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init: Minamo Model

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__pycache__
result
node_modules

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*.js

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{
"printWidth": 80,
"tabWidth": 4,
"useTabs": false,
"semi": true,
"singleQuote": true,
"quoteProps": "as-needed",
"bracketSpacing": true,
"vueIndentScriptAndStyle": false,
"arrowParens": "avoid",
"trailingComma": "none",
"endOfLine": "auto"
}

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{
// 使用 IntelliSense 了解相关属性。
// 悬停以查看现有属性的描述。
// 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python 调试程序: 模块",
"type": "debugpy",
"request": "launch",
"module": "ginka.topology.test"
}
]
}

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Also add information on how to contact you by electronic and paper mail.
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# GINKA 地图生成器
GINKA Model 是一个用于生成网格状魔塔地图的模型，采用 UNet 网络，允许输入自然语言，指定地图大小。
GINKA Model 内部集成了 Minamo Model 用于判别两个地图的相似性，用于计算损失值，指导 GINKA Model 训练，避免了传统拓扑图相似度计算的不可微性质，提高模型训练性能。
## 贡献 GINKA Model 数据集
对于 HTML5 魔塔，如果你想要贡献数据集，需要对你的魔塔进行手动数据处理，流程如下：
1. 选择楼层，可以是剧情层、战斗层等，但是需要满足下述条件
2. 楼层除边缘外不应出现墙壁堆叠（例如 2\*2，边缘可以有重叠）
3. 楼层中不应该有闲置怪，不应该有连续 3 个以上的怪物，不应该有无法到达的区域，不宜有过多的入口
4. 最外面一层围上一圈墙壁（箭头楼层切换除外）
5. 将所有的墙壁换成黄墙（数字 1）
6. 将所有的血瓶换成红血瓶（数字 31），所有红宝石换成最基础的红宝石（数字 27），蓝宝石换成最基础的蓝宝石（数字 28），删除除此之外的资源，剑盾可以当成红蓝宝石看待
7. 所有钥匙换成黄钥匙（数字 21），所有门换成黄门（数字 81）
8. 所有箭头换成样板原版箭头（数字 161 至 164），所有上下楼梯换成样板原版楼梯（数字 87 和 88）
9. 怪物分为三个强度，弱怪，中怪，强怪，弱怪换为绿头怪（数字 201），中怪换成红头怪（数字 202），强怪换成青头怪（数字 203）
10. 在 `project` 文件夹下创建 `ginka-config.json` 文件，双击进入编辑，粘贴如下模板：
```json
{
"clip": {
"defaults": [0, 0, 13, 13],
"special": {
"MT11": [3, 3, 7, 7]
}
},
"data": {
"MT1": ["MT1 楼层的第一个描述", "MT1 楼层的第二个描述"]
}
}
```
其中，`clip` 属性表示你的每张地图的那一部分会被当成数据集，例如填写 `[0, 0, 13, 13]` 就会让坐标为 `(0, 0)`，长宽为 `(13, 13)` 的矩形内容作为数据集。`special` 属性允许你针对单独的某几层设置不同的裁剪方式，例如设置 `MT11` 为 `[3, 3, 7, 7]` 等，如果没有设置默认使用 `defaults` 的裁剪方式。最好保证每个楼层大小一致，不然我还要手动分类。
`data` 是你的每一层的楼层描述，要求每一层都要有描述，每层描述可以有多个，要求可以准确或粗略描述楼层的一部分特征（不需要是全部，不然的话文字量会很大），不超过 64 个 token（每个中文字约 0.6 token，每个英文字母约 0.3 token），可以详细可以简略，推荐每层有三个描述以上，而且描述不要有过多的语义重复。
11. 在全塔属性中的楼层列表中去除不在数据集内的楼层
12. 将 `project` 文件夹打包发给我即可
## 贡献 Minamo Model 数据集
首先需要对你的塔的地图进行处理，参考 [GINKA Model 数据处理方式](#贡献-ginka-model-数据集)的 1-9 和 11 步，同时最好每张地图至少有几个空格（没有也没有影响，只不过会导致这个地图参与训练的次数降低）
与 GINKA Model 类似，在 `project` 文件夹中添加 `minamo-config.json` 文件，打开后将如下模板粘贴进去：
```json
{
"clip": {
"defaults": [0, 0, 13, 13],
"special": {
"MT11": [3, 3, 7, 7]
}
}
}
```
其中 `clip` 属性与 GINKA Model 的定义相同，参考上一小节即可。
将以上内容全部设置完毕后，将 `project` 文件夹打包发送给我，添加 `ginka-config.json` 时也可以作为 GINKA Model 训练集。
## 训练
如果你想自行训练模型，首先需要安装 `reqiurements.txt` 中所需的库，然后按照以下顺序操作：
1. 准备 Minamo Model 数据集，放置在根目录下，命名为 `minamo-dataset.json`
2. 执行 `python -m minamo.train`，等待训练完毕
3. 准备 GINKA Model 数据集，放置在根目录下，命名为 `ginka-dataset.json`
4. 执行 `python -m ginka.train`，等待训练完毕
5. 目录 `result/ginka.pth` 即为训练完毕的 GINKA 模型
6. （可选）准备 Minamo Model / GINKA Model 验证集，放置在根目录下，命名为 `minamo-eval.json` / `ginka-eval.json`，训练时每 10 个 epoch 会进行一次验证推理，建议使用与训练集不同的塔数据作为验证集。
准备训练集和验证集时，可以使用命令行脚本自动处理。首先进入 `data` 文件夹，然后运行 `pnpm i` 安装所有依赖，然后执行下面的脚本：
```bash
pnpm ginka "../ginka-dataset.json" "MyTower/project" # 通过 ginka-config.json 生成 GINKA 训练集
pnpm ginka "../ginka-eval.json" "MyTower/project" # 通过 ginka-config.json 生成 GINKA 验证集
pnpm minamo "../minamo-dataset.json" "MyTower/project" # 通过 minamo-config.json 生成 Minamo 训练集
pnpm minamo "../minamo-eval.json" "MyTower/project" # 通过 minamo-config.json 生成 Minamo 验证集
```

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"version": "1.0.0",
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"minamo": "tsx ./src/minamo.ts",
"test:topo": "tsx ./src/topology/test.ts",
"test:vision": "tsx ./src/vision/test.ts"
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"license": "ISC",
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'@esbuild/freebsd-x64': 0.25.1
'@esbuild/linux-arm': 0.25.1
'@esbuild/linux-arm64': 0.25.1
'@esbuild/linux-ia32': 0.25.1
'@esbuild/linux-loong64': 0.25.1
'@esbuild/linux-mips64el': 0.25.1
'@esbuild/linux-ppc64': 0.25.1
'@esbuild/linux-riscv64': 0.25.1
'@esbuild/linux-s390x': 0.25.1
'@esbuild/linux-x64': 0.25.1
'@esbuild/netbsd-arm64': 0.25.1
'@esbuild/netbsd-x64': 0.25.1
'@esbuild/openbsd-arm64': 0.25.1
'@esbuild/openbsd-x64': 0.25.1
'@esbuild/sunos-x64': 0.25.1
'@esbuild/win32-arm64': 0.25.1
'@esbuild/win32-ia32': 0.25.1
'@esbuild/win32-x64': 0.25.1
estree-walker@3.0.3:
dependencies:
'@types/estree': 1.0.6
expect-type@1.2.0: {}
fs-extra@11.3.0:
dependencies:
graceful-fs: 4.2.11
jsonfile: 6.1.0
universalify: 2.0.1
fsevents@2.3.3:
optional: true
get-tsconfig@4.10.0:
dependencies:
resolve-pkg-maps: 1.0.0
graceful-fs@4.2.11: {}
jsonfile@6.1.0:
dependencies:
universalify: 2.0.1
optionalDependencies:
graceful-fs: 4.2.11
loupe@3.1.3: {}
magic-string@0.30.17:
dependencies:
'@jridgewell/sourcemap-codec': 1.5.0
ms@2.1.3: {}
nanoid@3.3.9: {}
pathe@2.0.3: {}
pathval@2.0.0: {}
picocolors@1.1.1: {}
postcss@8.5.3:
dependencies:
nanoid: 3.3.9
picocolors: 1.1.1
source-map-js: 1.2.1
resolve-pkg-maps@1.0.0: {}
rollup@4.35.0:
dependencies:
'@types/estree': 1.0.6
optionalDependencies:
'@rollup/rollup-android-arm-eabi': 4.35.0
'@rollup/rollup-android-arm64': 4.35.0
'@rollup/rollup-darwin-arm64': 4.35.0
'@rollup/rollup-darwin-x64': 4.35.0
'@rollup/rollup-freebsd-arm64': 4.35.0
'@rollup/rollup-freebsd-x64': 4.35.0
'@rollup/rollup-linux-arm-gnueabihf': 4.35.0
'@rollup/rollup-linux-arm-musleabihf': 4.35.0
'@rollup/rollup-linux-arm64-gnu': 4.35.0
'@rollup/rollup-linux-arm64-musl': 4.35.0
'@rollup/rollup-linux-loongarch64-gnu': 4.35.0
'@rollup/rollup-linux-powerpc64le-gnu': 4.35.0
'@rollup/rollup-linux-riscv64-gnu': 4.35.0
'@rollup/rollup-linux-s390x-gnu': 4.35.0
'@rollup/rollup-linux-x64-gnu': 4.35.0
'@rollup/rollup-linux-x64-musl': 4.35.0
'@rollup/rollup-win32-arm64-msvc': 4.35.0
'@rollup/rollup-win32-ia32-msvc': 4.35.0
'@rollup/rollup-win32-x64-msvc': 4.35.0
fsevents: 2.3.3
siginfo@2.0.0: {}
source-map-js@1.2.1: {}
stackback@0.0.2: {}
std-env@3.8.1: {}
tinybench@2.9.0: {}
tinyexec@0.3.2: {}
tinypool@1.0.2: {}
tinyrainbow@2.0.0: {}
tinyspy@3.0.2: {}
tsx@4.19.3:
dependencies:
esbuild: 0.25.1
get-tsconfig: 4.10.0
optionalDependencies:
fsevents: 2.3.3
undici-types@6.20.0: {}
universalify@2.0.1: {}
vite-node@3.0.8(@types/node@22.13.10)(tsx@4.19.3):
dependencies:
cac: 6.7.14
debug: 4.4.0
es-module-lexer: 1.6.0
pathe: 2.0.3
vite: 6.2.2(@types/node@22.13.10)(tsx@4.19.3)
transitivePeerDependencies:
- '@types/node'
- jiti
- less
- lightningcss
- sass
- sass-embedded
- stylus
- sugarss
- supports-color
- terser
- tsx
- yaml
vite@6.2.2(@types/node@22.13.10)(tsx@4.19.3):
dependencies:
esbuild: 0.25.1
postcss: 8.5.3
rollup: 4.35.0
optionalDependencies:
'@types/node': 22.13.10
fsevents: 2.3.3
tsx: 4.19.3
vitest@3.0.8(@types/node@22.13.10)(tsx@4.19.3):
dependencies:
'@vitest/expect': 3.0.8
'@vitest/mocker': 3.0.8(vite@6.2.2(@types/node@22.13.10)(tsx@4.19.3))
'@vitest/pretty-format': 3.0.8
'@vitest/runner': 3.0.8
'@vitest/snapshot': 3.0.8
'@vitest/spy': 3.0.8
'@vitest/utils': 3.0.8
chai: 5.2.0
debug: 4.4.0
expect-type: 1.2.0
magic-string: 0.30.17
pathe: 2.0.3
std-env: 3.8.1
tinybench: 2.9.0
tinyexec: 0.3.2
tinypool: 1.0.2
tinyrainbow: 2.0.0
vite: 6.2.2(@types/node@22.13.10)(tsx@4.19.3)
vite-node: 3.0.8(@types/node@22.13.10)(tsx@4.19.3)
why-is-node-running: 2.3.0
optionalDependencies:
'@types/node': 22.13.10
transitivePeerDependencies:
- jiti
- less
- lightningcss
- msw
- sass
- sass-embedded
- stylus
- sugarss
- supports-color
- terser
- tsx
- yaml
why-is-node-running@2.3.0:
dependencies:
siginfo: 2.0.0
stackback: 0.0.2

