt_strict: true

configs/drf/drf_scoliosis1k.yaml

@@ -0,0 +1,106 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_1116.json
+  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: DRF
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 2
+    sample_type: all_ordered
+    frames_all_limit: 720
+  metric: euc
+  transform:
+    - type: BaseSilCuttingTransform
+    - type: NoOperation
+
+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
+  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
+
+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: DRF
+  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
+    - type: NoOperation

configs/drf/drf_scoliosis1k_smoke.yaml

@@ -0,0 +1,113 @@
+data_cfg:
+  dataset_name: Scoliosis1K
+  dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl
+  dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_1116.json
+  num_workers: 1
+  remove_no_gallery: false
+  test_dataset_name: Scoliosis1K
+
+logger_cfg:
+  use_tensorboard: true
+  use_wandb: false
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_name: DRF_smoke
+  eval_func: evaluate_scoliosis
+  sampler:
+    batch_shuffle: false
+    batch_size: 2
+    sample_type: all_ordered
+    frames_all_limit: 720
+  metric: euc
+  transform:
+    - type: BaseSilCuttingTransform
+    - type: NoOperation
+
+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
+  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
+
+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:
+  find_unused_parameters: false
+  enable_float16: true
+  fix_BN: false
+  with_test: false
+  log_iter: 1
+  restore_ckpt_strict: true
+  optimizer_reset: false
+  scheduler_reset: false
+  restore_hint: 0
+  save_iter: 1
+  save_name: DRF_smoke
+  sync_BN: true
+  total_iter: 1
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 4
+      - 4
+    frames_num_fixed: 30
+    sample_type: fixed_unordered
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform
+    - type: NoOperation

datasets/Scoliosis1K/README.md

@@ -83,3 +83,55 @@ datasets/pretreatment_heatmap.py \
   --save_root=<output_path> \
   --dataset_name=OUMVLP
 ```
+
+## DRF Preprocessing
+
+For the DRF model, OpenGait expects a combined runtime dataset with:
+
+* `0_heatmap.pkl`: the two-channel skeleton map sequence
+* `1_pav.pkl`: the paper-style Postural Asymmetry Vector (PAV), repeated along the sequence axis so it matches OpenGait's multi-input loader contract
+
+The PAV pass is implemented from the paper:
+
+1. convert pose to COCO17 if needed
+2. pad missing joints
+3. pelvis-center and height normalize the sequence
+4. compute vertical, midline, and angular deviations for the 8 symmetric joint pairs
+5. apply IQR filtering per metric
+6. average over time
+7. min-max normalize across the dataset, or across `TRAIN_SET` when `--stats_partition` is provided
+
+Run:
+
+```bash
+uv run python datasets/pretreatment_scoliosis_drf.py \
+  --pose_data_path=<path_to_pose_pkl> \
+  --output_path=<path_to_drf_pkl> \
+  --stats_partition=./datasets/Scoliosis1K/Scoliosis1K_1116.json
+```
+
+The output layout is:
+
+```text
+<path_to_drf_pkl>/
+├── pav_stats.pkl
+├── 00000/
+│   ├── Positive/
+│   │   ├── 000_180/
+│   │   │   ├── 0_heatmap.pkl
+│   │   │   └── 1_pav.pkl
+```
+
+Point `configs/drf/drf_scoliosis1k.yaml:data_cfg.dataset_root` to this output directory before training or testing.
+
+## DRF Training and Testing
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+uv run python -m torch.distributed.launch --nproc_per_node=4 \
+opengait/main.py --cfgs configs/drf/drf_scoliosis1k.yaml --phase train
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+uv run python -m torch.distributed.launch --nproc_per_node=4 \
+opengait/main.py --cfgs configs/drf/drf_scoliosis1k.yaml --phase test
+```

datasets/__init__.py

@@ -0,0 +1 @@
+

datasets/pretreatment_scoliosis_drf.py

@@ -0,0 +1,228 @@
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import pickle
+import sys
+from glob import glob
+from pathlib import Path
+from typing import Any, TypedDict
+
+import numpy as np
+import yaml
+from jaxtyping import Float
+from numpy.typing import NDArray
+from torchvision import transforms as T
+from tqdm import tqdm
+
+if __package__ in {None, ""}:
+    sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+from datasets import pretreatment_heatmap as heatmap_prep
+
+
+JOINT_PAIRS = (
+    (1, 2),   # eyes
+    (3, 4),   # ears
+    (5, 6),   # shoulders
+    (7, 8),   # elbows
+    (9, 10),  # wrists
+    (11, 12), # hips
+    (13, 14), # knees
+    (15, 16), # ankles
+)
+EPS = 1e-6
+FloatArray = NDArray[np.float32]
+
+
+class SequenceRecord(TypedDict):
+    pose_path: str
+    pid: str
+    seq_parts: list[str]
+    frames: int
+    raw_pav: Float[FloatArray, "pairs metrics"]
+
+
+def get_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Generate DRF inputs for Scoliosis1K from pose .pkl files."
