Upload folder using huggingface_hub

.claude/.nfs0000000676cbbb9200039239

@@ -0,0 +1,13 @@
+{
+  "permissions": {
+    "allow": [
+      "Read(//home/dschneider/workspace/PROJECTS/omnifall/hf/**)",
+      "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/omnifall/hf/**)",
+      "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/wanfall/labels/**)",
+      "Bash(source ~/miniconda3/bin/activate ccode)",
+      "Bash(source:*)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

.claude/settings.local.json

@@ -4,7 +4,9 @@
       "Read(//home/dschneider/workspace/PROJECTS/omnifall/hf/**)",
       "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/omnifall/hf/**)",
       "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/wanfall/labels/**)",
-      "Bash(source ~/miniconda3/bin/activate ccode)"
+      "Bash(source ~/miniconda3/bin/activate ccode)",
+      "Bash(source:*)",
+      "WebSearch"
     ],
     "deny": [],
     "ask": []

.gitignore

@@ -3,3 +3,5 @@ create_demographic_plots.py
 create_splits.py
 export_via_to_csv.py
 extract_jsonl_metadata.py
+test_wanfall_builder.py
+

data_files/frame_wise_labels.tar.zst

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f6025389eade4c047b56a92c37b3f34b43e380fe9214a421f9b50660ecc3113
+size 356098