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const numMap: Record<number, number> = {
0: 0, // 空地
1: 1, // 墙壁
21: 2, // 钥匙
27: 3, // 红宝石
28: 4, // 蓝宝石
31: 5, // 血瓶
81: 6, // 门
201: 7, // 弱怪
202: 8, // 中怪
203: 9, // 强怪
87: 10, // 楼梯
88: 10, // 楼梯
161: 11, // 箭头
162: 11, // 箭头
163: 11, // 箭头
164: 11 // 箭头
};
export function convertFloor(
map: number[][],
[x, y, w, h]: [number, number, number, number],
name: string,
floorId: string
) {
const clipped: number[][] = [];
for (let nx = x; nx < x + w; nx++) {
const row: number[] = [];
for (let ny = y; ny < y + h; ny++) {
const num = numMap[map[nx][ny]];
if (num === void 0) {
console.log(
`⚠️ 魔塔 ${name} 的楼层 ${floorId} 中出现未知图块类型：${map[nx][ny]}`
);
}
row.push(num ?? 0);
}
clipped.push(row);
}
return clipped;
}

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import { exists, writeFile } from 'fs-extra';
import { readFile } from 'node:fs/promises';
import { join } from 'node:path';
import { convertFloor } from './floor';
import { mergeDataset } from './utils';
interface GinkaConfig {
clip: {
defaults: [number, number, number, number];
special: Record<string, [number, number, number, number]>;
};
data: Record<string, string[]>;
}
interface GinkaTrainData {
text: string[];
map: number[][];
size: [number, number];
}
interface GinkaDataset {
datasetId: number;
data: Record<string, GinkaTrainData>;
}
const [output, ...list] = process.argv.slice(2);
async function parseOneFloor(
path: string,
name: string,
floorId: string,
config: GinkaConfig
): Promise<GinkaTrainData> {
const floorFile = await readFile(path, 'utf-8');
const floor: any = JSON.parse(floorFile.split('\n').slice(1).join('\n'));
const map = floor.map as number[][];
const clip = config.clip.special[floorId] ?? config.clip.defaults;
const clipped = convertFloor(map, clip, name, floorId);
if (!config.data[floorId]) {
console.log(`⚠️ 魔塔 ${name} 的楼层 ${floorId} 不存在描述文本！`);
}
const data: GinkaTrainData = {
text: config.data[floorId],
map: clipped,
size: [clipped[0].length, clipped.length]
};
return data;
}
async function parseOne(path: string): Promise<GinkaDataset> {
const dataFile = await readFile(join(path, 'data.js'), 'utf-8');
const configFile = await readFile(join(path, 'ginka-config.json'), 'utf-8');
const data: any = JSON.parse(dataFile.split('\n').slice(1).join('\n'));
const config = JSON.parse(configFile) as GinkaConfig;
const floorIds = data.main.floorIds as string[];
const name = data.firstData.name as string;
const datas = await Promise.all(
floorIds.map(v =>
parseOneFloor(join(path, 'floors', `${v}.js`), name, v, config)
)
);
const dataset: GinkaDataset = {
datasetId: Math.floor(Math.random() * 1e12),
data: Object.fromEntries(datas.map((v, i) => [floorIds[i], v]))
};
return dataset;
}
(async () => {
const results = await Promise.all(list.map(v => parseOne(v)));
const dataset = mergeDataset(...results);
await writeFile(output, JSON.stringify(dataset, void 0), 'utf-8');
const size = Object.keys(dataset.data).length;
console.log(`✅ 已处理 ${list.length} 个塔，共 ${size} 个地图`);
})();

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import { readFile, writeFile } from 'fs-extra';
import { join } from 'path';
import { convertFloor } from './floor';
import { mergeDataset } from './utils';
import { compareMap } from './topology/compare';
import { mirrorMapX, mirrorMapY, rotateMap } from './topology/transform';
import { directions, tileType } from './topology/graph';
import { calculateVisualSimilarity } from './vision/similarity';
interface MinamoConfig {
clip: {
defaults: [number, number, number, number];
special: Record<string, [number, number, number, number]>;
};
// data: Record<string, Record<string, number>>;
}
interface MinamoTrainData {
map1: number[][];
map2: number[][];
topoSimilarity: number;
visionSimilarity: number;
size: [number, number];
}
interface MinamoDataset {
datasetId: number;
data: Record<string, MinamoTrainData>;
}
const [output, ...list] = process.argv.slice(2);
function chooseFrom<T>(arr: T[], n: number): T[] {
const copy = arr.slice();
for (let i = copy.length - 1; i > 0; i--) {
let randIndex = Math.floor(Math.random() * (i + 1));
[copy[i], copy[randIndex]] = [copy[randIndex], copy[i]];
}
return copy.slice(0, n);
}
function choosePair(n: number) {
const totalCount = Math.round((n * (n - 1)) / 2);
const count = Math.min(totalCount, 1000);
const pairs: number[] = [];
for (let i = 0; i < n; i++) {
for (let j = i + 1; j < n; j++) {
pairs.push(i * n + j);
}
}
// 直接打乱后取前 count 个
for (let i = pairs.length - 1; i > 0; i--) {
let randIndex = Math.floor(Math.random() * (i + 1));
[pairs[i], pairs[randIndex]] = [pairs[randIndex], pairs[i]];
}
return pairs.slice(0, count);
}
function transform(map: number[][], rot: number, flip: number) {
let res = map;
for (let i = 0; i < rot; i++) {
res = rotateMap(res);
}
if (flip & 0b01) {
res = mirrorMapX(res);
}
if (flip & 0b10) {
res = mirrorMapY(res);
}
return res;
}
function generateTransformData(
id1: string,
id2: string,
map1: number[][],
map2: number[][],
simi: number
) {
const types: [rot: number, flip: number][] = [];
for (const rot of [0, 1, 2, 3]) {
for (const flip of [0b00, 0b01, 0b10, 0b11]) {
if (rot === 0 && flip === 0) continue;
types.push([rot, flip]);
}
}
// 随机抽取最多两个
const trans = chooseFrom(types, Math.floor(Math.random() * 3));
return trans
.map(([rot, flip]) => {
const com1 = `${id1}.${rot}.${flip}:${id1}`;
const com2 = `${id1}.${rot}.${flip}:${id2}`;
const com3 = `${id2}.${rot}.${flip}:${id1}`;
const com4 = `${id2}.${rot}.${flip}:${id2}`;
const choose = chooseFrom(
[com1, com2, com3, com4],
Math.floor(Math.random() * 2)
);
const res: [id: string, data: MinamoTrainData][] = [];
if (choose.includes(com1)) {
const t = transform(map1, rot, flip);
res.push([
com1,
{
map1: t,
map2: map1,
topoSimilarity: 1,
visionSimilarity: calculateVisualSimilarity(map1, t),
size: [map1[0].length, map1.length]
}
]);
}
if (choose.includes(com2)) {
const t = transform(map1, rot, flip);
res.push([
com2,
{
map1: t,
map2: map2,
topoSimilarity: simi,
visionSimilarity: calculateVisualSimilarity(t, map2),
size: [map1[0].length, map1.length]
}
]);
}
if (choose.includes(com3)) {
const t = transform(map2, rot, flip);
res.push([
com3,
{
map1: t,
map2: map1,
topoSimilarity: simi,
visionSimilarity: calculateVisualSimilarity(t, map1),
size: [map1[0].length, map1.length]
}
]);
}
if (choose.includes(com4)) {
const t = transform(map2, rot, flip);
res.push([
com4,
{
map1: t,
map2: map2,
topoSimilarity: 1,
visionSimilarity: calculateVisualSimilarity(t, map2),
size: [map1[0].length, map1.length]
}
]);
}
return res;
})
.flat();
}
function generateSimilarData(id: string, map: number[][]) {
// 生成最多五个微调地图
const width = map[0].length;
const height = map.length;
const num = Math.floor(Math.random() * 6);
const res: [id: string, data: MinamoTrainData][] = [];
for (let i = 0; i < num; i++) {
const clone = map.map(v => v.slice());
const prob = Math.random() * 0.3;
for (let ny = 0; ny < height; ny++) {
for (let nx = 0; nx < width; nx++) {
if (Math.random() > prob) {
// 有一定的概率进行微调
continue;
}
if (Math.random() < 0.2) {
// 20% 概率与旁边图块互换位置
const [dx, dy] =
directions[
Math.floor(Math.random() * directions.length)
];
const px = nx + dx;
const py = ny + dy;
if (px < 0 || px >= width || py < 0 || py >= height) {
continue;
}
[clone[ny][nx], clone[py][px]] = [
clone[py][px],
clone[ny][nx]
];
} else {
// 80% 概率替换当前图块
clone[ny][nx] = Math.floor(Math.random() * tileType.size);
}
}
}
const id2 = `${id}.S${i}`;
const sid = `${id}:${id2}`;
const simi = compareMap(id, id2, map, clone);
res.push([
sid,
{
map1: map,
map2: clone,
size: [width, height],
topoSimilarity: simi,
visionSimilarity: calculateVisualSimilarity(map, clone)
}
]);
}
return res;
}
function generateDataset(
floors: Map<string, number[][]>,
pairs: number[],
floorIds: string[],
config: MinamoConfig
): Record<string, MinamoTrainData> {
const data: Record<string, MinamoTrainData> = {};
pairs.forEach(v => {
const num1 = Math.floor(v / floorIds.length);
const num2 = v % floorIds.length;
const id1 = floorIds[num1];
const id2 = floorIds[num2];
const map1 = floors.get(id1);
const map2 = floors.get(id2);
if (!map1 || !map2) return;
const [w1, h1] = [map1[0].length, map1.length];
const [w2, h2] = [map2[0].length, map2.length];
if (w1 !== w2 || h1 !== h2) return;
const topoSimilarity = compareMap(id1, id2, map1, map2);
const visionSimilarity = calculateVisualSimilarity(map1, map2);
const train: MinamoTrainData = {
map1,
map2,
topoSimilarity,
visionSimilarity,
size: [w1, h1]
};
data[`${id1}:${id2}`] = train;
// 自身与自身对比的训练集，保证模型对相同地图输出 1
const self1 = `${id1}:${id1}`;
const self2 = `${id2}:${id2}`;
const selfTrain = chooseFrom(
[self1, self2],
Math.floor(Math.random() * 3)
);
if (selfTrain.includes(self1) && !data[`${id1}:${id1}`]) {
const selfTrain1: MinamoTrainData = {
map1: map1,
map2: map1,
topoSimilarity: 1,
visionSimilarity: 1,
size: [w1, h1]
};
data[`${id1}:${id1}`] = selfTrain1;
}
if (selfTrain.includes(self2) && !data[`${id2}:${id2}`]) {
const selfTrain2: MinamoTrainData = {
map1: map2,
map2: map2,
topoSimilarity: 1,
visionSimilarity: 1,
size: [w1, h1]
};
data[`${id2}:${id2}`] = selfTrain2;
}
// 翻转、旋转训练集
Object.assign(
data,
Object.fromEntries(
generateTransformData(id1, id2, map1, map2, topoSimilarity)
)
);
// 地图微调训练集
Object.assign(data, Object.fromEntries(generateSimilarData(id1, map1)));
// Object.assign(data, Object.fromEntries(generateSimilarData(id2, map2)));
});
return data;
}
async function parseOne(path: string): Promise<MinamoDataset> {
const dataFile = await readFile(join(path, 'data.js'), 'utf-8');
const configFile = await readFile(
join(path, 'minamo-config.json'),
'utf-8'
);
const data: any = JSON.parse(dataFile.split('\n').slice(1).join('\n'));
const config = JSON.parse(configFile) as MinamoConfig;
const floorIds = data.main.floorIds as string[];
const name = data.firstData.name as string;
const length = floorIds.length;
const totalCount = Math.round((length * (length - 1)) / 2);
const pairs = choosePair(length);
console.log(
`✅ 在 ${name} 中发现 ${length} 个楼层，共 ${totalCount} 种组合，选取 ${pairs.length} 个组合`
);
const floors = new Map(
await Promise.all(
floorIds.map<Promise<[string, number[][]]>>(async v => {
const file = await readFile(
join(path, 'floors', `${v}.js`),
'utf-8'
);
const data = file.split('\n').slice(1).join('\n');
const json = JSON.parse(data);
const map = json.map;
const clip = config.clip.special[v] ?? config.clip.defaults;
// 裁剪
const clipped = convertFloor(map, clip, name, v);
return [v, clipped];
})
)
);
const trainData = generateDataset(floors, pairs, floorIds, config);
const dataset: MinamoDataset = {
datasetId: Math.floor(Math.random() * 1e12),
data: trainData
};
return dataset;
}
(async () => {
const results = await Promise.all(list.map(v => parseOne(v)));
const dataset = mergeDataset(...results);
await writeFile(output, JSON.stringify(dataset, void 0), 'utf-8');
const size = Object.keys(dataset.data).length;
console.log(`✅ 已处理 ${list.length} 个塔，共 ${size} 个组合`);
})();

