More cutmix/mixup overhaul, ready to kick-off some trials.

timm/data/mixup.py

@@ -15,17 +15,6 @@ import numpy as np
 import torch
 import math
 import numbers
-from enum import IntEnum
-
-
-class MixupMode(IntEnum):
-    MIXUP = 0
-    CUTMIX = 1
-    RANDOM = 2
-
-    @classmethod
-    def from_str(cls, value):
-        return cls[value.upper()]
 
 
 def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
@@ -50,30 +39,49 @@ def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disab
     return input, target
 
 
-def calc_ratio(lam, minmax=None):
+def rand_bbox(size, lam, border=0., count=None):
     ratio = math.sqrt(1 - lam)
-    if minmax is not None:
+    img_h, img_w = size[-2:]
-        if isinstance(minmax, numbers.Number):
+    cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
-            minmax = (minmax, 1 - minmax)
+    margin_y, margin_x = int(border * cut_h), int(border * cut_w)
-        ratio = np.clip(ratio, minmax[0], minmax[1])
+    cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
-    return ratio
+    cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
-
+    yl = np.clip(cy - cut_h // 2, 0, img_h)
-
+    yh = np.clip(cy + cut_h // 2, 0, img_h)
-def rand_bbox(size, ratio):
+    xl = np.clip(cx - cut_w // 2, 0, img_w)
-    H, W = size[-2:]
+    xh = np.clip(cx + cut_w // 2, 0, img_w)
-    cut_h, cut_w = int(H * ratio), int(W * ratio)
-    cy, cx = np.random.randint(H), np.random.randint(W)
-    yl, yh = np.clip(cy - cut_h // 2, 0, H), np.clip(cy + cut_h // 2, 0, H)
-    xl, xh = np.clip(cx - cut_w // 2, 0, W), np.clip(cx + cut_w // 2, 0, W)
     return yl, yh, xl, xh
 
 
+def rand_bbox_minmax(size, minmax, count=None):
+    assert len(minmax) == 2
+    img_h, img_w = size[-2:]
+    cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
+    cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
+    yl = np.random.randint(0, img_h - cut_h, size=count)
+    xl = np.random.randint(0, img_w - cut_w, size=count)
+    yu = yl + cut_h
+    xu = xl + cut_w
+    return yl, yu, xl, xu
+
+
+def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
+    if ratio_minmax is not None:
+        yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
+    else:
+        yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
+    if correct_lam or ratio_minmax is not None:
+        bbox_area = (yu - yl) * (xu - xl)
+        lam = 1. - bbox_area / (img_shape[-2] * img_shape[-1])
+    return (yl, yu, xl, xu), lam
+
+
 def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, correct_lam=False):
     lam = 1.
     if not disable:
         lam = np.random.beta(alpha, alpha)
     if lam != 1:
-        yl, yh, xl, xh = rand_bbox(input.size(), calc_ratio(lam))
+        yl, yh, xl, xh = rand_bbox(input.size(), lam)
         input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh]
         if correct_lam:
             lam = 1 - (yh - yl) * (xh - xl) / (input.shape[-2] * input.shape[-1])
@@ -81,101 +89,135 @@ def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disa
     return input, target
 
 
-def _resolve_mode(mode):
-    mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
-    if mode == MixupMode.RANDOM:
-        mode = MixupMode(np.random.rand() > 0.7)
-    return mode  # will be one of cutmix or mixup
-
-
 def mix_batch(
-        input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, mode=MixupMode.MIXUP):
+        input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5,
-    mode = _resolve_mode(mode)
+        num_classes=1000, smoothing=0.1, disable=False):
-    if mode == MixupMode.CUTMIX:
+    # FIXME test this version
-        return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
+    if np.random.rand() > prob:
+        return input, target
+    use_cutmix = cutmix_alpha > 0. and np.random.rand() <= switch_prob
+    if use_cutmix:
+        return cutmix_batch(input, target, cutmix_alpha, num_classes, smoothing, disable)
     else:
-        return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
+        return mixup_batch(input, target, mixup_alpha, num_classes, smoothing, disable)
 
 
 class FastCollateMixup:
-    """Fast Collate Mixup that applies different params to each element + flipped pair
+    """Fast Collate Mixup/Cutmix that applies different params to each element or whole batch
 
