main.py

import argparse
import os
import sys

import torch
import torchvision.transforms as transforms
import torchvision.datasets as dset

from gradient_descent_network import *
from neumann_network import *


parser = argparse.ArgumentParser()
parser.add_argument('--datadir', required=True, default='data', help='directory to image dataset')
parser.add_argument('--ckptdir', required=False, default='out', help='output dir')
parser.add_argument('--epochs', type=int, default=100, dest='epochs', help='Number of epochs to train')
parser.add_argument('--blocks', type=int, default=6, dest='blocks', help='Number of blocks (iterations)')
parser.add_argument('--bs', type=int, default=10, help='Batch size')
parser.add_argument('--lr', type=float, default=1e-5, help='Learning rate')
parser.add_argument('--eta', type=float, default=None, help='If not specified, will be estimated by landweber solver. Recommended: 0.1')
parser.add_argument('--size', required=False, type=int, default=320, help='the size of the input image to network.')
parser.add_argument('--angles', required=False, type=int, default=180, help='full-view projection angles.')
parser.add_argument('--det_size', required=False, type=int, help='detector pixel number, default: image size.')
parser.add_argument('--src_dist', required=False, type=int, default=1800, help='source distance of fan beam projector.')
parser.add_argument('--det_dist', required=False, type=int, default=0, help='distance of fan beam detector.')
parser.add_argument('--beam', required=False, type=str, default='parallel', help='parallel: parallel beam; fan: fan beam.')
parser.add_argument('--net', required=False, type=str, default='GD', help='GD: Unrolled Gradiant Descent; NN: Neumann Network')
parser.add_argument('--rate', type=int, default=8, help='undersample rate')
parser.add_argument('--load', dest='load', type=int, default=-1, help='Load model from a .pth file by epoch #')
args = parser.parse_args()


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device {device}.')

dataset = dset.ImageFolder(root=args.datadir,
                            transform=transforms.Compose([  # to 0~1
                                transforms.Resize((args.size,args.size)),
                                transforms.Grayscale(),
                                transforms.ToTensor(),
#                                 transforms.Normalize((0.5,), (0.5,))
                            ]))
assert dataset
print(f"Dataset contains {len(dataset)} images.")
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=args.bs, num_workers=4)


try:
    if not os.path.exists(os.path.join(args.ckptdir, 'ckpt')):
        os.makedirs(os.path.join(args.ckptdir, 'ckpt'))
        print('Created checkpoint directory')
except OSError:
    pass


args.det_size = args.size if args.det_size == None else args.det_size


try:
    if args.net == 'GD':
        m = GradientDescentNet(args=args, dataloader=dataloader, device=device)
    elif args.net == 'NN':
        m = NeumannNet(args=args, dataloader=dataloader, device=device)
    m.train()
except KeyboardInterrupt:
    print('Interrupted')
    try:
        sys.exit(0)
    except SystemExit:
        os._exit(0)