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import { buildTopologicalGraph } from './graph';
import { GinkaTopologicalGraphs } from './interface';
import { overallSimilarity } from './similarity';
const cache = new Map<string, GinkaTopologicalGraphs>();
export function getTopologicalGraph(
floorId: string,
map: number[][]
): GinkaTopologicalGraphs {
if (cache.has(floorId)) return cache.get(floorId)!;
const graphs = buildTopologicalGraph(map);
cache.set(floorId, graphs);
return graphs;
}
export function compareMap(
floorId1: string,
floorId2: string,
map1: number[][],
map2: number[][]
) {
const graph1 = getTopologicalGraph(floorId1, map1);
const graph2 = getTopologicalGraph(floorId2, map2);
const kernel = overallSimilarity(graph1, graph2);
return kernel;
}

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import {
ResourceArea,
GinkaGraph,
BranchNode,
GinkaTopologicalGraphs,
ResourceNode
} from './interface';
export const tileType = new Set(
Array(12)
.fill(0)
.map((_, i) => i)
);
const branchType = new Set([6, 7, 8, 9]);
const entranceType = new Set([10, 11]);
const resourceType = new Set([0, 2, 3, 4, 5, 10, 11]);
export const directions: [number, number][] = [
[-1, 0],
[1, 0],
[0, -1],
[0, 1]
];
function buildGraphFromEntrance(
map: number[][],
entrance: number,
resourceMap: Map<number, number>,
areaMap: ResourceArea[]
): GinkaGraph {
const width = map[0].length;
const height = map[1].length;
const visitedEntrance = new Set<number>([entrance]);
const visited = new Set<number>();
const queue: [number, number][] = [];
queue.push([entrance % width, Math.floor(entrance / width)]);
const branchNodes = new Set<number>();
// 1. BFS 检测所有分支节点
while (queue.length > 0) {
const item = queue.shift();
if (!item) continue;
const [nx, ny] = item;
const index = ny * width + nx;
if (visited.has(index)) continue;
const tile = map[ny][nx];
if (entranceType.has(tile)) {
visitedEntrance.add(index);
}
if (branchType.has(tile)) {
branchNodes.add(index);
}
visited.add(index);
for (const [dx, dy] of directions) {
const px = dx + nx;
const py = dy + ny;
if (px < 0 || px >= width || py < 0 || py >= height) {
continue;
}
const tile = map[py][px];
if (tile !== 1) {
// 非墙区域可通行
queue.push([px, py]);
}
}
}
// 2. 从分支节点构建拓扑图
const graph = new Map<number, BranchNode | ResourceNode>();
branchNodes.forEach(v => {
const nx = v % width;
const ny = Math.floor(v / width);
if (!graph.get(v)) {
graph.set(v, {
type: 'branch',
neighbor: new Set(),
tile: map[ny][nx]
});
}
const node = graph.get(v)!;
for (const [dx, dy] of directions) {
const px = nx + dx;
const py = ny + dy;
if (px < 0 || px >= width || py < 0 || py >= height) {
continue;
}
const index = py * width + px;
// 先检查临近节点是不是分支节点，是的话链接到自己
if (branchNodes.has(index)) {
node.neighbor.add(index);
} else {
// 检查是不是资源节点
const pointer = resourceMap.get(index);
if (pointer === void 0) continue;
const area = areaMap[pointer];
if (!area) continue;
area.neighbor.add(v);
area.members.forEach(v => {
node.neighbor.add(v);
});
}
}
});
// 3. 把资源节点拆分成并排，并放入拓扑图
areaMap.forEach(v => {
v.members.forEach(index => {
const nx = index % width;
const ny = Math.floor(index / width);
const tile = map[ny][nx];
if (tile === 0) return;
const node: ResourceNode = {
type: 'resource',
resourceType: tile,
neighbor: v.neighbor,
resourceArea: v
};
graph.set(index, node);
});
});
return { graph, resourceMap, areaMap, visitedEntrance, visited };
}
function findResourceNodes(map: number[][]) {
const width = map[0].length;
const height = map[1].length;
const visited = new Set<number>();
const areas: ResourceArea[] = [];
const resourcesMap: Map<number, number> = new Map();
for (let ny = 0; ny < height; ny++) {
for (let nx = 0; nx < width; nx++) {
const tile = map[ny][nx];
const index = ny * width + nx;
if (visited.has(index) || !resourceType.has(tile)) {
continue;
}
const queue: [number, number][] = [];
queue.push([nx, ny]);
const area: ResourceArea = {
type: 'resource',
resources: new Map([[tile, 1]]),
members: new Set([index]),
neighbor: new Set()
};
while (queue.length > 0) {
const item = queue.shift();
if (!item) continue;
const [nx, ny] = item;
const index = ny * width + nx;
if (visited.has(index)) {
continue;
}
const tile = map[ny][nx];
if (!resourceType.has(tile)) {
continue;
}
visited.add(index);
const exists = area.resources.get(tile);
if (!exists) {
area.resources.set(tile, 1);
} else {
area.resources.set(tile, exists + 1);
}
area.members.add(index);
resourcesMap.set(index, areas.length);
for (const [dx, dy] of directions) {
const px = nx + dx;
const py = ny + dy;
if (px < 0 || px >= width || py < 0 || py >= height) {
continue;
}
queue.push([px, py]);
}
}
areas.push(area);
}
}
return { areaMap: areas, resourcesMap };
}
export function buildTopologicalGraph(map: number[][]): GinkaTopologicalGraphs {
const width = map[0].length;
const height = map[1].length;
// 1. 找到所有入口
const entrances = new Set<number>();
for (let ny = 0; ny < height; ny++) {
for (let nx = 0; nx < width; nx++) {
const tile = map[ny][nx];
if (entranceType.has(tile)) {
entrances.add(ny * width + nx);
}
}
}
// 2. 找到所有的资源节点
const { areaMap, resourcesMap } = findResourceNodes(map);
// 3. 对每个入口计算拓扑图
const graphs: GinkaGraph[] = [];
const usedEntrance = new Set<number>();
const totalVisited = new Set<number>();
/** 入口位置到拓扑图的映射 */
const entranceMap = new Map<number, GinkaGraph>();
entrances.forEach(v => {
if (usedEntrance.has(v)) {
return;
}
const nx = v % width;
const ny = Math.floor(v / width);
const entranceGraph = buildGraphFromEntrance(
map,
v,
resourcesMap,
areaMap
);
const { graph, visited, visitedEntrance } = entranceGraph;
graphs.push(entranceGraph);
// 标记已经探索到的入口，并标记这个入口对应了哪个图
visitedEntrance.forEach(v => {
usedEntrance.add(v);
entranceMap.set(v, entranceGraph);
});
visited.forEach(v => {
totalVisited.add(v);
});
});
// 3. 计算不可到达区域
const unreachable = new Set<number>();
for (let ny = 0; ny < height; ny++) {
for (let nx = 0; nx < width; nx++) {
const index = ny * width + nx;
if (!totalVisited.has(index) && map[ny][nx] !== 1) {
unreachable.add(index);
}
}
}
return { graphs, entranceMap, unreachable };
}

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export interface ResourceArea {
type: 'resource';
resources: Map<number, number>;
members: Set<number>;
neighbor: Set<number>;
}
export interface BranchNode {
type: 'branch';
neighbor: Set<number>;
tile: number;
}
export interface ResourceNode {
type: 'resource';
resourceType: number;
neighbor: Set<number>;
resourceArea: ResourceArea;
}
export type GinkaNode = BranchNode | ResourceNode;
export interface GinkaGraph {
/** 拓扑图内容，键表示位置，值表示这一点的节点 */
graph: Map<number, GinkaNode>;
/** 资源指针，键表示位置，值表示这一点对应的资源节点在 areaMap 的索引 */
resourceMap: Map<number, number>;
/** 资源区域列表 */
areaMap: ResourceArea[];
/** 这个拓扑图包含的入口位置 */
visitedEntrance: Set<number>;
/** 这个拓扑图能够造访的所有位置 */
visited: Set<number>;
}
export interface GinkaTopologicalGraphs {
/** 这个地图包含的所有独立的图 */
graphs: GinkaGraph[];
/** 每个入口对应哪个图 */
entranceMap: Map<number, GinkaGraph>;
/** 这个图从入口开始的不可到达区域 */
unreachable: Set<number>;
}

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import { cosineSimilarity } from 'src/utils';
import { GinkaGraph, GinkaTopologicalGraphs } from './interface';
interface WLNode {
originalPos: number;
originalLabel: string;
currentLabel: string;
neighbors: WLNode[];
}
function encodeNodeLabels(graph: GinkaGraph) {
const nodes: WLNode[] = [];
const nodeMap = new Map<number, WLNode>();
graph.graph.forEach((node, pos) => {
let label: string;
// 编码为唯一哈希值（用字符串就行，V8 会自动帮你算哈希）
if (node.type === 'branch') {
label = `B:${node.tile}`;
} else {
label = `R:${node.resourceType}`;
}
const wlNode: WLNode = {
originalPos: pos,
originalLabel: label,
currentLabel: label,
neighbors: []
};
nodeMap.set(pos, wlNode);
nodes.push(wlNode);
});
// 映射邻居节点
nodes.forEach(node => {
const ginkaNode = graph.graph.get(node.originalPos);
ginkaNode?.neighbor.forEach(v => {
const wl = nodeMap.get(v);
if (wl) node.neighbors.push(wl);
});
});
return nodes;
}
function weisfeilerLehmanIteration(
nodes: WLNode[],
iterations: number,
decay: number = 0.6 // 衰减权重，减小长距离图的权重
) {
const labelHistory: string[][] = [];
for (let i = 0; i < iterations; i++) {
const newLabels: string[] = [];
// 生成新标签
nodes.forEach(node => {
const neighborLabels = node.neighbors
.map(n => n.currentLabel)
.sort();
const compositeLabel = `${node.currentLabel}|${neighborLabels.join(
','
)}`;
newLabels.push(compositeLabel);
});
// 更新节点标签并记录
nodes.forEach((node, idx) => {
node.currentLabel = newLabels[idx];
});
labelHistory.push([...newLabels]);
}
// 统计每个节点的数量
let weight = 1;
const numMap = new Map<string, number>();
labelHistory.forEach(iter => {
iter.forEach(v => {
if (!numMap.has(v)) {
numMap.set(v, weight);
} else {
numMap.set(v, numMap.get(v)! + weight);
}
});
weight *= decay;
});
// 把每个节点的原始标签也加上，权重使用最远权重，可以认为是资源重复率
nodes.forEach(node => {
if (!numMap.has(node.originalLabel)) {
numMap.set(node.originalLabel, weight);
} else {
numMap.set(
node.originalLabel,
numMap.get(node.originalLabel)! + weight
);
}
});
return numMap;
}
function vectorizeFeatures(features: Map<string, number>, vocab: string[]) {
const vec: number[] = new Array(vocab.length).fill(0);
features.forEach((count, label) => {
const index = vocab.indexOf(label);
if (index !== -1) {
vec[index] += count;
}
});
return vec;
}
function wlKernel(
graphA: GinkaGraph,
graphB: GinkaGraph,
iterations = 3
): number {
// 编码节点
const nodesA = encodeNodeLabels(graphA);
const nodesB = encodeNodeLabels(graphB);
// 迭代生成标签
const featuresA = weisfeilerLehmanIteration(nodesA, iterations);
const featuresB = weisfeilerLehmanIteration(nodesB, iterations);
// 构建特征向量
const vocab = [...new Set([...featuresA.keys(), ...featuresB.keys()])];
const vecA = vectorizeFeatures(featuresA, vocab);
const vecB = vectorizeFeatures(featuresB, vocab);
// 计算余弦相似度
return cosineSimilarity(vecA, vecB);
}
export function overallSimilarity(
a: GinkaTopologicalGraphs,
b: GinkaTopologicalGraphs
) {
// 使用 Weisfeiler-Lehman Kernel 方式计算拓扑图相似度
const graphsA = a.graphs;
const graphsB = b.graphs;
let totalSimilarity = 0;
const comparedGraph = new Set<GinkaGraph>();
graphsA.forEach(ga => {
let maxSimilarity = 0;
let maxGraph: GinkaGraph | null = null;
// 图之间两两比较，找到最接近的作为相似度
for (const gb of graphsB) {
if (comparedGraph.has(gb)) continue;
// 计算迭代次数
const min = Math.min(ga.graph.size, gb.graph.size);
const iterations = Math.ceil(Math.max(1, Math.log(min)));
const similarity = wlKernel(ga, gb, iterations);
if (similarity > maxSimilarity) {
maxSimilarity = similarity;
maxGraph = gb;
}
if (similarity === 1) break;
}
totalSimilarity += maxSimilarity;
if (maxGraph) comparedGraph.add(maxGraph);
});
// 取根号使结果更接近线性
return Math.sqrt(totalSimilarity / graphsA.length);
}

