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train.py
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import os
import argparse
import json
import torch.utils.data
from torch.utils.data import DataLoader
from tqdm import tqdm
import numpy as np
import torch.backends.cudnn as cudnn
from torchvision.utils import save_image
from utils import prepare_sub_folder
from datasets import get_datasets
from models import create_model
import scipy.io as sio
import csv
import pdb
parser = argparse.ArgumentParser(description='DuDoRNet')
# model name
parser.add_argument('--experiment_name', type=str, default='train_DRDN_1Do_1pT1', help='give a experiment name before training')
parser.add_argument('--model_type', type=str, default='model_recurrent_dual', help='model type') # model_recurrent_dual / model_recurrent_single / model_cascade / model_cnn
parser.add_argument('--resume', type=str, default=None, help='Filename of the checkpoint to resume')
# dataset
parser.add_argument('--data_root', type=str, default='../Data/PROC/', help='data root folder')
parser.add_argument('--protocol_ref', type=str, default='T1', help='prior modality dataset name') # T1 / T2 / FLAIR
parser.add_argument('--protocol_tag', type=str, default='T2', help='recon modality dataset name') # T1 / T2 / FLAIR
parser.add_argument('--dataset', type=str, default='Cartesian', help='dataset name') # Cartesian / Radial / Spiral
# model architectures
parser.add_argument('--net_G', type=str, default='DRDN', help='generator network') # DRDN / SCNN
parser.add_argument('--n_recurrent', type=int, default=5, help='Number of reccurent block in model')
parser.add_argument('--use_prior', default=False, action='store_true', help='use prior') # True / False
# loss options
parser.add_argument('--wr_L1', type=float, default=1, help='weight for reconstruction L1 loss')
# training options
parser.add_argument('--n_epochs', type=int, default=1000, help='number of epoch')
parser.add_argument('--batch_size', type=int, default=3, help='training batch size')
# evaluation options
parser.add_argument('--eval_epochs', type=int, default=4, help='evaluation epochs')
parser.add_argument('--save_epochs', type=int, default=4, help='save evaluation for every number of epochs')
parser.add_argument('--center_fractions', type=float, default=1.0/8.0, help='Cartesian: cernter fraction')
parser.add_argument('--accelerations', type=float, default=5.0, help='Cartesian: acceleration rate')
parser.add_argument('--n_lines', type=float, default=np.round(256 * 0.16), help='Radial: number of radial lines')
parser.add_argument('--n_interleaves', type=float, default=11, help='Spiral: number of interleaves')
# optimizer
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for ADAM')
parser.add_argument('--beta2', type=float, default=0.999, help='beta2 for ADAM')
parser.add_argument('--weight_decay', type=float, default=0, help='weight decay')
# learning rate policy
parser.add_argument('--lr_policy', type=str, default='step', help='learning rate decay policy')
parser.add_argument('--step_size', type=int, default=1000, help='step size for step scheduler')
parser.add_argument('--gamma', type=float, default=0.5, help='decay ratio for step scheduler')
# logger options
parser.add_argument('--snapshot_epochs', type=int, default=10, help='save model for every number of epochs')
parser.add_argument('--log_freq', type=int, default=100, help='save model for every number of epochs')
parser.add_argument('--output_path', default='./', type=str, help='Output path.')
# other
parser.add_argument('--num_workers', type=int, default=8, help='number of threads to load data')
parser.add_argument('--gpu_ids', type=int, nargs='+', default=[0], help='list of gpu ids')
opts = parser.parse_args()
options_str = json.dumps(opts.__dict__, indent=4, sort_keys=False)
print("------------------- Options -------------------")
print(options_str[2:-2])
print("-----------------------------------------------")
cudnn.benchmark = True
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = create_model(opts)
model.setgpu(opts.gpu_ids)
num_param = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('Number of parameters: {} \n'.format(num_param))
if opts.resume is None:
model.initialize()
ep0 = -1
total_iter = 0
else:
ep0, total_iter = model.resume(opts.resume)
model.set_scheduler(opts, ep0)
ep0 += 1
print('Start training at epoch {} \n'.format(ep0))
# select dataset
train_set, val_set, test_set = get_datasets(opts)
train_loader = DataLoader(dataset=train_set, num_workers=opts.num_workers, batch_size=opts.batch_size, shuffle=True)
val_loader = DataLoader(dataset=val_set, num_workers=opts.num_workers, batch_size=1, shuffle=False)
test_loader = DataLoader(dataset=test_set, num_workers=opts.num_workers, batch_size=1, shuffle=False)
# Setup directories
output_directory = os.path.join(opts.output_path, 'outputs', opts.experiment_name)
checkpoint_directory, image_directory = prepare_sub_folder(output_directory)
with open(os.path.join(output_directory, 'options.json'), 'w') as f:
f.write(options_str)
with open(os.path.join(output_directory, 'train_loss.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(model.loss_names)
# training loop
for epoch in range(ep0, opts.n_epochs + 1):
train_bar = tqdm(train_loader)
model.train()
model.set_epoch(epoch)
for it, data in enumerate(train_bar):
total_iter += 1
model.set_input(data)
model.optimize()
train_bar.set_description(desc='[Epoch {}]'.format(epoch) + model.loss_summary)
if it % opts.log_freq == 0:
with open(os.path.join(output_directory, 'train_loss.csv'), 'a') as f:
writer = csv.writer(f)
writer.writerow(model.get_current_losses().values())
model.update_learning_rate()
# save checkpoint
if (epoch+1) % opts.snapshot_epochs == 0:
checkpoint_name = os.path.join(checkpoint_directory, 'model_{}.pt'.format(epoch))
model.save(checkpoint_name, epoch, total_iter)
# evaluation
print('Validation Evaluation ......')
if (epoch+1) % opts.eval_epochs == 0:
pred = os.path.join(image_directory, 'pred_{:03d}.png'.format(epoch))
gt = os.path.join(image_directory, 'gt_{:03d}.png'.format(epoch))
input_sub = os.path.join(image_directory, 'input_{:03d}.png'.format(epoch))
if opts.wr_L1 > 0:
print(model.recon.detach().shape)
vis_pred = (model.recon.detach()[:, 0:1, :, :] ** 2 + model.recon.detach()[:, 1:2, :, :] ** 2).sqrt()
save_image(vis_pred, pred, normalize=True, scale_each=True, padding=5)
vis_gt = (model.tag_image_full.detach()[:, 0:1, :, :] ** 2 + model.tag_image_full.detach()[:, 1:2, :, :] ** 2).sqrt()
save_image(vis_gt, gt, normalize=True, scale_each=True, padding=5)
vis_input = (model.tag_image_sub.detach()[:, 0:1, :, :] ** 2 + model.tag_image_sub.detach()[:, 1:2, :, :] ** 2).sqrt()
save_image(vis_input, input_sub, normalize=True, scale_each=True, padding=5)
model.eval()
with torch.no_grad():
model.evaluate(val_loader)
with open(os.path.join(output_directory, 'metrics.csv'), 'a') as f:
writer = csv.writer(f)
writer.writerow([epoch, model.psnr_recon, model.ssim_recon])
if (epoch+1) % opts.save_epochs == 0:
sio.savemat(os.path.join(image_directory, 'eval.mat'), model.results)