README.md

ManifoldMixup

Unofficial implementation of ManifoldMixup (Proceedings of ICML 19) in PyTorch with support for Interpolated Adversarial training.

Results of adversarial training on MNIST

Dataset	Adversarial training	Normal training
MNIST	0.732	0.0069

Adversarial training done using Interpolated adversarial training framework with FGSM attack (eps=0.3) and MixupManifold

Update

Following discussion with author the following features are added:

1. Module level control for user which allows deciding specifically which layers to consider for mixup
2. Warning is raised if a module is called more than once in forward pass, and mixup is done at first instance only
3. If for some reason, you cannot pre-decide which modules to use for mixup, pass mixup_all=True while creating ManifoldMixupModel instance

Usage

ManifoldMixup training

from manifold_mixup import ManifoldMixupDataset, ManifoldMixupModel, ManifoldMixupLoss, MixupModule
"""
(optional)
Wrap modules which you want to use for mixup using MixupModule
Example:
class Model(nn.Module):
    def __init__(self, in_dims, hid_dims, out_dims):
        super(Model, self).__init__()
        self.m = nn.Sequential(Flatten(),
                              LinearLayer(in_dims, hid_dims, use_bn=True),
                              MixupModule(LinearLayer(hid_dims, hid_dims, use_bn=True)),
                              MixupModule(LinearLayer(hid_dims, hid_dims, use_bn=True)),
                              nn.Linear(hid_dims, out_dims))
    def forward(self, x):
        return self.m(x)
"""
"""
Wrap your dataset, model and loss in ManifoldMixup classes that's it!
"""
mixup_ds = ManifoldMixupDataset(trn_ds)
mixup_model = ManifoldMixupModel(model, alpha=0.2)
mixup_criterion = ManifoldMixupLoss(criterion)
"""
Now train as usual using mixup dataset, model and loss
"""

Interpolated Adversarial training with ManifoldMixup

from manifold_mixup import ManifoldMixupDataset, ManifoldMixupModel
from adversarial_attacks import FGSM
from interpolated_adversarial import InterpolatedAdversarialLoss

mixup_ds = ManifoldMixupDataset(trn_ds)
mixup_model = ManifoldMixupModel(model, alpha=0.2, interpolated_adv=True)

"""
To define loss for interpolated adversarial training, you need to pass attack and
your original loss function
"""
adv_loss = nn.CrossEntropyLoss()
model_loss = nn.CrossEntropyLoss()
fgsm = FGSM(adv_loss)
adv_criterion = InterpolatedAdversarialLoss(model_loss, fgsm)
"""
Now train as usual with new model, dataset and loss
"""

Creating custom adversarial attack

from adversarial_attacks import BlackBoxAdversarialAttack
"""
BlackBoxAdversarialAttack automatically handles your model's parameter's requirement for grad
and creates a callable compatible with InterpolatedAdversarialLoss
"""
class MyNewAttack(BlackBoxAdversarialAttack):
  def run(self, x, y, model):
    """
    Logic for attack
    """

Fastai implementation by @nestordemeure

https://github.com/nestordemeure/ManifoldMixup