From b3cb5f32752c22312e0abcbfb599eabc2e14a4bf Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Sun, 16 Feb 2020 20:08:17 -0800
Subject: [PATCH 1/5] Working on CutMix impl as per #8, integrating with Mixup,
 currently experimenting...

---
 timm/data/__init__.py |   2 +-
 timm/data/dataset.py  |   2 +-
 timm/data/mixup.py    | 184 +++++++++++++++++++++++++++++++++++++++---
 train.py              |  11 ++-
 4 files changed, 183 insertions(+), 16 deletions(-)

diff --git a/timm/data/__init__.py b/timm/data/__init__.py
index ee2240b4..8e261617 100644
--- a/timm/data/__init__.py
+++ b/timm/data/__init__.py
@@ -4,6 +4,6 @@ from .dataset import Dataset, DatasetTar, AugMixDataset
 from .transforms import *
 from .loader import create_loader
 from .transforms_factory import create_transform
-from .mixup import mixup_batch, FastCollateMixup
+from .mixup import mix_batch, FastCollateMixup, FastCollateMixupBatchwise, FastCollateMixupElementwise
 from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
     rand_augment_transform, auto_augment_transform
diff --git a/timm/data/dataset.py b/timm/data/dataset.py
index 2ce79e7e..4c6bef96 100644
--- a/timm/data/dataset.py
+++ b/timm/data/dataset.py
@@ -89,7 +89,7 @@ class Dataset(data.Dataset):
         return img, target
 
     def __len__(self):
-        return len(self.imgs)
+        return len(self.samples)
 
     def filenames(self, indices=[], basename=False):
         if indices:
diff --git a/timm/data/mixup.py b/timm/data/mixup.py
index 4678472d..1b298af0 100644
--- a/timm/data/mixup.py
+++ b/timm/data/mixup.py
@@ -1,5 +1,30 @@
+""" Mixup and Cutmix
+
+Papers:
+mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
+
+CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
+
+Code Reference:
+CutMix: https://github.com/clovaai/CutMix-PyTorch
+
+Hacked together by Ross Wightman
+"""
+
 import numpy as np
 import torch
+import math
+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'):
@@ -12,7 +37,7 @@ def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
     on_value = 1. - smoothing + off_value
     y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device)
     y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device)
-    return lam*y1 + (1. - lam)*y2
+    return y1 * lam + y2 * (1. - lam)
 
 
 def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False):
@@ -24,28 +49,167 @@ def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disab
     return input, target
 
 
+def rand_bbox(size, ratio):
+    H, W = size[-2:]
+    ratio = max(min(ratio, 0.8), 0.2)
+    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 cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False):
+    lam = 1.
+    if not disable:
+        lam = np.random.beta(alpha, alpha)
+    if lam != 1:
+        ratio = math.sqrt(1. - lam)
+        yl, yh, xl, xh = rand_bbox(input.size(), ratio)
+        input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh]
+    target = mixup_target(target, num_classes, lam, smoothing)
+    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() > 0.5)
+    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):
+    mode = _resolve_mode(mode)
+    if mode == MixupMode.CUTMIX:
+        return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
+    else:
+        return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
+
+
 class FastCollateMixup:
+    """Fast Collate Mixup that applies different params to each element + flipped pair
 
