mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 04:41:12 +08:00
242 lines
11 KiB
Python
242 lines
11 KiB
Python
import argparse
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import os
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import sys
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import random
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import math
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from datetime import datetime
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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import cv2
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import numpy as np
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from torch_geometric.loader import DataLoader
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from tqdm import tqdm
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from .transformer.maskGIT import GinkaMaskGIT
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from .vae_rnn.loss import VAELoss
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from .vae_rnn.scheduler import VAEScheduler
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from .dataset import GinkaRNNDataset
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from shared.image import matrix_to_image_cv
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# 手工标注标签定义（暂时不用）：
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# 0. 蓝海, 1. 红海, 2: 室内, 3. 野外, 4. 左右对称, 5. 上下对称, 6. 伪对称, 7. 咸鱼层,
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# 8. 剧情层, 9. 水层, 10. 爽塔, 11. Boss层, 12. 纯Boss层, 13. 多房间, 14. 多走廊, 15. 道具风
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# 16. 区域入口, 17. 区域连接, 18. 有机关门, 19. 道具层, 20. 斜向对称, 21. 左右通道, 22. 上下通道, 23. 多机关门
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# 24. 中心对称, 25. 部分对称, 26. 鱼骨
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# 自动标注标签定义（暂时不用）：
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# 0. 左右对称, 1. 上下对称, 2. 中心对称, 3. 斜向对称, 4. 伪对称, 5. 多房间, 6. 多走廊
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# 32. 平面塔, 33. 转换塔, 34. 道具塔
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# 标量值定义：
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# 0. 整体密度，非空白图块/地图面积，空白图块还包括装饰图块
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# 1. 墙体密度，墙壁/地图面积
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# 2. 装饰密度，装饰数量/地图面积
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# 3. 门密度，门数量/地图面积
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# 4. 怪物密度，怪物数量/地图面积
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# 5. 资源密度，资源数量/地图面积
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# 6. 宝石密度，宝石数量/地图面积
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# 7. 血瓶密度，血瓶数量/地图面积
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# 8. 钥匙密度，钥匙数量/地图面积
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# 9. 道具密度，道具数量/地图面积
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# 10. 入口数量
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# 11. 机关门数量
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# 12. 咸鱼门数量（多层咸鱼门只算一个）
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# 图块定义：
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# 0. 空地, 1. 墙壁, 2. 门, 3. 钥匙, 4. 红宝石, 5. 蓝宝石, 6. 绿宝石, 7. 血瓶
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# 8. 道具, 9. 怪物, 10. 入口, 15. 掩码 token
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BATCH_SIZE = 16
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VAL_BATCH_DIVIDER = 16
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NUM_CLASSES = 16
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MASK_TOKEN = 15
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GENERATE_STEP = 8
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MAP_SIZE = 13 * 13
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device = torch.device(
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"cuda:1" if torch.cuda.is_available()
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else "mps" if torch.mps.is_available()
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else "cpu"
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)
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os.makedirs("result", exist_ok=True)
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os.makedirs("result/transformer", exist_ok=True)
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os.makedirs("result/transformer_img", exist_ok=True)
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disable_tqdm = not sys.stdout.isatty()
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def parse_arguments():
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parser = argparse.ArgumentParser(description="training codes")
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parser.add_argument("--resume", type=bool, default=False)
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parser.add_argument("--state_ginka", type=str, default="result/vae/ginka-100.pth")
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parser.add_argument("--train", type=str, default="ginka-dataset.json")
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parser.add_argument("--validate", type=str, default="ginka-eval.json")
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parser.add_argument("--epochs", type=int, default=100)
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parser.add_argument("--checkpoint", type=int, default=5)
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parser.add_argument("--load_optim", type=bool, default=True)
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args = parser.parse_args()
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return args
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def train():
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print(f"Using {device.type} to train model.")
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args = parse_arguments()
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model = GinkaMaskGIT(num_classes=NUM_CLASSES).to(device)
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dataset = GinkaRNNDataset(args.train, device)
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dataset_val = GinkaRNNDataset(args.validate, device)
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dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
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dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // VAL_BATCH_DIVIDER, shuffle=True)
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optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-2)
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# 自定义调度器允许在 self_prob 提高时重置调度器信息并提高学习率以适应学习
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=1e-5)
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# 用于生成图片
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tile_dict = dict()
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for file in os.listdir('tiles2'):
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name = os.path.splitext(file)[0]
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tile_dict[name] = cv2.imread(f"tiles2/{file}", cv2.IMREAD_UNCHANGED)
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if args.resume:
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data_ginka = torch.load(args.state_ginka, map_location=device)
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model.load_state_dict(data_ginka["model_state"], strict=False)
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if args.load_optim:
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if data_ginka.get("optim_state") is not None:
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optimizer.load_state_dict(data_ginka["optim_state"])
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print("Train from loaded state.")
