data_config = icg.utils.loadYaml(args.data_config, ['data_config'])
global_config = icg.utils.loadYaml(args.global_config, ['global_config'])
# Tensorflow config
tf_config = tf.compat.v1.ConfigProto(log_device_placement=False)
tf_config.gpu_options.allow_growth = global_config['tf_allow_gpu_growth']
# define the output locations
base_name = os.path.basename(args.network_config).split('.')[0]
suffix = base_name + '_' + time.strftime('%Y-%m-%d--%H-%M-%S')
vn.setupLogDirs(suffix, args, checkpoint_config)
# load data
filename_producer = VnMriFilenameProducer(data_config)
data = VnMriReconstructionData(
data_config,
filename_dequeue_op=filename_producer.dequeue_op,
queue_capacity=global_config['data_queue_capacity'])
network_config['sampling_pattern'] = data_config['sampling_pattern']
# Create a queue runner that will run 4 threads in parallel to enqueue examples.
qr_data = tf.train.QueueRunner(data.queue, [data.enqueue_op] *
global_config['data_num_threads'])
# Create a queue runner to produce the filenames
qr_filenames = tf.train.QueueRunner(filename_producer.queue,
[filename_producer.enqueue_op])
# Create a coordinator, launch the queue runner threads.
coord = tf.train.Coordinator()
# define parameters
print(traceback.print_exc())
continue
# extract a few ops and variables to be used in evaluation
u_op = tf.get_collection('u_op')[0]
u_var = tf.get_collection('u_var')
g_var = tf.get_collection('g_var')
c_var = tf.get_collection('c_var')
m_var = tf.get_collection('m_var')
f_var = tf.get_collection('f_var')
# create data object
data = VnMriReconstructionData(data_config,
u_var=u_var,
f_var=f_var,
g_var=g_var,
c_var=c_var,
m_var=m_var,
load_eval_data=False)
# Evaluate the performance
for dataset in eval_datasets:
eval_patients = dataset['eval_patients']
if not os.path.exists(eval_output_dir +
'/%s' % dataset['name']):
os.makedirs(eval_output_dir + '/%s' % dataset['name'])
print(
"Evaluating performance {:s} for {:s}, epoch {:d}".format(
suffix, dataset['name'], epoch))
epoch = vn.utils.loadCheckpoint(sess, ckpt_dir, epoch=epoch)
except Exception as e:
print(traceback.print_exc())
u_op = tf.compat.v1.get_collection('u_op')[0]
u_var = tf.compat.v1.get_collection('u_var')
c_var = tf.compat.v1.get_collection('c_var')
m_var = tf.compat.v1.get_collection('m_var')
f_var = tf.compat.v1.get_collection('f_var')
g_var = tf.compat.v1.get_collection('g_var')
# create data object
data = VnMriReconstructionData(data_config,
u_var=u_var,
f_var=f_var,
c_var=c_var,
m_var=m_var,
g_var=g_var,
load_eval_data=False,
load_target=False)
# run the model
print('start reconstruction')
eval_start_time = time.time()
u_volume = []
path = os.path.expanduser(data_config['base_dir'] + '/' +
data_config['dataset']['name'] + '/')
num_slices = len(
glob.glob(path +
'/%d/rawdata*.mat' % data_config['dataset']['patient']))
except Exception as e:
print(traceback.print_exc())
# extract operators and variables from the graph
u_op = tf.get_collection('u_op')[0]
u_var = tf.get_collection('u_var')
c_var = tf.get_collection('c_var')
m_var = tf.get_collection('m_var')
f_var = tf.get_collection('f_var')
g_var = tf.get_collection('g_var')
# create data object
data = VnMriReconstructionData(data_config,
u_var=u_var,
f_var=f_var,
c_var=c_var,
m_var=m_var,
g_var=g_var,
load_eval_data=False,
load_target=True)
# load data
kspace, coil_sens, x_adj, ref, mask, norm \
= data.get_test_data(data_config['dataset'],
data_config['dataset']['patient'],
data_config['dataset']['slice'])
# compile functions
def val_df(x_adj, kspace, coil_sens, mask, label):
tf_label = tf.placeholder(u_op.dtype)
loss = tf.nn.l2_loss(tf.abs(tf_label - u_op))
grad = tf.gradients(loss, u_var[0])