-    def __init__(self, mixup_alpha=1., label_smoothing=0.1, num_classes=1000):
+    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):
         self.mixup_alpha = mixup_alpha
         self.label_smoothing = label_smoothing
         self.num_classes = num_classes
+        self.mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
         self.mixup_enabled = True
+        self.correct_lam = False  # correct lambda based on clipped area for cutmix
+
+    def _do_mix(self, tensor, batch):
+        batch_size = len(batch)
+        lam_out = torch.ones(batch_size)
+        for i in range(batch_size//2):
+            j = batch_size - i - 1
+            lam = 1.
+            if self.mixup_enabled:
+                lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+
+            if _resolve_mode(self.mode) == MixupMode.CUTMIX:
+                mixed_i, mixed_j = batch[i][0].astype(np.float32), batch[j][0].astype(np.float32)
+                ratio = math.sqrt(1. - lam)
+                if lam != 1:
+                    yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
+                    mixed_i[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
+                    mixed_j[:, yl:yh, xl:xh] = batch[i][0][:, yl:yh, xl:xh].astype(np.float32)
+                    if self.correct_lam:
+                        lam_corrected = (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
+                        lam_out[i] -= lam_corrected
+                        lam_out[j] -= lam_corrected
+                    else:
+                        lam_out[i] = lam
+                        lam_out[j] = lam
+            else:
+                mixed_i = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                mixed_j = batch[j][0].astype(np.float32) * lam + batch[i][0].astype(np.float32) * (1 - lam)
+                lam_out[i] = lam
+                lam_out[j] = lam
+            np.round(mixed_i, out=mixed_i)
+            np.round(mixed_j, out=mixed_j)
+            tensor[i] += torch.from_numpy(mixed_i.astype(np.uint8))
+            tensor[j] += torch.from_numpy(mixed_j.astype(np.uint8))
+        return lam_out
 
     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)
+        lam = self._do_mix(tensor, batch)
+        target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
+        target = mixup_target(target, self.num_classes, lam.unsqueeze(1), self.label_smoothing, device='cpu')
+
+        return tensor, target
+
+
+class FastCollateMixupElementwise(FastCollateMixup):
+    """Fast Collate Mixup that applies different params to each batch element
+
+    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(FastCollateMixupElementwise, self).__init__(mixup_alpha, label_smoothing, num_classes, mode)
+
+    def _do_mix(self, tensor, batch):
+        batch_size = len(batch)
+        lam_out = torch.ones(batch_size)
+        for i in range(batch_size):
+            lam = 1.
+            if self.mixup_enabled:
+                lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+
+            if _resolve_mode(self.mode) == MixupMode.CUTMIX:
+                mixed = batch[i][0].astype(np.float32)
+                ratio = math.sqrt(1. - lam)
+                if lam != 1:
+                    yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
+                    mixed[:, yl:yh, xl:xh] = batch[batch_size - i - 1][0][:, yl:yh, xl:xh].astype(np.float32)
+                    if self.correct_lam:
+                        lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
+                    else:
+                        lam_out[i] = lam
+            else:
+                mixed = batch[i][0].astype(np.float32) * lam + \
+                        batch[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
+                lam_out[i] = lam
+            np.round(mixed, out=mixed)
+            tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
+        return lam_out
+
+
+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_out = torch.ones(batch_size)
         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 and self.correct_lam:
+                ratio = math.sqrt(1. - lam)
+                yl, yh, xl, xh = rand_bbox(batch[0][0].shape, ratio)
+                lam = 1 - (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
 
-        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')
-
-        tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
         for i in range(batch_size):
-            mixed = batch[i][0].astype(np.float32) * lam + \
-                    batch[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
+            if cutmix:
+                mixed = batch[i][0].astype(np.float32)
+                if lam != 1:
+                    mixed[:, yl:yh, xl:xh] = batch[batch_size - i - 1][0][:, yl:yh, xl:xh].astype(np.float32)
+                    lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
+            else:
+                mixed = batch[i][0].astype(np.float32) * lam + \
+                        batch[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
             np.round(mixed, out=mixed)
             tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
-
-        return tensor, target
+        return lam
diff --git a/train.py b/train.py
index 32beb418..f8fdf698 100755
--- a/train.py
+++ b/train.py
@@ -28,7 +28,8 @@ except ImportError:
     from torch.nn.parallel import DistributedDataParallel as DDP
     has_apex = False
 
