main_autoencoder.py

import argparse
import logging

import torch
import torch.nn as nn
from torch import cuda
from torch.autograd import Variable
from torch.utils.data import DataLoader,Dataset
import torchvision

from autoencoder import AutoEncoder 
from AD_Standard_3DRandomPatch import AD_Standard_3DRandomPatch

logging.basicConfig(
    format='%(asctime)s %(levelname)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO)

parser = argparse.ArgumentParser(description="Starter code for AutoEncoder")

parser.add_argument("--learning_rate", "-lr", default=1e-3, type=float,
                    help="Learning rate of the optimization. (default=0.01)")
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument("--batch_size", default=1, type=int,
                    help="Batch size for training. (default=1)")
parser.add_argument("--gpuid", default=[0], nargs='+', type=int,
                    help="ID of gpu device to use. Empty implies cpu usage.")
parser.add_argument("--num_classes", default=2, type=int,
                    help="Number of classes.")
parser.add_argument("--epochs", default=20, type=int,
                    help="Epochs through the data. (default=20)")
parser.add_argument("--estop", default=1e-4, type=float,
                    help="Early stopping criteria on the development set. (default=1e-4)")  

def main(options):

    if options.num_classes == 2:
        TRAINING_PATH = 'train_2classes.txt'
    else:
        TRAINING_PATH = 'train.txt'
    IMG_PATH = './Whole'

    dset_train = AD_Standard_3DRandomPatch(IMG_PATH, TRAINING_PATH)

    train_loader = DataLoader(dset_train,
                              batch_size = options.batch_size,
                              shuffle = True,
                              num_workers = 4,
                              drop_last = True
                              )
    sparsity = 0.05
    beta = 0.5

    mean_square_loss = nn.MSELoss()
    #kl_div_loss = nn.KLDivLoss(reduce=False)

    use_gpu = len(options.gpuid)>=1
    autoencoder = AutoEncoder()

    if(use_gpu):
        autoencoder = autoencoder.cuda()
    else:
        autoencoder = autoencoder.cpu()

    #autoencoder.load_state_dict(torch.load("./autoencoder_pretrained_model19"))

    optimizer = torch.optim.Adam(autoencoder.parameters(), lr=options.learning_rate, weight_decay=options.weight_decay)
    
    last_train_loss = 1e-4
    f = open("autoencoder_loss", 'a')
    for epoch in range(options.epochs):
        train_loss = 0.
        print("At {0}-th epoch.".format(epoch))
        for i, patches in enumerate(train_loader):
            patch = patches['patch']
            for b, batch in enumerate(patch):
                batch = Variable(batch).cuda()
                output, s_ = autoencoder(batch)
                loss1 = mean_square_loss(output, batch)
                s = Variable(torch.ones(s_.shape)*sparsity).cuda()
                loss2 = (s*torch.log(s/(s_+1e-8)) + (1-s)*torch.log((1-s)/((1-s_+1e-8)))).sum()/options.batch_size
                #kl_div_loss(mean_activitaion, sparsity)
                loss = loss1 + beta * loss2
                train_loss += loss
                logging.info("batch {0} training loss is : {1:.5f}, {2:.5f}".format(i*1000+b, loss1.data[0], loss2.data[0]))
                f.write("batch {0} training loss is : {1:.3f}\n".format(i*1000+b, loss.data[0]))
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
        train_avg_loss = train_loss/(len(train_loader)*1000)
        print("Average training loss is {0:.5f} at the end of epoch {1}".format(train_avg_loss.data[0], epoch))
        if (abs(train_avg_loss.data[0] - last_training_loss) <= options.estop) or ((epoch+1)%20==0):
            torch.save(autoencoder.state_dict(), open("autoencoder_pretrained_model"+str(epoch), 'wb'))
        last_train_loss = train_avg_loss.data[0]
    f.close()
if __name__ == "__main__":
  ret = parser.parse_known_args()
  options = ret[0]
  if ret[1]:
    logging.warning("unknown arguments: {0}".format(parser.parse_known_args()[1]))
  main(options)