import argparse import os import sys import random import math from datetime import datetime import torch import torch.nn.functional as F import torch.optim as optim import cv2 import numpy as np from tqdm import tqdm from torch.utils.data import DataLoader from .maskGIT.model import GinkaMaskGIT from .dataset import GinkaMaskGITDataset from shared.image import matrix_to_image_cv from .maskGIT.mask import MapMask # 标量值定义： # 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 # 1. 墙体密度，墙壁/地图面积 # 2. 门密度，门数量/地图面积 # 3. 怪物密度，怪物数量/地图面积 # 4. 资源密度，资源数量/地图面积 # 5. 宝石密度，宝石数量/地图面积 # 6. 血瓶密度，血瓶数量/地图面积 # 7. 钥匙密度，钥匙数量/地图面积 # 8. 道具密度，道具数量/地图面积 # 9. 入口数量 # 图块定义： # 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 # 8. 道具, 9. 怪物, 10. 入口, 15. 掩码 token # 热力图定义 # 0. 墙壁热力图, 1. 怪物热力图, 2. 资源热力图, 3. 血瓶热力图, 4. 宝石热力图, 5. 钥匙热力图 # 6. 道具热力图, 7. 入口热力图, 8. 门热力图 BATCH_SIZE = 128 VAL_BATCH_DIVIDER = 64 NUM_CLASSES = 16 MASK_TOKEN = 15 GENERATE_STEP = 8 MAP_SIZE = 13 * 13 HEATMAP_CHANNEL = 9 LABEL_SMOOTHING = 0 BLUR_MIN_SIZE = 3 BLUR_MAX_SIZE = 9 RAND_RATIO = 0.3 MASK_PROBS = [0.5, 0.5] # 纯随机，分块随机 NUM_LAYERS = 4 D_MODEL = 192 device = torch.device( "cuda:1" if torch.cuda.is_available() else "mps" if torch.mps.is_available() else "cpu" ) os.makedirs("result", exist_ok=True) os.makedirs("result/transformer", exist_ok=True) os.makedirs("result/transformer_img", exist_ok=True) disable_tqdm = not sys.stdout.isatty() def parse_arguments(): parser = argparse.ArgumentParser(description="training codes") parser.add_argument("--resume", type=bool, default=False) parser.add_argument("--state_ginka", type=str, default="result/transformer/ginka-100.pth") parser.add_argument("--train", type=str, default="ginka-dataset.json") parser.add_argument("--validate", type=str, default="ginka-eval.json") parser.add_argument("--epochs", type=int, default=100) parser.add_argument("--checkpoint", type=int, default=5) parser.add_argument("--load_optim", type=bool, default=True) args = parser.parse_args() return args def train(): print(f"Using {device.type} to train model.") args = parse_arguments() model = GinkaMaskGIT(num_classes=NUM_CLASSES, heatmap_channel=HEATMAP_CHANNEL, num_layers=NUM_LAYERS, d_model=D_MODEL).to(device) masker = MapMask([0.5, 0.5]) dataset = GinkaMaskGITDataset(args.train, sigma_rand=RAND_RATIO, blur_min=BLUR_MIN_SIZE, blur_max=BLUR_MAX_SIZE) dataset_val = GinkaMaskGITDataset(args.validate, sigma_rand=RAND_RATIO, blur_min=BLUR_MIN_SIZE, blur_max=BLUR_MAX_SIZE) dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True) dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // VAL_BATCH_DIVIDER, shuffle=True) optimizer = optim.AdamW(model.parameters(), lr=2e-4, weight_decay=1e-2) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=1e-6) # 用于生成图片 tile_dict = dict() for file in os.listdir('tiles'): name = os.path.splitext(file)[0] tile_dict[name] = cv2.imread(f"tiles/{file}", cv2.IMREAD_UNCHANGED) # 接续训练 if args.resume: data_ginka = torch.load(args.state_ginka, map_location=device) model.load_state_dict(data_ginka["model_state"], strict=False) if args.load_optim: if data_ginka.get("optim_state") is not None: optimizer.load_state_dict(data_ginka["optim_state"]) print("Train from loaded state.") for epoch in tqdm(range(args.epochs), desc="MaskGIT Training", disable=disable_tqdm): loss_total = torch.Tensor([0]).to(device) for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm): target_map = batch["target_map"].to(device) heatmap = batch["heatmap"].to(device) B, H, W = target_map.shape target_map = target_map.view(B, H * W) mask = np.zeros((B, H * W)) for i in range(B): mask[i] = masker.mask(H, W) mask = torch.from_numpy(mask).to(torch.bool).to(device) # 掩码 masked_input = target_map.clone() masked_input[mask] = MASK_TOKEN # 填充为 [MASK] 标记 logits = model(masked_input, heatmap) loss = F.cross_entropy(logits.permute(0, 2, 1), target_map, reduction='none', label_smoothing=LABEL_SMOOTHING) loss = (loss * mask).sum() / (mask.sum() + 1e-6) optimizer.zero_grad() loss.backward() optimizer.step() loss_total += loss.detach() scheduler.step() avg_loss = loss_total.item() / len(dataloader) tqdm.write( f"[Epoch {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " + f"E: {epoch + 1} | Loss: {avg_loss:.6f} | " + f"LR: {scheduler.get_last_lr()[0]:.6f}" ) # 每若干轮输出一次图片，并保存检查点 if (epoch + 1) % args.checkpoint == 0: # 保存检查点 torch.save({ "model_state": model.state_dict(), "optim_state": optimizer.state_dict(), }, f"result/transformer/ginka-{epoch + 1}.pth") val_loss_total = torch.Tensor([0]).to(device) model.eval() with torch.no_grad(): idx = 0 gap = 5 color = (255, 255, 255) # 白色 vline = np.full((416, gap, 3), color, dtype=np.uint8) # 垂直分割线 for batch in tqdm(dataloader_val, desc="Validating", leave=False, disable=disable_tqdm): # 1. 常规生成 target_map = batch["target_map"].to(device) heatmap = batch["heatmap"].to(device) B, H, W = target_map.shape target_map = target_map.view(B, H * W) mask = np.zeros((B, H * W)) for i in range(B): mask[i] = masker.mask(H, W) mask = torch.from_numpy(mask).to(torch.bool).to(device) # 2. 生成掩码矩阵 masked_input = target_map.clone() masked_input[mask] = MASK_TOKEN # 填充为 [MASK] 标记 logits = model(masked_input, heatmap) loss = F.cross_entropy(logits.permute(0, 2, 1), target_map, reduction='none', label_smoothing=LABEL_SMOOTHING) loss = (loss * mask).sum() / (mask.sum() + 1e-6) val_loss_total += loss.detach() fake_map = torch.argmax(logits, dim=2).view(B, H, W).cpu().numpy() fake_img = matrix_to_image_cv(fake_map[0], tile_dict) real_map = target_map.view(B, H, W).cpu().numpy() real_img = matrix_to_image_cv(real_map[0], tile_dict) img = np.block([[real_img], [vline], [fake_img]]) cv2.imwrite(f"result/transformer_img/{idx}.png", img) idx += 1 # 2. 从头完整生成 map = torch.full((B, MAP_SIZE), MASK_TOKEN).to(device) for i in range(GENERATE_STEP): # 1. 预测 logits = model(map, heatmap) # [1, H * W, num_classes] probs = F.softmax(logits, dim=-1) # 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） dist = torch.distributions.Categorical(probs) sampled_tiles = dist.sample() # [1, H * W] # 3. 计算置信度 (模型对采样结果的信心程度) confidences = torch.gather(probs, -1, sampled_tiles.unsqueeze(-1)).squeeze(-1) # 4. 决定本轮要固定多少个格子 (上凸函数逻辑) ratio = math.cos(((i + 1) / GENERATE_STEP) * math.pi / 2) num_to_mask = math.floor(ratio * MAP_SIZE) # 5. 更新画布：保留置信度最高的部分，其余位置设回 MASK # 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 if num_to_mask > 0: _, mask_indices = torch.topk(confidences, k=num_to_mask, largest=False) sampled_tiles = sampled_tiles.scatter(1, mask_indices, MASK_TOKEN) map = sampled_tiles if (map == MASK_TOKEN).sum() == 0: break generated_img = matrix_to_image_cv(map.view(B, H, W)[0].cpu().numpy(), tile_dict) img = np.block([[real_img], [vline], [generated_img]]) cv2.imwrite(f"result/transformer_img/g-{idx}.png", img) avg_loss_val = val_loss_total.item() / len(dataloader_val) tqdm.write( f"[Validate {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] E: {epoch + 1} | " + f"Loss: {avg_loss_val:.6f}" ) print("Train ended.") torch.save({ "model_state": model.state_dict(), }, f"result/ginka_transformer.pth") if __name__ == "__main__": torch.set_num_threads(4) train()