/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "

from fastai.vision.all import *

reduce_loss[source]

reduce_loss(loss, reduction='mean')

Reduce the loss based on reduction

class MixHandler[source]

MixHandler(alpha=0.5) :: Callback

A handler class for implementing MixUp style scheduling

Most Mix variants will perform the data augmentation on the batch, so to implement your Mix you should adjust the before_batch event with however your training regiment requires. Also if a different loss function is needed, you should adjust the lf as well.

class MixUp[source]

MixUp(alpha=0.4) :: MixHandler

Implementation of https://arxiv.org/abs/1710.09412

First we'll look at a very minimalistic example to show how our data is being generated with the PETS dataset:

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)

We can examine the results of our Callback by grabbing our data during fit at before_batch like so:

mixup = MixUp(1.)
with Learner(dls, nn.Linear(3,4), loss_func=CrossEntropyLossFlat(), cbs=mixup) as learn:
    learn.epoch,learn.training = 0,True
    learn.dl = dls.train
    b = dls.one_batch()
    learn._split(b)
    learn('before_train')
    learn('before_batch')

_,axs = plt.subplots(3,3, figsize=(9,9))
dls.show_batch(b=(mixup.x,mixup.y), ctxs=axs.flatten())

epoch	train_loss	valid_loss	time
0	00:00

We can see that every so often an image gets "mixed" with another.

How do we train? You can pass the Callback either to Learner directly or to cbs in your fit function:

learn = cnn_learner(dls, resnet18, loss_func=CrossEntropyLossFlat(), metrics=[error_rate])
learn.fit_one_cycle(1, cbs=mixup)

epoch	train_loss	valid_loss	error_rate	time
0	2.041960	0.495492	0.162382	00:12

class CutMix[source]

CutMix(alpha=1.0) :: MixHandler

Implementation of https://arxiv.org/abs/1905.04899

Similar to MixUp, CutMix will cut a random box out of two images and swap them together. We can look at a few examples below:

cutmix = CutMix(1.)
with Learner(dls, nn.Linear(3,4), 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_train')
    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

We train with it in the exact same way as well

learn = cnn_learner(dls, resnet18, loss_func=CrossEntropyLossFlat(), metrics=[accuracy, error_rate])
learn.fit_one_cycle(1, cbs=cutmix)

epoch	train_loss	valid_loss	accuracy	error_rate	time
0	3.440883	0.793059	0.769959	0.230041	00:12