+    )
+    _ = parser.add_argument(
+        "--pose_data_path",
+        type=str,
+        required=True,
+        help="Root directory containing Scoliosis1K pose .pkl files.",
+    )
+    _ = parser.add_argument(
+        "--output_path",
+        type=str,
+        required=True,
+        help="Output root for the DRF runtime dataset.",
+    )
+    _ = parser.add_argument(
+        "--heatmap_cfg_path",
+        type=str,
+        default="configs/skeletongait/pretreatment_heatmap.yaml",
+        help="Heatmap preprocessing config used to build the skeleton map branch.",
+    )
+    _ = parser.add_argument(
+        "--stats_partition",
+        type=str,
+        default=None,
+        help="Optional dataset partition JSON. When set, PAV min/max stats use TRAIN_SET ids only.",
+    )
+    return parser.parse_args()
+
+
+def load_heatmap_cfg(cfg_path: str) -> dict[str, Any]:
+    with open(cfg_path, "r", encoding="utf-8") as stream:
+        cfg = yaml.safe_load(stream)
+    replaced = heatmap_prep.replace_variables(cfg, cfg)
+    return dict(replaced)
+
+
+def build_pose_transform(cfg: dict[str, Any]) -> T.Compose:
+    return T.Compose([
+        heatmap_prep.COCO18toCOCO17(**cfg["coco18tococo17_args"]),
+        heatmap_prep.PadKeypoints(**cfg["padkeypoints_args"]),
+        heatmap_prep.CenterAndScaleNormalizer(**cfg["norm_args"]),
+    ])
+
+
+def iter_pose_paths(pose_root: str) -> list[str]:
+    return sorted(glob(os.path.join(pose_root, "*/*/*/*.pkl")))
+
+
+def read_pose(pose_path: str) -> Float[FloatArray, "frames joints channels"]:
+    with open(pose_path, "rb") as handle:
+        pose = pickle.load(handle)
+    return np.asarray(pose, dtype=np.float32)
+
+
+def compute_raw_pav(
+    normalized_pose: Float[FloatArray, "frames joints channels"],
+) -> Float[FloatArray, "pairs metrics"]:
+    coords = normalized_pose[..., :2]
+    hip_center_x = coords[:, [11, 12], 0].mean(axis=1)
+    raw_pav = np.zeros((len(JOINT_PAIRS), 3), dtype=np.float32)
+
+    for pair_idx, (left_idx, right_idx) in enumerate(JOINT_PAIRS):
+        left = coords[:, left_idx]
+        right = coords[:, right_idx]
+        dy = left[:, 1] - right[:, 1]
+        dx = left[:, 0] - right[:, 0]
+        safe_dx = np.where(np.abs(dx) < EPS, np.where(dx < 0, -EPS, EPS), dx)
+
+        vertical = np.abs(dy)
+        midline = np.abs((left[:, 0] + right[:, 0]) / 2.0 - hip_center_x)
+        angular = np.abs(np.arctan(dy / safe_dx))
+
+        metrics = np.stack([vertical, midline, angular], axis=-1)
+        raw_pav[pair_idx] = iqr_mean(metrics)
+
+    return raw_pav
+
+
+def iqr_mean(values: Float[FloatArray, "frames metrics"]) -> Float[FloatArray, "metrics"]:
+    refined = np.zeros(values.shape[1], dtype=np.float32)
+    for metric_idx in range(values.shape[1]):
+        metric_values = values[:, metric_idx]
+        q1, q3 = np.percentile(metric_values, [25, 75])
+        iqr = q3 - q1
+        lower = q1 - 1.5 * iqr
+        upper = q3 + 1.5 * iqr
+        filtered = metric_values[(metric_values >= lower) & (metric_values <= upper)]
+        if filtered.size == 0:
+            filtered = metric_values
+        refined[metric_idx] = float(filtered.mean())
+    return refined
+
+
+def normalize_pav(
+    raw_pav: Float[FloatArray, "pairs metrics"],
+    pav_min: Float[FloatArray, "pairs metrics"],
+    pav_max: Float[FloatArray, "pairs metrics"],
+) -> Float[FloatArray, "pairs metrics"]:
+    denom = np.maximum(pav_max - pav_min, EPS)
+    return np.clip((raw_pav - pav_min) / denom, 0.0, 1.0).astype(np.float32)
+
+
+def rel_seq_parts(pose_path: str) -> list[str]:
+    norm_path = os.path.normpath(pose_path)
+    return norm_path.split(os.sep)[-4:-1]
+
+
+def build_stats_mask(records: list[SequenceRecord], stats_partition: str | None) -> NDArray[np.bool_]:
+    if stats_partition is None:
+        return np.ones(len(records), dtype=bool)
+
+    with open(stats_partition, "r", encoding="utf-8") as handle:
+        partition = json.load(handle)
+    train_ids = set(partition["TRAIN_SET"])
+    return np.asarray([record["pid"] in train_ids for record in records], dtype=bool)
+
+
+def main() -> None:
+    args = get_args()
+    os.makedirs(args.output_path, exist_ok=True)
+
+    heatmap_cfg = load_heatmap_cfg(args.heatmap_cfg_path)
+    pose_transform = build_pose_transform(heatmap_cfg)
+    heatmap_transform = heatmap_prep.GenerateHeatmapTransform(
+        coco18tococo17_args=heatmap_cfg["coco18tococo17_args"],
+        padkeypoints_args=heatmap_cfg["padkeypoints_args"],
+        norm_args=heatmap_cfg["norm_args"],
+        heatmap_generator_args=heatmap_cfg["heatmap_generator_args"],
