File size: 8,157 Bytes
5a169ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | 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 CosineAnnealingWarmRestarts
from torch.cuda.amp import GradScaler, autocast
from sklearn.metrics import mean_absolute_error
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
class BrainAgeTrainer(BaseConfig):
"""
A trainer class for brain age prediction models.
This class handles the complete training pipeline including model setup,
data loading, training loop, and validation.
Inherits from BaseConfig for configuration management.
"""
def __init__(self):
"""Initialize the trainer with model, data, and training setup."""
super().__init__()
self.setup_wandb()
self.setup_model()
self.setup_data()
self.setup_training()
## setup wandb logger
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):
"""
Set up the model architecture.
Initializes the backbone and classifier blocks, and loads
checkpoints
"""
self.backbone = Backbone()
self.classifier = Classifier(d_model=2048)
self.model = SingleScanModel(self.backbone, self.classifier)
# Load BrainIACs weights
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!")
# Freeze backbone if specified
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)
def setup_data(self):
"""
Set up data loaders for training and validation.
Inherit configuration from the base config
"""
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):
"""
Set up training config with loss, scheduler, optimizer.
"""
config = self.get_config()
self.criterion = nn.MSELoss()
self.optimizer = optim.Adam(
self.model.parameters(),
lr=config['optim']['lr'],
weight_decay=config["optim"]["weight_decay"]
)
self.scheduler = CosineAnnealingWarmRestarts(self.optimizer, T_0=50, T_mult=2)
self.scaler = GradScaler()
def train(self):
"""
main training loop
"""
config = self.get_config()
max_epochs = config['optim']['max_epochs']
best_val_loss = float('inf')
best_val_mae = float('inf')
for epoch in range(max_epochs):
train_loss = self.train_epoch(epoch, max_epochs)
val_loss, mae = self.validate_epoch(epoch, max_epochs)
# Save best model
if (val_loss <= best_val_loss) and (mae <= best_val_mae):
print(f"Improved Val Loss from {best_val_loss:.4f} to {val_loss:.4f}")
print(f"Improved Val MAE from {best_val_mae:.4f} to {mae:.4f}")
best_val_loss = val_loss
best_val_mae = mae
self.save_checkpoint(epoch, val_loss, mae)
wandb.finish()
def train_epoch(self, epoch, max_epochs):
"""
Train pass.
Args:
epoch (int): Current epoch number
max_epochs (int): Total number of epochs
Returns:
float: Average training loss for the epoch
"""
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'].float().to(self.device)
self.optimizer.zero_grad()
with autocast():
outputs = self.model(inputs)
loss = self.criterion(outputs, labels.unsqueeze(1))
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
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
def validate_epoch(self, epoch, max_epochs):
"""
Validation pass.
Args:
epoch (int): Current epoch number
max_epochs (int): Total number of epochs
Returns:
tuple: (validation_loss, mean_absolute_error)
"""
self.model.eval()
val_loss = 0.0
all_labels = []
all_preds = []
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'].float().to(self.device)
outputs = self.model(inputs)
loss = self.criterion(outputs, labels.unsqueeze(1))
val_loss += loss.item() * inputs.size(0)
all_labels.extend(labels.cpu().numpy().flatten())
all_preds.extend(outputs.cpu().numpy().flatten())
val_loss = val_loss / len(self.val_loader.dataset)
mae = mean_absolute_error(all_labels, all_preds)
wandb.log({"Val Loss": val_loss, "MAE": mae})
self.scheduler.step(val_loss)
print(f"Epoch {epoch}/{max_epochs-1} Val Loss: {val_loss:.4f} MAE: {mae:.4f}")
return val_loss, mae
def save_checkpoint(self, epoch, loss, mae):
"""
Save model checkpoint.
"""
config = self.get_config()
checkpoint = {
'model_state_dict': self.model.state_dict(),
'loss': loss,
'epoch': epoch,
}
save_path = os.path.join(
config['logger']['save_dir'],
config['logger']['save_name'].format(epoch=epoch, loss=loss, metric=mae)
)
torch.save(checkpoint, save_path)
if __name__ == "__main__":
trainer = BrainAgeTrainer()
trainer.train() |