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import pandas as pd
from torch.utils.data import DataLoader
import torch
import torch.optim as optim
from tqdm import tqdm
import yaml
import wandb
import math
from torch.optim.lr_scheduler import LambdaLR
from monai.losses import DiceLoss, DiceCELoss
from monai.metrics import DiceMetric
from monai.transforms import Activations, AsDiscrete
from models.swinunetr import SwinUNETRMultiTask
from dataloading.dataloader2D import NiftiSegmentationDataset
from monai.transforms import (
Activations,
AsDiscrete,
Compose
)
from dataloading.collate_function import custom_collate
from odelia_breast_mri.scripts.main_predict import evaluate
import torch.nn.functional as F
# -------------------------
# Warmup Cosine Scheduler
# -------------------------
def warmup_cosine_lr_scheduler(optimizer, warmup_epochs, total_epochs):
def lr_lambda(current_epoch):
if current_epoch < warmup_epochs:
return float(current_epoch) / float(max(1, warmup_epochs))
else:
return 0.5 * (1. + math.cos(math.pi * (current_epoch - warmup_epochs) / (total_epochs - warmup_epochs)))
return LambdaLR(optimizer, lr_lambda)
# -------------------------
# Load Config
# -------------------------
with open("/workspace/ClassifierSegmenter/config2d.yaml", "r") as f:
config = yaml.safe_load(f)
wandb.init(project=config['project_name'], config=config, name=config['run_name'], notes=config['notes'])
cfg = wandb.config
device = torch.device(cfg.device if torch.cuda.is_available() else "cpu")
# # -------------------------
# # Model Setup
# # -------------------------
in_channels = len(cfg.channel_keys) if isinstance(cfg.channel_keys, list) else 1
def confidence_loss(logits):
probs = F.softmax(logits, dim=1)
entropy = -torch.sum(probs * torch.log(probs + 1e-6), dim=1)
return torch.mean(entropy)
model =SwinUNETRMultiTask(img_size=(256, 256), in_channels=in_channels, out_seg_channels=2, out_cls_classes=3).to(device)
optimizer = optim.Adam(model.parameters(), lr=cfg.learning_rate, weight_decay=1e-5)
scheduler = warmup_cosine_lr_scheduler(optimizer, cfg.warmup_epochs, cfg.epochs)
# # -------------------------
# # Compute Weights and Configure Loss function
# # -------------------------
segmentation_loss = DiceCELoss(sigmoid=True, to_onehot_y=True)
df = pd.read_csv(cfg.csv_file_train)
labels = df['label'].values
class_sample_counts = np.bincount(labels)
weights = 1.0 / class_sample_counts
weights = weights / weights.sum() # normalize
weights = torch.tensor(weights, dtype=torch.float32)
print('Weights:', weights)
classification_loss = torch.nn.CrossEntropyLoss(weight=weights.to(device))
# classification_loss = torch.nn.CrossEntropyLoss()
post_pred = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
dice_metric = DiceMetric(include_background=False, reduction="mean", get_not_nans=False)
# -------------------------
# Dataloaders
# -------------------------
train_dataset = NiftiSegmentationDataset(cfg.csv_file_train, channel_keys=cfg.channel_keys)
train_loader = DataLoader(train_dataset, batch_size=cfg.batch_size, shuffle=True, collate_fn=custom_collate, num_workers=cfg.num_workers)
val_dataset = NiftiSegmentationDataset(cfg.csv_file_val, channel_keys=cfg.channel_keys, augment=False)
val_loader = DataLoader(val_dataset, batch_size=cfg.batch_size, collate_fn=custom_collate, shuffle=False)
best_val_loss = float('inf')
best_val_score = float('-inf')
# -------------------------
# Training Loop
# -------------------------
for epoch in range(cfg.epochs):
model.train()
total_loss = 0.0
total_loss, correct, total = 0, 0, 0
all_preds = []
all_probs = []
all_targets = []
for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{cfg.epochs}"):
x = batch['image'].to(device)
y = batch['cls_label'].to(device) if batch['cls_label'] is not None else None
has_label = batch['has_cls_label'] if batch['has_cls_label'] is not None else None
mask = batch['mask'].to(device) if batch['mask'] is not None else None
has_mask = batch['has_mask'].to(device) if batch['has_mask'] is not None else None
optimizer.zero_grad()
seg_preds, cls_preds, _ = model(x)