-from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mixup_batch, AugMixDataset
+from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mix_batch, AugMixDataset,\
+    FastCollateMixupElementwise, FastCollateMixupBatchwise
 from timm.models import create_model, resume_checkpoint, convert_splitbn_model
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
@@ -134,6 +135,8 @@ 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',
+                    help='Mixup mode ("mixup", "cutmix", "random", default: "mixup")')
 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,
@@ -352,7 +355,7 @@ def main():
     collate_fn = None
     if args.prefetcher and args.mixup > 0:
         assert not num_aug_splits  # collate conflict (need to support deinterleaving in collate mixup)
-        collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes)
+        collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes, args.mixup_mode)
 
     if num_aug_splits > 1:
         dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
@@ -504,10 +507,10 @@ def train_epoch(
         if not args.prefetcher:
             input, target = input.cuda(), target.cuda()
             if args.mixup > 0.:
-                input, target = mixup_batch(
+                input, target = mix_batch(
                     input, target,
                     alpha=args.mixup, num_classes=args.num_classes, smoothing=args.smoothing,
-                    disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch)
+                    disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch, mode=args.mixup_mode)
 
         output = model(input)
 

From 670c61b28ffd123267df4126fc700fd8f2837d22 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 17 Feb 2020 11:00:54 -0800
Subject: [PATCH 2/5] Some cutmix/mixup cleanup/fixes

---
 timm/data/mixup.py | 55 +++++++++++++++++++++++++++-------------------
 1 file changed, 32 insertions(+), 23 deletions(-)

diff --git a/timm/data/mixup.py b/timm/data/mixup.py
index 1b298af0..a59fa1f3 100644
--- a/timm/data/mixup.py
+++ b/timm/data/mixup.py
@@ -14,6 +14,7 @@ Hacked together by Ross Wightman
 import numpy as np
 import torch
 import math
+import numbers
 from enum import IntEnum
 
 
@@ -49,9 +50,17 @@ def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disab
     return input, target
 
 
+def calc_ratio(lam, minmax=None):
+    ratio = math.sqrt(1 - lam)
+    if minmax is not None:
+        if isinstance(minmax, numbers.Number):
+            minmax = (minmax, 1 - minmax)
+        ratio = np.clip(ratio, minmax[0], minmax[1])
+    return ratio
+
+
 def rand_bbox(size, ratio):
     H, W = size[-2:]
-    ratio = max(min(ratio, 0.8), 0.2)
     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)
@@ -59,14 +68,15 @@ def rand_bbox(size, ratio):
     return yl, yh, xl, xh
 
 
-def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False):
+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:
-        ratio = math.sqrt(1. - lam)
-        yl, yh, xl, xh = rand_bbox(input.size(), ratio)
+        yl, yh, xl, xh = rand_bbox(input.size(), calc_ratio(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])
     target = mixup_target(target, num_classes, lam, smoothing)
     return input, target
 
@@ -82,9 +92,9 @@ def mix_batch(
         input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, mode=MixupMode.MIXUP):
     mode = _resolve_mode(mode)
     if mode == MixupMode.CUTMIX:
-        return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
-    else:
         return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
+    else:
+        return mixup_batch(input, target, alpha, num_classes, smoothing, disable)
 
 
 class FastCollateMixup:
@@ -99,6 +109,7 @@ class FastCollateMixup:
         self.mode = MixupMode.from_str(mode) if isinstance(mode, str) else mode
         self.mixup_enabled = True
         self.correct_lam = False  # correct lambda based on clipped area for cutmix
+        self.ratio_minmax = None  # (0.2, 0.8)
 
     def _do_mix(self, tensor, batch):
         batch_size = len(batch)
@@ -111,7 +122,7 @@ class FastCollateMixup:
 
             if _resolve_mode(self.mode) == MixupMode.CUTMIX:
                 mixed_i, mixed_j = batch[i][0].astype(np.float32), batch[j][0].astype(np.float32)
-                ratio = math.sqrt(1. - lam)
+                ratio = calc_ratio(lam, self.ratio_minmax)
                 if lam != 1:
                     yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
                     mixed_i[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
@@ -132,7 +143,7 @@ class FastCollateMixup:
             np.round(mixed_j, out=mixed_j)
             tensor[i] += torch.from_numpy(mixed_i.astype(np.uint8))
             tensor[j] += torch.from_numpy(mixed_j.astype(np.uint8))
-        return lam_out
+        return lam_out.unsqueeze(1)
 
