Source code for mmaction.models.utils.blending_utils
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from functools import partial
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from mmengine.utils import digit_version
from torch.distributions.beta import Beta
from mmaction.registry import MODELS
from mmaction.utils import SampleList
if digit_version(torch.__version__) < digit_version('1.8.0'):
floor_div = torch.floor_divide
else:
floor_div = partial(torch.div, rounding_mode='floor')
__all__ = ['BaseMiniBatchBlending', 'MixupBlending', 'CutmixBlending']
[docs]class BaseMiniBatchBlending(metaclass=ABCMeta):
"""Base class for Image Aliasing.
Args:
num_classes (int): Number of classes.
"""
def __init__(self, num_classes: int) -> None:
self.num_classes = num_classes
[docs] @abstractmethod
def do_blending(self, imgs: torch.Tensor, label: torch.Tensor,
**kwargs) -> Tuple:
"""Blending images process."""
raise NotImplementedError
def __call__(self, imgs: torch.Tensor, batch_data_samples: SampleList,
**kwargs) -> Tuple:
"""Blending data in a mini-batch.
Images are float tensors with the shape of (B, N, C, H, W) for 2D
recognizers or (B, N, C, T, H, W) for 3D recognizers.
Besides, labels are converted from hard labels to soft labels.
Hard labels are integer tensors with the shape of (B, ) and all of the
elements are in the range [0, num_classes - 1].
Soft labels (probability distribution over classes) are float tensors
with the shape of (B, num_classes) and all of the elements are in
the range [0, 1].
Args:
imgs (torch.Tensor): Model input images, float tensor with the
shape of (B, N, C, H, W) or (B, N, C, T, H, W).
batch_data_samples (List[:obj:`ActionDataSample`]): The batch
data samples. It usually includes information such
as `gt_label`.
Returns:
mixed_imgs (torch.Tensor): Blending images, float tensor with the
same shape of the input imgs.
batch_data_samples (List[:obj:`ActionDataSample`]): The modified
batch data samples. ``gt_label`` in each data sample are
converted from a hard label to a blended soft label, float
tensor with the shape of (num_classes, ) and all elements are
in range [0, 1].
"""
label = [x.gt_label for x in batch_data_samples]
# single-label classification
if label[0].size(0) == 1:
label = torch.tensor(label, dtype=torch.long).to(imgs.device)
one_hot_label = F.one_hot(label, num_classes=self.num_classes)
# multi-label classification
else:
one_hot_label = torch.stack(label)
mixed_imgs, mixed_label = self.do_blending(imgs, one_hot_label,
**kwargs)
for label_item, sample in zip(mixed_label, batch_data_samples):
sample.set_gt_label(label_item)
return mixed_imgs, batch_data_samples
[docs]@MODELS.register_module()
class MixupBlending(BaseMiniBatchBlending):
"""Implementing Mixup in a mini-batch.
This module is proposed in `mixup: Beyond Empirical Risk Minimization
<https://arxiv.org/abs/1710.09412>`_.
Code Reference https://github.com/open-mmlab/mmclassification/blob/master/mmcls/models/utils/mixup.py # noqa
Args:
num_classes (int): The number of classes.
alpha (float): Parameters for Beta distribution.
"""
def __init__(self, num_classes: int, alpha: float = .2) -> None:
super().__init__(num_classes=num_classes)
self.beta = Beta(alpha, alpha)
[docs] def do_blending(self, imgs: torch.Tensor, label: torch.Tensor,
**kwargs) -> Tuple:
"""Blending images with mixup.
Args:
imgs (torch.Tensor): Model input images, float tensor with the
shape of (B, N, C, H, W) or (B, N, C, T, H, W).
label (torch.Tensor): One hot labels, integer tensor with the shape
of (B, num_classes).
Returns:
tuple: A tuple of blended images and labels.
"""
assert len(kwargs) == 0, f'unexpected kwargs for mixup {kwargs}'
lam = self.beta.sample()
batch_size = imgs.size(0)
rand_index = torch.randperm(batch_size)
mixed_imgs = lam * imgs + (1 - lam) * imgs[rand_index, :]
mixed_label = lam * label + (1 - lam) * label[rand_index, :]
return mixed_imgs, mixed_label
[docs]@MODELS.register_module()
class CutmixBlending(BaseMiniBatchBlending):
"""Implementing Cutmix in a mini-batch.
This module is proposed in `CutMix: Regularization Strategy to Train Strong
Classifiers with Localizable Features <https://arxiv.org/abs/1905.04899>`_.