+        align_args=heatmap_cfg["align_args"],
+    )
+
+    pose_paths = iter_pose_paths(args.pose_data_path)
+    if not pose_paths:
+        raise FileNotFoundError(f"No pose .pkl files found under {args.pose_data_path}")
+
+    records: list[SequenceRecord] = []
+    for pose_path in tqdm(pose_paths, desc="Pass 1/2: computing raw PAV"):
+        pose = read_pose(pose_path)
+        normalized_pose = pose_transform(pose)
+        records.append({
+            "pose_path": pose_path,
+            "pid": rel_seq_parts(pose_path)[0],
+            "seq_parts": rel_seq_parts(pose_path),
+            "frames": pose.shape[0],
+            "raw_pav": compute_raw_pav(normalized_pose),
+        })
+
+    stats_mask = build_stats_mask(records, args.stats_partition)
+    if not stats_mask.any():
+        raise ValueError("No sequences matched the requested stats partition.")
+
+    pav_stack = np.stack([record["raw_pav"] for record, use in zip(records, stats_mask) if use], axis=0)
+    pav_min = pav_stack.min(axis=0).astype(np.float32)
+    pav_max = pav_stack.max(axis=0).astype(np.float32)
+
+    stats_path = os.path.join(args.output_path, "pav_stats.pkl")
+    with open(stats_path, "wb") as handle:
+        pickle.dump({
+            "joint_pairs": JOINT_PAIRS,
+            "pav_min": pav_min,
+            "pav_max": pav_max,
+            "stats_partition": args.stats_partition,
+        }, handle)
+
+    for record in tqdm(records, desc="Pass 2/2: writing DRF dataset"):
+        pose = read_pose(record["pose_path"])
+        heatmap = heatmap_transform(pose)
+        pav = normalize_pav(record["raw_pav"], pav_min, pav_max)
+        pav_seq = np.repeat(pav[np.newaxis, ...], record["frames"], axis=0)
+
+        save_dir = os.path.join(args.output_path, *record["seq_parts"])
+        os.makedirs(save_dir, exist_ok=True)
+        with open(os.path.join(save_dir, "0_heatmap.pkl"), "wb") as handle:
+            pickle.dump(heatmap, handle)
+        with open(os.path.join(save_dir, "1_pav.pkl"), "wb") as handle:
+            pickle.dump(pav_seq, handle)
+
+
+if __name__ == "__main__":
+    main()

opengait/main.py

@@ -24,13 +24,27 @@ opt = parser.parse_args()
 def initialization(cfgs, training):
     msg_mgr = get_msg_mgr()
     engine_cfg = cfgs['trainer_cfg'] if training else cfgs['evaluator_cfg']
+    logger_cfg = cfgs.get('logger_cfg', {})
     output_path = os.path.join('output/', cfgs['data_cfg']['dataset_name'],
                                cfgs['model_cfg']['model'], engine_cfg['save_name'])
     if training:
-        msg_mgr.init_manager(output_path, opt.log_to_file, engine_cfg['log_iter'],
-                             engine_cfg['restore_hint'] if isinstance(engine_cfg['restore_hint'], (int)) else 0)
+        msg_mgr.init_manager(
+            output_path,
+            opt.log_to_file,
+            engine_cfg['log_iter'],
+            engine_cfg['restore_hint'] if isinstance(engine_cfg['restore_hint'], (int)) else 0,
+            logger_cfg=logger_cfg,
+            config=cfgs,
+            phase='train',
+        )
     else:
-        msg_mgr.init_logger(output_path, opt.log_to_file)
+        msg_mgr.init_logger(
+            output_path,
+            opt.log_to_file,
+            logger_cfg=logger_cfg,
+            config=cfgs,
+            phase='test',
+        )
 
     msg_mgr.log_info(engine_cfg)
 
@@ -55,7 +69,10 @@ def run_model(cfgs, training):
     if training:
         Model.run_train(model)
     else:
-        Model.run_test(model)
+        result_dict = Model.run_test(model)
+        if result_dict:
+            msg_mgr.write_to_tensorboard(result_dict)
+            msg_mgr.write_to_wandb(result_dict)
 
 
 if __name__ == '__main__':

opengait/modeling/base_model.py

@@ -431,6 +431,7 @@ class BaseModel(MetaModel, nn.Module):
                         model.fix_BN()
                     if result_dict:
                         model.msg_mgr.write_to_tensorboard(result_dict)
+                        model.msg_mgr.write_to_wandb(result_dict)
                     model.msg_mgr.reset_time()
             if model.iteration >= model.engine_cfg['total_iter']:
                 break

opengait/modeling/models/drf.py

@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+from jaxtyping import Float, Int
+
+from einops import rearrange
+
+from ..base_model import BaseModel
+from ..modules import (
+    HorizontalPoolingPyramid,
+    PackSequenceWrapper,
+    SeparateBNNecks,
+    SeparateFCs,
+    SetBlockWrapper,
+)
+
+
+class DRF(BaseModel):
+    """Dual Representation Framework from arXiv:2509.00872v1."""
