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14daa35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | import torch
import torch.nn as nn
LATENT_DIM = 100
NUM_CLASSES = 10
class Generator(nn.Module):
def __init__(self, latent_dim=LATENT_DIM, num_classes=NUM_CLASSES):
super().__init__()
self.latent_dim = latent_dim
self.label_embedding = nn.Embedding(num_classes, 32)
self.project = nn.Sequential(
nn.Linear(latent_dim + 32, 128 * 7 * 7),
nn.BatchNorm1d(128 * 7 * 7),
nn.ReLU(inplace=True),
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(32, 1, kernel_size=4, stride=2, padding=1),
nn.Tanh(),
)
def forward(self, noise, labels):
if noise.ndim > 2:
noise = noise.flatten(start_dim=1)
conditioned = torch.cat([noise, self.label_embedding(labels)], dim=1)
features = self.project(conditioned).view(-1, 128, 7, 7)
return self.decoder(features)
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