wanfall.py

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+"""WanFall: A Synthetic Activity Recognition Dataset
+
+This dataset builder provides access to the WanFall synthetic activity recognition dataset,
+featuring 12,000 videos with dense temporal annotations across 16 activity classes.
+
+The dataset includes rich demographic and scene metadata, enabling research in fair and
+robust activity recognition across diverse populations.
+"""
+
+import pandas as pd
+import datasets
+from datasets import BuilderConfig, GeneratorBasedBuilder, Features, Value, ClassLabel, SplitGenerator, Split
+
+
+# Dataset metadata
+_CITATION = """\
+TBD
+"""
+
+_DESCRIPTION = """\
+WanFall is a large-scale synthetic activity recognition dataset designed for fall detection
+and activities of daily living research. The dataset features computer-generated videos of
+human actors performing various activities in controlled virtual environments.
+
+**Key Features:**
+- 12,000 video clips with dense temporal annotations
+- 16 activity classes including falls, posture transitions, and static states
+- 19,228 temporal segments with frame-level precision
+- 5.0625 seconds per video clip (81 frames @ 16 fps)
+- Rich demographic metadata (soft labels): age, gender, ethnicity, body type, height, skin tone
+- Scene attributes: environment, camera angle, frame rate
+- Multiple evaluation splits: random (80/10/10) and cross-demographic (age, ethnicity, BMI)
+
+**Use Cases:**
+- Fall detection research
+- Activity recognition with temporal segmentation
+- Bias and fairness analysis across demographics
+- Cross-demographic generalization studies
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/simplexsigil2/wanfall"
+_LICENSE = "cc-by-nc-4.0"
+
+# Activity class labels (16 classes)
+_ACTIVITY_LABELS = [
+    "walk",         # 0: Walking movement
+    "fall",         # 1: Falling down action
+    "fallen",       # 2: Person on ground after fall
+    "sit_down",     # 3: Transitioning to sitting
+    "sitting",      # 4: Stationary sitting posture
+    "lie_down",     # 5: Intentionally lying down
+    "lying",        # 6: Stationary lying posture
+    "stand_up",     # 7: Rising, mostly to standing, but also from lying to sitting.
+    "standing",     # 8: Stationary standing posture
+    "other",        # 9: Unclassified activities
+    "kneel_down",   # 10: Transitioning to kneeling
+    "kneeling",     # 11: Stationary kneeling posture
+    "squat_down",   # 12: Transitioning to squatting
+    "squatting",    # 13: Stationary squatting posture
+    "crawl",        # 14: Crawling movement
+    "jump",         # 15: Jumping action
+]
+
+# Demographic and scene metadata categories
+_AGE_GROUPS = ["toddlers_1_4", "children_5_12", "teenagers_13_17",
+               "young_adults_18_34", "middle_aged_35_64", "elderly_65_plus"]
+_GENDERS = ["male", "female"]
+_SKIN_TONES = [f"mst{i}" for i in range(1, 11)]  # Monk Skin Tone scale (1-10)
+_ETHNICITIES = ["white", "black", "asian", "hispanic_latino", "aian", "nhpi", "mena"]
+_BMI_BANDS = ["underweight", "normal", "overweight", "obese"]
+_HEIGHT_BANDS = ["short", "avg", "tall"]
+_ENVIRONMENTS = ["indoor", "outdoor"]
+_CAMERA_ELEVATIONS = ["eye", "low", "high", "top"]
+_CAMERA_AZIMUTHS = ["front", "rear", "left", "right"]
+_CAMERA_DISTANCES = ["medium", "far"]
+_CAMERA_SHOTS = ["static_wide", "static_medium_wide"]
+_SPEEDS = ["24fps_rt", "25fps_rt", "30fps_rt", "std_rt"]
+
+
+class WanFallConfig(BuilderConfig):
+    """BuilderConfig for WanFall dataset.
+
+    Args:
+        split_type: Type of data to load ("labels", "metadata", or split name like "random")
+        paths_only: If True, only return video paths for split configs (no label merging)
+        **kwargs: Keyword arguments forwarded to super.
+    """
+
+    def __init__(self, split_type="labels", paths_only=False, **kwargs):
+        super().__init__(**kwargs)
+        self.split_type = split_type
+        self.paths_only = paths_only
+
+
+class WanFall(GeneratorBasedBuilder):
+    """WanFall synthetic activity recognition dataset builder."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = WanFallConfig
+    BUILDER_CONFIGS = [
+        WanFallConfig(
+            name="labels",
+            version=VERSION,
+            description="All temporal segment labels with metadata (19,228 segments)",
+            split_type="labels",
+        ),
+        WanFallConfig(
+            name="metadata",
+            version=VERSION,
+            description="Video-level metadata without temporal segments (12,000 videos)",
+            split_type="metadata",
+        ),
+        WanFallConfig(
+            name="random",
+            version=VERSION,
+            description="Random 80/10/10 train/val/test split",
+            split_type="random",
+        ),
+        WanFallConfig(
+            name="cross_age",
+            version=VERSION,
+            description="Cross-age evaluation: train on young/middle-aged, test on children/elderly",
+            split_type="cross_age",
+        ),
+        WanFallConfig(
+            name="cross_ethnicity",
+            version=VERSION,
+            description="Cross-ethnicity evaluation: train on white/asian/hispanic, test on black/mena/nhpi",
+            split_type="cross_ethnicity",
+        ),
+        WanFallConfig(
+            name="cross_bmi",
+            version=VERSION,
+            description="Cross-BMI evaluation: train on normal/underweight, test on obese",
+            split_type="cross_bmi",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "random"
+
+    def _info(self):
+        """Specify dataset metadata and features schema."""
+
+        # Define features based on config type
+        if self.config.split_type == "metadata":
+            features = self._get_metadata_features()
+        elif self.config.paths_only:
+            features = self._get_paths_only_features()
+        else:
+            features = self._get_full_features()
+
+        # Create id2label and label2id mappings
+        id2label = {i: label for i, label in enumerate(_ACTIVITY_LABELS)}
+        label2id = {label: i for i, label in enumerate(_ACTIVITY_LABELS)}
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            # Note: Label mappings are accessible via dataset.info.features["label"].names
+        )
+
+    def _get_full_features(self):
+        """Complete feature schema with all 19 fields (temporal segments + metadata)."""
+        return Features({
+            # Core identity and temporal fields
+            "path": Value("string"),
+            "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
+            "start": Value("float32"),
+            "end": Value("float32"),
+            "subject": Value("int32"),  # -1 for WanFall (no subject tracking)
+            "cam": Value("int32"),      # -1 for WanFall (single view)
+            "dataset": Value("string"), # "wanfall" constant
+
+            # Demographic metadata
+            "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+            "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+            "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+            "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+            "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+            "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+
+            # Scene metadata
+            "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+            "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+            "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+            "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+            "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+            "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+        })
