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import torch.nn as nn
import torch.nn.functional as F
# NOTE: LATENT_DIM doit être le même que celui utilisé pour l'entraînement (128)
class VAE(nn.Module):
def __init__(self, latent_dim=128):
super(VAE, self).__init__()
self.latent_dim = latent_dim
# ENCODEUR
self.encoder = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.Flatten()
)
self.fc_mu = nn.Linear(256 * 4 * 4, latent_dim)
self.fc_logvar = nn.Linear(256 * 4 * 4, latent_dim)
# DÉCODEUR
self.decoder_input = nn.Linear(latent_dim, 256 * 4 * 4)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1), nn.ReLU(),
nn.ConvTranspose2d(32, 3, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def decode(self, z):
h = self.decoder_input(z)
h = h.view(-1, 256, 4, 4)
return self.decoder(h)
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