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for epoch in tqdm(range(args.epochs), desc="VAE Training", disable=disable_tqdm):
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loss_total = torch.Tensor([0]).to(device)
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# for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm):
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# target_map = batch["target_map"].to(device)
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# cond = batch["val_cond"].to(device)
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# B, H, W = target_map.shape
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# target_map = target_map.view(B, H * W)
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# # 1. 随机采样掩码比例 r (遵循余弦调度效果更好)
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# r = torch.rand(B).to(device)
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# r = torch.cos(r * math.pi / 2).unsqueeze(1) # 产生更多高掩码比例的样本
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# # 2. 生成掩码矩阵
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# masks = torch.rand(target_map.shape).to(device) < r
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# masked_input = target_map.clone()
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# masked_input[masks] = MASK_TOKEN # 填充为 [MASK] 标记
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# logits = model(masked_input, cond)
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# loss = F.cross_entropy(logits.permute(0, 2, 1), target_map, reduction='none', label_smoothing=1)
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# loss = (loss * masks).sum() / (masks.sum() + 1e-6)
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# optimizer.zero_grad()
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# loss.backward()
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# optimizer.step()
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# loss_total += loss.detach()
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scheduler.step()
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avg_loss = loss_total.item() / len(dataloader)
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tqdm.write(
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f"[Epoch {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " +
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f"E: {epoch + 1} | Loss: {avg_loss:.6f} | " +
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f"LR: {scheduler.get_last_lr()[0]:.6f}"
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)
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# 每若干轮输出一次图片，并保存检查点
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if (epoch + 1) % args.checkpoint == 0:
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# 保存检查点
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torch.save({
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"model_state": model.state_dict(),
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"optim_state": optimizer.state_dict(),
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}, f"result/transformer/ginka-{epoch + 1}.pth")
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val_loss_total = torch.Tensor([0]).to(device)
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model.eval()
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with torch.no_grad():
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idx = 0
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gap = 5
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color = (255, 255, 255) # 白色
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vline = np.full((416, gap, 3), color, dtype=np.uint8) # 垂直分割线
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for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
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# 1. 常规生成
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target_map = batch["target_map"].to(device)
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cond = batch["val_cond"].to(device)
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B, H, W = target_map.shape
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target_map = target_map.view(B, H * W)
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# 1. 随机采样掩码比例 r (遵循余弦调度效果更好)
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r = torch.rand(B).to(device)
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r = torch.cos(r * math.pi / 2).unsqueeze(1) # 产生更多高掩码比例的样本
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# 2. 生成掩码矩阵
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masks = torch.rand(target_map.shape).to(device) < r
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masked_input = target_map.clone()
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masked_input[masks] = MASK_TOKEN # 填充为 [MASK] 标记
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logits = model(masked_input, cond)
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loss = F.cross_entropy(logits.permute(0, 2, 1), target_map, reduction='none', label_smoothing=0.1)
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loss = (loss * masks.view(-1)).sum() / (masks.sum() + 1e-6)
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val_loss_total += loss.detach()
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fake_map = torch.argmax(logits, dim=2).view(B, H, W).cpu().numpy()
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fake_img = matrix_to_image_cv(fake_map[0], tile_dict)
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real_map = target_map.view(B, H, W).cpu().numpy()
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real_img = matrix_to_image_cv(real_map[0], tile_dict)
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img = np.block([[real_img], [vline], [fake_img]])
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cv2.imwrite(f"result/transformer_img/{idx}.png", img)
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idx += 1
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# 2. 从头完整生成
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map = torch.full((1, MAP_SIZE), MASK_TOKEN).to(device)
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for i in range(GENERATE_STEP):
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# 1. 预测
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logits = model(map, cond) # [1, H * W, num_classes]
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probs = F.softmax(logits, dim=-1)
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# 2. 采样（为了多样性，这里可以使用概率采样而不是取最大值）
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dist = torch.distributions.Categorical(probs)
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sampled_tiles = dist.sample() # [1, H * W]
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# 3. 计算置信度 (模型对采样结果的信心程度)
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confidences = torch.gather(probs, -1, sampled_tiles.unsqueeze(-1)).squeeze(-1)
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# 4. 决定本轮要固定多少个格子 (上凸函数逻辑)
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ratio = math.cos(((i + 1) / GENERATE_STEP) * math.pi / 2)
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num_to_mask = math.floor(ratio * MAP_SIZE)
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# 5. 更新画布：保留置信度最高的部分，其余位置设回 MASK
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# 注意：这里逻辑上通常是保留当前步预测中置信度最高的，并结合已有的非 mask 部分
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if num_to_mask > 0:
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_, mask_indices = torch.topk(confidences, k=num_to_mask, largest=False)
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sampled_tiles = sampled_tiles.scatter(1, mask_indices, MASK_TOKEN)
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map = sampled_tiles
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if (map == MASK_TOKEN).sum() == 0:
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break
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generated_img = matrix_to_image_cv(map.view(1, H, W)[0].cpu().numpy(), tile_dict)
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cv2.imwrite(f"result/transformer_img/g-{idx}.png", generated_img)
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avg_loss_val = val_loss_total.item() / len(dataloader_val)
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tqdm.write(
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f"[Validate {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] E: {epoch + 1} | " +
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f"Loss: {avg_loss_val:.6f}"
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)
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print("Train ended.")
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torch.save({
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"model_state": model.state_dict(),
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}, f"result/ginka_transformer.pth")
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if __name__ == "__main__":
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torch.set_num_threads(4)
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train()
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