if y is not None:
valid_idx = has_label.nonzero(as_tuple=True)[0]
if len(valid_idx) > 0:
cls_loss = classification_loss(cls_preds[valid_idx], y[valid_idx])
else:
cls_loss = 0.0
else:
cls_loss = 0.0
# conf_loss = confidence_loss(logits=cls_preds)
if cfg.propagate_segmentation_loss:
# Segmentation loss (computed only where masks are valid)
if mask is not None:
valid_idx = has_mask.nonzero(as_tuple=True)[0]
if len(valid_idx) > 0:
seg_loss = segmentation_loss(seg_preds[valid_idx], mask[valid_idx])
else:
seg_loss = 0.0
else:
seg_loss = 0.0
loss = cls_loss + seg_loss
else:
# If segmentation loss is not propagated, only use classification loss
loss = cls_loss
loss.backward()
optimizer.step()
total_loss += loss.item() * x.size(0)
##### classification metric
if y is not None:
preds = torch.argmax(cls_preds, dim=1)
correct += (preds == y).sum().item()
total += y.size(0)
# --- Collect predictions ---
probs = torch.softmax(cls_preds, dim=1) # Probabilities per class
all_preds.append(preds.cpu().detach())
all_probs.append(probs.cpu().detach())
all_targets.append(y.cpu().detach())
# After loop, concatenate all
all_preds = torch.cat(all_preds)
all_probs = torch.cat(all_probs)
all_targets = torch.cat(all_targets)
train_accuracy = correct / total
train_auc, train_sensitivity, train_specificity = evaluate(all_targets, all_preds, all_probs)
avg_train_loss = total_loss / len(train_loader.dataset)
# -------------------------
# Validation
# -------------------------
model.eval()
val_loss, val_correct, val_total = 0, 0, 0
all_preds = []
all_probs = []
all_targets = []
with torch.no_grad():
for batch in tqdm(val_loader, desc="Validation"):
x_val = batch['image'].to(device)
y_cls = batch['cls_label'].to(device)
y_mask = batch['mask'].to(device) if batch['mask'] is not None else None
has_mask = batch['has_mask'].to(device) if batch['has_mask'] is not None else None
seg_preds, cls_preds, _ = model(x_val)
# Classification loss
cls_loss = classification_loss(cls_preds, y_cls)
val_loss += cls_loss.item() * x_val.size(0)
preds = torch.argmax(cls_preds, dim=1)
val_correct += (preds == y_cls).sum().item()
val_total += y_cls.size(0)
probs = torch.softmax(cls_preds, dim=1) # Probabilities per class
all_preds.append(preds.cpu().detach())
all_probs.append(probs.cpu().detach())
all_targets.append(y_cls.cpu().detach())
# After loop, concatenate all
all_preds = torch.cat(all_preds)
all_probs = torch.cat(all_probs)
all_targets = torch.cat(all_targets)
avg_val_loss = val_loss / val_total
val_accuracy = val_correct / val_total
val_auc, val_sensitivity, val_specificity = evaluate(all_targets, all_preds, all_probs)
avg_val_loss = val_loss / len(val_loader.dataset)
# -------------------------
# Logging & Visualization
# -------------------------
print(
f"Epoch {epoch+1} Summary:\n"
f" Train Loss : {avg_train_loss:.4f} | Val Loss : {avg_val_loss:.4f}\n"
f" Train Accuracy : {train_accuracy:.4f} | Val Accuracy : {val_accuracy:.4f}\n"
f" Train AUC : {train_auc:.4f} | Val AUC : {val_auc:.4f}\n"
f" Train Sensitivity: {train_sensitivity:.4f} | Val Sensitivity: {val_sensitivity:.4f}\n"
f" Train Specificity: {train_specificity:.4f} | Val Specificity: {val_specificity:.4f}"
)
wandb.log({
"epoch": epoch + 1,
"train_loss": avg_train_loss,
"train_accuracy": train_accuracy,
"train_auc": train_auc,
"train_sensitivity": train_sensitivity,
"train_specificity": train_specificity,
"val_accuracy": val_accuracy,
"val_auc": val_auc,
"val_sensitivity": val_sensitivity,
"val_specificity": val_specificity,
"val_loss": avg_val_loss,
"lr": scheduler.get_last_lr()[0]
})
scheduler.step()
mean_score = (val_auc + val_sensitivity + val_specificity)/3
torch.save(model.state_dict(), '/workspace/Classifier/checkpoints/latest_model.pth')
if avg_val_loss < best_val_loss:
best_val_loss = avg_val_loss
torch.save(model.state_dict(), '/workspace/Classifier/checkpoints/best_model.pth')
print("✅ Saved best model.")
if mean_score > best_val_score:
best_val_score = mean_score
torch.save(model.state_dict(), '/workspace/Classifier/checkpoints/best_score_model.pth')
print("✅ Saved best score model.") |