diff --git a/Configs/train_3photon.yaml b/Configs/train_3photon.yaml index db7760b..b71de23 100644 --- a/Configs/train_3photon.yaml +++ b/Configs/train_3photon.yaml @@ -7,9 +7,9 @@ data: energy: 12 ### in keV batch_size_train: 4096 - batch_size_val: 1024 - batch_size_test: 1024 - num_workers: 32 + batch_size_val: 8192 + batch_size_test: 8192 + num_workers: 16 train_file_range: [0, 12] val_file_range: [13, 14] test_file_range: [15, 15] @@ -17,15 +17,16 @@ data: n_size: 9 ### size of sub-images containing 3 photons model: - version: "260529" + version: "260611" # 260529 training: epochs: 1000 learning_rate: 1.0e-3 - weight_decay: 1.0e-4 + weight_decay: 0 scheduler_factor: 0.7 scheduler_patience: 5 - checkpoint_epochs: [10, 30, 50, 100, 150, 300, 500, 1000] + checkpoint_epochs: [10, 20, 50, 100, 150, 300, 500, 1000] loss: - type: "three_point_set_loss_smooth_l1" \ No newline at end of file + type: "three_point_set_loss_smooth_l1" + smooth_l1_beta: 0.01 \ No newline at end of file diff --git a/Train_3Photon.py b/Train_3Photon.py index 8979beb..40c06b5 100644 --- a/Train_3Photon.py +++ b/Train_3Photon.py @@ -29,38 +29,98 @@ def prepare_output_folder(conf): # find the next index for experiment name exp_index = 0 while True: - exp_name = f'{date}_3ph_{conf.data.energy}keV_v{conf.model.version}_{exp_index:02d}' - if not Path(f'Results/{exp_name}').exists(): + exp_name = f'{date}_{conf.data.energy}keV_v{conf.model.version}_{exp_index:02d}' + if not Path(f'Results/3ph/{exp_name}').exists(): break exp_index += 1 - Path(f'Results/{exp_name}').mkdir(parents=True, exist_ok=True) - Path(f'Results/{exp_name}/Models').mkdir(parents=True, exist_ok=True) - Path(f'Results/{exp_name}/Plots').mkdir(parents=True, exist_ok=True) - OmegaConf.save(conf, f'Results/{exp_name}/config.yaml') + Path(f'Results/3ph/{exp_name}').mkdir(parents=True, exist_ok=True) + Path(f'Results/3ph/{exp_name}/Models').mkdir(parents=True, exist_ok=True) + Path(f'Results/3ph/{exp_name}/Plots').mkdir(parents=True, exist_ok=True) + OmegaConf.save(conf, f'Results/3ph/{exp_name}/config.yaml') return exp_name def get_loss_function(conf): if conf.loss.type == "three_point_set_loss_smooth_l1": def three_point_set_loss_smooth_l1(pred_xy, gt_xy): - loss_fn = torch.nn.SmoothL1Loss(reduction='none') + loss_fn = torch.nn.SmoothL1Loss(reduction='none', beta=conf.loss.smooth_l1_beta) p1, p2, p3 = pred_xy[:,0], pred_xy[:,1], pred_xy[:,2] g1, g2, g3 = gt_xy[:,0], gt_xy[:,1], gt_xy[:,2] - c_a = loss_fn(p1, g1).sum(dim=-1) + loss_fn(p2, g2).sum(dim=-1) + loss_fn(p3, g3).sum(dim=-1) - c_b = loss_fn(p1, g2).sum(dim=-1) + loss_fn(p2, g1).sum(dim=-1) + loss_fn(p3, g3).sum(dim=-1) - c_c = loss_fn(p1, g3).sum(dim=-1) + loss_fn(p2, g2).sum(dim=-1) + loss_fn(p3, g1).sum(dim=-1) - c_d = loss_fn(p1, g1).sum(dim=-1) + loss_fn(p2, g3).sum(dim=-1) + loss_fn(p3, g2).sum(dim=-1) - c_e = loss_fn(p1, g2).sum(dim=-1) + loss_fn(p2, g3).sum(dim=-1) + loss_fn(p3, g1).sum(dim=-1) - c_f = loss_fn(p1, g3).sum(dim=-1) + loss_fn(p2, g1).sum(dim=-1) + loss_fn(p3, g2).sum(dim=-1) - - return torch.minimum(torch.minimum(torch.minimum(torch.minimum(torch.minimum(c_a, c_b), c_c), c_d), c_e), c_f).mean() + # 1. 