+
+    def build_network(self, model_cfg: dict[str, Any]) -> None:
+        self.Backbone = self.get_backbone(model_cfg["backbone_cfg"])
+        self.Backbone = SetBlockWrapper(self.Backbone)
+        self.FCs = SeparateFCs(**model_cfg["SeparateFCs"])
+        self.BNNecks = SeparateBNNecks(**model_cfg["SeparateBNNecks"])
+        self.TP = PackSequenceWrapper(torch.max)
+        self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg["bin_num"])
+        self.PGA = PAVGuidedAttention(
+            in_channels=model_cfg["SeparateFCs"]["out_channels"],
+            parts_num=model_cfg["SeparateFCs"]["parts_num"],
+            num_pairs=model_cfg.get("num_pairs", 8),
+            num_metrics=model_cfg.get("num_metrics", 3),
+        )
+
+    def forward(
+        self,
+        inputs: tuple[
+            list[torch.Tensor],
+            Int[torch.Tensor, "batch"],
+            list[str],
+            list[str],
+            Int[torch.Tensor, "1 batch"] | None,
+        ],
+    ) -> dict[str, dict[str, Any]]:
+        ipts, pids, labels, _, seqL = inputs
+        label_ids = torch.as_tensor(
+            [LABEL_MAP[str(label).lower()] for label in labels],
+            device=pids.device,
+            dtype=torch.long,
+        )
+
+        heatmaps = ipts[0]
+        if heatmaps.ndim == 4:
+            heatmaps = heatmaps.unsqueeze(1)
+        else:
+            heatmaps = rearrange(heatmaps, "n s c h w -> n c s h w")
+
+        pav_seq = ipts[1]
+        pav = aggregate_sequence_features(pav_seq, seqL)
+
+        outs = self.Backbone(heatmaps)
+        outs = self.TP(outs, seqL, options={"dim": 2})[0]
+
+        feat = self.HPP(outs)
+        embed_1 = self.FCs(feat)
+        embed_1 = self.PGA(embed_1, pav)
+
+        embed_2, logits = self.BNNecks(embed_1)
+        del embed_2
+
+        return {
+            "training_feat": {
+                "triplet": {"embeddings": embed_1, "labels": pids},
+                "softmax": {"logits": logits, "labels": label_ids},
+            },
+            "visual_summary": {
+                "image/sils": rearrange(heatmaps, "n c s h w -> (n s) c h w"),
+            },
+            "inference_feat": {
+                "embeddings": logits,
+            },
+        }
+
+
+class PAVGuidedAttention(nn.Module):
+    channel_att: nn.Sequential
+    spatial_att: nn.Sequential
+
+    def __init__(
+        self,
+        in_channels: int = 256,
+        parts_num: int = 16,
+        num_pairs: int = 8,
+        num_metrics: int = 3,
+    ) -> None:
+        super().__init__()
+        pav_dim = num_pairs * num_metrics
+        self.channel_att = nn.Sequential(
+            nn.Linear(pav_dim, in_channels),
+            nn.Sigmoid(),
+        )
+        self.spatial_att = nn.Sequential(
+            nn.Conv1d(pav_dim, parts_num, kernel_size=1),
+            nn.Sigmoid(),
+        )
+
+    def forward(
+        self,
+        embeddings: Float[torch.Tensor, "batch channels parts"],
+        pav: Float[torch.Tensor, "batch pairs metrics"],
+    ) -> Float[torch.Tensor, "batch channels parts"]:
+        pav_flat = pav.flatten(1)
+
+        channel_att = self.channel_att(pav_flat).unsqueeze(-1)
+        spatial_att = self.spatial_att(pav_flat.unsqueeze(-1)).transpose(1, 2)
+
+        return embeddings * channel_att * spatial_att
+
+
+def aggregate_sequence_features(
+    sequence_features: Float[torch.Tensor, "batch seq pairs metrics"],
+    seqL: Int[torch.Tensor, "1 batch"] | None,
+) -> Float[torch.Tensor, "batch pairs metrics"]:
+    if seqL is None:
+        return sequence_features.mean(dim=1)
+
+    lengths = seqL[0].tolist()
+    flattened = sequence_features.squeeze(0)
+    aggregated = []
+    start = 0
+    for length in lengths:
+        end = start + int(length)
+        aggregated.append(flattened[start:end].mean(dim=0))
+        start = end
+    return torch.stack(aggregated, dim=0)
+
+
+LABEL_MAP: dict[str, int] = {
+    "negative": 0,
+    "neutral": 1,
+    "positive": 2,
+}

opengait/utils/msg_manager.py

@@ -1,41 +1,93 @@
+from __future__ import annotations
+
+import atexit
+import logging
+import os.path as osp
 import time