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import { buildTopologicalGraph } from './graph';
import { mirrorMapX, mirrorMapY, rotateMap } from './transform';
import { overallSimilarity } from './similarity';
(() => {
// MT3
const map1 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 3, 1, 10, 7, 3, 1],
[1, 6, 1, 5, 1, 7, 1],
[1, 9, 1, 8, 1, 6, 1],
[1, 5, 8, 0, 1, 7, 1],
[1, 2, 1, 5, 7, 10, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT6
const map2 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 6, 4, 1, 10, 1],
[1, 6, 1, 9, 1, 5, 1],
[1, 8, 0, 6, 0, 8, 1],
[1, 5, 1, 10, 1, 2, 1],
[1, 9, 3, 1, 4, 9, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT8
// const map2 = [
// [1, 1, 1, 1, 1, 1, 1],
// [1, 5, 8, 10, 7, 2, 1],
// [1, 2, 1, 5, 1, 7, 1],
// [1, 3, 1, 3, 6, 4, 1],
// [1, 6, 1, 6, 1, 8, 1],
// [1, 10, 7, 5, 1, 5, 1],
// [1, 1, 1, 1, 1, 1, 1]
// ];
// MT3 微调
// const map2 = [
// [1, 1, 1, 1, 1, 1, 1],
// [1, 3, 1, 10, 7, 3, 1],
// [1, 6, 1, 5, 1, 7, 1],
// [1, 9, 1, 8, 1, 6, 1],
// [1, 5, 8, 0, 1, 7, 1],
// [1, 2, 1, 4, 7, 10, 1],
// [1, 1, 1, 1, 1, 1, 1]
// ];
// 1. 两张图与自身对比
const graph1 = buildTopologicalGraph(map1);
const graph2 = buildTopologicalGraph(map2);
console.log(`map1 vs map1: ${overallSimilarity(graph1, graph1)}`);
console.log(`map2 vs map2: ${overallSimilarity(graph2, graph2)}`);
// 2. 两张图相互对比
console.log(`map1 vs map2: ${overallSimilarity(graph1, graph2)}`);
console.log(`map2 vs map1: ${overallSimilarity(graph2, graph1)}`);
// 3. x镜像对比
const xFlipped1 = mirrorMapX(map1);
const xFlipped2 = mirrorMapX(map2);
const graphX1 = buildTopologicalGraph(xFlipped1);
const graphX2 = buildTopologicalGraph(xFlipped2);
console.log(`map1:x vs map1: ${overallSimilarity(graphX1, graph1)}`);
console.log(`map1:x vs map2: ${overallSimilarity(graphX1, graph2)}`);
console.log(`map1 vs map2:x: ${overallSimilarity(graph1, graphX2)}`);
console.log(`map2:x vs map2: ${overallSimilarity(graphX2, graph2)}`);
console.log(`map2:x vs map1: ${overallSimilarity(graphX2, graph2)}`);
console.log(`map2 vs map1:x: ${overallSimilarity(graph2, graphX1)}`);
// 4. y镜像对比
const yFlipped1 = mirrorMapY(map1);
const yFlipped2 = mirrorMapY(map2);
const graphY1 = buildTopologicalGraph(yFlipped1);
const graphY2 = buildTopologicalGraph(yFlipped2);
console.log(`map1:y vs map1: ${overallSimilarity(graphY1, graph1)}`);
console.log(`map1:y vs map2: ${overallSimilarity(graphY1, graph2)}`);
console.log(`map1 vs map2:y: ${overallSimilarity(graph1, graphY2)}`);
console.log(`map2:y vs map2: ${overallSimilarity(graphY2, graph2)}`);
console.log(`map2:y vs map1: ${overallSimilarity(graphY2, graph1)}`);
console.log(`map2 vs map1:y: ${overallSimilarity(graph2, graphY1)}`);
// 5. xy 镜像混合对比
console.log(`map1:x vs map1:y: ${overallSimilarity(graphX1, graphY1)}`);
console.log(`map1:y vs map2:x: ${overallSimilarity(graphY1, graphX2)}`);
console.log(`map1:x vs map2:x: ${overallSimilarity(graphX1, graphX2)}`);
console.log(`map1:x vs map2:y: ${overallSimilarity(graphX1, graphY2)}`);
// 6. 旋转对比
const rot901 = rotateMap(map1);
const rot902 = rotateMap(map2);
const graph901 = buildTopologicalGraph(rot901);
const graph902 = buildTopologicalGraph(rot902);
console.log(`map1:90 vs map1: ${overallSimilarity(graph1, graph901)}`);
console.log(`map2:90 vs map2: ${overallSimilarity(graph2, graph902)}`);
})();

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export function mirrorMapX(map: number[][]) {
return map.map(v => [...v].reverse());
}
export function mirrorMapY(map: number[][]) {
return [...map].reverse();
}
export function rotateMap(map: number[][]) {
return [
...map[0].map((_, colIndex) => map.map(row => row[colIndex]))
].reverse();
}

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interface DatasetMergable<T> {
datasetId: number;
data: Record<string, T>;
}
export function mergeDataset<T>(
...datasets: DatasetMergable<T>[]
): DatasetMergable<T> {
const data: Record<string, T> = {};
datasets.forEach(v => {
for (const [key, value] of Object.entries(v.data)) {
const dataKey = `${v.datasetId}/${key}`;
data[dataKey] = value;
}
});
const dataset: DatasetMergable<T> = {
datasetId: Math.floor(Math.random() * 1e12),
data: data
};
return dataset;
}
export function cosineSimilarity(vecA: number[], vecB: number[]): number {
if (vecA.length !== vecB.length) {
throw new Error('Vectors must have same dimension');
}
let dot = 0,
normA = 0,
normB = 0;
for (let i = 0; i < vecA.length; i++) {
dot += vecA[i] * vecB[i];
normA += vecA[i] ** 2;
normB += vecB[i] ** 2;
}
return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

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interface VisualSimilarityConfig {
// 类型重要性权重（需根据游戏设定调整）
typeWeights: { [key: number]: number };
// 是否启用视觉焦点增强
enableVisualFocus: boolean;
// 是否启用密度感知
enableDensityAwareness: boolean;
}
const DEFAULT_CONFIG: VisualSimilarityConfig = {
typeWeights: {
0: 0.2, // 空地
1: 0.3, // 墙壁
2: 0.6, // 钥匙
3: 0.7, // 红宝石
4: 0.7, // 蓝宝石
5: 0.5, // 血瓶
6: 0.4, // 门
7: 0.5, // 弱怪
8: 0.6, // 中怪
9: 0.6, // 强怪
10: 0.4, // 楼梯
11: 0.4 // 箭头
},
enableVisualFocus: true,
enableDensityAwareness: true
};
export function calculateVisualSimilarity(
map1: number[][],
map2: number[][],
config = DEFAULT_CONFIG
): number {
// 尺寸校验
if (map1.length !== map2.length || map1[0]?.length !== map2[0]?.length) {
return 0; // 或抛出异常
}
const rows = map1.length;
const cols = map1[0].length;
let totalScore = 0;
let maxPossibleScore = 0;
// 视觉焦点权重图
const focusWeights = config.enableVisualFocus
? generateFocusWeights(rows, cols)
: Array(rows)
.fill(1)
.map(() => Array(cols).fill(1));
// 类型密度分布计算
const densityMap = config.enableDensityAwareness
? calculateDensityImpact(map1, map2, config.typeWeights)
: Array(rows)
.fill(1)
.map(() => Array(cols).fill(1));
for (let i = 0; i < rows; i++) {
for (let j = 0; j < cols; j++) {
const type1 = map1[i][j];
const type2 = map2[i][j];
// 基础类型权重
const baseWeight = Math.max(
config.typeWeights[type1] || 0.5,
config.typeWeights[type2] || 0.5
);
// 空间权重组合
const spatialWeight = focusWeights[i][j] * densityMap[i][j];
// 类型匹配得分
const typeScore = type1 === type2 ? 1 : 0;
totalScore += typeScore * baseWeight * spatialWeight;
maxPossibleScore += baseWeight * spatialWeight;
}
}
return maxPossibleScore > 0 ? totalScore / maxPossibleScore : 0;
}
/**
* 生成视觉焦点权重图（基于人类视觉注意力分布）
*/
function generateFocusWeights(rows: number, cols: number): number[][] {
const weights = [];
const centerX = cols / 2;
const centerY = rows / 2;
const maxDist = Math.sqrt(centerX ** 2 + centerY ** 2) * 0.7;
for (let i = 0; i < rows; i++) {
const rowWeights = [];
for (let j = 0; j < cols; j++) {
// 使用高斯分布模拟视觉焦点
const dx = (j - centerX) / cols;
const dy = (i - centerY) / rows;
const distance = Math.sqrt(dx ** 2 + dy ** 2);
const gaussian = Math.exp(-(distance ** 2) / (2 * 0.3 ** 2));
rowWeights.push(1.0 + 0.6 * gaussian); // 中心区域最高1.6倍权重
}
weights.push(rowWeights);
}
return weights;
}
/**
* 计算类型密度影响权重
*/
function calculateDensityImpact(
map1: number[][],
map2: number[][],
typeWeights: { [key: number]: number }
): number[][] {
const rows = map1.length;
const cols = map1[0].length;
const densityMap = Array(rows)
.fill(0)
.map(() => Array(cols).fill(0));
// 滑动窗口分析局部密度
const windowSize = 3;
const halfWindow = Math.floor(windowSize / 2);
for (let i = 0; i < rows; i++) {
for (let j = 0; j < cols; j++) {
let density = 0;
for (let di = -halfWindow; di <= halfWindow; di++) {
for (let dj = -halfWindow; dj <= halfWindow; dj++) {
const ni = i + di;
const nj = j + dj;
if (ni >= 0 && ni < rows && nj >= 0 && nj < cols) {
const weight1 = typeWeights[map1[ni][nj]] || 0.5;
const weight2 = typeWeights[map2[ni][nj]] || 0.5;
density += (weight1 + weight2) / 2;
}
}
}
// 密度权重：高密度区域增强对比度
densityMap[i][j] = 1.0 + 0.4 * (density / windowSize ** 2);
}
}
return densityMap;
}