匹配阶段：计算全部 6 种排列的 L2 物理距离 (不参与梯度计算) + with torch.no_grad(): + def calc_cost(ga, gb, gc): + return ((p1 - ga)**2).sum(dim=-1) + \ + ((p2 - gb)**2).sum(dim=-1) + \ + ((p3 - gc)**2).sum(dim=-1) + + # 枚举 3个光子的 6 种全排列 (Permutations) + c0 = calc_cost(g1, g2, g3) + c1 = calc_cost(g1, g3, g2) + c2 = calc_cost(g2, g1, g3) + c3 = calc_cost(g2, g3, g1) + c4 = calc_cost(g3, g1, g2) + c5 = calc_cost(g3, g2, g1) + + # 将 6 种 cost 堆叠起来，形状变为 [B, 6] + costs = torch.stack([c0, c1, c2, c3, c4, c5], dim=1) + # 找到每个 batch 中 cost 最小的那个排列的索引 (0 到 5) + min_indices = torch.argmin(costs, dim=1) # [B] + + # 预定义好 6 种排列对应的物理索引 (0代表g1, 1代表g2, 2代表g3) + perms = torch.tensor([ + [0, 1, 2], # c0 + [0, 2, 1], # c1 + [1, 0, 2], # c2 + [1, 2, 0], # c3 + [2, 0, 1], # c4 + [2, 1, 0] # c5 + ], device=pred_xy.device) + + # 获取当前 batch 最优的排列索引组合，形状为 [B, 3] + best_perms = perms[min_indices] + + # 将 Ground Truth 堆叠为 [B, 3, 2] 的统一张量 + g_all = torch.stack([g1, g2, g3], dim=1) + + # 利用高级索引一次性完成重组 + batch_idx = torch.arange(pred_xy.size(0), device=pred_xy.device).unsqueeze(-1) + g_matched = g_all[batch_idx, best_perms] # [B, 3, 2] + + # 2. 惩罚阶段：对重组后完美对齐的坐标计算 L2 Loss + loss_p1 = loss_fn(p1, g_matched[:, 0]).sum(dim=-1) + loss_p2 = loss_fn(p2, g_matched[:, 1]).sum(dim=-1) + loss_p3 = loss_fn(p3, g_matched[:, 2]).sum(dim=-1) + loss = (loss_p1 + loss_p2 + loss_p3).mean() / 3.0 + + return loss return three_point_set_loss_smooth_l1 +def get_mse(pred_xy, gt_xy): + p1, p2, p3 = pred_xy[:,0], pred_xy[:,1], pred_xy[:,2] + g1, g2, g3 = gt_xy[:,0], gt_xy[:,1], gt_xy[:,2] + + with torch.no_grad(): + def calc_cost(ga, gb, gc): + return ((p1 - ga)**2).sum(dim=-1) + \ + ((p2 - gb)**2).sum(dim=-1) + \ + ((p3 - gc)**2).sum(dim=-1) + c0 = calc_cost(g1, g2, g3) + c1 = calc_cost(g1, g3, g2) + c2 = calc_cost(g2, g1, g3) + c3 = calc_cost(g2, g3, g1) + c4 = calc_cost(g3, g1, g2) + c5 = calc_cost(g3, g2, g1) + costs = torch.stack([c0, c1, c2, c3, c4, c5], dim=1) + min_costs, _ = torch.min(costs, dim=1) # [B] + mse_1d = min_costs.mean() / 6.0 # num_photons=3, num_axes=2 + return mse_1d + def train(model, trainLoader, optimizer, loss_fn): model.train() batchLoss = 0 + sum_squared_error_1d = 0; event_count = 0 for batch_idx, (sample, label) in enumerate(trainLoader): sample, label = sample.cuda(), label.cuda() x1, y1, z1, e1 = label[:, 0, 0], label[:, 0, 1], label[:, 0, 2], label[:, 0, 3] @@ -74,13 +134,18 @@ def train(model, trainLoader, optimizer, loss_fn): loss.backward() optimizer.step() batchLoss += loss.item() * sample.shape[0] + mse_1d = get_mse(pred_xy, gt_xy) + sum_squared_error_1d += mse_1d.item() * sample.shape[0] + event_count += sample.shape[0] avgLoss = batchLoss / len(trainLoader.dataset) / 6 ### divide by 6 to get the average loss per photon per axis - print(f"[Train]\t Average Loss: {avgLoss:.6f} (RMS = {np.sqrt(avgLoss):.6f})") - return avgLoss + rms_1d = np.sqrt(sum_squared_error_1d / event_count) + print(f"[Train]\t Average Loss: {avgLoss:.6f} (RMS = {rms_1d:.6f})") + return avgLoss, rms_1d def evaluate(model, valLoader, loss_fn): model.eval() batchLoss = 0 + sum_squared_error_1d = 0; event_count = 0 with torch.no_grad(): for batch_idx, (sample, label) in enumerate(valLoader): sample, label = sample.cuda(), label.cuda() @@ -91,10 +156,14 @@ def evaluate(model, valLoader, loss_fn): output = model(sample) pred_xy = torch.stack((output[:,0:2], output[:,2:4], output[:,4:6]), axis=1) loss = loss_fn(pred_xy, gt_xy) + mse_1d = get_mse(pred_xy, gt_xy) + sum_squared_error_1d += mse_1d.item() * sample.shape[0] + event_count += sample.shape[0] batchLoss += loss.item() * sample.shape[0] avgLoss = batchLoss / len(valLoader.dataset) / 6 ### divide by 6 to get the average loss per photon per axis - print(f"[Val]\t Average Loss: {avgLoss:.6f} (RMS = {np.sqrt(avgLoss):.6f})") - return avgLoss + rms_1d = np.sqrt(sum_squared_error_1d / event_count) + print(f"[Val]\t Average Loss: {avgLoss:.6f} (RMS = {rms_1d:.6f})") + return avgLoss, rms_1d def get_dataloaders(conf): """construct all dataloaders""" @@ -136,7 +205,23 @@ def plot_loss_curves(train_losses, val_losses, test_loss, exp_name, conf): plt.legend() plt.grid() plotName = f'loss_curve_triplePhoton_{conf.model.version}.png' - plt.savefig(f'Results/{exp_name}/Plots/{plotName}') + plt.savefig(f'Results/3ph/{exp_name}/Plots/{plotName}') + plt.close() + +def plot_rms_curves(train_rms, val_rms, test_rms, exp_name, conf): + import matplotlib.pyplot as plt + plt.figure(figsize=(8,6)) + plt.plot(train_rms, label='Train RMS') + plt.plot(val_rms, label='Val RMS') + if test_rms > 0: + plt.axhline(y=test_rms, color='green', linestyle='--', label='Test RMS') + plt.xlabel('Epoch') + plt.ylabel('RMS Error [pixels]') + plt.yscale('log') + plt.legend() + plt.grid() + plotName = f'rms_curve_triplePhoton_{conf.model.version}.png' + plt.savefig(f'Results/3ph/{exp_name}/Plots/{plotName}') plt.close() def get_model_name(conf): @@ -155,17 +240,23 @@ if __name__ == "__main__": trainLoader, valLoader, testLoader = get_dataloaders(conf) TrainLosses, ValLosses = [], [] + train_rms, val_rms = [], [] + for epoch in tqdm(range(1, conf.training.epochs + 1)): - train_loss = train(model, trainLoader, optimizer, loss_fn) - val_loss = evaluate(model, valLoader, loss_fn) + train_loss, train_rms_epoch = train(model, trainLoader, optimizer, loss_fn) + val_loss, val_rms_epoch = evaluate(model, valLoader, loss_fn) TrainLosses.append(train_loss) ValLosses.append(val_loss) + train_rms.append(train_rms_epoch) + val_rms.append(val_rms_epoch) scheduler.step(val_loss) print(f"Learning Rate: {optimizer.param_groups[0]['lr']:.2e}") if epoch in conf.training.checkpoint_epochs or epoch == conf.training.epochs: modelName = get_model_name(conf) - torch.save(model.state_dict(), f'Results/{exp_name}/Models/{modelName}_E{epoch}.pth') + torch.save(model.state_dict(), f'Results/3ph/{exp_name}/Models/{modelName}_E{epoch}.pth') print(f"Saved model checkpoint: {modelName}_E{epoch}.pth") plot_loss_curves(TrainLosses, ValLosses, test_loss=-1, exp_name=exp_name, conf=conf) - test_loss = evaluate(model, testLoader, loss_fn) - plot_loss_curves(TrainLosses, ValLosses, test_loss=test_loss, exp_name=exp_name, conf=conf) \ No newline at end of file + plot_rms_curves(train_rms, val_rms, test_rms=-1, exp_name=exp_name, conf=conf) + test_loss, test_rms = evaluate(model, testLoader, loss_fn) + plot_loss_curves(TrainLosses, ValLosses, test_loss=test_loss, exp_name=exp_name, conf=conf) + plot_rms_curves(train_rms, val_rms, test_rms=test_rms, exp_name=exp_name, conf=conf) \ No newline at end of file