test_data = normalize(test_data, mu, std)
if len(test_data.shape) < 3:
test_data = test_data[np.newaxis, ...]
"""
Load the network
"""
print('[%s] Loading network' % (datetime.datetime.now()))
net = load_net(args.net)
"""
Segmentation
"""
print('[%s] Starting segmentation' % (datetime.datetime.now()))
segmentation = segment(test_data,
net,
args.input_size,
batch_size=args.batch_size,
crf_iterations=args.crf_iterations,
mu=mu,
std=std)
"""
Validate the segmentation
"""
print('[%s] Validating segmentation' % (datetime.datetime.now()))
test_data_labels = read_tif(args.data_labels, dtype='uint8')
test_data_labels = normalize(test_data_labels, 0, 255)
j = jaccard(segmentation, test_data_labels)
d = dice(segmentation, test_data_labels)
a, p, r, f = accuracy_metrics(segmentation, test_data_labels)
print('[%s] RESULTS:' % (datetime.datetime.now()))
print('[%s] Jaccard: %f' % (datetime.datetime.now(), j))
print('[%s] Dice: %f' % (datetime.datetime.now(), d))
lambdas=args.lambdas,
optimizer=optimizer,
scheduler=scheduler,
epochs=args.epochs,
test_freq=args.test_freq,
print_stats=args.print_stats,
log_dir=args.log_dir)
"""
Validate the trained network
"""
test_data = tar_test.data
test_labels = tar_test.labels
print('[%s] Validating the trained network' % (datetime.datetime.now()))
seg_net = net.get_segmentation_net()
segmentation_last_checkpoint = segment(test_data,
seg_net,
args.input_size,
batch_size=args.test_batch_size)
j = jaccard(segmentation_last_checkpoint, test_labels)
d = dice(segmentation_last_checkpoint, test_labels)
print('[%s] Network performance (last checkpoint): Jaccard=%f - Dice=%f' %
(datetime.datetime.now(), j, d))
if args.write_dir is not None:
print('[%s] Writing the output' % (datetime.datetime.now()))
imwrite3D(segmentation_last_checkpoint,
os.path.join(args.write_dir, 'tar_segmentation_last'),
rescale=True)
net = torch.load(os.path.join(args.log_dir, 'best_checkpoint.pytorch'))
seg_net = net.get_segmentation_net()
segmentation_best_checkpoint = segment(test_data,
seg_net,
args.input_size,
input_sizes = (args.input_size_xy, args.input_size_zx, args.input_size_yz)
for i, o in enumerate(orientations):
"""
Load the network
"""
print('[%s] Loading network' % (datetime.datetime.now()))
net = load_net(args.nets[i])
"""
Segmentation
"""
print('[%s] Starting segmentation' % (datetime.datetime.now()))
segmentation_cum += np.transpose(
segment(np.transpose(test_data, o),
net,
input_sizes[i],
batch_size=args.batch_size,
crf_iterations=args.crf_iterations,
mu=mu,
std=std), orientations_inv[i])
segmentation = segmentation_cum / 3
"""
Validate the segmentation
"""
print('[%s] Validating segmentation' % (datetime.datetime.now()))
test_data_labels = read_tif(args.data_labels, dtype='uint8')
test_data_labels = normalize(test_data_labels, 0, 255)
j = jaccard(segmentation, test_data_labels)
d = dice(segmentation, test_data_labels)
a, p, r, f = accuracy_metrics(segmentation, test_data_labels)
print('[%s] RESULTS:' % (datetime.datetime.now()))