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import { calculateVisualSimilarity } from './similarity';
(() => {
// MT3
const map1 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 3, 1, 10, 7, 3, 1],
[1, 6, 1, 5, 1, 7, 1],
[1, 9, 1, 8, 1, 6, 1],
[1, 5, 8, 0, 1, 7, 1],
[1, 2, 1, 5, 7, 10, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT6
const map2 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 6, 4, 1, 10, 1],
[1, 6, 1, 9, 1, 5, 1],
[1, 8, 0, 6, 0, 8, 1],
[1, 5, 1, 10, 1, 2, 1],
[1, 9, 3, 1, 4, 9, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT8
const map3 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 8, 10, 7, 2, 1],
[1, 2, 1, 5, 1, 7, 1],
[1, 3, 1, 3, 6, 4, 1],
[1, 6, 1, 6, 1, 8, 1],
[1, 10, 7, 5, 1, 5, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT3 微调
const map4 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 3, 1, 10, 7, 3, 1],
[1, 6, 1, 5, 1, 7, 1],
[1, 9, 1, 8, 1, 6, 1],
[1, 5, 8, 0, 1, 7, 1],
[1, 2, 1, 4, 7, 10, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// MT10
const map5 = [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 1, 10, 1, 5, 1],
[1, 6, 7, 7, 7, 6, 1],
[1, 1, 6, 5, 6, 1, 1],
[1, 4, 5, 9, 5, 4, 1],
[1, 3, 1, 1, 1, 3, 1],
[1, 1, 1, 1, 1, 1, 1]
];
// 测试自我对比
console.log(`map1 vs map1: ${calculateVisualSimilarity(map1, map1)}`);
console.log(`map2 vs map2: ${calculateVisualSimilarity(map2, map2)}`);
console.log(`map3 vs map3: ${calculateVisualSimilarity(map3, map3)}`);
console.log(`map4 vs map4: ${calculateVisualSimilarity(map4, map4)}`);
// 两两测试
console.log(`map1 vs map2: ${calculateVisualSimilarity(map1, map2)}`);
console.log(`map1 vs map3: ${calculateVisualSimilarity(map1, map3)}`);
console.log(`map1 vs map4: ${calculateVisualSimilarity(map1, map4)}`);
console.log(`map1 vs map5: ${calculateVisualSimilarity(map1, map5)}`);
console.log(`map2 vs map3: ${calculateVisualSimilarity(map2, map3)}`);
console.log(`map2 vs map4: ${calculateVisualSimilarity(map2, map4)}`);
console.log(`map2 vs map5: ${calculateVisualSimilarity(map2, map5)}`);
console.log(`map3 vs map4: ${calculateVisualSimilarity(map3, map4)}`);
console.log(`map3 vs map5: ${calculateVisualSimilarity(map3, map5)}`);
console.log(`map4 vs map5: ${calculateVisualSimilarity(map4, map5)}`);
// 测试交换性
console.log(`map2 vs map1: ${calculateVisualSimilarity(map2, map1)}`);
console.log(`map4 vs map2: ${calculateVisualSimilarity(map4, map2)}`);
})();

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{
"compilerOptions": {
"experimentalDecorators": true,
"target": "ESNext",
"useDefineForClassFields": true,
"module": "ESNext",
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"strict": true,
"resolveJsonModule": true,
"isolatedModules": true,
"esModuleInterop": true,
"lib": ["ESNext"],
"skipLibCheck": true,
"noEmit": true,
"baseUrl": "."
},
"include": ["src/**/*.ts", "src/**/*.d.ts"]
}

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import { defineConfig } from 'vitest/config';
export default defineConfig({
test: {
globals: true,
environment: 'node'
}
});

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{
"datasetId": 468713377296,
"data": {
"93529307366/MT1": {
"text": [
"左右对称结构，右侧包含一个红宝石，左侧包含一个蓝宝石，怪物全部是中等强度",
"画一个左右对称，怪物强度中等，需要开门才能上楼的地图",
"生成一个有两把黄钥匙，三个黄门，四个血瓶，四个怪物的左右对称地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 1, 10, 1, 5, 1],
[1, 8, 0, 2, 0, 8, 1],
[1, 4, 1, 6, 1, 3, 1],
[1, 8, 6, 2, 6, 8, 1],
[1, 5, 1, 10, 1, 5, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT2": {
"text": [
"画一个主干道上只有弱怪，没有宝石，包含咸鱼门的地图",
"宝石被中等怪物守护，也可以通过开咸鱼门获得，以弱怪为主的地图",
"这是一个主干道在地图边缘的地图，可以考虑开一个黄门提前获得宝石"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 7, 10, 0, 7, 1],
[1, 1, 5, 1, 1, 2, 1],
[1, 0, 7, 0, 1, 5, 1],
[1, 6, 1, 6, 1, 7, 1],
[1, 5, 1, 4, 8, 10, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT3": {
"text": [
"一个红宝石在黄门里面，门前有一个强怪守护，另一个红宝石可以打败一个中怪和两个弱怪后获得",
"画一个主干道在中间，左侧有一个强怪守护的红宝石，右侧有一个红宝石的地图",
"生成一个左侧怪物较强，右侧怪物较弱的地图，右侧有一个黄门捷径"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 3, 1, 10, 7, 3, 1],
[1, 6, 1, 5, 1, 7, 1],
[1, 9, 1, 8, 1, 6, 1],
[1, 5, 8, 0, 1, 7, 1],
[1, 2, 1, 5, 7, 10, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT4": {
"text": [
"生成一个左右异形对称，包含各种强度的怪物，有中怪守护红宝石，强怪守护蓝宝石的地图",
"防御宝石由强怪守护，攻击宝石由中怪守护，左右不完全对称的地图",
"画一个可以通过开门绕过一个中等强度怪物和两个弱怪，包含三个血瓶，攻防宝石各一个的地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 7, 10, 7, 0, 1],
[1, 8, 1, 6, 1, 8, 1],
[1, 3, 1, 5, 7, 5, 1],
[1, 1, 1, 6, 1, 1, 1],
[1, 10, 8, 0, 9, 4, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT5": {
"text": [
"画一个左上右下对称，中间全是门和宝石，周围全是怪物和血瓶的地图",
"中间的门构成一个叉号状，空余位置填充宝石，外围包含怪物和血瓶，几乎没有空地的地图",
"生成一个偏资源向的楼层，有很多资源集中在中间，但是需要开门才能获得，地图外围有怪物挡路"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 8, 5, 7, 0, 10, 1],
[1, 5, 6, 4, 6, 0, 1],
[1, 7, 3, 6, 3, 7, 1],
[1, 2, 6, 4, 6, 5, 1],
[1, 10, 2, 7, 5, 8, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT6": {
"text": [
"上楼梯在蓝门里面，需要开门才能上楼，怪物强度比较高，没有弱怪，宝石全由强怪守护，但是其中一个也可以咸门获得",
"画一个接近左右对称的地图，最好能好看点",
"生成一个有一把黄钥匙，怪物强度高的地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 6, 4, 1, 10, 1],
[1, 6, 1, 9, 1, 5, 1],
[1, 8, 0, 6, 0, 8, 1],
[1, 5, 1, 10, 1, 2, 1],
[1, 9, 3, 1, 4, 9, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT7": {
"text": [
"画一个几乎全由怪物组成，没有宝石，只有血瓶钥匙和门的地图",
"怪物强度高，但是血瓶数量多",
"可以直接开门上楼，没有宝石的地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 9, 2, 9, 5, 1],
[1, 8, 0, 10, 0, 8, 1],
[1, 5, 9, 6, 9, 5, 1],
[1, 0, 8, 10, 8, 0, 1],
[1, 6, 0, 5, 0, 6, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT8": {
"text": [
"以走廊为主，没有强怪，只有弱怪和中怪，有一定数量的宝石的地图",
"画一个蓝海风，以走廊为主的地图",
"生成一个资源比较丰富，有宝石、血瓶，需要开门才能上楼，有一些咸鱼门的地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 8, 10, 7, 2, 1],
[1, 2, 1, 5, 1, 7, 1],
[1, 3, 1, 3, 6, 4, 1],
[1, 6, 1, 6, 1, 8, 1],
[1, 10, 7, 5, 1, 5, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT9": {
"text": [
"左右基本对称，但是入口不对称，宝石种类不对称的地图",
"画一个左右基本对称，没有强怪的地图",
"生成一个有两个红宝石，一个蓝宝石，很多血瓶，没有强怪的地图"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 4, 1, 10, 1, 3, 1],
[1, 5, 7, 0, 7, 5, 1],
[1, 8, 6, 5, 6, 8, 1],
[1, 5, 8, 6, 8, 5, 1],
[1, 10, 5, 1, 5, 3, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
},
"93529307366/MT10": {
"text": [
"左右对称的 Boss 层",
"画一个 Boss 层，入口在上方，Boss 在下方，中间有几个弱怪挡路",
"生成一个 Boss 层，可以咸门提前拿资源"
],
"map": [
[1, 1, 1, 1, 1, 1, 1],
[1, 5, 1, 10, 1, 5, 1],
[1, 6, 7, 7, 7, 6, 1],
[1, 1, 6, 5, 6, 1, 1],
[1, 4, 5, 9, 5, 4, 1],
[1, 3, 1, 1, 1, 3, 1],
[1, 1, 1, 1, 1, 1, 1]
],
"size": [7, 7]
}
}
}

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import json
import random
import torch
from torch.utils.data import Dataset
from transformers import BertTokenizer
def load_data(path: str):
with open(path, 'r', encoding="utf-8") as f:
data = json.load(f)
data_list = []
for value in data["data"].values():
data_list.append(value)
return data_list
class GinkaDataset(Dataset):
def __init__(self, data_path: str, tokenizer: BertTokenizer, max_len=128):
self.data = load_data(data_path) # 自定义数据加载函数
self.tokenizer = tokenizer
self.max_len = max_len
self.max_size = 32
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
# 文本处理
text = random.choice(item["text"])
encoding = self.tokenizer(
text,
max_length=self.max_len,
padding="max_length",
truncation=True,
return_tensors="pt"
)
# 噪声生成
w, h = item["size"]
noise = torch.randn(h, w, 1)
# 目标矩阵填充
target = torch.full((self.max_size, self.max_size), -100) # 使用-100忽略填充区域
target[:h, :w] = torch.tensor(item["map"])
return {
"noise": noise,
"input_ids": encoding["input_ids"].squeeze(),
"attention_mask": encoding["attention_mask"].squeeze(),
"map_size": torch.tensor([h, w]),
"target": target
}

