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- # python main.py --net='LSNET' --acc=16 --data='DYNAMIC_V2_MULTICOIL' --gpu=2 --batch_size=1 --learnedSVT=True
- # python main.py --net='SNET' --acc=16 --data='DYNAMIC_V2_MULTICOIL' --gpu=2 --batch_size=1
- import os
- import argparse
- import tensorflow as tf
- from model import LplusS_Net, S_Net
- from dataset import get_dataset
- import scipy.io as scio
- import mat73
- import numpy as np
- from datetime import datetime
- import time
- from tools.tools import video_summary
-
- from tools.tools import tempfft, mse
-
-
- #tf.debugging.set_log_device_placement(True)
- #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
- #tf.debugging.set_log_device_placement(True)
-
- if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument('--num_epoch', metavar='int', nargs=1, default=['50'], help='number of epochs')
- parser.add_argument('--batch_size', metavar='int', nargs=1, default=['1'], help='batch size')
- parser.add_argument('--learning_rate', metavar='float', nargs=1, default=['0.001'], help='initial learning rate')
- parser.add_argument('--niter', metavar='int', nargs=1, default=['10'], help='number of network iterations')
- parser.add_argument('--acc', metavar='int', nargs=1, default=['16'], help='accelerate rate')
- parser.add_argument('--net', metavar='str', nargs=1, default=['LSNET'], help='L+S Net or S Net')
- parser.add_argument('--gpu', metavar='int', nargs=1, default=['2'], help='GPU No.')
- parser.add_argument('--data', metavar='str', nargs=1, default=['DYNAMIC_V2_MULTICOIL'], help='dataset name, \
- DYNAMIC_V2_MULTICOIL for multi-coil dataset, DYNAMIC_V2 for single-coil dataset.')
- parser.add_argument('--learnedSVT', metavar='bool', nargs=1, default=['True'], help='Learned SVT threshold or not')
-
-
- args = parser.parse_args()
-
- # GPU setup
- os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu[0]
- GPUs = tf.config.experimental.list_physical_devices('GPU')
- tf.config.experimental.set_memory_growth(GPUs[0], True)
-
- mode = 'training'
- dataset_name = args.data[0].upper()
- batch_size = int(args.batch_size[0])
- num_epoch = int(args.num_epoch[0])
- learning_rate = float(args.learning_rate[0])
-
- acc = int(args.acc[0])
- net_name = args.net[0].upper()
- niter = int(args.niter[0])
- learnedSVT = bool(args.learnedSVT[0])
-
-
- logdir = './logs'
- TIMESTAMP = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
- model_id = TIMESTAMP + net_name + '_' + dataset_name + str(acc)
- summary_writer = tf.summary.create_file_writer(os.path.join(logdir, mode, model_id + '/'))
-
- modeldir = os.path.join('models/', model_id)
- os.makedirs(modeldir)
-
- # prepare undersampling mask
- if dataset_name == 'DYNAMIC_V2':
- multi_coil = False
- mask_size = '18_192_192'
- elif dataset_name == 'DYNAMIC_V2_MULTICOIL':
- multi_coil = True
- mask_size = '18_192_192'
-
- mask_file = 'mask/vista_' + mask_size + '_acc_' + str(acc) + '.mat'
- mask = mat73.loadmat(mask_file)['mask']
- mask = tf.cast(tf.constant(mask), tf.complex64)
-
- # prepare dataset
- dataset = get_dataset(mode, dataset_name, batch_size, shuffle=True)
- tf.print('dataset loaded.')
-
- # initialize network
- if net_name == 'LSNET':
- net = LplusS_Net(mask, niter, learnedSVT)
- elif net_name == 'SNET':
- net = S_Net(mask, niter)
-
- tf.print('network initialized.')
-
- learning_rate_org = learning_rate
- learning_rate_decay = 0.95
-
- optimizer = tf.optimizers.Adam(learning_rate_org)
-
-
- # Iterate over epochs.
- total_step = 0
- param_num = 0
- loss = 0
-
- for epoch in range(num_epoch):
- for step, sample in enumerate(dataset):
-
- # forward
- t0 = time.time()
- csm = None
- with tf.GradientTape() as tape:
- if multi_coil:
- k0, label, csm = sample
- if k0 == None:
- continue
- else:
- k0, label = sample
- if k0.shape[0] < batch_size:
- continue
-
- label_abs = tf.abs(label)
-
- k0 = k0 * mask
-
- if net_name == 'SNET':
- recon = net(k0, csm)
-
- recon_abs = tf.abs(recon)
- loss_mse = mse(recon, label)
- else:
- L_recon, S_recon, LSrecon = net(k0, csm)
- recon = L_recon + S_recon
-
- L_recon_abs = tf.abs(L_recon)
- S_recon_abs = tf.abs(S_recon)
- recon_abs = tf.abs(LSrecon)
- loss_mse = mse(recon, label)
-
- loss = loss_mse #mse ok
-
-
- # backward
- grads = tape.gradient(loss, net.trainable_weights)
- optimizer.apply_gradients(zip(grads, net.trainable_weights))
-
- # record loss
- with summary_writer.as_default():
- tf.summary.scalar('loss/total', loss_mse.numpy(), step=total_step)
-
- # record gif
-
- if step % 20 == 0:
- with summary_writer.as_default():
- if net_name == 'SNET':
- combine_video = tf.concat([label_abs[0:1,:,:,:], recon_abs[0:1,:,:,:]], axis=0).numpy()
- else:
- combine_video = tf.concat([label_abs[0:1,:,:,:], recon_abs[0:1,:,:,:], \
- L_recon_abs[0:1,:,:,:], S_recon_abs[0:1,:,:,:]], axis=0).numpy()
- combine_video = np.expand_dims(combine_video, -1)
- video_summary('result', combine_video, step=total_step, fps=10)
-
- # calculate parameter number
- if total_step == 0:
- param_num = np.sum([np.prod(v.get_shape()) for v in net.trainable_variables])
-
- # log output
- tf.print('Epoch', epoch+1, '/', num_epoch, 'Step', step, 'loss =', loss.numpy(),
- 'time', time.time() - t0, 'lr = ', learning_rate, 'param_num', param_num)
- total_step += 1
-
- # learning rate decay for each epoch
- learning_rate = learning_rate_org * learning_rate_decay ** (epoch + 1)
- optimizer = tf.optimizers.Adam(learning_rate)
-
- # save model each epoch
- if (epoch+1) % 10 == 0:
- model_epoch_dir = os.path.join(modeldir,'epoch-'+str(epoch+1), 'ckpt')
- net.save_weights(model_epoch_dir, save_format='tf')
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