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#default_exp callback.cutmix
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#export
from torch.distributions.beta import Beta
from fastai2.vision.all import *
From the research paper, CutMix is a way to combine two images. It comes from MixUp and Cutout. In this data augmentation technique:
patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches
Also, from the paper:
By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on the ImageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances.
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#export
class CutMix(Callback):
"Implementation of `https://arxiv.org/abs/1905.04899`"
run_after,run_valid = [Normalize],False
def __init__(self, alpha=1.): self.distrib = Beta(tensor(alpha), tensor(alpha))
def before_fit(self):
self.stack_y = getattr(self.learn.loss_func, 'y_int', False)
if self.stack_y: self.old_lf,self.learn.loss_func = self.learn.loss_func,self.lf
def after_fit(self):
if self.stack_y: self.learn.loss_func = self.old_lf
def before_batch(self):
W, H = self.xb[0].size(3), self.xb[0].size(2)
lam = self.distrib.sample((1,)).squeeze().to(self.x.device)
lam = torch.stack([lam, 1-lam])
self.lam = lam.max()
shuffle = torch.randperm(self.y.size(0)).to(self.x.device)
xb1,self.yb1 = tuple(L(self.xb).itemgot(shuffle)),tuple(L(self.yb).itemgot(shuffle))
nx_dims = len(self.x.size())
x1, y1, x2, y2 = self.rand_bbox(W, H, self.lam)
self.learn.xb[0][:, :, x1:x2, y1:y2] = xb1[0][:, :, x1:x2, y1:y2]
self.lam = (1 - ((x2-x1)*(y2-y1))/float(W*H)).item()
if not self.stack_y:
ny_dims = len(self.y.size())
self.learn.yb = tuple(L(self.yb1,self.yb).map_zip(torch.lerp,weight=unsqueeze(self.lam, n=ny_dims-1)))
def lf(self, pred, *yb):
if not self.training: return self.old_lf(pred, *yb)
with NoneReduce(self.old_lf) as lf:
loss = torch.lerp(lf(pred,*self.yb1), lf(pred,*yb), self.lam)
return reduce_loss(loss, getattr(self.old_lf, 'reduction', 'mean'))
def rand_bbox(self, W, H, lam):
cut_rat = torch.sqrt(1. - lam)
cut_w = (W * cut_rat).type(torch.long)
cut_h = (H * cut_rat).type(torch.long)
# uniform
cx = torch.randint(0, W, (1,)).to(self.x.device)
cy = torch.randint(0, H, (1,)).to(self.x.device)
x1 = torch.clamp(cx - cut_w // 2, 0, W)
y1 = torch.clamp(cy - cut_h // 2, 0, H)
x2 = torch.clamp(cx + cut_w // 2, 0, W)
y2 = torch.clamp(cy + cut_h // 2, 0, H)
return x1, y1, x2, y2
How does the batch with CutMix data augmentation technique look like?¶
First, let's quickly create the dls using ImageDataLoaders.from_name_re DataBlocks API.
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path = untar_data(URLs.PETS)
pat = r'([^/]+)_\d+.*$'
fnames = get_image_files(path/'images')
item_tfms = [Resize(256, method='crop')]
batch_tfms = [*aug_transforms(size=224), Normalize.from_stats(*imagenet_stats)]
dls = ImageDataLoaders.from_name_re(path, fnames, pat, bs=64, item_tfms=item_tfms,
batch_tfms=batch_tfms)
Next, let's initialize the callback CutMix, create a learner, do one batch and display the images with the labels. CutMix inside updates the loss function based on the ratio of the cutout bbox to the complete image.
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cutmix = CutMix(alpha=1.)
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with Learner(dls, resnet18(), loss_func=CrossEntropyLossFlat(), cbs=cutmix) as learn:
learn.epoch,learn.training = 0,True
learn.dl = dls.train
b = dls.one_batch()
learn._split(b)
learn('before_batch')
_,axs = plt.subplots(3,3, figsize=(9,9))
dls.show_batch(b=(cutmix.x,cutmix.y), ctxs=axs.flatten())
| epoch | train_loss | valid_loss | time |
|---|---|---|---|
| 0 | 00:00 |
Using CutMix in Training¶
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learn = cnn_learner(dls, resnet18, loss_func=CrossEntropyLossFlat(), cbs=cutmix, metrics=[accuracy, error_rate])
# learn.fit_one_cycle(1)
Export -¶
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#hide
from nbdev.export import notebook2script
notebook2script()
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