mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 04:41:12 +08:00
189 lines
7.5 KiB
Python
189 lines
7.5 KiB
Python
import argparse
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import os
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import sys
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from datetime import datetime
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import torch
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import torch.optim as optim
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import cv2
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from torch_geometric.loader import DataLoader
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from tqdm import tqdm
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from .generator.rnn import GinkaRNNModel
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from .dataset import GinkaRNNDataset
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from .generator.loss import RNNGinkaLoss
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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. 装饰（用于野外装饰，视为空地）,
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# 3. 黄门, 4. 蓝门, 5. 红门, 6. 机关门, 其余种类的门如绿门都视为红门
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# 7-9. 黄蓝红门钥匙，机关门不使用钥匙开启
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# 10-12. 三种等级的红宝石
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# 13-15. 三种等级的蓝宝石
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# 16-18. 三种等级的绿宝石
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# 19-22. 四种等级的血瓶
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# 23-25. 三种等级的道具
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# 26-28. 三种等级的怪物
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# 29. 入口，不区分楼梯和箭头
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BATCH_SIZE = 96
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device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
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os.makedirs("result", exist_ok=True)
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os.makedirs("result/rnn", exist_ok=True)
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os.makedirs("result/ginka_rnn_img", exist_ok=True)
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disable_tqdm = not sys.stdout.isatty()
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def gt_prob(epoch: int, max_epoch: int) -> float:
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progress = epoch / max_epoch
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return 0.1 + 0.9 * progress
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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/rnn/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 {'cuda' if torch.cuda.is_available() else 'cpu'} to train model.")
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args = parse_arguments()
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ginka_rnn = GinkaRNNModel(device).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 // 8)
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optimizer_ginka = optim.AdamW(ginka_rnn.parameters(), lr=1e-4, weight_decay=1e-4)
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scheduler_ginka = optim.lr_scheduler.CosineAnnealingLR(optimizer_ginka, T_max=800, eta_min=1e-6)
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criterion = RNNGinkaLoss(32, device)
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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('tiles'):
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name = os.path.splitext(file)[0]
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tile_dict[name] = cv2.imread(f"tiles/{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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ginka_rnn.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_ginka.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="RNN Training", disable=disable_tqdm):
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loss_total_ginka = torch.Tensor([0]).to(device)
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iters = 0
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for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm):
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val_cond = batch["val_cond"].to(device)
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target_map = batch["target_map"].to(device)
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fake_logits, fake_map = ginka_rnn(val_cond, target_map, 1 - gt_prob(epoch, args.epochs))
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loss = criterion.rnn_loss(fake_logits, target_map)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(ginka_rnn.parameters(), max_norm=1.0)
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optimizer_ginka.step()
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loss_total_ginka += loss.detach()
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iters += 1
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# if iters % 50 == 0:
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# avg_loss_ginka = loss_total_ginka.item() / iters
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# tqdm.write(
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# f"[Iters {iters} {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " +
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# f"E: {epoch + 1} | Loss: {avg_loss_ginka:.6f} | " +
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# f"LR: {optimizer_ginka.param_groups[0]['lr']:.6f}"
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# )
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avg_loss_ginka = loss_total_ginka.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_ginka:.6f} | " +
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f"LR: {optimizer_ginka.param_groups[0]['lr']:.6f}"
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)
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scheduler_ginka.step()
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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": ginka_rnn.state_dict(),
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"optim_state": optimizer_ginka.state_dict(),
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}, f"result/rnn/ginka-{epoch + 1}.pth")
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val_loss_total = torch.Tensor([0]).to(device)
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with torch.no_grad():
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idx = 0
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for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
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val_cond = batch["val_cond"].to(device)
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target_map = batch["target_map"].to(device)
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fake_logits, fake_map = ginka_rnn(val_cond, target_map, 1 - gt_prob(epoch, args.epoch))
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val_loss_total += criterion.rnn_loss(fake_logits, target_map).detach()
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fake_map = fake_map.cpu().numpy()
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fake_img = matrix_to_image_cv(fake_map[0], tile_dict)
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cv2.imwrite(f"result/ginka_rnn_img/{idx}.png", fake_img)
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idx += 1
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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": ginka_rnn.state_dict(),
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}, f"result/ginka_rnn.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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