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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
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