alzheimers_nn/train_cnn.py

# MACHINE LEARNING
import torch
import torch.nn as nn
import torch.optim as optim

# GENERAL USE
import random as rand

# SYSTEM
import tomli as toml
import os

# DATA PROCESSING
from sklearn.model_selection import train_test_split

# CUSTOM MODULES
import utils.models.cnn as cnn
from utils.data.datasets import prepare_datasets, initalize_dataloaders
import utils.training as train

# CONFIGURATION
if os.getenv("ADL_CONFIG_PATH") is None:
    with open("config.toml", "rb") as f:
        config = toml.load(f)
else:
    with open(os.getenv("ADL_CONFIG_PATH"), "rb") as f:
        config = toml.load(f)

for i in range(config["training"]["runs"]):
    # Set up the model
    model = cnn.CNN(
        config["model"]["image_channels"],
        config["model"]["clin_data_channels"],
        config["hyperparameters"]["droprate"],
    ).float()
    criterion = nn.BCELoss()
    optimizer = optim.Adam(
        model.parameters(), lr=config["hyperparameters"]["learning_rate"]
    )

    # Generate seed for each run
    seed = rand.randint(0, 1000)

    # Prepare data
    train_dataset, val_dataset, test_dataset = prepare_datasets(
        config["paths"]["mri_data"],
        config["paths"]["xls_data"],
        config["dataset"]["validation_split"],
        seed,
    )
    train_dataloader, val_dataloader, test_dataloader = initalize_dataloaders(
        train_dataset,
        val_dataset,
        test_dataset,
        config["hyperparameters"]["batch_size"],
    )

    # Train the model
    history = train.train_model(
        model, train_dataloader, val_dataloader, criterion, optimizer, config
    )

    # Save model
    if not os.path.exists(
        config["paths"]["model_output"] + "/" + str(config["model"]["name"])
    ):
        os.makedirs(
            config["paths"]["model_output"] + "/" + str(config["model"]["name"])
        )

    torch.save(
        model,
        config["paths"]["model_output"]
        + "/"
        + str(config["model"]["name"])
        + "/"
        + str(i)
        + "_s-"
        + str(seed)
        + ".pt",
    )