Metadata-Version: 2.1
Name: memorywrap
Version: 1.0.4
Summary: Memory Wrap: an extension for image classification models
Home-page: UNKNOWN
Author: La Rosa Biagio
Author-email: larosa@diag.uniroma1.it
License: UNKNOWN
Description: # Description
        Memory Wrap is an extension to image classification models that improves both data-efficiency and model interpretability, adopting a sparse content-attention mechanism between the input and some memories of past training samples.
        
        # Installation
        This is a PyTorch implementation of Memory Wrap. To install Memory Wrap run the following command:
        ```
        pip install memorywrap
        ```
        
        The library contains two main classes:
        - *MemoryWrapLayer*: it is the Memory Wrap variant described in the paper that uses both the input encoding and the memory encoding to compute the output;
        - *BaselineMemory*: it is the baseline that uses only the memory encoding to compute the output.
        
        # Usage
        ## Instantiate the layer
        ```python
        memorywrap = MemoryWrapLayer(encoder_dim,output_dim,return_weights=False)
        ```
        or
        ```python
        memorywrap = BaselineMemory(encoder_dim,output_dim)
        ```
        where:
        - *encoder_dim* is the output dimension of the last layer of the encoder 
        - *output_dim* is the desired output dimensione. In the case of the paper *output_dim* is equal to the **number of classes**;
        - *return_weights* is a flag telling to the layer if it has to return the sparse content weights.
        
        ## Forward call
        Add the forward call to your forward function.
        ```python
        output_memorywrap = memorywrap(input_encoding,memory_encoding)
        ```
        where *input_encoding* and *memory_encoding* are the outputs of the the encoder of rispectively the current input and the memory set. <br>
        If you have set the flag *return_weights* to True, then *output_memorywrap* is a Tuple where the first element is the output and the second one are the content weights associated to each element in the memory_encoding.
        
        # Additional information
        Here you can find link to additional source of information about Memory Wrap:
        - <a href="https://arxiv.org/abs/2106.01440">Paper</a>
        - <a href="">GitHub repo</a>
        - <a href="https://colab.research.google.com/drive/1OPjcpTH7X8EV1ev361iuhVzd2Jfp9kFA">Jupyter notebook</a>
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
