mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-17 23:21:20 +08:00
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
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 torch.nn.utils import spectral_norm
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from torch_geometric.nn import global_max_pool, GCNConv, global_mean_pool
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from shared.constant import VISION_WEIGHT, TOPO_WEIGHT
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from shared.graph import batch_convert_soft_map_to_graph
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from .vision import MinamoVisionModel
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from .topo import MinamoTopoModel
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from ..common.cond import ConditionEncoder, ConditionInjector
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def print_memory(tag=""):
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print(f"{tag} | 当前显存: {torch.cuda.memory_allocated() / 1024**2:.2f} MB, 最大显存: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB")
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class CNNHead(nn.Module):
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def __init__(self, in_ch):
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super().__init__()
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self.cnn = nn.Sequential(
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spectral_norm(nn.Conv2d(in_ch, in_ch, 3)),
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nn.LeakyReLU(0.2),
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nn.AdaptiveMaxPool2d((2, 2))
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)
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self.fc = nn.Sequential(
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spectral_norm(nn.Linear(in_ch*2*2, 1))
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)
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self.proj = nn.Linear(256, in_ch*2*2)
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def forward(self, x, cond):
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x = self.cnn(x)
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B, C, H, W = x.shape
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x = x.view(B, -1)
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cond = self.proj(cond)
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proj = torch.sum(x * cond, dim=1, keepdim=True)
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x = self.fc(x) + proj
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return x
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class GCNHead(nn.Module):
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def __init__(self, in_dim):
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super().__init__()
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self.gcn = GCNConv(in_dim, in_dim)
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self.proj = nn.Linear(256, in_dim)
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self.fc = nn.Sequential(
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spectral_norm(nn.Linear(in_dim, 1))
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)
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def forward(self, x, graph, cond):
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x = self.gcn(x, graph.edge_index)
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x = F.leaky_relu(x, 0.2)
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x = global_max_pool(x, graph.batch)
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cond = self.proj(cond)
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proj = torch.sum(x * cond, dim=1, keepdim=True)
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x = self.fc(x) + proj
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return x
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class MinamoScoreHead(nn.Module):
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def __init__(self, vision_dim, topo_dim):
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super().__init__()
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self.vision_head = CNNHead(vision_dim)
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self.topo_head = GCNHead(topo_dim)
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def forward(self, vis, topo, graph, cond):
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vis_score = self.vision_head(vis, cond)
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topo_score = self.topo_head(topo, graph, cond)
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return vis_score, topo_score
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class MinamoModel(nn.Module):
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def __init__(self, tile_types=32):
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super().__init__()
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self.topo_model = MinamoTopoModel(tile_types)
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self.vision_model = MinamoVisionModel(tile_types)
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self.cond = ConditionEncoder(64, 16, 128, 256)
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# 输出层
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self.head1 = MinamoScoreHead(512, 512)
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self.head2 = MinamoScoreHead(512, 512)
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self.head3 = MinamoScoreHead(512, 512)
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def forward(self, map, graph, stage, tag_cond, val_cond):
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vision = self.vision_model(map)
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topo = self.topo_model(graph)
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cond = self.cond(tag_cond, val_cond)
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if stage == 1:
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vision_score, topo_score = self.head1(vision, topo, graph, cond)
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elif stage == 2:
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vision_score, topo_score = self.head2(vision, topo, graph, cond)
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elif stage == 3:
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vision_score, topo_score = self.head3(vision, topo, graph, cond)
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else:
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raise RuntimeError("Unknown critic stage.")
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score = VISION_WEIGHT * vision_score + TOPO_WEIGHT * topo_score
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return score, vision_score, topo_score
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# 检查显存占用
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if __name__ == "__main__":
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input = torch.randn((1, 32, 13, 13)).cuda()
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tag = torch.rand(1, 64).cuda()
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val = torch.rand(1, 16).cuda()
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# 初始化模型
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model = MinamoModel().cuda()
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print_memory("初始化后")
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# 前向传播
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output, _, _ = model(input, batch_convert_soft_map_to_graph(input), 1, tag, val)
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print_memory("前向传播后")
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print(f"输入形状: feat={input.shape}")
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print(f"输出形状: output={output.shape}")
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print(f"Cond parameters: {sum(p.numel() for p in model.cond.parameters())}")
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print(f"Topo parameters: {sum(p.numel() for p in model.topo_model.parameters())}")
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print(f"Vision parameters: {sum(p.numel() for p in model.vision_model.parameters())}")
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print(f"Head parameters: {sum(p.numel() for p in model.head1.parameters())}")
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print(f"Total parameters: {sum(p.numel() for p in model.parameters())}")
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