     def __call__(self, batch):
         batch_size = len(batch)
@@ -140,7 +151,7 @@ class FastCollateMixup:
         tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
         lam = self._do_mix(tensor, batch)
         target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
-        target = mixup_target(target, self.num_classes, lam.unsqueeze(1), self.label_smoothing, device='cpu')
+        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
 
         return tensor, target
 
@@ -157,27 +168,27 @@ class FastCollateMixupElementwise(FastCollateMixup):
         batch_size = len(batch)
         lam_out = torch.ones(batch_size)
         for i in range(batch_size):
+            j = batch_size - i - 1
             lam = 1.
             if self.mixup_enabled:
                 lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
 
             if _resolve_mode(self.mode) == MixupMode.CUTMIX:
                 mixed = batch[i][0].astype(np.float32)
-                ratio = math.sqrt(1. - lam)
                 if lam != 1:
+                    ratio = calc_ratio(lam)
                     yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
-                    mixed[:, yl:yh, xl:xh] = batch[batch_size - i - 1][0][:, yl:yh, xl:xh].astype(np.float32)
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
                     if self.correct_lam:
                         lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
                     else:
                         lam_out[i] = lam
             else:
-                mixed = batch[i][0].astype(np.float32) * lam + \
-                        batch[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
+                mixed = batch[i][0].astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
                 lam_out[i] = lam
             np.round(mixed, out=mixed)
             tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
-        return lam_out
+        return lam_out.unsqueeze(1)
 
 
 class FastCollateMixupBatchwise(FastCollateMixup):
@@ -191,25 +202,23 @@ class FastCollateMixupBatchwise(FastCollateMixup):
 
     def _do_mix(self, tensor, batch):
         batch_size = len(batch)
-        lam_out = torch.ones(batch_size)
         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 and self.correct_lam:
-                ratio = math.sqrt(1. - lam)
-                yl, yh, xl, xh = rand_bbox(batch[0][0].shape, ratio)
-                lam = 1 - (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
+            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[batch_size - i - 1][0][:, yl:yh, xl:xh].astype(np.float32)
-                    lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-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[batch_size - i - 1][0].astype(np.float32) * (1 - lam)
+                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

From f471c17c9d12dea9385edc395816fb5d57cf3412 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 11 Aug 2020 00:10:33 -0700
Subject: [PATCH 3/5] More cutmix/mixup overhaul, ready to kick-off some
 trials.

---
 timm/data/mixup.py | 240 ++++++++++++++++++++++++++-------------------
 train.py           |  24 +++--
 2 files changed, 159 insertions(+), 105 deletions(-)

diff --git a/timm/data/mixup.py b/timm/data/mixup.py
index cf6df6f6..de19c616 100644
--- a/timm/data/mixup.py
+++ b/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:
-        if isinstance(minmax, numbers.Number):
-            minmax = (minmax, 1 - minmax)
-        ratio = np.clip(ratio, minmax[0], minmax[1])
-    return ratio
-
-
-def rand_bbox(size, ratio):
-    H, W = size[-2:]
-    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)
+    img_h, img_w = size[-2:]
+    cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
+    margin_y, margin_x = int(border * cut_h), int(border * cut_w)
+    cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
+    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)
+    xl = np.clip(cx - cut_w // 2, 0, img_w)
+    xh = np.clip(cx + cut_w // 2, 0, img_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):
-    mode = _resolve_mode(mode)
-    if mode == MixupMode.CUTMIX:
-        return cutmix_batch(input, target, alpha, num_classes, smoothing, disable)
+        input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5,
+        num_classes=1000, smoothing=0.1, disable=False):
+    # FIXME test this version
+    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.mixup_enabled = True
-        self.correct_lam = True  # correct lambda based on clipped area for cutmix
-        self.ratio_minmax = None  # (0.2, 0.8)
+        self.elementwise = elementwise
+        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)
 
