Add a WIP NaFlex compatible mixup/cutmix for testing

timm/data/naflex_mixup.py

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+"""Variable‑size Mixup / CutMix utilities for NaFlex data loaders.
+
+This module provides:
+
+* `mix_batch_variable_size` – pixel‑level Mixup/CutMix that operates on a
+  list of images whose spatial sizes differ, mixing only their central overlap
+  so no resizing is required.
+* `pairwise_mixup_target` – builds soft‑label targets that exactly match the
+  per‑sample pixel provenance produced by the mixer.
+* `NaFlexMixup` – a callable functor that wraps the two helpers and stores
+  all augmentation hyper‑parameters in one place, making it easy to plug into
+  different dataset wrappers.
+
+"""
+import math
+import random
+from typing import Dict, List, Tuple
+
+import torch
+
+
+def mix_batch_variable_size(
+        imgs: List[torch.Tensor],
+        *,
+        mixup_alpha: float = 0.8,
+        cutmix_alpha: float = 1.0,
+        switch_prob: float = 0.5,
+        local_shuffle: int = 4,
+) -> Tuple[List[torch.Tensor], List[float], Dict[int, int], bool]:
+    """Apply Mixup or CutMix on a batch of variable‑sized images.
+
+    The function first sorts images by aspect ratio and pairs neighbouring
+    samples (optionally shuffling within small windows so pairs vary between
+    epochs).  Only the mutual central‑overlap region of each pair is mixed
+
+    Args:
+        imgs: List of transformed images shaped (C, H, W).  Heights and
+            widths may differ between samples.
+        mixup_alpha: Beta‑distribution *α* for Mixup.  Set to 0 to disable Mixup.
+        cutmix_alpha: Beta‑distribution *α* for CutMix.  Set to 0 to disable CutMix.
+        switch_prob: Probability of using CutMix when both Mixup and CutMix are enabled.
+        local_shuffle: Size of local windows that are randomly shuffled after aspect sorting.
+            A value of 0 turns shuffling off.
+
+    Returns:
+        mixed_imgs: List of mixed images.
+        lam_list: Per‑sample lambda values representing the degree of mixing.
+        pair_to: Mapping i -> j describing which sample was mixed with which (absent for unmatched odd sample).
+        use_cutmix: True if CutMix was used for this call, False if Mixup was used.
+    """
+    if len(imgs) < 2:
+        raise ValueError("Need at least two images to perform Mixup/CutMix.")
+
+    # Decide augmentation mode and raw λ
+    if mixup_alpha > 0.0 and cutmix_alpha > 0.0:
+        use_cutmix = torch.rand(()).item() < switch_prob
+        alpha = cutmix_alpha if use_cutmix else mixup_alpha
+    elif mixup_alpha > 0.0:
+        use_cutmix = False
+        alpha = mixup_alpha
+    elif cutmix_alpha > 0.0:
+        use_cutmix = True
+        alpha = cutmix_alpha
+    else:
+        raise ValueError("Both mixup_alpha and cutmix_alpha are zero – nothing to do.")
+
+    lam_raw = torch.distributions.Beta(alpha, alpha).sample().item()
+    lam_raw = max(0.0, min(1.0, lam_raw))  # numerical safety
+
+    # Pair images by nearest aspect ratio
+    order = sorted(range(len(imgs)), key=lambda i: imgs[i].shape[2] / imgs[i].shape[1])
+    if local_shuffle > 1:
+        for start in range(0, len(order), local_shuffle):
+            random.shuffle(order[start: start + local_shuffle])
+
+    pair_to: Dict[int, int] = {}
+    for a, b in zip(order[::2], order[1::2]):
+        pair_to[a] = b
+        pair_to[b] = a
+
+    odd_one = order[-1] if len(imgs) % 2 else None
+
+    mixed_imgs: List[torch.Tensor] = [None] * len(imgs)
+    lam_list: List[float] = [1.0] * len(imgs)
+
+    for i in range(len(imgs)):
+        if i == odd_one:
+            mixed_imgs[i] = imgs[i]
+            continue
+
+        j = pair_to[i]
+        xi, xj = imgs[i], imgs[j]
+        _, hi, wi = xi.shape
+        _, hj, wj = xj.shape
+        dest_area = hi * wi
+
+        # Central overlap common to both images
+        oh, ow = min(hi, hj), min(wi, wj)
+        overlap_area = oh * ow
+        top_i, left_i = (hi - oh) // 2, (wi - ow) // 2
+        top_j, left_j = (hj - oh) // 2, (wj - ow) // 2
+
+        xi = xi.clone()
+        if use_cutmix:
+            # CutMix: random rectangle inside the overlap
+            cut_ratio = math.sqrt(1.0 - lam_raw)
+            ch, cw = int(oh * cut_ratio), int(ow * cut_ratio)
+            cut_area = ch * cw
+            y_off = random.randint(0, oh - ch)
+            x_off = random.randint(0, ow - cw)
+
+            yl_i, xl_i = top_i + y_off, left_i + x_off
+            yl_j, xl_j = top_j + y_off, left_j + x_off
