ƒ() Functional#

The simplest way to use Composer’s algorithms is via the functional API. Composer’s algorithms can be grouped into three, broad classes:

  • data augmentations add additional transforms to the training data.

  • model surgery algorithms modify the network architecture.

  • training loop modifications change the logic in the training loop.

Data augmentations can be inserted either into the dataloader as a transform or after a batch has been loaded, depending on what the augmentation acts on. Here is an example of using 🎲 RandAugment with the functional API.

import torch
from torchvision import datasets, transforms

from composer import functional as cf

c10_transforms = transforms.Compose([cf.randaugment_image(), # <---- Add RandAugment
                                    transforms.Normalize(mean, std)])

dataset = datasets.CIFAR10('../data',
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1024)

Other data augmentations, such as CutMix, act on a batch of inputs. These can be inserted in the training loop after a batch is loaded from the dataloader as follows:

from composer import functional as cf

cutmix_alpha = 1
num_classes = 10
for batch_idx, (data, target) in enumerate(dataloader):
    data = cf.cutmix(  # <-- insert cutmix
    output = model(data)
    loss = loss_fn(output, target)

Model surgery algorithms make direct modifications to the network itself. Functionally, these can be called as follows, using BlurPool as an example

import torchvision.models as models

from composer import functional as cf

model = models.resnet18()

Each method card has a section describing how to use these methods in your own trainer loop.

Functional API for applying algorithms in your own training loop.

from composer import functional as cf
from torchvision import models

model = models.resnet50()

# replace some layers with blurpool
# replace some layers with squeeze-excite
cf.apply_squeeze_excite(model, latent_channels=64, min_channels=128)