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- import tensorflow as tf
- import os
- from model import LplusS_Net, S_Net
- from dataset 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, mse, tempfft
-
-
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('--mode', metavar='str', nargs=1, default=['test'], help='training or test')
- parser.add_argument('--batch_size', metavar='int', nargs=1, default=['1'], help='batch size')
- parser.add_argument('--niter', metavar='int', nargs=1, default=['10'], help='number of network iterations')
- parser.add_argument('--acc', metavar='int', nargs=1, default=['4'], help='accelerate rate')
- parser.add_argument('--net', metavar='str', nargs=1, default=['LSNet'], help='L+S Net or S Net')
- parser.add_argument('--weight', metavar='str', nargs=1, default=['models/stable/2020-09-02T11-38-47LSNET_DYNAMIC_V28_learnSVT/epoch-50/ckpt'], help='modeldir in ./models')
- parser.add_argument('--gpu', metavar='int', nargs=1, default=['6'], 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)
-
- dataset_name = args.data[0].upper()
- mode = args.mode[0]
- batch_size = int(args.batch_size[0])
- niter = int(args.niter[0])
- acc = int(args.acc[0])
- net_name = args.net[0].upper()
- weight_file = args.weight[0]
- learnedSVT = bool(args.learnedSVT[0])
-
- print('network: ', net_name)
- print('acc: ', acc)
- print('load weight file from: ', weight_file)
-
-
- result_dir = os.path.join('results/prospective', weight_file.split('/')[2] + net_name + str(acc))
- if not os.path.isdir(result_dir):
- os.makedirs(result_dir)
-
-
- for i in range(2,8):
- k0 = mat73.loadmat('data/prospective/ku'+str(i)+'.mat')['ku']
- csm = mat73.loadmat('data/prospective/csm'+str(i)+'.mat')['csm']
-
- k0 = tf.convert_to_tensor(k0, dtype=tf.complex64)
- csm = tf.convert_to_tensor(csm, dtype=tf.complex64)
- csm = tf.expand_dims(csm, 2)
-
- k0 = tf.expand_dims(k0, 0) #batch
- csm = tf.expand_dims(csm, 0) #batch
-
- k0 = tf.transpose(k0, [0,4,3,1,2])
- csm = tf.transpose(csm, [0,4,3,1,2])
-
- mask = tf.cast(tf.abs(k0) > 0, tf.complex64)
-
- # initialize network
- net = LplusS_Net(mask, niter, learnedSVT)
- net.load_weights(weight_file)
-
-
- t0 = time.time()
- L_recon, S_recon, LSrecon = net(k0, csm)
- t1 = time.time()
- recon = L_recon + S_recon
-
- L_recon_abs = tf.abs(L_recon)
- S_recon_abs = tf.abs(S_recon)
- recon_abs = tf.abs(LSrecon)
-
- result_file = os.path.join(result_dir, 'recon_'+str(i)+'.mat')
- datadict = {
- 'recon': np.squeeze(tf.transpose(LSrecon, [0,2,3,1]).numpy()),
- 'L':np.squeeze(tf.transpose(L_recon, [0,2,3,1]).numpy()),
- 'S':np.squeeze(tf.transpose(S_recon, [0,2,3,1]).numpy())
- }
- scio.savemat(result_file, datadict)
-
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