+            xi[:, yl_i: yl_i + ch, xl_i: xl_i + cw] = xj[:, yl_j: yl_j + ch, xl_j: xl_j + cw]
+            mixed_imgs[i] = xi
+
+            corrected_lam = 1.0 - cut_area / float(dest_area)
+            lam_list[i] = corrected_lam
+            #print(i, 'Doing cutmix', yl_i, xl_i, yl_j, xl_j, ch, cw, lam_raw, corrected_lam)
+        else:
+            # Mixup: blend the entire overlap region
+            patch_i = xi[:, top_i: top_i + oh, left_i: left_i + ow]
+            patch_j = xj[:, top_j: top_j + oh, left_j: left_j + ow]
+
+            blended = patch_i.mul(lam_raw).add_(patch_j, alpha=1.0 - lam_raw)
+            xi[:, top_i: top_i + oh, left_i: left_i + ow] = blended
+            mixed_imgs[i] = xi
+
+            corrected_lam = (dest_area - overlap_area) / dest_area + lam_raw * overlap_area / dest_area
+            lam_list[i] = corrected_lam
+            #print(i, 'Doing mixup', top_i, left_i, top_j, left_j, (oh, ow), (hi, wi), (hj, wj), lam_raw, corrected_lam)
+
+    return mixed_imgs, lam_list, pair_to, use_cutmix
+
+
+def pairwise_mixup_target(
+        labels: torch.Tensor,
+        pair_to: Dict[int, int],
+        lam_list: List[float],
+        *,
+        num_classes: int,
+        smoothing: float = 0.0,
+) -> torch.Tensor:
+    """Create soft targets that match the pixel‑level mixing performed.
+
+    Args:
+        labels: (B,) tensor of integer class indices.
+        pair_to: Mapping of sample index to its mixed partner as returned by mix_batch_variable_size().
+        lam_list: Per‑sample fractions of self pixels, also from the mixer.
+        num_classes: Total number of classes in the dataset.
+        smoothing: Label‑smoothing value in the range [0, 1).
+
+    Returns:
+        Tensor of shape (B, num_classes) whose rows sum to 1.
+    """
+    off_val = smoothing / num_classes
+    on_val = 1.0 - smoothing + off_val
+
+    y_onehot = torch.full((labels.size(0), num_classes), off_val, dtype=torch.float32, device=labels.device)
+    y_onehot.scatter_(1, labels.unsqueeze(1), on_val)
+
+    targets = y_onehot.clone()
+    for i, j in pair_to.items():
+        lam = lam_list[i]
+        targets[i].mul_(lam).add_(y_onehot[j], alpha=1.0 - lam)
+
+    return targets
+
+
+class NaFlexMixup:
+    """Callable wrapper that combines mixing and target generation."""
+
+    def __init__(
+            self,
+            *,
+            num_classes: int,
+            mixup_alpha: float = 0.8,
+            cutmix_alpha: float = 1.0,
+            switch_prob: float = 0.5,
+            local_shuffle: int = 4,
+            smoothing: float = 0.0,
+    ) -> None:
+        """Configure the augmentation.
+
+        Args:
+            num_classes: Total number of classes.
+            mixup_alpha: Beta α for Mixup. 0 disables Mixup.
+            cutmix_alpha: Beta α for CutMix. 0 disables CutMix.
+            switch_prob: Probability of selecting CutMix when both modes are enabled.
+            local_shuffle: Window size used to shuffle images after aspect sorting so pairings vary between epochs.
+            smoothing: Label‑smoothing value. 0 disables smoothing.
+        """
+        self.num_classes = num_classes
+        self.mixup_alpha = mixup_alpha
+        self.cutmix_alpha = cutmix_alpha
+        self.switch_prob = switch_prob
+        self.local_shuffle = local_shuffle
+        self.smoothing = smoothing
+
+    def __call__(
+            self,
+            imgs: List[torch.Tensor],
+            labels: torch.Tensor,
+    ) -> Tuple[List[torch.Tensor], torch.Tensor]:
+        """Apply the augmentation and generate matching targets.
+
+        Args:
+            imgs: List of already‑transformed images shaped (C, H, W).
+            labels: Hard labels with shape (B,).
+
+        Returns:
+            mixed_imgs: List of mixed images in the same order and shapes as the input.
+            targets: Soft‑label tensor shaped (B, num_classes) suitable for cross‑entropy with soft targets.
+        """
+        if isinstance(labels, (list, tuple)):
+            labels = torch.tensor(labels)
+
+        mixed_imgs, lam_list, pair_to, _ = mix_batch_variable_size(
+            imgs,
+            mixup_alpha=self.mixup_alpha,
+            cutmix_alpha=self.cutmix_alpha,
+            switch_prob=self.switch_prob,
+            local_shuffle=self.local_shuffle,
+        )
+
+        targets = pairwise_mixup_target(
+            labels,
+            pair_to,
+            lam_list,
+            num_classes=self.num_classes,
+            smoothing=self.smoothing,
+        )
+        return mixed_imgs, targets.unbind(0)