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import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import wandb
from tqdm import tqdm
from torch.optim.lr_scheduler import OneCycleLR
from torch.cuda.amp import GradScaler, autocast
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from dataset2 import MedicalImageDatasetBalancedIntensity3D, TransformationMedicalImageDatasetBalancedIntensity3D
from model import Backbone, SingleScanModel, Classifier
from utils import BaseConfig
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
def calculate_metrics(pred_probs, pred_labels, true_labels):
"""
Multi-class classification metrics.
Args:
pred_probs (numpy.ndarray): Predicted probabilities for each class
pred_labels (numpy.ndarray): Predicted labels
true_labels (numpy.ndarray): Ground truth labels
Returns:
dict: Dictionary containing accuracy, precision, recall, F1, and AUC
"""
accuracy = accuracy_score(true_labels, pred_labels)
precision = precision_score(true_labels, pred_labels, average='weighted')
recall = recall_score(true_labels, pred_labels, average='weighted')
f1 = f1_score(true_labels, pred_labels, average='weighted')
auc = roc_auc_score(true_labels, pred_probs, multi_class='ovr')
return {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1,
'auc': auc
}
#============================
# TRAINER CLASS
#============================
class SequenceTrainer(BaseConfig):
"""
Trainer class for sequence classification
"""
def __init__(self):
super().__init__()
self.setup_wandb()
self.setup_model()
self.setup_data()
self.setup_training()
def setup_wandb(self):
config = self.get_config()
wandb.init(
project=config['logger']['project_name'],
name=config['logger']['run_name'],
config=config
)
def setup_model(self):
self.backbone = Backbone()
# Change classifier to output 4 values for multi-class classification
self.classifier = Classifier(d_model=2048, num_classes=4)
self.model = SingleScanModel(self.backbone, self.classifier)
# Load weights from brainiac
config = self.get_config()
if config["train"]["finetune"] == "yes":
checkpoint = torch.load(config["train"]["weights"], map_location=self.device)
state_dict = checkpoint["state_dict"]
filtered_state_dict = {}
for key, value in state_dict.items():
new_key = key.replace("module.", "backbone.") if key.startswith("module.") else key
filtered_state_dict[new_key] = value
self.model.backbone.load_state_dict(filtered_state_dict, strict=False)
print("Pretrained weights loaded!")
if config["train"]["freeze"] == "yes":
for param in self.model.backbone.parameters():
param.requires_grad = False
print("Backbone weights frozen!")
self.model = self.model.to(self.device)
## spinup dataloaders
def setup_data(self):
config = self.get_config()
self.train_dataset = TransformationMedicalImageDatasetBalancedIntensity3D(
csv_path=config['data']['train_csv'],
root_dir=config["data"]["root_dir"]
)
self.val_dataset = MedicalImageDatasetBalancedIntensity3D(
csv_path=config['data']['val_csv'],
root_dir=config["data"]["root_dir"]
)
self.train_loader = DataLoader(
self.train_dataset,
batch_size=config["data"]["batch_size"],
shuffle=True,
collate_fn=self.custom_collate,
num_workers=config["data"]["num_workers"]
)
self.val_loader = DataLoader(
self.val_dataset,
batch_size=1,
shuffle=False,
collate_fn=self.custom_collate,
num_workers=1
)
def setup_training(self):
"""
training setup
"""
config = self.get_config()
# Cross Entropy Loss for multi-class classification
self.criterion = nn.CrossEntropyLoss().to(self.device)
self.optimizer = optim.AdamW(
self.model.parameters(),
lr=config['optim']['lr'],
weight_decay=config["optim"]["weight_decay"]
)
self.scheduler = OneCycleLR(
self.optimizer,
max_lr=config['optim']['lr'],
epochs=config['optim']['max_epochs'],
steps_per_epoch=len(self.train_loader)
)
self.scaler = GradScaler()
## main training loop
def train(self):
config = self.get_config()
max_epochs = config['optim']['max_epochs']
best_metrics = {
'val_loss': float('inf'),
'accuracy': 0,
'precision': 0,
'recall': 0,
'f1': 0,
'auc': 0
}
for epoch in range(max_epochs):
train_loss = self.train_epoch(epoch, max_epochs)
val_loss, metrics = self.validate_epoch(epoch, max_epochs)
# save model based on auc
if metrics['auc'] > best_metrics['auc']:
print(f"New best model found!")