-    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.
-            if self.mixup_enabled:
-                lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
-
-            if _resolve_mode(self.mode) == MixupMode.CUTMIX:
-                mixed = batch[i][0].astype(np.float32)
-                if lam != 1:
-                    ratio = calc_ratio(lam)
-                    yl, yh, xl, xh = rand_bbox(tensor.size(), ratio)
+            lam = lam_out[i]
+            mixed = batch[i][0].astype(np.float32)
+            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)
                     mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
-                    if self.correct_lam:
-                        lam_out[i] -= (yh - yl) * (xh - xl) / (tensor.shape[-2] * tensor.shape[-1])
-                    else:
-                        lam_out[i] = lam
+                    lam_out[i] = lam
+                else:
+                    mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - 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)
-                lam_out[i] = lam
-            np.round(mixed, out=mixed)
-            tensor[i] += torch.from_numpy(mixed.astype(np.uint8))
-        return lam_out.unsqueeze(1)
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam = lam_mix
+
+        if use_cutmix:
+            (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)
-        lam = self._do_mix(tensor, batch)
+        output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
+        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
-
-
-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)
+        return output, target
 
-    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
diff --git a/train.py b/train.py
index af1b81dd..3b038d53 100755
--- a/train.py
+++ b/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',
-                    help='Mixup mode. One of "mixup", "cutmix", "random" (default: "mixup")')
+parser.add_argument('--cutmix', type=float, default=0.0,
+                    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,
-                    disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch, mode=args.mixup_mode)
+                    mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, prob=args.mixup_prob,
+                    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)
 

From cd23f553974f0cda6b07761ba2d4a88e82236966 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 11 Aug 2020 12:17:43 -0700
Subject: [PATCH 4/5] Fix mixed prec issues with new mixup code

---
 timm/data/mixup.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/timm/data/mixup.py b/timm/data/mixup.py
index de19c616..a018ea07 100644
--- a/timm/data/mixup.py
+++ b/timm/data/mixup.py
@@ -72,7 +72,7 @@ def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, cou
         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])
+        lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
     return (yl, yu, xl, xu), lam
 
 
@@ -84,7 +84,7 @@ def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disa
         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])
+            lam = 1. - (yh - yl) * (xh - xl) / float(input.shape[-2] * input.shape[-1])
     target = mixup_target(target, num_classes, lam, smoothing)
     return input, target
 
@@ -139,7 +139,7 @@ class FastCollateMixup:
 
     def _mix_elem(self, output, batch):
         batch_size = len(batch)
-        lam_out = np.ones(batch_size)
+        lam_out = np.ones(batch_size, dtype=np.float32)
         use_cutmix = np.zeros(batch_size).astype(np.bool)
         if self.mixup_enabled:
             if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
@@ -155,22 +155,23 @@ class FastCollateMixup:
                 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)
+            lam_out = np.where(np.random.rand(batch_size) < self.prob, lam_mix.astype(np.float32), lam_out)
 
         for i in range(batch_size):
             j = batch_size - i - 1
             lam = lam_out[i]
-            mixed = batch[i][0].astype(np.float32)
+            mixed = batch[i][0]
             if lam != 1.:
                 if use_cutmix[i]:
+                    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].astype(np.float32)
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                     lam_out[i] = lam
                 else:
-                    mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
                     lam_out[i] = lam
-                np.round(mixed, out=mixed)
+                    np.round(mixed, out=mixed)
             output[i] += torch.from_numpy(mixed.astype(np.uint8))
         return torch.tensor(lam_out).unsqueeze(1)
 
