import math import sys from typing import Iterable import torch import os import util.misc as misc import util.lr_sched as lr_sched from monai.losses import DiceCELoss, DiceLoss import numpy as np from monai.metrics import DiceHelper import surface_distance from surface_distance import metrics from util.meter import DiceMeter, HausdorffMeter, SurfaceDistanceMeter # from monai.data import ImageDataset, create_test_image_3d, decollate_batch, DataLoader from monai.inferers import sliding_window_inference from torchmetrics.classification import ( BinarySpecificityAtSensitivity, BinarySensitivityAtSpecificity, ) # from monai.metrics import DiceMetric # from monai.transforms import Activations import pdb from sklearn.metrics import ( roc_auc_score, top_k_accuracy_score, f1_score, confusion_matrix, ) def train_one_epoch( model, data_loader, optimizer, device, epoch: int, loss_scaler, log_writer=None, args=None, ): model.train(True) metric_logger = misc.MetricLogger(delimiter=" ") metric_logger.add_meter("lr", misc.SmoothedValue(window_size=1, fmt="{value:.6f}")) header = "Epoch: [{}]".format(epoch) print_freq = 20 loss_cal = torch.nn.BCEWithLogitsLoss() optimizer.zero_grad() if log_writer is not None: print("log_dir: {}".format(log_writer.log_dir)) last_norm = 0.0 for data_iter_step, (img, zone_mask, gt) in enumerate( metric_logger.log_every(data_loader, print_freq, header) ): # we use a per iteration (instead of per epoch) lr scheduler img, zone_mask, gt = img.to(device, non_blocking=True), zone_mask.to(device, non_blocking=True), gt.to(device, non_blocking=True) gt = gt.float() lr_sched.adjust_learning_rate( optimizer, data_iter_step / len(data_loader) + epoch, args ) logit = model(img, zone_mask) if isinstance(logit, list): loss = loss_cal(logit[0], gt) + 0.4*loss_cal(logit[1], gt) else: loss = loss_cal(logit, gt) loss_value = loss.item() if not math.isfinite(loss_value): print( "nan", torch.isnan(logit).any(), torch.isnan(img).any(), last_norm, ) print( "inf", torch.isinf(logit).any(), torch.isinf(img).any(), last_norm, ) print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) optimizer.zero_grad() loss.backward() # torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() metric_logger.update(loss=loss_value) lr = optimizer.param_groups[0]["lr"] metric_logger.update(lr=lr) loss_value_reduce = misc.all_reduce_mean(loss_value) if log_writer is not None: """We use epoch_1000x as the x-axis in tensorboard. This calibrates different curves when batch size changes. """ epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) log_writer.add_scalar("train_loss", loss_value_reduce, epoch_1000x) log_writer.add_scalar("lr", lr, epoch_1000x) # gather the stats from all processes # metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def validation(model, data_loader_val, device, epoch, args): model.eval() loss_cal = torch.nn.BCEWithLogitsLoss() with torch.no_grad(): loss_summary = [] for idx, (img, zone_mask, gt) in enumerate(data_loader_val): img, zone_mask, gt = img.to(device, non_blocking=True), zone_mask.to(device, non_blocking=True), gt.to(device, non_blocking=True) gt = gt.float() logit = model(img, zone_mask) loss = loss_cal(logit, gt) loss_summary.append(loss.detach().cpu().numpy()) print( "epoch: {}/{}, iter: {}/{}".format( epoch, args.epochs, idx, len(data_loader_val) ) + " loss:" + str(loss_summary[-1].flatten()[0]) ) avg_loss = np.mean(loss_summary) print("Averaged stats:", str(avg_loss)) return avg_loss def test(model, test_loader, args, sliding_window=False): model.eval() filepath_best = os.path.join(args.output_dir, "best.pth.tar") model.load_state_dict(torch.load(filepath_best)["model"], weights_only=False) log_stats = {} with torch.no_grad(): prob, gts = [], [] for idx, (img, zone_mask, gt) in enumerate(test_loader): img, zone_mask, gt = img.to(args.device, non_blocking=True), zone_mask.to(args.device, non_blocking=True), gt.to(args.device, non_blocking=True) logit = model(img, zone_mask) prob.append(logit) gts.append(gt) prob = torch.cat(prob, 0) prob = torch.sigmoid(prob).cpu() gts = torch.cat(gts, 0).cpu() print("- Zone level: ") zone_prob = prob.reshape(-1, prob.shape[-1]) zone_gt = gts.reshape(-1, prob.shape[-1]) zone_auc = roc_auc_score(zone_prob, zone_gt) * 100 for i in [0.8, 0.9]: sig_spec = BinarySpecificityAtSensitivity(min_sensitivity=i, thresholds=None) sig_specificity, _ = sig_spec(zone_prob, zone_gt) sig_specificity = sig_specificity * 100 sig_sens = BinarySensitivityAtSpecificity(min_specificity=i, thresholds=None) sig_sensitivity, _ = sig_sens(zone_prob, zone_gt) sig_sensitivity = sig_sensitivity* 100 print(f"min: {i}") print(f"Specificity at Sensitivity \t Sensitivity at Specificity") print(f"{sig_specificity:.2f} \t {sig_sensitivity:.2f} ") log_stats[f"specificity_at_{i}"]=f"{sig_specificity:.2f}" log_stats[f"sensitivity_at_{i}"]=f"{sig_sensitivity:.2f}" print("- Patient level: ") p_prob = prob.max(1).values p_gt = gts.max(1).values p_auc = roc_auc_score(p_prob, p_gt) * 100 for i in [0.8, 0.9]: sig_spec = BinarySpecificityAtSensitivity(min_sensitivity=i, thresholds=None) sig_specificity, _ = sig_spec(p_prob, p_gt) sig_specificity = sig_specificity * 100 sig_sens = BinarySensitivityAtSpecificity(min_specificity=i, thresholds=None) sig_sensitivity, _ = sig_sens(p_prob, p_gt) sig_sensitivity = sig_sensitivity* 100 print(f"min: {i}") print(f"Specificity at Sensitivity \t Sensitivity at Specificity") print(f"{sig_specificity:.2f} \t {sig_sensitivity:.2f} ") log_stats[f"specificity_at_{i}"]=f"{sig_specificity:.2f}" log_stats[f"sensitivity_at_{i}"]=f"{sig_sensitivity:.2f}" return log_stats