Metadata-Version: 2.1
Name: dropblock
Version: 0.3.0
Summary: Implementation of DropBlock: A regularization method for convolutional networks in PyTorch. 
Home-page: https://github.com/miguelvr/dropblock
Author: Miguel Varela Ramos
Author-email: miguelvramos92@gmail.com
License: MIT
Description: # DropBlock
        
        ![build](https://travis-ci.org/miguelvr/dropblock.png?branch=master)
        
        
        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
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