@@ -190,7 +191,7 @@ class FastCollateMixup:
                 lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
             else:
                 assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
-            lam = lam_mix
+            lam = float(lam_mix)
 
         if use_cutmix:
             (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
@@ -198,13 +199,14 @@ class FastCollateMixup:
 
         for i in range(batch_size):
             j = batch_size - i - 1
-            mixed = batch[i][0].astype(np.float32)
+            mixed = batch[i][0]
             if lam != 1.:
                 if use_cutmix:
-                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh].astype(np.float32)
+                    mixed = mixed.copy()
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
                 else:
-                    mixed = mixed * lam + batch[j][0].astype(np.float32) * (1 - lam)
-                np.round(mixed, out=mixed)
+                    mixed = mixed.astype(np.float32) * 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
 

From 8c9814e3f500e8b37aae86dd4db10aba2c295bd2 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Wed, 12 Aug 2020 17:01:32 -0700
Subject: [PATCH 5/5] Final cleanup of mixup/cutmix. Element/batch modes
 working with both collate (prefetcher active) and without prefetcher.

---
 timm/data/__init__.py |   2 +-
 timm/data/mixup.py    | 214 ++++++++++++++++++++++++------------------
 train.py              |  41 ++++----
 3 files changed, 143 insertions(+), 114 deletions(-)

diff --git a/timm/data/__init__.py b/timm/data/__init__.py
index e1886fcc..15617859 100644
--- a/timm/data/__init__.py
+++ b/timm/data/__init__.py
@@ -4,7 +4,7 @@ from .dataset import Dataset, DatasetTar, AugMixDataset
 from .transforms import *
 from .loader import create_loader
 from .transforms_factory import create_transform
-from .mixup import mix_batch, FastCollateMixup
+from .mixup import Mixup, FastCollateMixup
 from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
     rand_augment_transform, auto_augment_transform
 from .real_labels import RealLabelsImagenet
diff --git a/timm/data/mixup.py b/timm/data/mixup.py
index a018ea07..63861bc7 100644
--- a/timm/data/mixup.py
+++ b/timm/data/mixup.py
@@ -10,11 +10,8 @@ CutMix: https://github.com/clovaai/CutMix-PyTorch
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
-
 import numpy as np
 import torch
-import math
-import numbers
 
 
 def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
@@ -30,20 +27,21 @@ def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
     return y1 * lam + y2 * (1. - lam)
 
 
-def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False):
-    lam = 1.
-    if not disable:
-        lam = np.random.beta(alpha, alpha)
-    input = input.mul(lam).add_(1 - lam, input.flip(0))
-    target = mixup_target(target, num_classes, lam, smoothing)
-    return input, target
-
+def rand_bbox(img_shape, lam, margin=0., count=None):
+    """ Standard CutMix bounding-box
+    Generates a random square bbox based on lambda value. This impl includes
+    support for enforcing a border margin as percent of bbox dimensions.
 
-def rand_bbox(size, lam, border=0., count=None):
-    ratio = math.sqrt(1 - lam)
-    img_h, img_w = size[-2:]
+    Args:
+        img_shape (tuple): Image shape as tuple
+        lam (float): Cutmix lambda value
+        margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
+        count (int): Number of bbox to generate
+    """
+    ratio = np.sqrt(1 - lam)
+    img_h, img_w = img_shape[-2:]
     cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
-    margin_y, margin_x = int(border * cut_h), int(border * cut_w)
+    margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
     cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
     cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
     yl = np.clip(cy - cut_h // 2, 0, img_h)
@@ -53,9 +51,20 @@ def rand_bbox(size, lam, border=0., count=None):
     return yl, yh, xl, xh
 
 
-def rand_bbox_minmax(size, minmax, count=None):
+def rand_bbox_minmax(img_shape, minmax, count=None):
+    """ Min-Max CutMix bounding-box
+    Inspired by Darknet cutmix impl, generates a random rectangular bbox
+    based on min/max percent values applied to each dimension of the input image.
+
+    Typical defaults for minmax are usually in the  .2-.3 for min and .8-.9 range for max.
+
+    Args:
+        img_shape (tuple): Image shape as tuple
+        minmax (tuple or list): Min and max bbox ratios (as percent of image size)
+        count (int): Number of bbox to generate
+    """
     assert len(minmax) == 2
-    img_h, img_w = size[-2:]
+    img_h, img_w = img_shape[-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)
@@ -66,6 +75,8 @@ def rand_bbox_minmax(size, minmax, count=None):
 
