src/trifuse/data/datasets.py

"""PyTorch datasets for the three modalities.

All datasets return a uniform batch dict so the trainer is modality-agnostic:
  {"slices": (T,3,H,W) | "vol": (1,D,H,W), "tab": (F,), "y": int, "subject_id": str}

Tabular features are standardized with statistics FIT ON THE TRAINING FOLD ONLY
(passed in as `tab_stats`) — never on the full dataset — to prevent leakage.
Missing tabular values are median/mode-imputed from training-fold stats too.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset

ROOT = Path(__file__).resolve().parents[3]
PROC2D = ROOT / "data" / "processed_2d"
PROC3D = ROOT / "data" / "processed_3d"

# imagenet stats for pretrained backbones (applied after per-volume z-score,
# so slices are re-scaled into the pretrained input regime)
IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)


def fit_tab_stats(df: pd.DataFrame, features) -> dict:
    """Compute train-fold imputation + standardization stats for tab features."""
    stats = {}
    for f in features:
        col = df[f].astype(float)
        med = float(col.median())
        vals = col.fillna(med)
        mu, sd = float(vals.mean()), float(vals.std())
        stats[f] = {"median": med, "mean": mu, "std": sd if sd > 1e-6 else 1.0}
    return stats


def _encode_tab(row, features, stats) -> np.ndarray:
    out = np.zeros(len(features), dtype=np.float32)
    for i, f in enumerate(features):
        v = row[f]
        s = stats[f]
        if pd.isna(v):
            v = s["median"]
        out[i] = (float(v) - s["mean"]) / s["std"]
    return out


class SliceDataset(Dataset):
    """2D / 2.5D tri-planar slices. planes/n_slices select which of the 27 to load.

    modality:
      slice2d  -> single center axial slice returned as (1,3,H,W)
      slice25d -> the requested planes x n_slices as (T,3,H,W)
    """

    def __init__(self, df, features, tab_stats, planes=("axial",), n_slices=9,
                 modality="slice25d", augment=False):
        self.df = df.reset_index(drop=True)
        self.features = features
        self.stats = tab_stats
        self.planes = planes
        self.n_slices = n_slices
        self.modality = modality
        self.augment = augment

    def __len__(self):
        return len(self.df)

    def _load_plane(self, sid, plane):
        arr = np.load(PROC2D / sid / plane / "slices.npy").astype(np.float32)  # (9,H,W)
        # select n_slices centered subset if fewer requested
        if self.n_slices < arr.shape[0]:
            start = (arr.shape[0] - self.n_slices) // 2
            arr = arr[start:start + self.n_slices]
        return arr

    def _to_rgb(self, sl):  # (H,W) -> (3,H,W) imagenet-normalized
        x = np.stack([sl, sl, sl], axis=0)
        x = (x - IMAGENET_MEAN[:, None, None]) / IMAGENET_STD[:, None, None]
        return x.astype(np.float32)

    def _augment(self, arr):  # light aug on a (n,H,W) stack
        if not self.augment:
            return arr
        if np.random.rand() < 0.5:
            arr = arr + np.random.normal(0, 0.02, arr.shape).astype(np.float32)
        if np.random.rand() < 0.5:
            arr = arr * np.random.uniform(0.95, 1.05)
        return arr

    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        sid = row["subject_id"]
        if self.modality == "slice2d":
            axial = self._load_plane(sid, "axial")
            center = axial[len(axial) // 2]
            slices = self._to_rgb(self._augment(center[None]))[0][None]  # (1,3,H,W)
        else:
            planes = []
            for p in self.planes:
                stack = self._augment(self._load_plane(sid, p))
                planes.append(np.stack([self._to_rgb(s) for s in stack]))  # (n,3,H,W)
            slices = np.concatenate(planes, axis=0)  # (T,3,H,W)
        return {
            "slices": torch.from_numpy(slices),
            "tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
            "y": int(row["class_id"]),
            "subject_id": sid,
        }


class VolumeDataset(Dataset):
    """Whole-brain 3D volumes for CNN3D / transformer / hybrid models."""

    def __init__(self, df, features, tab_stats, target="cnn3d", augment=False):
        self.df = df.reset_index(drop=True)
        self.features = features
        self.stats = tab_stats
        self.target = target  # cnn3d (128^3) or tf3d (96x112x112)
        self.augment = augment

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        sid = row["subject_id"]
        vol = np.load(PROC3D / self.target / f"{sid}.npy").astype(np.float32)
        if self.augment and np.random.rand() < 0.5:
            vol = vol + np.random.normal(0, 0.02, vol.shape).astype(np.float32)
        return {
            "vol": torch.from_numpy(vol[None]),  # (1,D,H,W)
            "tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
            "y": int(row["class_id"]),
            "subject_id": sid,
        }


class TabularDataset(Dataset):
    def __init__(self, df, features, tab_stats):
        self.df = df.reset_index(drop=True)
        self.features = features
        self.stats = tab_stats

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        return {
            "tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
            "y": int(row["class_id"]),
            "subject_id": row["subject_id"],
        }


def collate(items):
    """Uniform collate that stacks whichever tensors are present."""
    out = {"y": torch.tensor([it["y"] for it in items]),
           "subject_id": [it["subject_id"] for it in items]}
    for key in ("slices", "vol", "tab"):
        if key in items[0]:
            out[key] = torch.stack([it[key] for it in items])
    return out