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- import tensorflow as tf
- import os
- from model_net_v3 import Manifold_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, 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=['5'], help='number of network iterations')
- parser.add_argument('--acc', metavar='int', nargs=1, default=['8'], help='accelerate rate')
- parser.add_argument('--mask_pattern', metavar='str', nargs=1, default=['cartesian'], help='mask pattern: cartesian, radial, spiral, vsita')
- parser.add_argument('--net', metavar='str', nargs=1, default=['Manifold_Net'], help='Manifold_Net')
- parser.add_argument('--weight', metavar='str', nargs=1, default=['models/stable/2021-02-28T13-44-00_Manifold_Net_v3_correct_dc_v1_d3c5_acc_8_lr_0.001_N_factor_1.05_rank_17_cartesian/epoch-60/ckpt'], help='modeldir in ./models')
- parser.add_argument('--gpu', metavar='int', nargs=1, default=['2'], help='GPU No.')
- parser.add_argument('--data', metavar='str', nargs=1, default=['DYNAMIC_V2'], help='dataset name')
- parser.add_argument('--learnedSVT', metavar='bool', nargs=1, default=['True'], help='Learned SVT threshold or not')
- parser.add_argument('--SVT_favtor', metavar='float', nargs=1, default=['1.05'], help='SVT factor')
- 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])
- mask_pattern = args.mask_pattern[0]
- net_name = args.net[0]
- weight_file = args.weight[0]
- learnedSVT = bool(args.learnedSVT[0])
- N_factor = float(args.SVT_favtor[0])
-
- print('network: ', net_name)
- print('acc: ', acc)
- print('load weight file from: ', weight_file)
-
-
- result_dir = os.path.join('results/stable', weight_file.split('/')[2])
- if not os.path.isdir(result_dir):
- os.makedirs(result_dir)
-
- logdir = './logs'
- TIMESTAMP = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
- summary_writer = tf.summary.create_file_writer(os.path.join(logdir, mode, TIMESTAMP + net_name + str(acc) + '/'))
-
- # 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/wenqihuang/LplusSNet/mask_newdata/'+mask_pattern + '_' + mask_size + '_acc8.mat')['mask']
- elif acc == 10:
- mask = scio.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/cartesian_' + mask_size + '_acs4_acc10.mat')['mask']
- elif acc == 12:
- mask = scio.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/'+mask_pattern + '_' + mask_size + '_acc12.mat')['mask']
- """
- if acc == 8:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_8.mat')['mask']
- elif acc == 10:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_10.mat')['mask']
- elif acc == 12:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_12.mat')['mask']
- elif acc == 16:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_16.mat')['mask']
- elif acc == 20:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_20.mat')['mask']
- elif acc == 24:
- mask = mat73.loadmat('/data1/wenqihuang/LplusSNet/mask_newdata/vista_' + mask_size + '_acc_24.mat')['mask']
- """
-
- mask = tf.cast(tf.constant(mask), tf.complex64)
-
- # prepare dataset
- dataset = get_dataset(mode, dataset_name, batch_size, shuffle=False)
-
- # initialize network
- if net_name == 'Manifold_Net':
- net = Manifold_Net(mask, niter, learnedSVT, N_factor)
-
-
-
- net.load_weights(weight_file)
-
- # Iterate over epochs.
- for i, sample in enumerate(dataset):
- # forward
-
- k0 = None
- csm = None
- #with tf.GradientTape() as tape:
- if multi_coil:
- k0, label, csm = sample
- else:
- k0, label = sample
- label_abs = tf.abs(label)
-
- k0 = k0 * mask
-
-
- t0 = time.time()
- recon = net(k0, csm)
- t1 = time.time()
-
- recon_abs = tf.abs(recon)
-
- loss_total = mse(recon, label)
-
- tf.print(i, 'mse =', loss_total.numpy(), 'time = ', t1-t0)
-
- result_file = os.path.join(result_dir, 'recon_'+str(i+1)+'.mat')
-
- datadict = {'recon': np.squeeze(tf.transpose(recon, [0,2,3,1]).numpy())}
- scio.savemat(result_file, datadict)
-
- # record gif
- 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('convin-'+str(i+1), combine_video, step=1, fps=10)
-
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