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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_toolbelt import losses as L
def wall_border_loss(pred: torch.Tensor, probs: torch.Tensor, allow_border=[1, 11]):
"""地图最外层是否为墙"""
# 计算 softmax 概率
B, C, H, W = pred.shape
# 构造一个 [H, W] 的布尔 mask，选取最外圈的像素
border_mask = torch.zeros((H, W), dtype=torch.bool, device=pred.device)
border_mask[0, :] = True
border_mask[-1, :] = True
border_mask[:, 0] = True
border_mask[:, -1] = True
# 对允许的类别求概率和（即该像素为允许类别的总概率）
allowed_prob = probs[:, allow_border, :, :].sum(dim=1) # [B, H, W]
# 只计算边界区域的损失：对于边界上的每个像素，要求 allowed_prob 越高越好
border_allowed_prob = allowed_prob[:, border_mask] # [B, N_border_pixels]
# 损失为 -log(allowed_prob)
loss = 1 - border_allowed_prob.mean()
return loss
def internal_wall_loss(logits, probs, wall_class=1, threshold=2.5):
"""
针对内部区域（排除最外圈）设计的损失函数：
当内部任意 2×2 区域的 wall 类别概率之和超过阈值时，施加惩罚。
参数:
logits: 模型输出，形状 [B, C, H, W]
wall_class: 对应墙壁的类别索引（这里假设墙壁数字为1）
threshold: 2×2 区域概率之和的阈值，超过此值时施加惩罚。可根据实际情况调节。
返回:
loss: 内部墙壁连续区域的平均惩罚损失
"""
# 取出对应墙壁类别的概率图 [B, H, W]
wall_probs = probs[:, wall_class, :, :]
# 排除最外圈，取内部区域 (H, W 均减去2)
interior = wall_probs[:, 1:-1, 1:-1] # [B, H-2, W-2]
# 构造一个 2×2 的卷积核，全为 1，用于检测局部连续墙壁的概率之和
kernel = torch.ones((1, 1, 2, 2), device=logits.device)
# 对内部区域进行卷积操作，计算每个 2×2 区域内的概率和
# 需要将 interior 扩展一个通道维度
conv_result = F.conv2d(interior.unsqueeze(1), kernel, stride=1, padding=0)
# conv_result 的形状为 [B, 1, H-3, W-3]
# 对于每个 2×2 区域，如果概率和超过 threshold，则产生惩罚
# 这里采用 ReLU 计算超出部分，确保损失为非负
penalty = F.relu(conv_result - threshold)
# 取平均作为损失值
loss = penalty.mean()
return loss
def entrance_loss(logits, probs, stairs_class=10, arrow_class=11):
"""
针对地图生成的额外约束损失：
- 保证最外圈不出现楼梯类型入口（数字10）
- 保证内部区域不出现箭头类型入口（数字11）
参数:
logits: 模型输出，形状 [B, C, H, W]
stairs_class: 楼梯入口对应的类别（数字10）
arrow_class: 箭头入口对应的类别（数字11）
返回:
loss: 针对入口出现的惩罚损失
"""
# 先将 logits 转为概率分布
B, C, H, W = logits.shape
# 构造最外圈 mask：外圈为 True，其余为 False
outer_mask = torch.zeros((H, W), dtype=torch.bool, device=logits.device)
outer_mask[0, :] = True
outer_mask[-1, :] = True
outer_mask[:, 0] = True
outer_mask[:, -1] = True
# 内部区域 mask
interior_mask = ~outer_mask # 取反
# 提取对应类别的概率图
stairs_probs = probs[:, stairs_class, :, :] # 楼梯概率 [B, H, W]
arrow_probs = probs[:, arrow_class, :, :] # 箭头概率 [B, H, W]
# 从最外圈提取楼梯概率；用 mask 索引时：张量[:, mask] 会将每个样本的外圈像素展平
outer_stairs = stairs_probs[:, outer_mask] # [B, num_outer_pixels]
# 从内部区域提取箭头概率
interior_arrow = arrow_probs[:, interior_mask] # [B, num_interior_pixels]
# 损失设计：使得这些概率尽量接近 0，直接使用均值惩罚
outer_loss = outer_stairs.mean()
interior_loss = interior_arrow.mean()
total_loss = outer_loss + interior_loss
return total_loss
def entrance_distance_and_presence_loss(
logits, probs,
arrow_class=11, stairs_class=10,
arrow_min_threshold=0.5, stairs_min_threshold=0.5,
lambda_arrow_presence=1.0, lambda_stairs_presence=1.0
):
"""
入口损失同时考虑：
1. 局部距离约束：防止同一类型入口过于靠近
2. 存在性约束：鼓励至少放置一个入口
箭头入口要求局部 (9x9) 内最多只有一个入口；
楼梯入口要求在一个窗口（地图尺寸一半）内只出现一个楼梯入口。
参数:
logits: 模型输出, shape [B, C, H, W]
arrow_class: 箭头入口类别（默认 11）
stairs_class: 楼梯入口类别（默认 10）
arrow_min_threshold: 箭头入口全局最小平均概率要求（可根据任务调节）
stairs_min_threshold: 楼梯入口全局最小平均概率要求
lambda_arrow_presence: 箭头入口存在性损失权重
lambda_stairs_presence: 楼梯入口存在性损失权重
返回:
total_loss: 综合入口距离与存在性损失
"""
# 将 logits 转换为概率分布
probs = F.softmax(logits, dim=1) # [B, C, H, W]
B, C, H, W = logits.shape
# 提取箭头和楼梯的概率图
arrow_probs = probs[:, arrow_class, :, :] # [B, H, W]
stairs_probs = probs[:, stairs_class, :, :] # [B, H, W]
#### 局部距离约束 ####
# 箭头：构造 9x9 卷积核，半径 4
kernel_arrow = torch.ones((1, 1, 9, 9), device=logits.device)
local_arrow_sum = F.conv2d(arrow_probs.unsqueeze(1), kernel_arrow, padding=4)
# 减去自身概率，计算多余的局部累积
arrow_excess = local_arrow_sum - arrow_probs.unsqueeze(1)
arrow_distance_loss = F.relu(arrow_excess).mean()
# 楼梯：使用窗口大小为 (W//2, H//2)
kernel_size_stairs = (max(1, W // 2), max(1, H // 2))
kernel_stairs = torch.ones((1, 1, kernel_size_stairs[0], kernel_size_stairs[1]), device=logits.device)
pad_stairs = (kernel_size_stairs[0] // 2, kernel_size_stairs[1] // 2)
local_stairs_sum = F.conv2d(stairs_probs.unsqueeze(1), kernel_stairs, padding=pad_stairs)
stairs_excess = local_stairs_sum - stairs_probs.unsqueeze(1)
stairs_distance_loss = F.relu(stairs_excess).mean()
#### 存在性约束 ####
# 计算每个样本中箭头的最大概率
global_arrow_max = arrow_probs.view(B, -1).max(dim=1)[0] # [B]
global_stairs_max = stairs_probs.view(B, -1).max(dim=1)[0] # [B]
# 取 batch 平均（或者你可以对每个样本分别计算损失再求平均）
global_arrow_max = global_arrow_max.mean()
global_stairs_max = global_stairs_max.mean()
# 如果全局均值低于预期阈值，则施加额外惩罚
arrow_presence_loss = F.relu(arrow_min_threshold - global_arrow_max)
stairs_presence_loss = F.relu(stairs_min_threshold - global_stairs_max)
ap_weighted = lambda_arrow_presence * arrow_presence_loss
sp_weighted = lambda_stairs_presence * stairs_presence_loss
# 总入口损失：局部距离约束 + 存在性约束（加权）
total_loss = arrow_distance_loss + stairs_distance_loss \
+ min(ap_weighted, sp_weighted)
return total_loss
def monster_consecutive_loss(logits, probs, monster_classes=[7,8,9], threshold=2.9):
"""
检查横向和纵向是否存在连续超过三个的怪物（类别 7,8,9）。
参数:
logits: 模型输出，形状 [B, C, H, W]
monster_classes: 待检测的怪物类别列表
threshold: 滑动窗口内概率和的阈值，若超过则施加惩罚
（对于连续三个像素，如果每个像素概率接近 1，则窗口和接近 3）
返回:
loss: 惩罚损失（数值越高表示连续怪物区域越严重）
"""
# 将 logits 转换为概率分布
B, C, H, W = logits.shape
# 得到怪物整体概率图：将类别 7,8,9 的概率相加
monster_probs = probs[:, monster_classes, :].sum(dim=1) # [B, H, W]
# 注意：monster_probs 越高说明该像素更有可能是怪物
# --- 横向检测 ---
# 构造一个 (1,3) 的卷积核，全 1
kernel_horiz = torch.ones((1, 1, 1, 3), device=logits.device)
# 对 monster_probs 加一个 channel 维度，使形状为 [B, 1, H, W]
conv_horiz = F.conv2d(monster_probs.unsqueeze(1), kernel_horiz, padding=(0,1))
# conv_horiz 的每个值表示相邻三个像素的怪物概率和
# --- 纵向检测 ---
# 构造一个 (3,1) 的卷积核，全 1
kernel_vert = torch.ones((1, 1, 3, 1), device=logits.device)
conv_vert = F.conv2d(monster_probs.unsqueeze(1), kernel_vert, padding=(1,0))
# conv_vert 的每个值表示垂直连续三个像素的怪物概率和
# 对两个方向的窗口，如果概率和超过阈值，则计算超出部分的惩罚
penalty_horiz = F.relu(conv_horiz - threshold)
penalty_vert = F.relu(conv_vert - threshold)
# 将两个方向的惩罚损失取平均（或者直接相加）
loss = penalty_horiz.mean() + penalty_vert.mean()
return loss
def illegal_block_loss(logits ,probs, used_classes=12, mode='mean'):
"""
对未使用类别（例如 12 ~ 31）的预测概率施加惩罚，
鼓励模型输出仅集中在 0 ~ 11 上。
参数:
logits: 模型输出，形状 [B, num_classes, H, W]
used_classes: 已经使用的类别数（例如 12 表示只使用 0-11）
mode: 'mean' 使用平均概率，或 'mse' 使用均方误差
返回:
penalty: 标量惩罚损失
"""
# 选取非法类别的概率（注意：这一步会得到非法图块在每个像素上的概率）
illegal_probs = probs[:, range(used_classes, 32), :, :] # [B, len(illegal_classes), H, W]
# 我们可以将非法图块的概率在类别维度上求和，得到每个像素的非法激活值
illegal_activation = illegal_probs.sum(dim=1) # [B, H, W]
# 接下来我们计算整个图上非法激活的“数量”
# 例如，可以直接对整个 batch 内非法激活求和
total_illegal = illegal_activation.sum() # 标量
# 计算损失值：使用负指数函数。注意如果非法激活很小，总损失接近 exp(0)=1
loss = torch.sqrt(total_illegal)
return loss
def integrated_count_loss(probs, target, class_list=[0,1,2,3,4,5,6,7,8,9], tolerance=0.5):
"""
对每个类别分别计算数量匹配损失，再取平均。
参数:
probs: 模型输出的概率，形状 [B, num_classes, H, W]
target: 真实标签，形状 [B, H, W]，类别取值在 0 ~ 使用范围-1 内
class_list: 需要计算的类别列表
tolerance: 每个类别允许的相对误差（例如 0.15 表示 15%）
返回:
loss: 对每个类别数量匹配损失取平均后的标量
"""
total_loss = 0.0
count = 0
B, C, H, W = probs.shape
for cls in class_list:
# 预测数量：对于当前类别，所有像素的预测概率和
pred_count = probs[:, cls, :, :].sum()
# 真实数量：统计 target 中属于当前类别的像素数量
true_count = (target == cls).float().sum()
if true_count == 0:
# 参考地图中不包含该类别，允许最多出现 (sqrt(地图尺寸) / 2) 个单位的概率输出
cls_loss = F.relu(pred_count - math.sqrt(H * W) / 2)
else:
# 计算相对误差
rel_error = torch.abs(pred_count - true_count) / (true_count)
cls_loss = F.relu(rel_error - tolerance)
total_loss += cls_loss
count += 1
# 求平均每个类别的损失
avg_loss = total_loss / count
return avg_loss
class GinkaLoss(nn.Module):
def __init__(self, weight=[0.35, 0.1, 0.1, 0.1, 0.1, 0.05, 0.1, 0.1]):
"""Ginka Model 损失函数部分
Args:
weight (list, optional): 每一个损失函数的权重，从第 0 项开始，依次是：
1. 拓扑图损失
2. 外圈墙壁损失
3. 内层 2*2 墙壁损失
4. 要求外层只能有箭头，内层只能有楼梯的损失
5. 入口间距及存在性损失
6. 连续怪物损失
7. 非法图块损失
8. 怪物、道具、门数量损失
"""
super().__init__()
self.weight = weight
self.dice = L.DiceLoss(mode='multiclass')
self.ce = nn.CrossEntropyLoss()
def forward(self, pred, target):
probs = F.softmax(pred, dim=1)
# 拓扑结构损失
# structure_loss = topology_loss(pred, target)
# 地图结构损失
border_loss = wall_border_loss(pred, probs)
wall_loss = internal_wall_loss(pred, probs)
entry_loss = entrance_loss(pred, probs)
entry_dis_loss = entrance_distance_and_presence_loss(pred, probs)
enemy_loss = monster_consecutive_loss(pred, probs)
valid_block_loss = illegal_block_loss(pred, probs, used_classes=12, mode="mean")
count_loss = integrated_count_loss(probs, target)
print(
# structure_loss.item(),
border_loss.item(),
wall_loss.item(),
entry_loss.item(),
entry_dis_loss.item(),
enemy_loss.item(),
valid_block_loss.item(),
count_loss.item()
)
return (
# structure_loss * self.weight[0] +
border_loss * self.weight[1] +
wall_loss * self.weight[2] +
entry_loss * self.weight[3] +
entry_dis_loss * self.weight[4] +
enemy_loss * self.weight[5] +
valid_block_loss * self.weight[6] +
count_loss * self.weight[7]
)

