Pytorch_CNN-RNN/main.py

import torch
from torch import nn


# GENERAL PURPOSE
import numpy as np
from datetime import datetime
import pandas as pd
import os

# FOR TRAINING
import torch.optim as optim
import utils.models as models
from utils.training import train_model, test_model, initalize_dataloaders, plot_results


#Set Default GPU
cuda_device = torch.device('cuda:1')
torch.set_default_device(cuda_device)

print("--- RUNNING ---")
print("Pytorch Version: " + torch. __version__)

# data & training properties:
val_split = 0.2     # % of val and test, rest will be train
runs = 1
epochs = 30
seeds = [np.random.randint(0, 1000) for _ in range(runs)]

#Data Path
mri_path = '/data/data_wnx1/_Data/AlzheimersDL/CNN+RNN-2class-1cnn+data/PET_volumes_customtemplate_float32/'

#Local Path
local_path = '/export/home/nschense/alzheimers/Pytorch_CNN-RNN'

xls_path = local_path + '/LP_ADNIMERGE.csv'
saved_model_path = local_path + '/saved_models/'
plot_path = local_path + '/plots/'
training_record_path = local_path + '/training_records/'

DEBUG = False

model_CNN = models.CNN_Net(1, 2, 0.5).to(cuda_device)
criterion = nn.BCELoss()
optimizer = optim.Adam(model_CNN.parameters(), lr=0.001)

        
for seed in seeds:
    time_stamp = datetime.now().strftime('%Y%m%d_%H%M%S')

    train_loader, val_loader, test_loader = initalize_dataloaders(mri_path, xls_path, val_split, seed, cuda_device=cuda_device)
    train_results = train_model(model_CNN, seed, time_stamp, epochs, train_loader, val_loader, saved_model_path, "CNN", optimizer, criterion, cuda_device=cuda_device)
    test_model(model_CNN, test_loader, cuda_device=cuda_device)
    
    #Plot results
    plot_results(train_results["train_acc"], train_results["train_loss"], train_results["val_acc"], train_results["val_loss"], "CNN", time_stamp, plot_path)
    
    #Save training results
    if not os.path.exists(training_record_path):
        os.makedirs(training_record_path)
    train_results.to_csv(training_record_path + "CNN_t-" + time_stamp + "_s-" + str(seed) + "_e-" + str(epochs) + ".csv")
    
    
    

print("--- END ---")