 
 def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
+    """ Generate bbox and apply lambda correction.
+    """
     if ratio_minmax is not None:
         yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
     else:
@@ -76,52 +87,22 @@ def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, cou
     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(), lam)
-        input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh]
-        if correct_lam:
-            lam = 1. - (yh - yl) * (xh - xl) / float(input.shape[-2] * input.shape[-1])
-    target = mixup_target(target, num_classes, lam, smoothing)
-    return input, target
-
-
-def mix_batch(
-        input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5,
-        num_classes=1000, smoothing=0.1, disable=False):
-    # FIXME test this version
-    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, mixup_alpha, num_classes, smoothing, disable)
-
-
-class FastCollateMixup:
-    """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
+class Mixup:
+    """ Mixup/Cutmix that applies different params to each element or whole batch
 
+    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 (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
+        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):
-        """
-
-        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
@@ -129,7 +110,7 @@ class FastCollateMixup:
             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.mix_prob = prob
         self.switch_prob = switch_prob
         self.label_smoothing = label_smoothing
         self.num_classes = num_classes
@@ -137,10 +118,9 @@ class FastCollateMixup:
         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)
 
-    def _mix_elem(self, output, batch):
-        batch_size = len(batch)
-        lam_out = np.ones(batch_size, dtype=np.float32)
-        use_cutmix = np.zeros(batch_size).astype(np.bool)
+    def _params_per_elem(self, batch_size):
+        lam = np.ones(batch_size, dtype=np.float32)
+        use_cutmix = np.zeros(batch_size, dtype=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
@@ -151,35 +131,17 @@ class FastCollateMixup:
             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)
+                use_cutmix = np.ones(batch_size, dtype=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.astype(np.float32), lam_out)
-
-        for i in range(batch_size):
-            j = batch_size - i - 1
-            lam = lam_out[i]
-            mixed = batch[i][0]
-            if lam != 1.:
-                if use_cutmix[i]:
-                    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_out[i] = lam
-                else:
-                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - 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)
+            lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam)
+        return lam, use_cutmix
 
-    def _mix_batch(self, output, batch):
-        batch_size = len(batch)
+    def _params_per_batch(self):
         lam = 1.
         use_cutmix = False
-        if self.mixup_enabled and np.random.rand() < self.prob:
+        if self.mixup_enabled and np.random.rand() < self.mix_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 \
@@ -192,17 +154,84 @@ class FastCollateMixup:
             else:
                 assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
             lam = float(lam_mix)
+        return lam, use_cutmix
 
+    def _mix_elem(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size)
+        x_orig = x.clone()  # need to keep an unmodified original for mixing source
+        for i in range(batch_size):
+            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]
+                    lam_batch[i] = lam
+                else:
+                    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_batch(self, x):
+        lam, use_cutmix = self._params_per_batch()
+        if lam == 1.:
+            return 1.
         if use_cutmix:
             (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
-                output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+            x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
+        else:
+            x_flipped = x.flip(0).mul_(1. - lam)
+            x.mul_(lam).add_(x_flipped)
+        return lam
+
+    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)
+        target = mixup_target(target, self.num_classes, lam, self.label_smoothing)
+        return x, target
+
+
+class FastCollateMixup(Mixup):
+    """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch
 
+    A Mixup impl that's performed while collating the batches.
+    """
+
+    def _mix_elem_collate(self, output, batch):
+        batch_size = len(batch)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size)
         for i in range(batch_size):
             j = batch_size - i - 1
+            lam = lam_batch[i]
             mixed = batch[i][0]
             if lam != 1.:
-                if use_cutmix:
+                if use_cutmix[i]:
                     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)
+            output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        return torch.tensor(lam_batch).unsqueeze(1)
+
+    def _mix_batch_collate(self, output, batch):
+        batch_size = len(batch)
+        lam, use_cutmix = self._params_per_batch()
+        if use_cutmix:
+            (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]
+            if lam != 1.:
+                if use_cutmix:
+                    mixed = mixed.copy()  # don't want to modify the original while iterating
                     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)
@@ -210,16 +239,15 @@ class FastCollateMixup:
             output[i] += torch.from_numpy(mixed.astype(np.uint8))
         return lam
 