     NOTE once experiments are done, one of the three variants will remain with this class name
+
     """
-    def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
+    def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
+                 elementwise=False, correct_lam=True, label_smoothing=0.1, num_classes=1000):
+        """
+
+        Args:
+            mixup_alpha (float): mixup alpha value, mixup is active if > 0.
+            cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
+            cutmix_minmax (float): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None
+            prob (float): probability of applying mixup or cutmix per batch or element
+            switch_prob (float): probability of using cutmix instead of mixup when both active
+            elementwise (bool): apply mixup/cutmix params per batch element instead of per batch
+            label_smoothing (float):
+            num_classes (int):
+        """
         self.mixup_alpha = mixup_alpha
+        self.cutmix_alpha = cutmix_alpha
+        self.cutmix_minmax = cutmix_minmax
+        if self.cutmix_minmax is not None:
+            assert len(self.cutmix_minmax) == 2
+            # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
+            self.cutmix_alpha = 1.0
+        self.prob = prob
+        self.switch_prob = switch_prob
         self.label_smoothing = label_smoothing
         self.num_classes = num_classes
-        self.mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
+        self.elementwise = elementwise
-        self.mixup_enabled = True
+        self.correct_lam = correct_lam  # correct lambda based on clipped area for cutmix
-        self.correct_lam = True  # correct lambda based on clipped area for cutmix
+        self.mixup_enabled = True  # set to false to disable mixing (intended tp be set by train loop)
-        self.ratio_minmax = None  # (0.2, 0.8)
 
-    def _do_mix(self, tensor, batch):
+    def _mix_elem(self, output, batch):
         batch_size = len(batch)
-        lam_out = torch.ones(batch_size)
+        lam_out = np.ones(batch_size)
+        use_cutmix = np.zeros(batch_size).astype(np.bool)
+        if self.mixup_enabled:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand(batch_size) < self.switch_prob
+                lam_mix = np.where(
+                    use_cutmix,
+                    np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
+                    np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = np.ones(batch_size).astype(np.bool)
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
+            else:
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam_out = np.where(np.random.rand(batch_size) < self.prob, lam_mix, lam_out)
+
         for i in range(batch_size):
             j = batch_size - i - 1
-            lam = 1.
+            lam = lam_out[i]
-            if self.mixup_enabled:
+            mixed = batch[i][0].astype(np.float32)
-                lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            if lam != 1.:
-
+                if use_cutmix[i]:
-            if _resolve_mode(self.mode) == MixupMode.CUTMIX:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
-                mixed = batch[i][0].astype(np.float32)
+                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
-                if lam != 1:
-                    ratio = calc_ratio(lam)
-                    yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
                     mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
-                    if self.correct_lam:
+                    lam_out[i] = lam
-                        lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
+                else:
-                    else:
+                    mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
-                        lam_out[i] = lam
+                    lam_out[i] = lam
+                np.round(mixed, out=mixed)
+            output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        return torch.tensor(lam_out).unsqueeze(1)
+
+    def _mix_batch(self, output, batch):
+        batch_size = len(batch)
+        lam = 1.
+        use_cutmix = False
+        if self.mixup_enabled and np.random.rand() < self.prob:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand() < self.switch_prob
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
+                    np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = True
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
             else:
-                mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
-                lam_out[i] = lam
+            lam = lam_mix
-            np.round(mixed, out=mixed)
+
-            tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
+        if use_cutmix:
-        return lam_out.unsqueeze(1)
+            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+
+        for i in range(batch_size):
+            j = batch_size - i - 1
+            mixed = batch[i][0].astype(np.float32)
+            if lam != 1.:
+                if use_cutmix:
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
+                else:
+                    mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                np.round(mixed, out=mixed)
+            output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        return lam
 
