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34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
import torch
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from torch_geometric.data import Data
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def convert_soft_map_to_graph(map_probs: torch.Tensor):
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"""
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直接使用 Softmax 概率构建 soft 图结构
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"""
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C, H, W = map_probs.shape # [32, H, W]
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N = H * W
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device = map_probs.device
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# 计算 soft 节点特征
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node_features = map_probs.view(C, N).T # [N, C]
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# 计算 soft 邻接边（基于 soft 权重）
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edge_list = []
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for r in range(H):
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for c in range(W):
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node = r * W + c
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if c + 1 < W:
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right = node + 1
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edge_list.append([node, right])
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if r + 1 < H:
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down = node + W
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edge_list.append([node, down])
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edge_index = torch.tensor(edge_list).t().to(device)
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# 计算 soft 边权重（基于 Softmax 概率）
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soft_edge_weight = (map_probs[:, edge_index[0] // W, edge_index[0] % W] +
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map_probs[:, edge_index[1] // W, edge_index[1] % W]) / 2
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return Data(x=node_features, edge_index=edge_index, edge_attr=soft_edge_weight)
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