262 lines
12 KiB
Python
262 lines
12 KiB
Python
import sys
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sys.path.append('./src')
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import matplotlib
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matplotlib.use('Agg')
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from omegaconf import OmegaConf ### for yaml config parsing
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import torch
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import numpy as np
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import torch.optim as optim
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from tqdm import tqdm
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from torchinfo import summary
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from pathlib import Path
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from models import get_triple_photon_model_class
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from datasets import triplePhotonDataset
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### random seed for reproducibility
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torch.manual_seed(0)
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torch.cuda.manual_seed(0)
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np.random.seed(0)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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conf = OmegaConf.load("Configs/train_3photon.yaml")
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def prepare_output_folder(conf):
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from datetime import datetime
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date = datetime.now().strftime("%y%m%d") ## YYMMDD format
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# find the next index for experiment name
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exp_index = 0
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while True:
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exp_name = f'{date}_{conf.data.energy}keV_v{conf.model.version}_{exp_index:02d}'
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if not Path(f'Results/3ph/{exp_name}').exists():
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break
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exp_index += 1
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Path(f'Results/3ph/{exp_name}').mkdir(parents=True, exist_ok=True)
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Path(f'Results/3ph/{exp_name}/Models').mkdir(parents=True, exist_ok=True)
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Path(f'Results/3ph/{exp_name}/Plots').mkdir(parents=True, exist_ok=True)
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OmegaConf.save(conf, f'Results/3ph/{exp_name}/config.yaml')
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return exp_name
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def get_loss_function(conf):
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if conf.loss.type == "three_point_set_loss_smooth_l1":
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def three_point_set_loss_smooth_l1(pred_xy, gt_xy):
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loss_fn = torch.nn.SmoothL1Loss(reduction='none', beta=conf.loss.smooth_l1_beta)
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p1, p2, p3 = pred_xy[:,0], pred_xy[:,1], pred_xy[:,2]
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g1, g2, g3 = gt_xy[:,0], gt_xy[:,1], gt_xy[:,2]
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# 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)
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with torch.no_grad():
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def calc_cost(ga, gb, gc):
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return ((p1 - ga)**2).sum(dim=-1) + \
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((p2 - gb)**2).sum(dim=-1) + \
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((p3 - gc)**2).sum(dim=-1)
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# 枚举 3个光子的 6 种全排列 (Permutations)
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c0 = calc_cost(g1, g2, g3)
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c1 = calc_cost(g1, g3, g2)
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c2 = calc_cost(g2, g1, g3)
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c3 = calc_cost(g2, g3, g1)
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c4 = calc_cost(g3, g1, g2)
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c5 = calc_cost(g3, g2, g1)
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# 将 6 种 cost 堆叠起来，形状变为 [B, 6]
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costs = torch.stack([c0, c1, c2, c3, c4, c5], dim=1)
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# 找到每个 batch 中 cost 最小的那个排列的索引 (0 到 5)
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min_indices = torch.argmin(costs, dim=1) # [B]
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# 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)
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perms = torch.tensor([
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[0, 1, 2], # c0
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[0, 2, 1], # c1
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[1, 0, 2], # c2
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[1, 2, 0], # c3
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[2, 0, 1], # c4
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[2, 1, 0] # c5
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], device=pred_xy.device)
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# 获取当前 batch 最优的排列索引组合，形状为 [B, 3]
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best_perms = perms[min_indices]
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# 将 Ground Truth 堆叠为 [B, 3, 2] 的统一张量
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g_all = torch.stack([g1, g2, g3], dim=1)
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# 利用高级索引一次性完成重组
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batch_idx = torch.arange(pred_xy.size(0), device=pred_xy.device).unsqueeze(-1)
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g_matched = g_all[batch_idx, best_perms] # [B, 3, 2]
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# 2. 惩罚阶段：对重组后完美对齐的坐标计算 L2 Loss
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loss_p1 = loss_fn(p1, g_matched[:, 0]).sum(dim=-1)
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loss_p2 = loss_fn(p2, g_matched[:, 1]).sum(dim=-1)
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loss_p3 = loss_fn(p3, g_matched[:, 2]).sum(dim=-1)
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loss = (loss_p1 + loss_p2 + loss_p3).mean() / 3.0
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return loss
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return three_point_set_loss_smooth_l1
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def get_mse(pred_xy, gt_xy):
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p1, p2, p3 = pred_xy[:,0], pred_xy[:,1], pred_xy[:,2]