-import torch
+from time import localtime, strftime
+from typing import Any
 
 import numpy as np
+import torch
 import torchvision.utils as vutils
-import os.path as osp
-from time import strftime, localtime
-
 from torch.utils.tensorboard import SummaryWriter
-from .common import is_list, is_tensor, ts2np, mkdir, Odict, NoOp
-import logging
+try:
+    import wandb
+except ImportError:
+    wandb = None
+
+from .common import NoOp, Odict, is_list, is_tensor, mkdir, ts2np
 
 
 class MessageManager:
-    def __init__(self):
+    def __init__(self) -> None:
         self.info_dict = Odict()
-        self.writer_hparams = ['image', 'scalar']
+        self.writer_hparams = ["image", "scalar"]
         self.time = time.time()
+        self.logger = logging.getLogger("opengait")
+        self.writer: SummaryWriter | None = None
+        self.wandb_run: Any | None = None
+        self.iteration = 0
+        self.log_iter = 1
+        self._close_registered = False
 
-    def init_manager(self, save_path, log_to_file, log_iter, iteration=0):
+    def init_manager(
+        self,
+        save_path: str,
+        log_to_file: bool,
+        log_iter: int,
+        iteration: int = 0,
+        logger_cfg: dict[str, Any] | None = None,
+        config: dict[str, Any] | None = None,
+        phase: str = "train",
+    ) -> None:
         self.iteration = iteration
         self.log_iter = log_iter
-        mkdir(osp.join(save_path, "summary/"))
-        self.writer = SummaryWriter(
-            osp.join(save_path, "summary/"), purge_step=self.iteration)
-        self.init_logger(save_path, log_to_file)
 
-    def init_logger(self, save_path, log_to_file):
-        # init logger
-        self.logger = logging.getLogger('opengait')
+        logger_cfg = logger_cfg or {}
+        if logger_cfg.get("use_tensorboard", True):
+            mkdir(osp.join(save_path, "summary/"))
+            self.writer = SummaryWriter(
+                osp.join(save_path, "summary/"),
+                purge_step=self.iteration,
+            )
+        else:
+            self.writer = None
+
+        self.init_logger(
+            save_path,
+            log_to_file,
+            logger_cfg=logger_cfg,
+            config=config,
+            phase=phase,
+        )
+
+    def init_logger(
+        self,
+        save_path: str,
+        log_to_file: bool,
+        logger_cfg: dict[str, Any] | None = None,
+        config: dict[str, Any] | None = None,
+        phase: str = "test",
+    ) -> None:
+        self.logger = logging.getLogger("opengait")
         self.logger.setLevel(logging.INFO)
         self.logger.propagate = False
+        self.logger.handlers.clear()
+
         formatter = logging.Formatter(
-            fmt='[%(asctime)s] [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+            fmt="[%(asctime)s] [%(levelname)s]: %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
         if log_to_file:
             mkdir(osp.join(save_path, "logs/"))
             vlog = logging.FileHandler(
-                osp.join(save_path, "logs/", strftime('%Y-%m-%d-%H-%M-%S', localtime())+'.txt'))
+                osp.join(
+                    save_path,
+                    "logs/",
+                    strftime("%Y-%m-%d-%H-%M-%S", localtime()) + ".txt",
+                )
+            )
             vlog.setLevel(logging.INFO)
             vlog.setFormatter(formatter)
             self.logger.addHandler(vlog)
@@ -45,70 +97,170 @@ class MessageManager:
         console.setLevel(logging.DEBUG)
         self.logger.addHandler(console)
 
-    def append(self, info):
+        self.init_wandb(save_path, logger_cfg or {}, config, phase)
+
+    def init_wandb(
+        self,
+        save_path: str,
+        logger_cfg: dict[str, Any],
+        config: dict[str, Any] | None,
+        phase: str,
+    ) -> None:
+        if not logger_cfg.get("use_wandb", False):
+            self.wandb_run = None
+            return
+
+        if wandb is None:
+            raise ImportError(
+                "wandb logging is enabled but the package is not installed. "
+                "Install it with `uv sync --extra wandb`."
