mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 04:41:12 +08:00
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import time
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .encoder import VAEEncoder
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from .decoder import VAEDecoder
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from ..utils import print_memory
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class GinkaVAE(nn.Module):
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def __init__(self, device, tile_classes=32, latent_dim=32):
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super().__init__()
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self.encoder = VAEEncoder(device, tile_classes, latent_dim)
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self.decoder = VAEDecoder(device)
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def reparameterize(self, mu, logvar):
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std = torch.exp(0.5 * logvar)
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eps = torch.randn_like(std)
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return mu + eps * std
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def forward(self, target_map: torch.Tensor, use_self_probility=0):
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mu, logvar = self.encoder(target_map)
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z = self.reparameterize(mu, logvar)
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logits = self.decoder(z, target_map, use_self_probility)
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return logits, mu, logvar
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if __name__ == "__main__":
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device = torch.device("cpu")
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input = torch.randint(0, 32, [1, 13, 13]).to(device)
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# 初始化模型
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model = GinkaVAE(device).to(device)
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print_memory("初始化后")
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# 前向传播
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start = time.perf_counter()
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logits, mu, logvar = model(input)
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end = time.perf_counter()
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print_memory("前向传播后")
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print(f"推理耗时: {end - start}")
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print(f"输出形状: logits= {logits.shape}, mu={mu.shape}, logvar={logvar.shape}")
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print(f"Encoder parameters: {sum(p.numel() for p in model.encoder.parameters())}")
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print(f"Decoder parameters: {sum(p.numel() for p in model.decoder.parameters())}")
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print(f"Total parameters: {sum(p.numel() for p in model.parameters())}")
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