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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertModel
from ...shared.attention import CBAM, SpatialAttention
from .sample import HybridUpsample, FinalUpsample, GumbelSampler
class ResidualBlock(nn.Module):
"""残差块"""
def __init__(self, channels):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(channels, channels, 3, padding=1),
nn.GroupNorm(8, channels),
nn.GELU(),
nn.Conv2d(channels, channels, 3, padding=1),
nn.GroupNorm(8, channels)
)
def forward(self, x):
return x + self.conv(x)
class DynamicPadConv(nn.Module):
"""支持动态处理奇数尺寸的智能卷积"""
def __init__(self, in_ch, out_ch, kernel=3, stride=1):
super().__init__()
self.conv = nn.Conv2d(
in_ch, out_ch, kernel,
stride=stride,
padding=kernel//2
)
self.requires_pad = (stride > 1) # 仅在下采样时需要填充
def forward(self, x):
if self.requires_pad:
# 动态计算各维度需要填充的量
pad_h = x.size(-2) % 2
pad_w = x.size(-1) % 2
if pad_h or pad_w:
x = F.pad(x, (0, pad_w, 0, pad_h)) # 右下填充
return self.conv(x)
class ConditionInjector(nn.Module):
"""基于注意力机制的条件注入"""
def __init__(self, cond_dim=128, feat_dim=256):
super().__init__()
self.cond_proj = nn.Sequential(
nn.Linear(cond_dim, feat_dim * 2),
nn.GELU(),
nn.LayerNorm(feat_dim * 2)
)
self.channel_att = nn.Sequential(
nn.Conv2d(feat_dim, feat_dim//8, 1),
nn.GELU(),
nn.Conv2d(feat_dim//8, feat_dim, 1),
nn.Sigmoid()
)
def forward(self, x, cond):
# 投影条件向量
gamma, beta = self.cond_proj(cond).chunk(2, dim=1) # [B, D]
# 通道注意力调制
att = self.channel_att(x) # [B, C, H, W]
modulated = x * att
# 添加条件偏置
return modulated + beta.view(-1, gamma.size(1), 1, 1)
class GinkaEncoder(nn.Module):
"""编码器（下采样）部分"""
def __init__(self, in_ch, out_ch):
super().__init__()
self.encoder = nn.Sequential(
DynamicPadConv(in_ch, out_ch, stride=1),
ResidualBlock(out_ch),
CBAM(out_ch),
nn.GroupNorm(8, out_ch),
nn.GELU()
)
def forward(self, x):
return self.encoder(x)
class GinkaModel(nn.Module):
def __init__(self, in_ch=1, base_ch=64, num_classes=32):
"""Ginka Model 模型定义部分
Args:
in_ch (int, optional): 输入通道数，默认是 1
base_ch (int, optional): UNet 上下采样卷积基础通道数，默认 64
num_classes (int, optional): 图块种类数量，默认 32 预留出一部分以供后续拓展功能
"""
super().__init__()
# 轻量级文本编码器（使用BERT前4层）
self.bert = BertModel.from_pretrained('google-bert/bert-base-chinese', output_hidden_states=True)
self.text_proj = nn.Linear(768, 128)
# 动态尺寸处理系统
self.size_embed = nn.Embedding(32, 16) # 处理最大32的尺寸
# 编码器
self.enc1 = GinkaEncoder(in_ch, base_ch)
self.enc2 = GinkaEncoder(base_ch, base_ch * 2)
# self.enc3 = GinkaEncoder(base_ch * 2, base_ch * 4)
# 中间层
self.mid = nn.Sequential(
DynamicPadConv(base_ch * 2, base_ch * 4),
ConditionInjector(160, base_ch * 4)
)
# 解码器，解码器仅使用空间注意力
self.dec1 = HybridUpsample(base_ch * 4, base_ch * 2)
self.dec1_att = SpatialAttention()
self.dec2 = HybridUpsample(base_ch * 2, base_ch)
self.dec2_att = SpatialAttention()
# self.dec3 = HybridUpsample(base_ch * 2, base_ch)
# self.dec3_att = SpatialAttention()
# 输出层
self.out = FinalUpsample(base_ch, num_classes)
def forward(self, noise, input_ids, attention_mask, map_size):
"""
Args:
noise: 噪声输入 [BS, H, W, 1]
input_ids: 文本token id [BS, seq_len]
attention_mask: 文本attention mask [BS, seq_len]
map_size: 地图尺寸 [BS, 2] (height, width)
Returns:
logits: 输出logits [BS, num_classes, H, W]
"""
# 文本特征提取
with torch.no_grad(): # 冻结BERT参数
bert_outputs = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True
)
# 取前4层隐藏状态的平均
hidden_states = torch.stack(bert_outputs.hidden_states[1:5]) # [4, BS, seq_len, 768]
text_features = torch.mean(hidden_states, dim=0)[:, 0, :] # [BS, 768]
text_features = self.text_proj(text_features) # [BS, 128]
# 尺寸特征处理
h_emb = self.size_embed(map_size[:, 0]) # [BS, 16]
w_emb = self.size_embed(map_size[:, 1]) # [BS, 16]
size_features = torch.cat([h_emb, w_emb], dim=1) # [BS, 32]
# 特征融合
conditional = torch.cat([text_features, size_features], dim=1) # [BS, 160]
# 调整噪声输入维度
x = noise.permute(0, 3, 1, 2) # [BS, 1, H, W]
# 编码器路径
x1 = self.enc1(x) # [BS, 64, H / 2, W / 2]
x2 = self.enc2(x1) # [BS, 128, H / 4, W / 4]
# 中间层（注入条件）
x_mid = self.mid[0](x2) # [BS, 256, H / 4, W / 4]
x_mid = self.mid[1](x_mid, conditional)
# 解码器路径
d1 = self.dec1(x_mid, x2) # [BS, 128, H / 2, W / 2]
d1 = self.dec1_att(d1)
d2 = self.dec2(d1, x1) # [BS, 64, H, W]
d2 = self.dec2_att(d2)
# d3 = self.dec3(d2, x1)
# d3 = self.dec3_att(d3)
# 最终自适应上采样
h, w = noise.shape[1:3] # 获取原始输入尺寸
return self.out(d2, (h, w))

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.ops as ops
class HybridUpsample(nn.Module):
"""自适应尺寸的混合上采样"""
def __init__(self, in_ch, out_ch, skip_ch=None):
super().__init__()
# 子像素卷积上采样
self.subpixel = nn.Sequential(
nn.Conv2d(in_ch, out_ch * 4, 3, padding=1),
nn.PixelShuffle(2) # 2倍上采样
)
# 跳跃连接处理
self.skip_conv = nn.Conv2d(skip_ch, out_ch, 1) if skip_ch else None
self.adaptive_pool = nn.AdaptiveAvgPool2d(None)
def forward(self, x, skip=None):
x = self.subpixel(x) # [B, out_ch, 2H, 2W]
if skip is not None and self.skip_conv:
# 自动对齐尺寸
if x.shape[-2:] != skip.shape[-2:]:
skip = F.interpolate(skip, size=x.shape[-2:], mode='nearest')
# 融合特征
x = x + self.skip_conv(skip)
return x
class DiscreteAwareUpsample(nn.Module):
"""离散感知的智能上采样模块"""
def __init__(self, in_ch, out_ch, base_size=16):
super().__init__()
self.base_size = base_size
self.scale_factors = [2, 4, 8] # 支持放大倍数
# 可变形卷积增强几何感知
self.deform_conv = ops.DeformConv2d(in_ch, in_ch, kernel_size=3, padding=1)
# 多尺度特征融合
self.multi_scale = nn.ModuleList([
nn.Sequential(
nn.Conv2d(in_ch, in_ch//4, 1),
nn.Upsample(scale_factor=s, mode='nearest')
) for s in self.scale_factors
])
# 门控上采样机制
self.gate_conv = nn.Conv2d(in_ch*2, len(self.scale_factors)+1, 3, padding=1)
# 离散化输出层
self.final_conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch*4, 3, padding=1),
nn.PixelShuffle(2), # 亚像素卷积
nn.Conv2d(out_ch, out_ch, 3, padding=1)
)
def forward(self, x, target_size):
# 几何特征提取
deform_feat = self.deform_conv(x)
# 生成多尺度特征
scale_features = [f(deform_feat) for f in self.multi_scale]
# 动态门控选择
gate_map = F.softmax(self.gate_conv(torch.cat([x, deform_feat], dim=1)), dim=1)
# 加权融合多尺度特征
combined = sum(g * F.interpolate(f, size=target_size, mode='nearest')
for g, f in zip(gate_map.unbind(1), scale_features+[x]))
# 离散化上采样
out = self.final_conv(combined)
# 结构化约束（保持通道独立性）
return out.argmax(dim=1).unsqueeze(1).float() # 伪梯度保留

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import torch
from ..model.model import DynamicPadConv, ConditionInjector, HybridUpsample
def test_dynamic_conv():
conv = DynamicPadConv(3, 64, stride=2)
# 测试奇数尺寸
x = torch.randn(1, 3, 15, 17)
out = conv(x)
assert out.shape == (1, 64, 8, 9), f"Got {out.shape}"
# 测试偶数尺寸
x = torch.randn(1, 3, 16, 16)
out = conv(x)
assert out.shape == (1, 64, 8, 8)
def test_condition_injector():
injector = ConditionInjector(128, 256)
x = torch.randn(2, 256, 16, 16)
cond = torch.randn(2, 128)
out = injector(x, cond)
assert out.shape == x.shape
assert not torch.allclose(out, x) # 确保条件起作用
def test_hybrid_upsample():
# 带跳跃连接的情况
upsample = HybridUpsample(256, 128, skip_ch=64)
x = torch.randn(2, 256, 8, 8)
skip = torch.randn(2, 64, 16, 16)
out = upsample(x, skip)
assert out.shape == (2, 128, 16, 16)
# 无跳跃连接的情况
upsample = HybridUpsample(256, 128)
out = upsample(x)
assert out.shape == (2, 128, 16, 16)
def test_all():
test_dynamic_conv()
print("✅ 动态卷积测试完毕")
test_condition_injector()
print("✅ 条件注入测试完毕")
test_hybrid_upsample()
print("✅ 混合上采样测试完毕")

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from .test.model import test_all
if __name__ == "__main__":
test_all()

