def main():
parser = argparse.ArgumentParser(
description="calculate class probabilities with VoxResNet")
parser.add_argument("--input_file",
"-i",
type=str,
help="input json file of test dataset")
parser.add_argument("--output_suffix",
"-o",
type=str,
help="result of the segmentation")
parser.add_argument(
"--model",
"-m",
type=str,
help="a file containing parameters of trained VoxResNet")
parser.add_argument(
"--input_shape",
type=int,
nargs="*",
action="store",
default=[80, 80, 80],
help="input patch shape of VoxResNet, default=[80, 80, 80]")
parser.add_argument(
"--output_shape",
type=int,
nargs="*",
action="store",
default=[60, 60, 60],
help="output patch shape of VoxResNet, default=[60, 60, 60]")
parser.add_argument("--gpu",
"-g",
default=-1,
type=int,
help="negative value indicates no gpu, default=-1")
parser.add_argument("--n_tiles",
type=int,
nargs="*",
action="store",
default=[5, 5, 5],
help="number of tiles along each axis")
args = parser.parse_args()
print(args)
with open(args.input_file) as f:
dataset = json.load(f)
test_df = pd.DataFrame(dataset["data"])
vrn = VoxResNet(dataset["in_channels"], dataset["n_classes"])
chainer.serializers.load_npz(args.model, vrn)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
vrn.to_gpu()
for image_path, subject in zip(test_df["image"], test_df["subject"]):
image, affine = load_nifti(image_path, with_affine=True)
output = feedforward(vrn, image, args.input_shape, args.output_shape,
args.n_tiles, dataset["n_classes"])
output /= np.sum(output, axis=0, keepdims=True)
nib.save(
nib.Nifti1Image(np.float32(output).transpose(1, 2, 3, 0), affine),
os.path.join(os.path.dirname(image_path),
subject + args.output_suffix))
def main():
parser = argparse.ArgumentParser(description="segment with VoxResNet")
parser.add_argument("--input_file",
"-i",
type=str,
help="input json file of test dataset")
parser.add_argument(
"--output_suffix",
"-o",
type=str,
default="_segTRI_predict.nii.gz",
help="result of the segmentation, default=_segTRI_predict.nii.gz")
parser.add_argument(
"--model",
"-m",
type=str,
help="a file containing parameters of trained VoxResNet")
parser.add_argument(
"--shape",
type=int,
nargs="*",
action="store",
default=[80, 80, 80],
help="input patch shape of VoxResNet, default=[80, 80, 80]")
parser.add_argument("--gpu",
"-g",
default=-1,
type=int,
help="negative value indicates no gpu, default=-1")
args = parser.parse_args()
print(args)
with open(args.input_file) as f:
dataset = json.load(f)
test_df = pd.DataFrame(dataset["data"])
vrn = VoxResNet(dataset["in_channels"], dataset["n_classes"])
chainer.serializers.load_npz(args.model, vrn)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
vrn.to_gpu()
xp = chainer.cuda.cupy
else:
xp = np
for image_path, subject in zip(test_df["image"], test_df["subject"]):
image, affine = load_nifti(image_path, with_affine=True)
image = image.transpose(3, 0, 1, 2)
slices = [[], [], []]
for img_len, patch_len, slices_ in zip(image.shape[1:], args.shape,
slices):
assert img_len > patch_len, (img_len, patch_len)
stride = int((img_len - patch_len) / int(img_len / patch_len))
for i in range(int(img_len / patch_len)):
slices_.append(slice(i * stride, i * stride + patch_len))
slices_.append(slice(img_len - patch_len, img_len))
output = np.zeros((dataset["n_classes"], ) + image.shape[1:])
for xslice, yslice, zslice in itertools.product(*slices):
patch = image[slice(None), xslice, yslice, zslice]
patch = np.expand_dims(patch, 0)
x = xp.asarray(patch)
output[slice(None), xslice, yslice,
zslice] += chainer.cuda.to_cpu(vrn(x).data[0])
y = np.argmax(output, axis=0)
nib.save(
nib.Nifti1Image(np.int32(y), affine),
os.path.join(os.path.dirname(image_path),
subject + args.output_suffix))