More flexible mixup mode, add 'half' mode.

timm/data/mixup.py

@@ -96,13 +96,13 @@ class Mixup:
         cutmix_minmax (List[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 switching to cutmix instead of mixup when both are active
-        elementwise (bool): apply mixup/cutmix params per batch element instead of per batch
+        mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
         correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
         label_smoothing (float): apply label smoothing to the mixed target tensor
         num_classes (int): number of classes for target
     """
     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):
+                 mode='batch', correct_lam=True, label_smoothing=0.1, num_classes=1000):
         self.mixup_alpha = mixup_alpha
         self.cutmix_alpha = cutmix_alpha
         self.cutmix_minmax = cutmix_minmax
@@ -114,7 +114,7 @@ class Mixup:
         self.switch_prob = switch_prob
         self.label_smoothing = label_smoothing
         self.num_classes = num_classes
-        self.elementwise = elementwise
+        self.mode = mode
         self.correct_lam = correct_lam  # 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)
 
@@ -173,6 +173,26 @@ class Mixup:
                     x[i] = x[i] * lam + x_orig[j] * (1 - lam)
         return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
 
+    def _mix_pair(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
+        x_orig = x.clone()  # need to keep an unmodified original for mixing source
+        for i in range(batch_size // 2):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
+                    x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
+                    x[j] = x[j] * lam + x_orig[i] * (1 - lam)
+        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
+        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
+
     def _mix_batch(self, x):
         lam, use_cutmix = self._params_per_batch()
         if lam == 1.:
@@ -188,7 +208,12 @@ class Mixup:
 
     def __call__(self, x, target):
         assert len(x) % 2 == 0, 'Batch size should be even when using this'
-        lam = self._mix_elem(x) if self.elementwise else self._mix_batch(x)
+        if self.mode == 'elem':
+            lam = self._mix_elem(x)
+        elif self.mode == 'pair':
+            lam = self._mix_pair(x)
+        else:
+            lam = self._mix_batch(x)
         target = mixup_target(target, self.num_classes, lam, self.label_smoothing)
         return x, target
 
@@ -199,25 +224,57 @@ class FastCollateMixup(Mixup):
     A Mixup impl that's performed while collating the batches.
     """
 
-    def _mix_elem_collate(self, output, batch):
+    def _mix_elem_collate(self, output, batch, half=False):
         batch_size = len(batch)
-        lam_batch, use_cutmix = self._params_per_elem(batch_size)
-        for i in range(batch_size):
+        num_elem = batch_size // 2 if half else batch_size
+        assert len(output) == num_elem
+        lam_batch, use_cutmix = self._params_per_elem(num_elem)
+        for i in range(num_elem):
             j = batch_size - i - 1
             lam = lam_batch[i]
             mixed = batch[i][0]
             if lam != 1.:
                 if use_cutmix[i]:
-                    mixed = mixed.copy()
+                    if not half:
+                        mixed = mixed.copy()
                     (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                         output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
                     mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                     lam_batch[i] = lam
                 else:
                     mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
-                    lam_batch[i] = lam
-                    np.round(mixed, out=mixed)
+                    np.rint(mixed, out=mixed)
             output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        if half:
+            lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))
+        return torch.tensor(lam_batch).unsqueeze(1)
+
+    def _mix_pair_collate(self, output, batch):
+        batch_size = len(batch)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
+        for i in range(batch_size // 2):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            mixed_i = batch[i][0]
+            mixed_j = batch[j][0]
+            assert 0 <= lam <= 1.0
+            if lam < 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    patch_i = mixed_i[:, yl:yh, xl:xh].copy()
+                    mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
+                    mixed_j[:, yl:yh, xl:xh] = patch_i
+                    lam_batch[i] = lam
+                else:
+                    mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam)
+                    mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam)
+                    mixed_i = mixed_temp
+                    np.rint(mixed_j, out=mixed_j)
+                    np.rint(mixed_i, out=mixed_i)
+            output[i] += torch.from_numpy(mixed_i.astype(np.uint8))
+            output[j] += torch.from_numpy(mixed_j.astype(np.uint8))
+        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
         return torch.tensor(lam_batch).unsqueeze(1)
 
     def _mix_batch_collate(self, output, batch):
@@ -235,19 +292,25 @@ class FastCollateMixup(Mixup):
                     mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                 else:
                     mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
-                    np.round(mixed, out=mixed)
+                    np.rint(mixed, out=mixed)
             output[i] += torch.from_numpy(mixed.astype(np.uint8))
         return lam
 
     def __call__(self, batch, _=None):
         batch_size = len(batch)
         assert batch_size % 2 == 0, 'Batch size should be even when using this'
+        half = 'half' in self.mode
+        if half:
+            batch_size //= 2
         output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
-        if self.elementwise:
-            lam = self._mix_elem_collate(output, batch)
+        if self.mode == 'elem' or self.mode == 'half':
+            lam = self._mix_elem_collate(output, batch, half=half)
+        elif self.mode == 'pair':
+            lam = self._mix_pair_collate(output, batch)
         else:
             lam = self._mix_batch_collate(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')
+        target = target[:batch_size]
         return output, target
 

train.py

@@ -176,8 +176,8 @@ 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-mode', type=str, default='batch',
+                    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
 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,
@@ -444,7 +444,7 @@ def main():
     if mixup_active:
         mixup_args = dict(
             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,
+            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
             label_smoothing=args.smoothing, num_classes=args.num_classes)
         if args.prefetcher:
             assert not num_aug_splits  # collate conflict (need to support deinterleaving in collate mixup)