+
+    def _get_metadata_features(self):
+        """Feature schema for metadata config (video-level, no temporal segments)."""
+        return Features({
+            # Core identity (no temporal fields)
+            "path": Value("string"),
+            "dataset": Value("string"),
+
+            # Demographic metadata
+            "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+            "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+            "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+            "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+            "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+            "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+
+            # Scene metadata
+            "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+            "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+            "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+            "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+            "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+            "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+        })
+
+    def _get_paths_only_features(self):
+        """Minimal feature schema for paths-only mode."""
+        return Features({
+            "path": Value("string"),
+        })
+
+    def _split_generators(self, dl_manager):
+        """Define data splits and their source files."""
+
+        # Handle different config types
+        if self.config.split_type == "labels":
+            # Labels config: single split with all temporal segments
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": "labels/wanfall.csv",
+                        "split_name": "labels",
+                    },
+                ),
+            ]
+
+        elif self.config.split_type == "metadata":
+            # Metadata config: single split with video-level metadata
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": "videos/metadata.csv",
+                        "split_name": "metadata",
+                    },
+                ),
+            ]
+
+        else:
+            # Split configs (random, cross_age, cross_ethnicity, cross_bmi)
+            split_dir = f"splits/{self.config.split_type}"
+
+            # If paths_only mode, just load split files
+            # Otherwise, we need both split files and labels for merging
+            base_kwargs = {
+                "split_dir": split_dir,
+                "labels_path": "labels/wanfall.csv" if not self.config.paths_only else None,
+            }
+
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        **base_kwargs,
+                        "split_file": f"{split_dir}/train.csv",
+                        "split_name": "train",
+                    },
+                ),
+                SplitGenerator(
+                    name=Split.VALIDATION,
+                    gen_kwargs={
+                        **base_kwargs,
+                        "split_file": f"{split_dir}/val.csv",
+                        "split_name": "val",
+                    },
+                ),
+                SplitGenerator(
+                    name=Split.TEST,
+                    gen_kwargs={
+                        **base_kwargs,
+                        "split_file": f"{split_dir}/test.csv",
+                        "split_name": "test",
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath=None, split_file=None, labels_path=None,
+                          split_name=None, split_dir=None):
+        """Generate examples from CSV files.
+
+        Args:
+            filepath: Direct path to CSV file (for labels/metadata configs)
+            split_file: Path to split file containing video paths (for split configs)
+            labels_path: Path to labels file for merging (for split configs with full data)
+            split_name: Name of the split being generated
+            split_dir: Directory containing split files
+        """
+
+        # Case 1: Direct file loading (labels or metadata config)
+        if filepath is not None:
+            df = pd.read_csv(filepath)
+
+            # For metadata config, filter out temporal segment columns if they exist
+            if self.config.split_type == "metadata":
+                # Keep only metadata columns (exclude label, start, end, subject, cam if present)
+                metadata_cols = ["path", "age_group", "gender_presentation",
+                               "monk_skin_tone", "race_ethnicity_omb", "bmi_band", "height_band",
+                               "environment_category", "camera_shot", "speed",
+                               "camera_elevation", "camera_azimuth", "camera_distance"]
+                # Only keep columns that exist in the dataframe
+                available_cols = [col for col in metadata_cols if col in df.columns]
+                df = df[available_cols].drop_duplicates(subset=["path"]).reset_index(drop=True)
+                # Add dataset column manually
+                df["dataset"] = "wanfall"
+
+            # Yield examples
+            for idx, row in df.iterrows():
+                yield idx, self._row_to_example(row)
+
+        # Case 2: Split file loading with optional merging
+        elif split_file is not None:
+            # Load split paths
+            split_df = pd.read_csv(split_file)
+
+            # Paths-only mode: just return paths
+            if self.config.paths_only or labels_path is None:
+                for idx, row in split_df.iterrows():
+                    yield idx, {"path": row["path"]}
+
+            # Full mode: merge with labels
+            else:
+                # Load all labels
+                labels_df = pd.read_csv(labels_path)
+
+                # Merge split paths with labels
+                merged_df = pd.merge(split_df, labels_df, on="path", how="left")
+
+                # Yield examples
+                for idx, row in merged_df.iterrows():
+                    yield idx, self._row_to_example(row)
+
+    def _row_to_example(self, row):
+        """Convert a DataFrame row to an example dictionary with proper types.
+
+        Args:
+            row: pandas Series representing one row
+
+        Returns:
+            Dictionary with properly typed values for the features schema
+        """
+        example = {}
+
+        # Always include path
+        example["path"] = str(row["path"])
+
+        # Include fields based on what's available in the row
+        if "label" in row and pd.notna(row["label"]):
+            example["label"] = int(row["label"])
+
+        if "start" in row and pd.notna(row["start"]):
+            example["start"] = float(row["start"])
+
+        if "end" in row and pd.notna(row["end"]):
+            example["end"] = float(row["end"])
+
+        if "subject" in row and pd.notna(row["subject"]):
+            example["subject"] = int(row["subject"])
+
+        if "cam" in row and pd.notna(row["cam"]):
+            example["cam"] = int(row["cam"])
+
+        if "dataset" in row and pd.notna(row["dataset"]):
+            example["dataset"] = str(row["dataset"])
+
+        # Demographic metadata (categorical - keep as strings, ClassLabel handles conversion)
+        demographic_fields = [
+            "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band"
+        ]
+        for field in demographic_fields:
+            if field in row and pd.notna(row[field]):
+                example[field] = str(row[field])
+
+        # Scene metadata (categorical)
+        scene_fields = [
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance"
+        ]
+        for field in scene_fields:
+            if field in row and pd.notna(row[field]):
+                example[field] = str(row[field])
+
+        return example