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g1, g2, g3 = gt_xy[:,0], gt_xy[:,1], gt_xy[:,2]
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with torch.no_grad():
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def calc_cost(ga, gb, gc):
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return ((p1 - ga)**2).sum(dim=-1) + \
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((p2 - gb)**2).sum(dim=-1) + \
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((p3 - gc)**2).sum(dim=-1)
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c0 = calc_cost(g1, g2, g3)
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c1 = calc_cost(g1, g3, g2)
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c2 = calc_cost(g2, g1, g3)
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c3 = calc_cost(g2, g3, g1)
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c4 = calc_cost(g3, g1, g2)
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c5 = calc_cost(g3, g2, g1)
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costs = torch.stack([c0, c1, c2, c3, c4, c5], dim=1)
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min_costs, _ = torch.min(costs, dim=1) # [B]
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mse_1d = min_costs.mean() / 6.0 # num_photons=3, num_axes=2
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return mse_1d
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def train(model, trainLoader, optimizer, loss_fn):
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model.train()
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batchLoss = 0
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sum_squared_error_1d = 0; event_count = 0
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for batch_idx, (sample, label) in enumerate(trainLoader):
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sample, label = sample.cuda(), label.cuda()
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x1, y1, z1, e1 = label[:, 0, 0], label[:, 0, 1], label[:, 0, 2], label[:, 0, 3]
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x2, y2, z2, e2 = label[:, 1, 0], label[:, 1, 1], label[:, 1, 2], label[:, 1, 3]
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x3, y3, z3, e3 = label[:, 2, 0], label[:, 2, 1], label[:, 2, 2], label[:, 2, 3]
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gt_xy = torch.stack((torch.stack((x1, y1), axis=1), torch.stack((x2, y2), axis=1), torch.stack((x3, y3), axis=1)), axis=1)
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optimizer.zero_grad()
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output = model(sample)
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pred_xy = torch.stack((output[:,0:2], output[:,2:4], output[:,4:6]), axis=1)
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loss = loss_fn(pred_xy, gt_xy)
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loss.backward()
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optimizer.step()
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batchLoss += loss.item() * sample.shape[0]
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mse_1d = get_mse(pred_xy, gt_xy)
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sum_squared_error_1d += mse_1d.item() * sample.shape[0]
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event_count += sample.shape[0]
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avgLoss = batchLoss / len(trainLoader.dataset) / 6 ### divide by 6 to get the average loss per photon per axis
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rms_1d = np.sqrt(sum_squared_error_1d / event_count)
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print(f"[Train]\t Average Loss: {avgLoss:.6f} (RMS = {rms_1d:.6f})")
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return avgLoss, rms_1d
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def evaluate(model, valLoader, loss_fn):
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model.eval()
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batchLoss = 0
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sum_squared_error_1d = 0; event_count = 0
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with torch.no_grad():
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for batch_idx, (sample, label) in enumerate(valLoader):
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sample, label = sample.cuda(), label.cuda()
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x1, y1, z1, e1 = label[:, 0, 0], label[:, 0, 1], label[:, 0, 2], label[:, 0, 3]
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x2, y2, z2, e2 = label[:, 1, 0], label[:, 1, 1], label[:, 1, 2], label[:, 1, 3]
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x3, y3, z3, e3 = label[:, 2, 0], label[:, 2, 1], label[:, 2, 2], label[:, 2, 3]
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gt_xy = torch.stack((torch.stack((x1, y1), axis=1), torch.stack((x2, y2), axis=1), torch.stack((x3, y3), axis=1)), axis=1)
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output = model(sample)
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pred_xy = torch.stack((output[:,0:2], output[:,2:4], output[:,4:6]), axis=1)
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loss = loss_fn(pred_xy, gt_xy)
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mse_1d = get_mse(pred_xy, gt_xy)
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sum_squared_error_1d += mse_1d.item() * sample.shape[0]
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event_count += sample.shape[0]
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batchLoss += loss.item() * sample.shape[0]
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avgLoss = batchLoss / len(valLoader.dataset) / 6 ### divide by 6 to get the average loss per photon per axis
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rms_1d = np.sqrt(sum_squared_error_1d / event_count)
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print(f"[Val]\t Average Loss: {avgLoss:.6f} (RMS = {rms_1d:.6f})")
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return avgLoss, rms_1d
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def get_dataloaders(conf):
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"""construct all dataloaders"""
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datasets = {}
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loaders = {}
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splits = ['Train', 'Val', 'Test']
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keys = ['train_files', 'val_files', 'test_files']
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batch_keys = ['batch_size_train', 'batch_size_val', 'batch_size_test']
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file_range_keys = ['train_file_range', 'val_file_range', 'test_file_range']
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for split, key, batch_key, file_range_key in zip(splits, keys, batch_keys, file_range_keys):
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files = [f"{conf.data.sample_folder}/pileupOf3phs_sample_{i}.npz" for i in range(conf.data[file_range_key][0], conf.data[file_range_key][1] + 1)]