     def __call__(self, batch):
         batch_size = len(batch)
         assert batch_size % 2 == 0, 'Batch size should be even when using this'
-        tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
+        output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
-        lam = self._do_mix(tensor, batch)
+        if self.elementwise:
+            lam = self._mix_elem(output, batch)
+        else:
+            lam = self._mix_batch(output, batch)
         target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
         target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
 
-        return tensor, target
+        return output, target
-
-
-class FastCollateMixupBatchwise(FastCollateMixup):
-    """Fast Collate Mixup that applies same params to whole batch
-
-    NOTE this is for experimentation, may remove at some point
-    """
-
-    def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000, mode=MixupMode.MIXUP):
-        super(FastCollateMixupBatchwise, self).__init__(mixup_alpha, label_smoothing, num_classes, mode)
 
-    def _do_mix(self, tensor, batch):
-        batch_size = len(batch)
-        lam = 1.
-        cutmix = _resolve_mode(self.mode) == MixupMode.CUTMIX
-        if self.mixup_enabled:
-            lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
-            if cutmix:
-                yl, yh, xl, xh = rand_bbox(batch[0][0].shape, calc_ratio(lam))
-                if self.correct_lam:
-                    lam = 1 - (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
-
-        for i in range(batch_size):
-            j = batch_size - i - 1
-            if cutmix:
-                mixed = batch[i][0].astype(np.float32)
-                if lam != 1:
-                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
-            else:
-                mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
-            np.round(mixed, out=mixed)
-            tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
-        return lam

train.py

@@ -157,8 +157,16 @@ parser.add_argument('--resplit', action='store_true', default=False,
                     help='Do not random erase first (clean) augmentation split')
 parser.add_argument('--mixup', type=float, default=0.0,
                     help='mixup alpha, mixup enabled if > 0. (default: 0.)')
-parser.add_argument('--mixup-mode', type=str, default='mixup',
+parser.add_argument('--cutmix', type=float, default=0.0,
-                    help='Mixup mode. One of "mixup", "cutmix", "random" (default: "mixup")')
+                    help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
+parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
+                    help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
+parser.add_argument('--mixup-prob', type=float, default=1.0,
+                    help='Probability of performing mixup or cutmix when either/both is enabled')
+parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
+                    help='Probability of switching to cutmix when both mixup and cutmix enabled')
+parser.add_argument('--mixup-elem', action='store_true', default=False,
+                    help='Apply mixup/cutmix params uniquely per batch element instead of per batch.')
 parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                     help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
 parser.add_argument('--smoothing', type=float, default=0.1,
@@ -390,9 +398,12 @@ def main():
     dataset_train = Dataset(train_dir)
 
     collate_fn = None
-    if args.prefetcher and args.mixup > 0:
+    if args.prefetcher and (args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None):
         assert not num_aug_splits  # collate conflict (need to support deinterleaving in collate mixup)
-        collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes, args.mixup_mode)
+        collate_fn = FastCollateMixup(
+            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
+            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, elementwise=args.mixup_elem,
+            label_smoothing=args.smoothing, num_classes=args.num_classes)
 
     if num_aug_splits > 1:
         dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
@@ -555,8 +566,9 @@ def train_epoch(
             if args.mixup > 0.:
                 input, target = mix_batch(
                     input, target,
-                    alpha=args.mixup, num_classes=args.num_classes, smoothing=args.smoothing,
+                    mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, prob=args.mixup_prob,
-                    disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch, mode=args.mixup_mode)
+                    switch_prob=args.mixup_switch_prob, num_classes=args.num_classes, smoothing=args.smoothing,
+                    disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch)
 
         output = model(input)