+            )
+
+        data_cfg = (config or {}).get("data_cfg", {})
+        model_cfg = (config or {}).get("model_cfg", {})
+        default_name = "-".join(
+            [
+                str(data_cfg.get("dataset_name", "dataset")),
+                str(model_cfg.get("model", "model")),
+                phase,
+            ]
+        )
+
+        self.wandb_run = wandb.init(
+            project=logger_cfg.get("wandb_project", "OpenGait"),
+            entity=logger_cfg.get("wandb_entity"),
+            name=logger_cfg.get("wandb_name", default_name),
+            group=logger_cfg.get("wandb_group"),
+            job_type=logger_cfg.get("wandb_job_type", phase),
+            tags=logger_cfg.get("wandb_tags", []),
+            mode=logger_cfg.get("wandb_mode", "online"),
+            resume=logger_cfg.get("wandb_resume", "allow"),
+            id=logger_cfg.get("wandb_id"),
+            dir=save_path,
+            config=config,
+            reinit=True,
+        )
+
+        if not self._close_registered:
+            atexit.register(self.close)
+            self._close_registered = True
+
+    def append(self, info) -> None:
         for k, v in info.items():
             v = [v] if not is_list(v) else v
             v = [ts2np(_) if is_tensor(_) else _ for _ in v]
             info[k] = v
         self.info_dict.append(info)
 
-    def flush(self):
+    def flush(self) -> None:
         self.info_dict.clear()
-        self.writer.flush()
+        if self.writer is not None:
+            self.writer.flush()
 
-    def write_to_tensorboard(self, summary):
+    def write_to_tensorboard(self, summary) -> None:
+        if self.writer is None:
+            return
 
         for k, v in summary.items():
-            module_name = k.split('/')[0]
+            module_name = k.split("/")[0]
             if module_name not in self.writer_hparams:
                 self.log_warning(
-                    'Not Expected --Summary-- type [{}] appear!!!{}'.format(k, self.writer_hparams))
+                    "Not Expected --Summary-- type [{}] appear!!!{}".format(
+                        k, self.writer_hparams
+                    )
+                )
                 continue
-            board_name = k.replace(module_name + "/", '')
-            writer_module = getattr(self.writer, 'add_' + module_name)
+            board_name = k.replace(module_name + "/", "")
+            writer_module = getattr(self.writer, "add_" + module_name)
             v = v.detach() if is_tensor(v) else v
-            v = vutils.make_grid(
-                v, normalize=True, scale_each=True) if 'image' in module_name else v
-            if module_name == 'scalar':
+            v = (
+                vutils.make_grid(v, normalize=True, scale_each=True)
+                if "image" in module_name
+                else v
+            )
+            if module_name == "scalar":
                 try:
                     v = v.mean()
-                except:
+                except Exception:
                     v = v
             writer_module(board_name, v, self.iteration)
 
-    def log_training_info(self):
+    def write_to_wandb(self, summary) -> None:
+        if self.wandb_run is None:
+            return
+
+        wandb_summary = {}
+        for k, v in summary.items():
+            module_name = k.split("/")[0]
+            if module_name not in self.writer_hparams:
+                self.log_warning(
+                    "Not Expected --Summary-- type [{}] appear!!!{}".format(
+                        k, self.writer_hparams
+                    )
+                )
+                continue
+
+            if is_tensor(v):
+                v = v.detach().cpu()
+
+            if module_name == "scalar":
+                if is_tensor(v):
+                    wandb_summary[k] = float(v.mean().item())
+                elif isinstance(v, np.ndarray):
+                    wandb_summary[k] = float(np.mean(v))
+                else:
+                    wandb_summary[k] = float(v)
+                continue
+
+            grid = vutils.make_grid(v, normalize=True, scale_each=True)
+            wandb_summary[k] = wandb.Image(grid.permute(1, 2, 0).numpy())
+
+        if wandb_summary:
+            self.wandb_run.log(wandb_summary, step=self.iteration)
+
+    def log_training_info(self) -> None:
         now = time.time()
         string = "Iteration {:0>5}, Cost {:.2f}s".format(
-            self.iteration, now-self.time, end="")
+            self.iteration, now - self.time, end=""
+        )
         for i, (k, v) in enumerate(self.info_dict.items()):
-            if 'scalar' not in k:
+            if "scalar" not in k:
                 continue
-            k = k.replace('scalar/', '').replace('/', '_')
-            end = "\n" if i == len(self.info_dict)-1 else ""
+            k = k.replace("scalar/", "").replace("/", "_")
+            end = "\n" if i == len(self.info_dict) - 1 else ""
             string += ", {0}={1:.4f}".format(k, np.mean(v), end=end)
         self.log_info(string)
         self.reset_time()
 
-    def reset_time(self):
+    def reset_time(self) -> None:
         self.time = time.time()
 
-    def train_step(self, info, summary):
+    def train_step(self, info, summary) -> None:
         self.iteration += 1
         self.append(info)
         if self.iteration % self.log_iter == 0:
             self.log_training_info()