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import os
from datetime import datetime
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from transformers import BertTokenizer
from tqdm import tqdm
from .model.model import GinkaModel
from .model.loss import GinkaLoss
from .dataset import GinkaDataset
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs("result", exist_ok=True)
epochs = 100
def collate_fn(batch):
# 动态填充噪声到最大尺寸
max_h = max([b["noise"].shape[0] for b in batch])
max_w = max([b["noise"].shape[1] for b in batch])
padded_batch = {}
for key in ["noise", "target"]:
padded = []
for b in batch:
tensor = b[key]
pad_h = max_h - tensor.shape[0]
pad_w = max_w - tensor.shape[1]
padded.append(F.pad(tensor, (0, pad_w, 0, pad_h), value=-100 if key=="target" else 0))
padded_batch[key] = torch.stack(padded)
# 其他字段直接堆叠
for key in ["input_ids", "attention_mask", "map_size"]:
padded_batch[key] = torch.stack([b[key] for b in batch])
return padded_batch
def train():
print(f"Using {"cuda" if torch.cuda.is_available() else "cpu"} to train model.")
model = GinkaModel()
model.to(device)
# 准备数据集
tokenizer = BertTokenizer.from_pretrained('google-bert/bert-base-chinese')
dataset = GinkaDataset("F:/github-ai/ginka-generator/dataset.json", tokenizer)
dataloader = DataLoader(
dataset,
batch_size=4,
shuffle=True,
collate_fn=collate_fn,
num_workers=0
)
# 设定优化器与调度器
optimizer = optim.AdamW(model.parameters(), lr=3e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
criterion = GinkaLoss()
# 开始训练
for epoch in tqdm(range(epochs)):
model.train()
total_loss = 0
# 温度退火
model.gumbel.tau = max(0.1, 1.0 - 0.9 * epoch / epochs)
for batch in dataloader:
# 数据迁移到设备
noise = batch["noise"].to(device)
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
map_size = batch["map_size"].to(device)
target = batch["target"].to(device)
# 前向传播
optimizer.zero_grad()
outputs = model(noise, input_ids, attention_mask, map_size)
print(torch.argmax(torch.softmax(outputs, dim=1), dim=1))
# print(sampled[0, :, :, 1])
# 构建拓扑图
# with torch.no_grad():
# pred_graphs = build_topology_graph(outputs.argmax(1))
# ref_graphs = build_topology_graph(target)
# 计算损失
loss = criterion(
outputs, # 调整为 [BS, C, H, W]
target
)
# 反向传播
loss.backward()
optimizer.step()
total_loss += loss.item()
tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Epoch: {epoch} | loss: {total_loss:.6f} | lr: {(optimizer.param_groups[0]['lr']):.6f}")
# 学习率调整
scheduler.step()
print("Train ended.")
torch.save({
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, f"result/ginka.pth")
if __name__ == "__main__":
torch.set_num_threads(8)
train()

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import json
import torch
from torch.utils.data import Dataset
def load_data(path: str):
with open(path, 'r', encoding="utf-8") as f:
data = json.load(f)
data_list = []
for value in data["data"].values():
data_list.append(value)
return data_list
class MinamoDataset(Dataset):
def __init__(self, data_path: str):
self.data = load_data(data_path) # 自定义数据加载函数
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
return (
torch.LongTensor(item['map1']),
torch.LongTensor(item['map2']),
torch.FloatTensor([item['visionSimilarity']]),
torch.FloatTensor([item['topoSimilarity']])
)

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import torch.nn as nn
class MinamoLoss(nn.Module):
def __init__(self, vision_weight=0.4, topo_weight=0.6):
super().__init__()
self.vision_weight = vision_weight
self.topo_weight = topo_weight
self.mse = nn.MSELoss()
def forward(self, vis_pred, topo_pred, vis_true, topo_true):
# print(vis_pred[0].item(), topo_pred[0].item(), vis_true[0].item(), topo_true[0].item())
vis_loss = self.mse(vis_pred, vis_true)
topo_loss = self.mse(topo_pred, topo_true)
return self.vision_weight * vis_loss + self.topo_weight * topo_loss

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import torch
import torch.nn as nn
import torch.nn.functional as F
class DualAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
# 空间注意力
self.spatial = nn.Sequential(
nn.Conv2d(in_channels, 1, 1),
nn.Sigmoid()
)
# 通道注意力
self.channel = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, in_channels//8, 1),
nn.ReLU(),
nn.Conv2d(in_channels//8, in_channels, 1),
nn.Sigmoid()
)
def forward(self, x):
return x * self.spatial(x) + x * self.channel(x)
class DirectionalAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.direction_convs = nn.ModuleDict({
dir: nn.Conv2d(1, 1, kernel_size, padding=kernel_size//2,
padding_mode='replicate')
for dir in ['h', 'v', 'd1', 'd2']
})
def forward(self, x):
B, C, H, W = x.shape
# 各方向特征
h_att = self.direction_convs['h'](x.mean(1, keepdim=True))
v_att = self.direction_convs['v'](x.mean(1, keepdim=True))
d1_att = self.direction_convs['d1'](x.mean(1, keepdim=True))
d2_att = self.direction_convs['d2'](x.mean(1, keepdim=True))
# 动态融合
combined = torch.stack([h_att, v_att, d1_att, d2_att], dim=1) # [B,4,1,H,W]
att_weights = F.softmax(combined.mean([3,4]), dim=1) # [B,4]
return x * (combined * att_weights.unsqueeze(-1).unsqueeze(-1)).sum(1)
class MinamoModel(nn.Module):
def __init__(self, num_tile_types, embedding_dim=64, conv_channels=256):
super().__init__()
# 嵌入层处理不同图块类型
self.embedding = nn.Embedding(num_tile_types, embedding_dim)
# 共享特征提取的卷积层
self.conv_layers = nn.Sequential(
nn.Conv2d(embedding_dim, conv_channels, 3, padding=1),
DualAttention(conv_channels),
DirectionalAttention(),
nn.ReLU(),
nn.BatchNorm2d(conv_channels),
nn.Conv2d(conv_channels, conv_channels*2, 3, padding=1),
DualAttention(conv_channels*2),
DirectionalAttention(),
nn.ReLU(),
nn.BatchNorm2d(conv_channels*2),
nn.Conv2d(conv_channels*2, conv_channels*4, 3, padding=1),
DualAttention(conv_channels*4),
DirectionalAttention(),
nn.ReLU(),
nn.BatchNorm2d(conv_channels*4),
)
# 自适应池化处理任意尺寸
self.pool = nn.ModuleDict({
'avg': nn.AdaptiveAvgPool2d((1,1)),
'max': nn.AdaptiveMaxPool2d((1,1))
})
# 多任务预测头
head_dim = conv_channels * 4 * 2 * 4 # 2个池化，四个交互项
self.vision_head = nn.Sequential(
nn.Linear(head_dim, 512),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(512, 1),
nn.Sigmoid()
)
self.topo_head = nn.Sequential(
nn.Linear(head_dim, 512),
nn.GELU(),
nn.Dropout(0.3),
nn.Linear(512, 256),
nn.GELU(),
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, map1, map2):
# 增强特征提取
def process_map(x):
x = self.embedding(x).permute(0,3,1,2)
x = self.conv_layers(x)
return torch.cat([
self.pool['avg'](x),
self.pool['max'](x)
], dim=1).flatten(1)
f1 = process_map(map1)
f2 = process_map(map2)
# 特征融合
combined = torch.cat([f1, f2, f1-f2, f1*f2], dim=1) # [B, 256]
# 多任务输出
vision_sim = self.vision_head(combined)
topo_sim = self.topo_head(combined)
return vision_sim, topo_sim

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import os
from datetime import datetime
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from .model.model import MinamoModel
from .model.loss import MinamoLoss
from .dataset import MinamoDataset
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs("result", exist_ok=True)
epochs = 100
def collate_fn(batch):
"""动态处理不同尺寸地图的批处理"""
map1_batch = [item[0] for item in batch]
map2_batch = [item[1] for item in batch]
vis_sim = torch.cat([item[2] for item in batch])
topo_sim = torch.cat([item[3] for item in batch])
# 保持批次内地图尺寸一致（根据问题描述）
assert all(m.shape == map1_batch[0].shape for m in map1_batch), \
"对比地图必须尺寸相同"
return (
torch.stack(map1_batch), # (B, H, W)
torch.stack(map2_batch), # (B, H, W)
vis_sim,
topo_sim
)
def train():
print(f"Using {"cuda" if torch.cuda.is_available() else "cpu"} to train model.")
model = MinamoModel(32)
model.to(device)
# 准备数据集
dataset = MinamoDataset("F:/github-ai/ginka-generator/minamo-dataset.json")
val_dataset = MinamoDataset("F:/github-ai/ginka-generator/minamo-eval.json")
dataloader = DataLoader(
dataset,
batch_size=32,
shuffle=True
)
val_loader = DataLoader(
val_dataset,
batch_size=32,
shuffle=True
)
# 设定优化器与调度器
optimizer = optim.AdamW(model.parameters(), lr=3e-5, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
criterion = MinamoLoss()
# 开始训练
for epoch in tqdm(range(epochs)):
model.train()
total_loss = 0
for batch in dataloader:
# 数据迁移到设备
map1, map2, vision_simi, topo_simi = batch
map1 = map1.to(device) # 转为 [B, C, H, W]
map2 = map2.to(device)
topo_simi = topo_simi.to(device)
vision_simi = vision_simi.to(device)
# print(map1.shape, map2.shape)
# 前向传播
optimizer.zero_grad()
vision_pred, topo_pred = model(map1, map2)
# 计算损失
loss = criterion(vision_pred, topo_pred, vision_simi, topo_simi)
# 反向传播
loss.backward()
optimizer.step()
total_loss += loss.item()
total_norm = 0
for p in model.parameters():
if p.grad is not None:
param_norm = p.grad.detach().data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm ** 0.5
# tqdm.write(f"Gradient Norm: {total_norm:.4f}") # 正常应保持在1~100之间
ave_loss = total_loss / len(dataloader)
tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Epoch: {epoch} | loss: {ave_loss:.6f} | lr: {(optimizer.param_groups[0]['lr']):.6f}")
# 学习率调整
scheduler.step()
# 每十轮推理一次验证集
if (epoch + 1) % 10 == 0:
model.eval()
val_loss = 0
with torch.no_grad():
for val_batch in val_loader:
map1_val, map2_val, vision_simi_val, topo_simi_val = val_batch
map1_val = map1_val.to(device)
map2_val = map2_val.to(device)
vision_simi_val = vision_simi_val.to(device)
topo_simi_val = topo_simi_val.to(device)
vision_pred_val, topo_pred_val = model(map1_val, map2_val)
loss_val = criterion(
vision_pred_val, topo_pred_val,
vision_simi_val, topo_simi_val
)
val_loss += loss_val.item()
avg_val_loss = val_loss / len(val_loader)
tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Validation :: loss: {avg_val_loss:.6f}")
print("Train ended.")
torch.save({
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, "result/minamo.pth")
if __name__ == "__main__":
torch.set_num_threads(2)
train()

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import torch
import torch.nn as nn
class ChannelAttention(nn.Module):
"""通道注意力模块"""
def __init__(self, channels, reduction=8):
super().__init__()
# 通道注意力
self.channel_att = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(channels, channels//reduction, 1),
nn.ReLU(),
nn.Conv2d(channels//reduction, channels, 1),
nn.Sigmoid()
)
def forward(self, x):
# 通道注意力
c_att = self.channel_att(x)
x = x * c_att
return x
class SpatialAttention(nn.Module):
"""空间注意力模块"""
def __init__(self):
super().__init__()
# 空间注意力
self.spatial_att = nn.Sequential(
nn.Conv2d(2, 1, 7, padding=3),
nn.Sigmoid()
)
def forward(self, x):
# 空间注意力
max_pool = torch.max(x, dim=1, keepdim=True)[0]
avg_pool = torch.mean(x, dim=1, keepdim=True)
s_att = self.spatial_att(torch.cat([max_pool, avg_pool], dim=1))
return x * s_att
class CBAM(nn.Module):
"""通道与空间注意力结合"""
def __init__(self, channels, reduction=8):
super().__init__()
# 通道注意力
self.channel_att = ChannelAttention(channels, reduction)
# 空间注意力
self.spatial_att = SpatialAttention()
def forward(self, x):
# 通道注意力
c_att = self.channel_att(x)
x = x * c_att
# 空间注意力
s_att = self.spatial_att(x)
return x * s_att