Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/miguelvr/dropblock

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
https://github.com/miguelvr/dropblock

computer-vision convolutional-neural-networks dropblock dropout machine-learning pytorch pytorch-implementation regularization

Last synced: 12 days ago
JSON representation

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Awesome Lists containing this project

README 

        
# DropBlock



![build](https://travis-ci.org/miguelvr/dropblock.png?branch=master)

[![Downloads](https://pepy.tech/badge/dropblock)](https://pepy.tech/project/dropblock)



Implementation of [DropBlock: A regularization method for convolutional networks](https://arxiv.org/pdf/1810.12890.pdf) 

in PyTorch.



## Abstract



Deep 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%.



## Installation



Install directly from PyPI:



    pip install dropblock

    

or the bleeding edge version from github:



    pip install git+https://github.com/miguelvr/dropblock.git#egg=dropblock



**NOTE**: Implementation and tests were done in Python 3.6, if you have problems with other versions of python please open an issue.



## Usage



For 2D inputs (DropBlock2D):



```python

import torch

from dropblock import DropBlock2D



# (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):



```python

import torch

from dropblock import DropBlock3D



# (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:



```python

import torch

from dropblock import DropBlock2D, LinearScheduler



# (bsize, n_feats, depth, height, width)

loader = [torch.rand(20, 10, 16, 16) for _ in range(10)]



drop_block = LinearScheduler(

                DropBlock2D(block_size=3, drop_prob=0.),

                start_value=0.,

                stop_value=0.25,

                nr_steps=5

            )



probs = []

for x in loader:

    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 the `step()` call at the start of the batch loop, 

or at the the start of a model's `forward` call. 



Check [examples/resnet-cifar10.py](examples/resnet-cifar10.py) to

see an implementation example.



## Implementation details



We use `drop_prob` instead of `keep_prob` as 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.



## Benchmark



Refer to [BENCHMARK.md](BENCHMARK.md)



## Reference

[Ghiasi et al., 2018] DropBlock: A regularization method for convolutional networks



## TODO

- [x] Scheduled DropBlock

- [x] Get benchmark numbers

- [x] Extend the concept for 3D images