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- import tensorflow as tf
- import os
- from model import S_Net, SLR_Net
- from dataset_tfrecord import get_dataset
- import argparse
- 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, loss_function_ISTA
-
-
- #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=['12'], help='accelerate rate')
- parser.add_argument('--net', metavar='str', nargs=1, default=['SLRNET'], help='SLR Net or S Net')
- parser.add_argument('--gpu', metavar='int', nargs=1, default=['0'], help='GPU No.')
- parser.add_argument('--data', metavar='str', nargs=1, default=['DYNAMIC_V2_MULTICOIL'], help='dataset name')
- 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) + '_lr_' + str(learning_rate)
- summary_writer = tf.summary.create_file_writer(os.path.join(logdir, mode, model_id + '/'))
-
- modeldir = os.path.join('models/stable/', 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'
- elif dataset_name == 'FLOW':
- multi_coil = False
- mask_size = '20_180_180'
-
- """
- if acc == 8:
- mask = scio.loadmat('/data1/ziwenke/SLRNet/mask_newdata/cartesian_' + mask_size + '_acs4_acc8.mat')['mask']
- elif acc == 10:
- mask = scio.loadmat('/data1/ziwenke/SLRNet/mask_newdata/cartesian_' + mask_size + '_acs4_acc10.mat')['mask']
- elif acc == 12:
- mask = scio.loadmat('/data1/ziwenke/SLRNet/mask_newdata/cartesian_' + mask_size + '_acs4_acc12.mat')['mask']
- """
- if acc == 8:
- mask = mat73.loadmat('/data1/ziwenke/SLRNet/mask_newdata/vista_' + mask_size + '_acc_8.mat')['mask']
- elif acc == 10:
- mask = mat73.loadmat('/data1/ziwenke/SLRNet/mask_newdata/vista_' + mask_size + '_acc_10.mat')['mask']
- elif acc == 12:
- mask = mat73.loadmat('/data1/ziwenke/SLRNet/mask_newdata/vista_' + mask_size + '_acc_12.mat')['mask']
-
-
- mask = tf.cast(tf.constant(mask), tf.complex64)
-
- # prepare dataset
- dataset = get_dataset(mode, dataset_name, batch_size, shuffle=True, full=True)
- #dataset = get_dataset('test', dataset_name, batch_size, shuffle=True, full=True)
- tf.print('dataset loaded.')
-
- # initialize network
- if net_name == 'SLRNET':
- net = SLR_Net(mask, niter, learnedSVT)
-
-
- 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()
- k0 = None
- 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
-
- recon, X_SYM = net(k0, csm)
- recon_abs = tf.abs(recon)
-
- loss = loss_function_ISTA(recon, label, X_SYM, niter)
- loss_mse = mse(recon, label)
-
- # 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():
- combine_video = tf.concat([label_abs[0:1,:,:,:], 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(), loss_mse.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)#(total_step / decay_steps)
- optimizer = tf.optimizers.Adam(learning_rate)
-
- # save model each epoch
- #if epoch in [0, num_epoch-1, num_epoch]:
- 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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