mirror of
https://github.com/unanmed/ginka-generator.git
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70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import time
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import torch
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import torch.nn as nn
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from ..utils import print_memory
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from .cond import GinkaMaskGITCond
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from .maskGIT import MaskGIT
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class GinkaMaskGIT(nn.Module):
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def __init__(
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self, num_classes=16, heatmap_channel=4, d_model=256,
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dim_ff=512, nhead=8, num_layers=4, map_size=13*13
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):
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super().__init__()
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self.tile_embedding = nn.Embedding(num_classes, d_model)
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self.pos_embedding = nn.Parameter(torch.randn(1, map_size, d_model))
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self.cond_encoder = GinkaMaskGITCond(heatmap_channel=heatmap_channel, output_dim=d_model)
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self.transformer = MaskGIT(d_model=d_model, dim_ff=dim_ff, nhead=nhead, num_layers=num_layers)
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self.output_fc = nn.Sequential(
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nn.Linear(d_model, num_classes)
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)
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def forward(self, map: torch.Tensor, heatmap: torch.Tensor):
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# map: [B, H * W]
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# heatmap: [B, C, H, W]
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# output: [B, H * W, num_classes]
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heatmap = self.cond_encoder(heatmap)
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# cond: [B, d_model]
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# heatmap: [B, d_model, H, W]
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B, C, H, W = heatmap.shape
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heatmap = heatmap.view(B, C, H * W).permute(0, 2, 1)
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x = self.tile_embedding(map) + heatmap
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x = x + self.pos_embedding
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x = self.transformer(x)
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logits = self.output_fc(x)
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return logits
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if __name__ == "__main__":
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device = torch.device("cpu")
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map = torch.randint(0, 16, [1, 169]).to(device)
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heatmap = torch.rand(1, 4, 13, 13).to(device)
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# 初始化模型
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model = GinkaMaskGIT().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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output = model(map, heatmap)
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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: output={output.shape}")
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print(f"Tile Embedding parameters: {sum(p.numel() for p in model.tile_embedding.parameters())}")
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print(f"Condition Encoder parameters: {sum(p.numel() for p in model.cond_encoder.parameters())}")
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print(f"MaskGIT parameters: {sum(p.numel() for p in model.transformer.parameters())}")
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print(f"Output parameters: {sum(p.numel() for p in model.output_fc.parameters())}")
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print(f"Total parameters: {sum(p.numel() for p in model.parameters())}")
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