vpredicto 0.1.0

A library for video frame prediction using PredRNN++, MIM, and Causal LSTM.

<title>Predicto</title>

Predicto

Predicto is a Python library for video frame prediction, featuring three state-of-the-art models: PredRNN++, MIM, and Causal LSTM. This library is designed to cater to both expert and non-expert users, providing an API for developers and a simple interface for non-experts.

<h2>Features</h2>
<ul>
    <li>Three video frame prediction models: PredRNN++, MIM, and Causal LSTM.</li>
    <li>Easy-to-use interface for training and testing models.</li>
    <li>Supports custom dataloaders or default to MovingMNIST dataset.</li>
    <li>Pre and post-processing for input and output in each model.</li>
</ul>

<h2>Installation</h2>
<pre><code>pip install predicto</code></pre>

<h2>Usage</h2>
<h3>Quick Start</h3>
<pre><code>from predicto import PredRNN, MIM, CausalLSTM, Predicto

Create a model object

model_object = MIM()

Initialize Predicto with the model object

model = Predicto(model_object)

Train the model

model.train(train_loader)

Test the model

model.test(test_loader)

<h3>Custom Dataloader</h3>
<pre><code>from predicto import PredRNN, MIM, CausalLSTM, Predicto

Define your custom dataloader

class CustomDataLoader: def init(self, ...): ...

def __iter__(self):
    ...

Create a model object

model_object = CausalLSTM()

Initialize Predicto with the model object and custom dataloader

model = Predicto(model_object, dataloader=CustomDataLoader())

Train the model

model.train(train_loader)

Test the model

model.test(test_loader)

<h2>Models</h2>
<ul>
    <li><strong>PredRNN++</strong>: A recurrent neural network model for video frame prediction.</li>
    <li><strong>MIM</strong>: Memory In Memory network for spatiotemporal predictive learning.</li>
    <li><strong>Causal LSTM</strong>: A causal LSTM model for video frame prediction.</li>
</ul>