Memory Wrap: an extension for image classification models
Project 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
memorywrap = MemoryWrapLayer(encoder_output_dim, output_dim, classifier=None, distance='cosine')
or, for the baseline that uses only the memory to output the prediction:
memorywrap = BaselineMemory(encoder_output_dim, output_dim, classifier=None, distance='cosine')
where
-
encoder_output_dim (int) is the output dimension of the last layer of the encoder
-
output_dim (int) is the desired output dimensione. In the case of the paper output_dim is equal to the number of classes;
-
classifier (torch.nn.Module): Classifier on top of MemoryWrap. Inputs dimensions must be equal at encoder_output_dim*2 for MemoryWrapLayer and encoder_output_dim for BaselineMemory. By default is an MLP as described in the paper. An alternative is to use a linear layer. (e.g. torch.nn.Linear(encoder_output_dim*2, output_dim). Default: torch.nn.Sequential( torch.nn.Linear(encoder_output_dim*2, encoder_output_dim*4), torch.nn.ReLU(), torch.nn.Linear(encoder_output_dim*4, output_dim)
-
distance (str): Distance to use to compute the similarity between input and memory set. Allowed values are: cosine, l2 and dot for respectively cosine similarity, l2 distance and dot product distance. Default=cosine
Forward call
Add the forward call to your forward function.
output_memorywrap = memorywrap(input_encoding, memory_encoding, return_weights=False)
where input_encoding and memory_encoding are the outputs of the the encoder of rispectively the current input and the memory set.
The last argument of the Memory Wrap's call function is a boolean flag controlling the number of outputs returned. If the flag is True, then the layer returns both the output and the sparse attention weight associated to each memory sample; if the flag is False, then the layer return only the output.
Additional information
Here you can find link to additional source of information about Memory Wrap:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file memorywrap-1.1.7.tar.gz
.
File metadata
- Download URL: memorywrap-1.1.7.tar.gz
- Upload date:
- Size: 4.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ca84627a2bb13c941f4866b8d0b834122d3c09bb03ea12fdb07fa81d25afb51c |
|
MD5 | eb4e8fad768d21b708acc759abe5a48f |
|
BLAKE2b-256 | 2b42fd37d1ba911d3604c4c7fb573cb5a1899f490285ef58148ae6d5ac254193 |
File details
Details for the file memorywrap-1.1.7-py3-none-any.whl
.
File metadata
- Download URL: memorywrap-1.1.7-py3-none-any.whl
- Upload date:
- Size: 4.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | bf00dd1e9152f51ef018d37217d4e63481a0048b289128b9fb7eccd010a15576 |
|
MD5 | dea629639e7a96dd078968b805b80267 |
|
BLAKE2b-256 | 27816092b3d7be1297dec69a5d57af145cc77b7ebf92f02015c88a9f4769a6a9 |