Pytorch VQVAE implementation
Example
from vqvae import VQVAE, sequential_encoder, sequential_decoder
from torch.optim import Adam
from functools import partial
input_channels = 3
output_channels = 3
embedding_length = 256
hidden_channels = 64
beta = 0.25
embedding_size = 512
opt = partial(Adam, lr=2e-4)
encoder = sequential_encoder(input_channels, embedding_size, hidden_channels) # Encoder from the paper
decoder = sequential_decoder(embedding_size, output_channels, hidden_channels) # Decoder from the paper
vqvae = VQVAE(encoder, decoder, opt, beta, embedding_length, embedding_size) # Pytorch-Lightning module,
# hence usable to train the model
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Release files for vqvae 1.0.2
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Source distribution (sdist)
| File | Size | Uploaded | |
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| vqvae-1.0.2.tar.gz | 11.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vqvae-1.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.2 kB
Release files / vqvae-1.0.2.tar.gz
| Download URL | vqvae-1.0.2.tar.gz |
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| Size | 11.6 kB |
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Release files / vqvae-1.0.2-py3-none-any.whl
| Download URL | vqvae-1.0.2-py3-none-any.whl |
|---|---|
| Size | 1.6 kB |
| Tags | Python 3 |
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