Package: dropblock
DropBlockImplementation ofDropBlock: A regularization method for convolutional networksin PyTorch.AbstractDeep neural networks often work well when they are over-parameterized
and trained with a massive amount of noise and regularization, such as
weight decay and dropout. Although dropout is widely used as a regularization
technique for fully connected layers, it is often less effective for convolutional layers.
This lack of success of dropout for convolutional layers is perhaps due to the fact
that activation units in convolutional layers are spatially correlated so
information can still flow through convolutional networks despite dropout.
Thus a structured form of dropout is needed to regularize convolutional networks.
In this paper, we introduce DropBlock, a form of structured dropout, where units in a
contiguous region of a feature map are dropped together.
We found that applying DropBlock in skip connections in addition to the
convolution layers increases the accuracy. Also, gradually increasing number
of dropped units during training leads to better accuracy and more robust to hyperparameter choices.
Extensive experiments show that DropBlock works better than dropout in regularizing
convolutional networks. On ImageNet classification, ResNet-50 architecture with
DropBlock achieves 78.13% accuracy, which is more than 1.6% improvement on the baseline.
On COCO detection, DropBlock improves Average Precision of RetinaNet from 36.8% to 38.4%.InstallationInstall directly from PyPI:pip install dropblockor the bleeding edge version from github:pip install git+https://github.com/miguelvr/dropblock.git#egg=dropblockNOTE: Implementation and tests were done in Python 3.6, if you have problems with other versions of python please open an issue.UsageFor 2D inputs (DropBlock2D):importtorchfromdropblockimportDropBlock2D# (bsize, n_feats, height, width)x=torch.rand(100,10,16,16)drop_block=DropBlock2D(block_size=3,drop_prob=0.3)regularized_x=drop_block(x)For 3D inputs (DropBlock3D):importtorchfromdropblockimportDropBlock3D# (bsize, n_feats, depth, height, width)x=torch.rand(100,10,16,16,16)drop_block=DropBlock3D(block_size=3,drop_prob=0.3)regularized_x=drop_block(x)Scheduled Dropblock:importtorchfromdropblockimportDropBlock2D,LinearScheduler# (bsize, n_feats, depth, height, width)loader=[torch.rand(20,10,16,16)for_inrange(10)]drop_block=LinearScheduler(DropBlock2D(block_size=3,drop_prob=0.),start_value=0.,stop_value=0.25,nr_steps=5)probs=[]forxinloader:drop_block.step()regularized_x=drop_block(x)probs.append(drop_block.dropblock.drop_prob)print(probs)The drop probabilities will be:>>> [0.    , 0.0625, 0.125 , 0.1875, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]The user should include thestep()call at the start of the batch loop,
or at the the start of a model'sforwardcall.Checkexamples/resnet-cifar10.pyto
see an implementation example.Implementation detailsWe usedrop_probinstead ofkeep_probas a matter of preference,
and to keep the argument consistent with pytorch's dropout.
Regardless, everything else should work similarly to what is described in the paper.BenchmarkRefer toBENCHMARK.mdReference[Ghiasi et al., 2018] DropBlock: A regularization method for convolutional networksTODOScheduled DropBlockGet benchmark numbersExtend the concept for 3D images