import argparse import math import os import sys from datetime import datetime import numpy as np import torch import torch.nn.functional as F import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm from .dataset import GinkaJointDataset from .heatmap.diffusion import Diffusion from .heatmap.model import GinkaHeatmapModel from .maskGIT.model import GinkaMaskGIT BATCH_SIZE = 64 VAL_BATCH_DIVIDER = 64 NUM_CLASSES = 16 MASK_TOKEN = 15 GENERATE_STEP = 8 MAP_W = 13 MAP_H = 13 HEATMAP_CHANNEL = 9 LABEL_SMOOTHING = 0 NUM_LAYERS = 4 D_MODEL = 192 NUM_LAYERS_DIFFUSION = 4 D_MODEL_DIFFUSION = 128 T_DIFFUSION = 100 MIN_MASK = 0 MAX_MASK = 1 CE_WEIGHT = 0.5 DROP_RATE = 0.2 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/joint", exist_ok=True) disable_tqdm = not sys.stdout.isatty() def parse_arguments(): parser = argparse.ArgumentParser(description="joint training codes") parser.add_argument("--resume", type=bool, default=False) parser.add_argument("--state_heatmap", type=str, default="result/ginka_heatmap.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=50) parser.add_argument("--checkpoint", type=int, default=5) parser.add_argument("--load_optim", type=bool, default=True) parser.add_argument("--maskgit_path", type=str, default="result/ginka_transformer.pth") args = parser.parse_args() return args def load_heatmap_checkpoint(model, optimizer, args): if not args.state_heatmap: return if not os.path.exists(args.state_heatmap): raise FileNotFoundError(f"Heatmap checkpoint not found: {args.state_heatmap}") checkpoint = torch.load(args.state_heatmap, map_location=device) model.load_state_dict(checkpoint["model_state"], strict=False) if args.resume and args.load_optim and checkpoint.get("optim_state") is not None: optimizer.load_state_dict(checkpoint["optim_state"]) print("Loaded Diffusion model state.") def freeze_module(module: torch.nn.Module): module.eval() for parameter in module.parameters(): parameter.requires_grad = False def predict_x0(diffusion: Diffusion, x_t: torch.Tensor, pred_noise: torch.Tensor, t: torch.Tensor): sqrt_ab = diffusion.sqrt_ab[t][:, None, None, None] sqrt_one_minus_ab = diffusion.sqrt_one_minus_ab[t][:, None, None, None] x0 = (x_t - sqrt_one_minus_ab * pred_noise) / sqrt_ab return torch.clamp(x0, 0.0, 1.0) def maskgit_joint_loss(maskgit, generated_heatmap: torch.Tensor, target_map: torch.Tensor): batch_size, height, width = target_map.shape target_tokens = target_map.view(batch_size, height * width) canvas = torch.full_like(target_tokens, MASK_TOKEN) losses = [] for step in range(GENERATE_STEP): current_mask = canvas == MASK_TOKEN if current_mask.sum().item() == 0: break logits = maskgit(canvas, generated_heatmap) ce = F.cross_entropy( logits.permute(0, 2, 1), target_tokens, reduction='none', label_smoothing=LABEL_SMOOTHING ) ce = (ce * current_mask).sum() / (current_mask.sum() + 1e-6) losses.append(ce) with torch.no_grad(): probs = F.softmax(logits, dim=-1) sampled_tiles = torch.argmax(probs, dim=-1) confidences = torch.gather(probs, -1, sampled_tiles.unsqueeze(-1)).squeeze(-1) ratio = math.cos(((step + 1) / GENERATE_STEP) * math.pi / 2) num_to_mask = math.floor(ratio * target_tokens.shape[1]) 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) canvas = sampled_tiles if not losses: return torch.zeros((), device=generated_heatmap.device) return torch.stack(losses).mean() def validate(model, maskgit, diffusion, dataloader, ce_weight): model.eval() total_loss = 0.0 total_diffusion_loss = 0.0 total_maskgit_loss = 0.0 with torch.no_grad(): for batch in tqdm(dataloader, desc="Validating", leave=False, disable=disable_tqdm): cond_heatmap = batch["cond_heatmap"].to(device) target_heatmap = batch["target_heatmap"].to(device) target_map = batch["target_map"].to(device) batch_size = target_heatmap.shape[0] t = torch.randint(1, T_DIFFUSION, [batch_size], device=device) noise = torch.randn_like(target_heatmap) x_t = diffusion.q_sample(target_heatmap, t, noise) pred_noise = model(x_t, cond_heatmap, t) diffusion_loss = F.mse_loss(pred_noise, noise) generated_heatmap = predict_x0(diffusion, x_t, pred_noise, t) maskgit_loss = maskgit_joint_loss(maskgit, generated_heatmap, target_map) loss = diffusion_loss + ce_weight * maskgit_loss total_loss += loss.item() total_diffusion_loss += diffusion_loss.item() total_maskgit_loss += maskgit_loss.item() size = max(len(dataloader), 1) return { "loss": total_loss / size, "diffusion_loss": total_diffusion_loss / size, "maskgit_loss": total_maskgit_loss / size, } def train(): print(f"Using {device.type} to train model.") args = parse_arguments() maskgit = GinkaMaskGIT( num_classes=NUM_CLASSES, heatmap_channel=HEATMAP_CHANNEL, num_layers=NUM_LAYERS, d_model=D_MODEL, ).to(device) if not os.path.exists(args.maskgit_path): raise FileNotFoundError(f"MaskGIT checkpoint not found: {args.maskgit_path}") maskgit_state = torch.load(args.maskgit_path, map_location=device) maskgit.load_state_dict(maskgit_state["model_state"]) freeze_module(maskgit) print("Loaded and froze MaskGIT model state.") model = GinkaHeatmapModel( T=T_DIFFUSION, heatmap_dim=HEATMAP_CHANNEL, d_model=D_MODEL_DIFFUSION, num_layers=NUM_LAYERS_DIFFUSION, ).to(device) diffusion = Diffusion(device, T=T_DIFFUSION) dataset = GinkaJointDataset(args.train, min_mask=MIN_MASK, max_mask=MAX_MASK) dataset_val = GinkaJointDataset(args.validate, min_mask=MIN_MASK, max_mask=MAX_MASK) dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True) dataloader_val = DataLoader( dataset_val, batch_size=max(1, BATCH_SIZE // VAL_BATCH_DIVIDER), shuffle=True, ) optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-2) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, T_max=args.epochs, eta_min=1e-6, ) load_heatmap_checkpoint(model, optimizer, args) for epoch in tqdm(range(args.epochs), desc="Joint Training", disable=disable_tqdm): model.train() epoch_loss = 0.0 epoch_diffusion_loss = 0.0 epoch_maskgit_loss = 0.0 for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm): cond_heatmap = batch["cond_heatmap"].to(device) target_heatmap = batch["target_heatmap"].to(device) target_map = batch["target_map"].to(device) batch_size = target_heatmap.shape[0] optimizer.zero_grad() t = torch.randint(1, T_DIFFUSION, [batch_size], device=device) noise = torch.randn_like(target_heatmap) x_t = diffusion.q_sample(target_heatmap, t, noise) cond_for_diffusion = cond_heatmap use_unconditional_branch = False if np.random.rand() < DROP_RATE: cond_for_diffusion = torch.zeros_like(cond_heatmap) use_unconditional_branch = True pred_noise = model(x_t, cond_for_diffusion, t) diffusion_loss = F.mse_loss(pred_noise, noise) pred_noise_for_joint = pred_noise if use_unconditional_branch: pred_noise_for_joint = model(x_t, cond_heatmap, t) generated_heatmap = predict_x0(diffusion, x_t, pred_noise_for_joint, t) maskgit_loss = maskgit_joint_loss(maskgit, generated_heatmap, target_map) loss = diffusion_loss + CE_WEIGHT * maskgit_loss loss.backward() optimizer.step() epoch_loss += loss.item() epoch_diffusion_loss += diffusion_loss.item() epoch_maskgit_loss += maskgit_loss.item() scheduler.step() train_size = max(len(dataloader), 1) tqdm.write( f"[Epoch {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " f"E: {epoch + 1} | " f"Loss: {epoch_loss / train_size:.6f} | " f"Diffusion: {epoch_diffusion_loss / train_size:.6f} | " f"MaskGIT: {epoch_maskgit_loss / train_size:.6f} | " f"LR: {scheduler.get_last_lr()[0]:.6f}" ) if (epoch + 1) % args.checkpoint == 0: checkpoint_path = f"result/joint/ginka-joint-{epoch + 1}.pth" torch.save( { "model_state": model.state_dict(), "optim_state": optimizer.state_dict(), }, checkpoint_path, ) metrics = validate(model, maskgit, diffusion, dataloader_val, CE_WEIGHT) tqdm.write( f"[Validate {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " f"E: {epoch + 1} | " f"Loss: {metrics['loss']:.6f} | " f"Diffusion: {metrics['diffusion_loss']:.6f} | " f"MaskGIT: {metrics['maskgit_loss']:.6f}" ) print("Train ended.") torch.save( { "model_state": model.state_dict(), "optim_state": optimizer.state_dict(), }, "result/ginka_joint_heatmap.pth", ) if __name__ == "__main__": torch.set_num_threads(4) train()