def test_regular_grids(self):
out = _create_affine_features([3, 3], [2])
expected = [
np.array([0., 0., 0., 1., 1., 1., 2., 2., 2.], dtype=np.float32),
np.array([0., 1., 2., 0., 1., 2., 0., 1., 2.], dtype=np.float32),
np.array([1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=np.float32)]
self.assertAllClose(expected, out)
def layer_op(self,
fixed_image,
moving_image,
base_grid=None,
is_training=True,
**unused_kwargs):
"""
:param fixed_image:
:param moving_image:
:param base_grid:
:param is_training:
:return: estimated dense displacement fields
"""
spatial_rank = infer_spatial_rank(fixed_image)
spatial_shape = fixed_image.get_shape().as_list()[1:-1]
check_spatial_dims(fixed_image, lambda x: x % 16 == 0)
# resize the moving image to match the fixed
moving_image = Resize(spatial_shape)(moving_image)
img = tf.concat([moving_image, fixed_image], axis=-1)
down_res_0, conv_0_0, _ = \
DownRes(self.fea[0], kernel_size=7, **self.down_res_param)(img, is_training)
down_res_1, conv_0_1, _ = \
DownRes(self.fea[1], **self.down_res_param)(down_res_0, is_training)
down_res_2, conv_0_2, _ = \
DownRes(self.fea[2], **self.down_res_param)(down_res_1, is_training)
down_res_3, conv_0_3, _ = \
DownRes(self.fea[3], **self.down_res_param)(down_res_2, is_training)
conv_4 = Conv(n_output_chns=self.fea[4],
kernel_size=self.k_conv,
**self.down_res_param)(down_res_3, is_training)
up_res_0 = UpRes(self.fea[3], **self.up_res_param)(conv_4, conv_0_3,
is_training)
up_res_1 = UpRes(self.fea[2], **self.up_res_param)(up_res_0, conv_0_2,
is_training)
up_res_2 = UpRes(self.fea[1], **self.up_res_param)(up_res_1, conv_0_1,
is_training)
up_res_3 = UpRes(self.fea[0], **self.up_res_param)(up_res_2, conv_0_0,
is_training)
if self.multi_scale_fusion:
output_list = [up_res_3, up_res_2, up_res_1, up_res_0, conv_4]
else:
output_list = [up_res_3]
# converting all output layers to displacement fields
dense_fields = []
for scale_out in output_list:
field = Conv(n_output_chns=spatial_rank,
kernel_size=self.k_conv,
with_bias=True,
with_bn=False,
acti_func=None,
**self.disp_param)(scale_out)
resized_field = Resize(new_size=spatial_shape)(field)
dense_fields.append(resized_field)
if base_grid is None:
# adding a reference grid if it doesn't exist
in_spatial_size = [None] * spatial_rank
base_grid = _create_affine_features(output_shape=spatial_shape,
source_shape=in_spatial_size)
base_grid = np.asarray(base_grid[:-1])
base_grid = np.reshape(base_grid.T,
[-1] + spatial_shape + [spatial_rank])
base_grid = tf.constant(base_grid, dtype=resized_field.dtype)
if self.multi_scale_fusion and len(dense_fields) > 1:
dense_field = tf.reduce_sum(dense_fields, axis=0)
else:
dense_field = dense_fields[0]
# TODO filtering
if self.smoothing_func is not None:
dense_field = self.smoothing_func(dense_field, spatial_rank)
tf.add_to_collection('bending_energy',
_computing_bending_energy(dense_field))
tf.add_to_collection('gradient_norm',
_computing_gradient_norm(dense_field))
dense_field = dense_field + base_grid
return dense_field
def layer_op(self,
fixed_image,
moving_image,
base_grid=None,
is_training=True):
"""
:param fixed_image:
:param moving_image:
:param base_grid:
:param is_training:
:return: estimated dense displacement fields
"""
spatial_rank = infer_spatial_rank(fixed_image)
spatial_shape = fixed_image.get_shape().as_list()[1:-1]
check_spatial_dims(fixed_image, lambda x: x % 16 == 0)
# resize the moving image to match the fixed
moving_image = Resize(spatial_shape)(moving_image)
img = tf.concat([moving_image, fixed_image], axis=-1)
down_res_0, conv_0_0, _ = \
DownRes(self.fea[0], kernel_size=7, **self.down_res_param)(img, is_training)
down_res_1, conv_0_1, _ = \
DownRes(self.fea[1], **self.down_res_param)(down_res_0, is_training)
down_res_2, conv_0_2, _ = \
DownRes(self.fea[2], **self.down_res_param)(down_res_1, is_training)
down_res_3, conv_0_3, _ = \
DownRes(self.fea[3], **self.down_res_param)(down_res_2, is_training)
conv_4 = Conv(n_output_chns=self.fea[4],
kernel_size=self.k_conv,
**self.down_res_param)(down_res_3, is_training)
up_res_0 = UpRes(self.fea[3], **self.up_res_param)(
conv_4, conv_0_3, is_training)
up_res_1 = UpRes(self.fea[2], **self.up_res_param)(
up_res_0, conv_0_2, is_training)
up_res_2 = UpRes(self.fea[1], **self.up_res_param)(
up_res_1, conv_0_1, is_training)
up_res_3 = UpRes(self.fea[0], **self.up_res_param)(
up_res_2, conv_0_0, is_training)
if self.multi_scale_fusion:
output_list = [up_res_3, up_res_2, up_res_1, up_res_0, conv_4]
else:
output_list = [up_res_3]
# converting all output layers to displacement fields
dense_fields = []
for scale_out in output_list:
field = Conv(n_output_chns=spatial_rank,
kernel_size=self.k_conv,
with_bias=True,
with_bn=False,
acti_func=None,
**self.disp_param)(scale_out)
resized_field = Resize(new_size=spatial_shape)(field)
dense_fields.append(resized_field)
if base_grid is None:
# adding a reference grid if it doesn't exist
in_spatial_size = [None] * spatial_rank
base_grid = _create_affine_features(output_shape=spatial_shape,
source_shape=in_spatial_size)
base_grid = np.asarray(base_grid[:-1])
base_grid = np.reshape(
base_grid.T, [-1] + spatial_shape + [spatial_rank])
base_grid = tf.constant(base_grid, dtype=resized_field.dtype)
if self.multi_scale_fusion and len(dense_fields) > 1:
dense_field = tf.reduce_sum(dense_fields, axis=0)
else:
dense_field = dense_fields[0]
# TODO filtering
if self.smoothing_func is not None:
dense_field = self.smoothing_func(dense_field, spatial_rank)
tf.add_to_collection('bending_energy',
_computing_bending_energy(dense_field))
tf.add_to_collection('gradient_norm',
_computing_gradient_norm(dense_field))
dense_field = dense_field + base_grid
return dense_field