             self.flush()
             self.write_to_tensorboard(summary)
+            self.write_to_wandb(summary)
 
-    def log_debug(self, *args, **kwargs):
+    def log_debug(self, *args, **kwargs) -> None:
         self.logger.debug(*args, **kwargs)
 
-    def log_info(self, *args, **kwargs):
+    def log_info(self, *args, **kwargs) -> None:
         self.logger.info(*args, **kwargs)
 
-    def log_warning(self, *args, **kwargs):
+    def log_warning(self, *args, **kwargs) -> None:
         self.logger.warning(*args, **kwargs)
 
+    def close(self) -> None:
+        if self.writer is not None:
+            self.writer.close()
+            self.writer = None
+        if self.wandb_run is not None:
+            self.wandb_run.finish()
+            self.wandb_run = None
+
 
 msg_mgr = MessageManager()
 noop = NoOp()

pyproject.toml

@@ -30,6 +30,9 @@ torch = [
 parquet = [
     "pyarrow",
 ]
+wandb = [
+    "wandb",
+]
 
 [tool.setuptools]
 packages = ["opengait"]

uv.lock

@@ -3,13 +3,17 @@ revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and sys_platform != 'win32'",
     "python_full_version == '3.12.*' and sys_platform == 'win32'",
-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
-    "python_full_version == '3.11.*' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version < '3.11' and sys_platform == 'win32'",
-    "python_full_version < '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 
 [[package]]
@@ -21,6 +25,15 @@ wheels = [
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
 [[package]]
 name = "backports-zstd"
 version = "1.3.0"
@@ -128,14 +141,14 @@ wheels = [
 
 [[package]]
 name = "basedpyright"
-version = "1.38.1"
+version = "1.38.2"
 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
 dependencies = [
     { name = "nodejs-wheel-binaries" },
 ]
-sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/17/ea/4d45e3c66c609496f3069a7c9e5fbd1f9ba54097c41b89048af0d8021ea6/basedpyright-1.38.1.tar.gz", hash = "sha256:e4876aa3ef7c76569ffdcd908d4e260b8d1a1deaa8838f2486f91a10b60d68d6", size = 25267403, upload-time = "2026-02-18T09:20:45.563Z" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e4/a3/20aa7c4e83f2f614e0036300f3c352775dede0655c66814da16c37b661a9/basedpyright-1.38.2.tar.gz", hash = "sha256:b433b2b8ba745ed7520cdc79a29a03682f3fb00346d272ece5944e9e5e5daa92", size = 25277019, upload-time = "2026-02-26T11:18:43.594Z" }
 wheels = [
-    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/28/92/42f4dc30a28c052a70c939d8dbb34102674b48c89369010442038d3c888b/basedpyright-1.38.1-py3-none-any.whl", hash = "sha256:24f21661d2754687b64f3bc35efcc78781e11b08c8b2310312ed92bf178ea627", size = 12311610, upload-time = "2026-02-18T09:20:50.09Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ac/12/736cab83626fea3fe65cdafb3ef3d2ee9480c56723f2fd33921537289a5e/basedpyright-1.38.2-py3-none-any.whl", hash = "sha256:153481d37fd19f9e3adedc8629d1d071b10c5f5e49321fb026b74444b7c70e24", size = 12312475, upload-time = "2026-02-26T11:18:40.373Z" },
 ]
 
 [[package]]
@@ -214,6 +227,11 @@ dependencies = [
 ]
 sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/84/85/57c314a6b35336efbbdc13e5fc9ae13f6b60a0647cfa7c1221178ac6d8ae/brotlicffi-1.2.0.0.tar.gz", hash = "sha256:34345d8d1f9d534fcac2249e57a4c3c8801a33c9942ff9f8574f67a175e17adb", size = 476682, upload-time = "2025-11-21T18:17:57.334Z" }
 wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7c/87/ba6298c3d7f8d66ce80d7a487f2a487ebae74a79c6049c7c2990178ce529/brotlicffi-1.2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b13fb476a96f02e477a506423cb5e7bc21e0e3ac4c060c20ba31c44056e38c68", size = 433038, upload-time = "2026-03-05T17:57:37.96Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/00/49/16c7a77d1cae0519953ef0389a11a9c2e2e62e87d04f8e7afbae40124255/brotlicffi-1.2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:17db36fb581f7b951635cd6849553a95c6f2f53c1a707817d06eae5aeff5f6af", size = 1541124, upload-time = "2026-03-05T17:57:39.488Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e8/17/fab2c36ea820e2288f8c1bf562de1b6cd9f30e28d66f1ce2929a4baff6de/brotlicffi-1.2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:40190192790489a7b054312163d0ce82b07d1b6e706251036898ce1684ef12e9", size = 1541983, upload-time = "2026-03-05T17:57:41.061Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/78/c9/849a669b3b3bb8ac96005cdef04df4db658c33443a7fc704a6d4a2f07a56/brotlicffi-1.2.0.0-cp314-cp314t-win32.whl", hash = "sha256:a8079e8ecc32ecef728036a1d9b7105991ce6a5385cf51ee8c02297c90fb08c2", size = 349046, upload-time = "2026-03-05T17:57:42.76Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a4/25/09c0fd21cfc451fa38ad538f4d18d8be566746531f7f27143f63f8c45a9f/brotlicffi-1.2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:ca90c4266704ca0a94de8f101b4ec029624273380574e4cf19301acfa46c61a0", size = 385653, upload-time = "2026-03-05T17:57:44.224Z" },
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e4/df/a72b284d8c7bef0ed5756b41c2eb7d0219a1dd6ac6762f1c7bdbc31ef3af/brotlicffi-1.2.0.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:9458d08a7ccde8e3c0afedbf2c70a8263227a68dea5ab13590593f4c0a4fd5f4", size = 432340, upload-time = "2025-11-21T18:17:42.277Z" },
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/74/2b/cc55a2d1d6fb4f5d458fba44a3d3f91fb4320aa14145799fd3a996af0686/brotlicffi-1.2.0.0-cp38-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:84e3d0020cf1bd8b8131f4a07819edee9f283721566fe044a20ec792ca8fd8b7", size = 1534002, upload-time = "2025-11-21T18:17:43.746Z" },
     { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e4/9c/d51486bf366fc7d6735f0e46b5b96ca58dc005b250263525a1eea3cd5d21/brotlicffi-1.2.0.0-cp38-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:33cfb408d0cff64cd50bef268c0fed397c46fbb53944aa37264148614a62e990", size = 1536547, upload-time = "2025-11-21T18:17:45.729Z" },
@@ -432,7 +450,8 @@ version = "1.3.2"
 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
 resolution-markers = [
     "python_full_version < '3.11' and sys_platform == 'win32'",
-    "python_full_version < '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
     { name = "numpy", version = "2.2.6", source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }, marker = "python_full_version < '3.11'" },
@@ -503,11 +522,14 @@ version = "1.3.3"
 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and sys_platform != 'win32'",
     "python_full_version == '3.12.*' and sys_platform == 'win32'",
-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
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-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
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 resolution-markers = [
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-    "python_full_version < '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
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-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
-    "python_full_version == '3.11.*' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux'",
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 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform != 'win32'" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
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 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'win32'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
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@@ -1591,9 +1649,9 @@ name = "nvidia-cusolver-cu12"
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 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
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-    { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'win32'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'win32'" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
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@@ -1604,7 +1662,7 @@ name = "nvidia-cusparse-cu12"
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 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'win32'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
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@@ -1699,6 +1757,9 @@ torch = [
     { name = "torch" },
     { name = "torchvision" },
 ]
+wandb = [
+    { name = "wandb" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -1730,8 +1791,9 @@ requires-dist = [
     { name = "torch", marker = "extra == 'torch'", specifier = ">=1.10" },
     { name = "torchvision", marker = "extra == 'torch'" },
     { name = "tqdm" },
+    { name = "wandb", marker = "extra == 'wandb'" },
 ]
-provides-extras = ["torch", "parquet"]
+provides-extras = ["torch", "parquet", "wandb"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -1851,6 +1913,15 @@ wheels = [
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+[[package]]
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+wheels = [
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+]
+
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 name = "pluggy"
 version = "1.6.0"
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 ]
 
+[[package]]
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+dependencies = [
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+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
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+[[package]]
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-    "python_full_version < '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
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 ]
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-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
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-    "python_full_version == '3.11.*' and sys_platform != 'win32'",
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-    "python_full_version == '3.12.*' and sys_platform != 'win32'",
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+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
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-    "python_full_version == '3.11.*' and sys_platform != 'win32'",
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 [[package]]
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 source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
 dependencies = [
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@@ -2923,9 +3169,9 @@ dependencies = [
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 ]
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+    { name = "packaging" },
+    { name = "platformdirs" },
+    { name = "protobuf" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "requests" },
+    { name = "sentry-sdk" },
+    { name = "typing-extensions" },
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