-    def __call__(self, batch):
+    def __call__(self, batch, _=None):
         batch_size = len(batch)
         assert batch_size % 2 == 0, 'Batch size should be even when using this'
         output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
         if self.elementwise:
-            lam = self._mix_elem(output, batch)
+            lam = self._mix_elem_collate(output, batch)
         else:
-            lam = self._mix_batch(output, batch)
+            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')
-
         return output, target
 
diff --git a/train.py b/train.py
index 3b038d53..c28bd266 100755
--- a/train.py
+++ b/train.py
@@ -28,7 +28,7 @@ except ImportError:
     from torch.nn.parallel import DistributedDataParallel as DDP
     has_apex = False
 
-from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mix_batch, AugMixDataset
+from timm.data import Dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
 from timm.models import create_model, resume_checkpoint, convert_splitbn_model
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
@@ -398,12 +398,18 @@ def main():
     dataset_train = Dataset(train_dir)
 
     collate_fn = None
-    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(
+    mixup_fn = None
+    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
+    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,
             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)
+            collate_fn = FastCollateMixup(**mixup_args)
+        else:
+            mixup_fn = Mixup(**mixup_args)
 
     if num_aug_splits > 1:
         dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
@@ -465,17 +471,14 @@ def main():
     if args.jsd:
         assert num_aug_splits > 1  # JSD only valid with aug splits set
         train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
-        validate_loss_fn = nn.CrossEntropyLoss().cuda()
-    elif args.mixup > 0.:
-        # smoothing is handled with mixup label transform
+    elif mixup_active:
+        # smoothing is handled with mixup target transform
         train_loss_fn = SoftTargetCrossEntropy().cuda()
-        validate_loss_fn = nn.CrossEntropyLoss().cuda()
     elif args.smoothing:
         train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
-        validate_loss_fn = nn.CrossEntropyLoss().cuda()
     else:
         train_loss_fn = nn.CrossEntropyLoss().cuda()
-        validate_loss_fn = train_loss_fn
+    validate_loss_fn = nn.CrossEntropyLoss().cuda()
 
     eval_metric = args.eval_metric
     best_metric = None
@@ -503,7 +506,7 @@ def main():
             train_metrics = train_epoch(
                 epoch, model, loader_train, optimizer, train_loss_fn, args,
                 lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
-                use_amp=use_amp, model_ema=model_ema)
+                use_amp=use_amp, model_ema=model_ema, mixup_fn=mixup_fn)
 
             if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                 if args.local_rank == 0:
@@ -543,11 +546,13 @@ def main():
 
 def train_epoch(
         epoch, model, loader, optimizer, loss_fn, args,
-        lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None):
+        lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None, mixup_fn=None):
 
-    if args.prefetcher and args.mixup > 0 and loader.mixup_enabled:
-        if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
+    if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
+        if args.prefetcher and loader.mixup_enabled:
             loader.mixup_enabled = False
+        elif mixup_fn is not None:
+            mixup_fn.mixup_enabled = False
 
     batch_time_m = AverageMeter()
     data_time_m = AverageMeter()
@@ -563,12 +568,8 @@ def train_epoch(
         data_time_m.update(time.time() - end)
         if not args.prefetcher:
             input, target = input.cuda(), target.cuda()
-            if args.mixup > 0.:
-                input, target = mix_batch(
-                    input, target,
-                    mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, prob=args.mixup_prob,
-                    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)
+            if mixup_fn is not None:
+                input, target = mixup_fn(input, target)
 
         output = model(input)