Code Reference https://github.com/clovaai/CutMix-PyTorch
Args:
num_classes (int): The number of classes.
alpha (float): Parameters for Beta distribution.
"""
def __init__(self, num_classes: int, alpha: float = .2) -> None:
super().__init__(num_classes=num_classes)
self.beta = Beta(alpha, alpha)
[docs] @staticmethod
def rand_bbox(img_size: torch.Size, lam: torch.Tensor) -> Tuple:
"""Generate a random boudning box."""
w = img_size[-1]
h = img_size[-2]
cut_rat = torch.sqrt(1. - lam)
cut_w = torch.tensor(int(w * cut_rat))
cut_h = torch.tensor(int(h * cut_rat))
# uniform
cx = torch.randint(w, (1, ))[0]
cy = torch.randint(h, (1, ))[0]
bbx1 = torch.clamp(cx - floor_div(cut_w, 2), 0, w)
bby1 = torch.clamp(cy - floor_div(cut_h, 2), 0, h)
bbx2 = torch.clamp(cx + floor_div(cut_w, 2), 0, w)
bby2 = torch.clamp(cy + floor_div(cut_h, 2), 0, h)
return bbx1, bby1, bbx2, bby2
[docs] def do_blending(self, imgs: torch.Tensor, label: torch.Tensor,
**kwargs) -> Tuple:
"""Blending images with cutmix.
Args:
imgs (torch.Tensor): Model input images, float tensor with the
shape of (B, N, C, H, W) or (B, N, C, T, H, W).
label (torch.Tensor): One hot labels, integer tensor with the shape
of (B, num_classes).
Returns:
tuple: A tuple of blended images and labels.
"""
assert len(kwargs) == 0, f'unexpected kwargs for cutmix {kwargs}'
batch_size = imgs.size(0)
rand_index = torch.randperm(batch_size)
lam = self.beta.sample()
bbx1, bby1, bbx2, bby2 = self.rand_bbox(imgs.size(), lam)
imgs[:, ..., bby1:bby2, bbx1:bbx2] = imgs[rand_index, ..., bby1:bby2,
bbx1:bbx2]
lam = 1 - (1.0 * (bbx2 - bbx1) * (bby2 - bby1) /
(imgs.size()[-1] * imgs.size()[-2]))
label = lam * label + (1 - lam) * label[rand_index, :]
return imgs, label
[docs]@MODELS.register_module()
class RandomBatchAugment(BaseMiniBatchBlending):
"""Randomly choose one batch augmentation to apply.
Args:
augments (dict | list): configs of batch
augmentations.
probs (float | List[float] | None): The probabilities of each batch
augmentations. If None, choose evenly. Defaults to None.
Example:
>>> augments_cfg = [
... dict(type='CutmixBlending', alpha=1., num_classes=10),
... dict(type='MixupBlending', alpha=1., num_classes=10)
... ]
>>> batch_augment = RandomBatchAugment(augments_cfg, probs=[0.5, 0.3])
>>> imgs = torch.randn(16, 3, 8, 32, 32)
>>> label = torch.randint(0, 10, (16, ))
>>> imgs, label = batch_augment(imgs, label)
.. note ::
To decide which batch augmentation will be used, it picks one of
``augments`` based on the probabilities. In the example above, the
probability to use CutmixBlending is 0.5, to use MixupBlending is 0.3,
and to do nothing is 0.2.
"""
def __init__(self,
augments: Union[dict, list],
probs: Optional[Union[float, List[float]]] = None) -> None:
if not isinstance(augments, (tuple, list)):
augments = [augments]
self.augments = []
for aug in augments:
assert isinstance(aug, dict), \
f'blending augment config must be a dict. Got {type(aug)}'
self.augments.append(MODELS.build(aug))
self.num_classes = augments[0].get('num_classes')
if isinstance(probs, float):
probs = [probs]
if probs is not None:
assert len(augments) == len(probs), \
'``augments`` and ``probs`` must have same lengths. ' \
f'Got {len(augments)} vs {len(probs)}.'
assert sum(probs) <= 1, \
'The total probability of batch augments exceeds 1.'
self.augments.append(None)
probs.append(1 - sum(probs))
self.probs = probs
[docs] def do_blending(self, imgs: torch.Tensor, label: torch.Tensor,
**kwargs) -> Tuple:
"""Randomly apply batch augmentations to the batch inputs and batch
data samples."""
aug_index = np.random.choice(len(self.augments), p=self.probs)
aug = self.augments[aug_index]
if aug is not None:
return aug.do_blending(imgs, label, **kwargs)
else:
return imgs, label