[Feature] Support resizemix. (#676)

* add resizemix

* skip torch.__version__ < 1.7.0

* Update mmcls/models/utils/augment/resizemix.py

Co-authored-by: Ma Zerun <mzr1996@163.com>

* Update mmcls/models/utils/augment/resizemix.py

Co-authored-by: Ma Zerun <mzr1996@163.com>

* resize -> F.interpolate

* fix docs

* fix test

* add Copyright

* add argument interpolation

Co-authored-by: Ma Zerun <mzr1996@163.com>

mmcls/models/utils/augment/__init__.py

@@ -3,5 +3,7 @@
 from .cutmix import BatchCutMixLayer
 from .identity import Identity
 from .mixup import BatchMixupLayer
+from .resizemix import BatchResizeMixLayer
 
-__all__ = ('Augments', 'BatchCutMixLayer', 'Identity', 'BatchMixupLayer')
+__all__ = ('Augments', 'BatchCutMixLayer', 'Identity', 'BatchMixupLayer',
+           'BatchResizeMixLayer')

mmcls/models/utils/augment/resizemix.py

@@ -0,0 +1,89 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from mmcls.models.utils.augment.builder import AUGMENT
+from .cutmix import BatchCutMixLayer
+from .utils import one_hot_encoding
+
+
+@AUGMENT.register_module(name='BatchResizeMix')
+class BatchResizeMixLayer(BatchCutMixLayer):
+    r"""ResizeMix Random Paste layer for batch ResizeMix.
+
+    The ResizeMix will resize an image to a small patch and paste it on another
+    image. More details can be found in `ResizeMix: Mixing Data with Preserved
+    Object Information and True Labels <https://arxiv.org/abs/2012.11101>`_
+
+    Args:
+        alpha (float): Parameters for Beta distribution. Positive(>0)
+        num_classes (int): The number of classes.
+        lam_min(float): The minimum value of lam. Defaults to 0.1.
+        lam_max(float): The maximum value of lam. Defaults to 0.8.
+        interpolation (str): algorithm used for upsampling:
+         'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'.
+         Default to 'bilinear'.
+        prob (float): mix probability. It should be in range [0, 1].
+            Default to 1.0.
+        cutmix_minmax (List[float], optional): cutmix min/max image ratio.
+            (as percent of image size). When cutmix_minmax is not None, we
+            generate cutmix bounding-box using cutmix_minmax instead of alpha
+        correct_lam (bool): Whether to apply lambda correction when cutmix bbox
+            clipped by image borders. Default to True
+        **kwargs: Any other parameters accpeted by :class:`BatchCutMixLayer`.
+
+    Note:
+        The :math:`\lambda` (``lam``) is the mixing ratio. It's a random
+        variable which follows :math:`Beta(\alpha, \alpha)` and is mapped
+        to the range [``lam_min``, ``lam_max``].
+
+        .. math::
+            \lambda = \frac{Beta(\alpha, \alpha)}
+            {\lambda_{max} - \lambda_{min}} + \lambda_{min}
+
+        And the resize ratio of source images is calculated by :math:`\lambda`:
+
+        .. math::
+            \text{ratio} = \sqrt{1-lam}
+    """
+
+    def __init__(self,
+                 alpha,
+                 num_classes,
+                 lam_min: float = 0.1,
+                 lam_max: float = 0.8,
+                 interpolation='bilinear',
+                 prob=1.0,
+                 cutmix_minmax=None,
+                 correct_lam=True,
+                 **kwargs):
+        super(BatchResizeMixLayer, self).__init__(
+            alpha=alpha,
+            num_classes=num_classes,
+            prob=prob,
+            cutmix_minmax=cutmix_minmax,
+            correct_lam=correct_lam,
+            **kwargs)
+        self.lam_min = lam_min
+        self.lam_max = lam_max
+        self.interpolation = interpolation
+
+    def cutmix(self, img, gt_label):
+        one_hot_gt_label = one_hot_encoding(gt_label, self.num_classes)
+
+        lam = np.random.beta(self.alpha, self.alpha)
+        lam = lam * (self.lam_max - self.lam_min) + self.lam_min
+        batch_size = img.size(0)
+        index = torch.randperm(batch_size)
+
+        (bby1, bby2, bbx1,
+         bbx2), lam = self.cutmix_bbox_and_lam(img.shape, lam)
+
+        img[:, :, bby1:bby2, bbx1:bbx2] = F.interpolate(
+            img[index],
+            size=(bby2 - bby1, bbx2 - bbx1),
+            mode=self.interpolation)
+        mixed_gt_label = lam * one_hot_gt_label + (
+            1 - lam) * one_hot_gt_label[index, :]
+        return img, mixed_gt_label

tests/test_models/test_utils/test_augment.py

@@ -8,6 +8,7 @@
     dict(type='BatchCutMix', alpha=1., prob=1.),
     dict(type='BatchMixup', alpha=1., prob=1.),
     dict(type='Identity', prob=1.),
+    dict(type='BatchResizeMix', alpha=1., prob=1.)
 ]
 
 
@@ -29,6 +30,14 @@ def test_augments():
     assert mixed_imgs.shape == torch.Size((4, 3, 32, 32))
     assert mixed_labels.shape == torch.Size((4, 10))
 
+    # Test resizemix
+    augments_cfg = dict(
+        type='BatchResizeMix', alpha=1., num_classes=10, prob=1.)
+    augs = Augments(augments_cfg)
+    mixed_imgs, mixed_labels = augs(imgs, labels)
+    assert mixed_imgs.shape == torch.Size((4, 3, 32, 32))
+    assert mixed_labels.shape == torch.Size((4, 10))
+
     # Test cutmixup
     augments_cfg = [
         dict(type='BatchCutMix', alpha=1., num_classes=10, prob=0.5),