print(f"Improved Val Loss from {best_metrics['val_loss']:.4f} to {val_loss:.4f}")
print(f"Improved F1 from {best_metrics['f1']:.4f} to {metrics['f1']:.4f}")
best_metrics.update(metrics)
best_metrics['val_loss'] = val_loss
self.save_checkpoint(epoch, val_loss, metrics)
wandb.finish()
## training pass
def train_epoch(self, epoch, max_epochs):
self.model.train()
train_loss = 0.0
for sample in tqdm(self.train_loader, desc=f"Training Epoch {epoch}/{max_epochs-1}"):
inputs = sample['image'].to(self.device)
labels = sample['label'].to(self.device) # No need for float() conversion
self.optimizer.zero_grad(set_to_none=True)
with autocast():
outputs = self.model(inputs)
loss = self.criterion(outputs, labels) # CrossEntropyLoss expects raw logits
self.scaler.scale(loss).backward()
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
self.scaler.step(self.optimizer)
self.scaler.update()
self.scheduler.step()
train_loss += loss.item() * inputs.size(0)
train_loss = train_loss / len(self.train_loader.dataset)
wandb.log({"Train Loss": train_loss})
return train_loss
## validation pass
def validate_epoch(self, epoch, max_epochs):
self.model.eval()
val_loss = 0.0
all_labels = []
all_preds = []
all_probs = []
with torch.no_grad():
for sample in tqdm(self.val_loader, desc=f"Validation Epoch {epoch}/{max_epochs-1}"):
inputs = sample['image'].to(self.device)
labels = sample['label'].to(self.device) # No need for float() conversion
outputs = self.model(inputs)
loss = self.criterion(outputs, labels) # CrossEntropyLoss expects raw logits
# Get probabilities and predictions for multi-class
probs = torch.softmax(outputs, dim=1).cpu().numpy()
preds = np.argmax(probs, axis=1)
val_loss += loss.item() * inputs.size(0)
all_labels.extend(labels.cpu().numpy())
all_preds.extend(preds)
all_probs.extend(probs)
val_loss = val_loss / len(self.val_loader.dataset)
metrics = calculate_metrics(
np.array(all_probs),
np.array(all_preds),
np.array(all_labels)
)
wandb.log({
"Val Loss": val_loss,
"Accuracy": metrics['accuracy'],
"Precision": metrics['precision'],
"Recall": metrics['recall'],
"F1 Score": metrics['f1'],
"AUC": metrics['auc']
})
print(f"Epoch {epoch}/{max_epochs-1}")
print(f"Val Loss: {val_loss:.4f}")
print(f"Accuracy: {metrics['accuracy']:.4f}")
print(f"Precision: {metrics['precision']:.4f}")
print(f"Recall: {metrics['recall']:.4f}")
print(f"F1 Score: {metrics['f1']:.4f}")
print(f"AUC: {metrics['auc']:.4f}")
return val_loss, metrics
## save checkpoint
def save_checkpoint(self, epoch, loss, metrics):
config = self.get_config()
checkpoint = {
'epoch': epoch,
'model_state_dict': self.model.state_dict(),
'metrics': metrics
}
save_path = os.path.join(
config['logger']['save_dir'],
config['logger']['save_name'].format(epoch=epoch, loss=loss, metric=metrics['f1'])
)
torch.save(checkpoint, save_path)
if __name__ == "__main__":
trainer = SequenceTrainer()
trainer.train() |