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datasets[split] = triplePhotonDataset(
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files,
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sampleRatio = conf.data.sample_ratio,
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datasetName = split.capitalize(),
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)
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loaders[split] = torch.utils.data.DataLoader(
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datasets[split],
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batch_size=conf.data[batch_key],
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shuffle=(split=='Train'),
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num_workers=conf.data.num_workers,
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pin_memory=True
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)
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return loaders['Train'], loaders['Val'], loaders['Test']
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def plot_loss_curves(train_losses, val_losses, test_loss, exp_name, conf):
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import matplotlib.pyplot as plt
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plt.figure(figsize=(8,6))
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plt.plot(train_losses, label='Train Loss')
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plt.plot(val_losses, label='Val Loss')
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if test_loss > 0:
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plt.axhline(y=test_loss, color='green', linestyle='--', label='Test Loss')
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plt.xlabel('Epoch')
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plt.ylabel('MSE Loss')
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plt.yscale('log')
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plt.legend()
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plt.grid()
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plotName = f'loss_curve_triplePhoton_{conf.model.version}.png'
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plt.savefig(f'Results/3ph/{exp_name}/Plots/{plotName}')
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plt.close()
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def plot_rms_curves(train_rms, val_rms, test_rms, exp_name, conf):
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import matplotlib.pyplot as plt
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plt.figure(figsize=(8,6))
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plt.plot(train_rms, label='Train RMS')
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plt.plot(val_rms, label='Val RMS')
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if test_rms > 0:
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plt.axhline(y=test_rms, color='green', linestyle='--', label='Test RMS')
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plt.xlabel('Epoch')
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plt.ylabel('RMS Error [pixels]')
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plt.yscale('log')
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plt.legend()
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plt.grid()
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plotName = f'rms_curve_triplePhoton_{conf.model.version}.png'
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plt.savefig(f'Results/3ph/{exp_name}/Plots/{plotName}')
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plt.close()
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def get_model_name(conf):
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modelName = f'triplePhoton{conf.model.version}_{conf.data.energy}keV'
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return modelName
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if __name__ == "__main__":
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exp_name = prepare_output_folder(conf)
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model = get_triple_photon_model_class(conf.model.version)().cuda()
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# summary(model, input_size=(128, 3, conf.data.n_size, conf.data.n_size))
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loss_fn = get_loss_function(conf)
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optimizer = torch.optim.Adam(model.parameters(), lr=conf.training.learning_rate, weight_decay=conf.training.weight_decay)
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scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=conf.training.scheduler_factor, patience=conf.training.scheduler_patience)
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trainLoader, valLoader, testLoader = get_dataloaders(conf)
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TrainLosses, ValLosses = [], []
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train_rms, val_rms = [], []
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for epoch in tqdm(range(1, conf.training.epochs + 1)):
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train_loss, train_rms_epoch = train(model, trainLoader, optimizer, loss_fn)
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val_loss, val_rms_epoch = evaluate(model, valLoader, loss_fn)
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TrainLosses.append(train_loss)
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ValLosses.append(val_loss)
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train_rms.append(train_rms_epoch)
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val_rms.append(val_rms_epoch)
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scheduler.step(val_loss)
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print(f"Learning Rate: {optimizer.param_groups[0]['lr']:.2e}")
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if epoch in conf.training.checkpoint_epochs or epoch == conf.training.epochs:
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modelName = get_model_name(conf)
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torch.save(model.state_dict(), f'Results/3ph/{exp_name}/Models/{modelName}_E{epoch}.pth')
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print(f"Saved model checkpoint: {modelName}_E{epoch}.pth")
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plot_loss_curves(TrainLosses, ValLosses, test_loss=-1, exp_name=exp_name, conf=conf)
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plot_rms_curves(train_rms, val_rms, test_rms=-1, exp_name=exp_name, conf=conf)
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test_loss, test_rms = evaluate(model, testLoader, loss_fn)
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plot_loss_curves(TrainLosses, ValLosses, test_loss=test_loss, exp_name=exp_name, conf=conf)
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plot_rms_curves(train_rms, val_rms, test_rms=test_rms, exp_name=exp_name, conf=conf) |