def run_comparison_torch(self, shape, points, order=3, sigma=25, crop=None, mode='constant', axis=None):
if torch is None:
raise unittest.SkipTest("PyTorch was not loaded.")
# generate random displacement vector
displacement = np.random.randn(len(shape) if axis is None else len(axis), *points) * sigma
# generate random data
X_val = np.random.rand(*shape)
# compute forward reference value
X_deformed_ref = elasticdeform.deform_grid(X_val, displacement, order=order, crop=crop, mode=mode, axis=axis)
# generate gradient
dX_deformed_val = np.random.rand(*X_deformed_ref.shape)
# compute backward reference value
dX_ref = elasticdeform.deform_grid_gradient(dX_deformed_val, displacement, order=order, crop=crop, mode=mode, axis=axis, X_shape=shape)
# compute PyTorch output
X = torch.tensor(X_val, requires_grad=True)
displacement = torch.tensor(displacement)
dX_deformed = torch.tensor(dX_deformed_val)
X_deformed = etorch.deform_grid(X, displacement, order=order, crop=crop, mode=mode, axis=axis)
X_deformed.backward(dX_deformed)
dX = X.grad
# convert back to numpy
X_deformed = X_deformed.detach().numpy()
dX = dX.detach().numpy()
np.testing.assert_almost_equal(X_deformed_ref, X_deformed)
np.testing.assert_almost_equal(dX_ref, dX)
def grad(*dys_disp_xs):
dys = dys_disp_xs[:len(xs)]
displacement = dys_disp_xs[len(xs)]
X_shape = [x.shape for x in dys_disp_xs[len(xs) + 1:]]
dXs = elasticdeform.deform_grid_gradient(list(dys),
displacement,
*args,
X_shape=X_shape,
**kwargs)
return [numpy.nan * displacement] + dXs
def run_comparison_tensorflow(self,
shape,
points,
order=3,
sigma=25,
crop=None,
mode='constant',
axis=None):
if tf is None or not hasattr(tf, 'py_func'):
raise unittest.SkipTest("TensorFlow 1 was not loaded.")
# generate random displacement vector
displacement = np.random.randn(
len(shape) if axis is None else len(axis), *points) * sigma
# generate random data
X_val = np.random.rand(*shape)
# compute forward reference value
X_deformed_ref = elasticdeform.deform_grid(X_val,
displacement,
order=order,
crop=crop,
mode=mode,
axis=axis)
# generate gradient
dY_val = np.random.rand(*X_deformed_ref.shape)
# compute backward reference value
dX_ref = elasticdeform.deform_grid_gradient(dY_val,
displacement,
order=order,
crop=crop,
mode=mode,
axis=axis,
X_shape=shape)
# build tensorflow graph
X = tf.Variable(X_val)
dY = tf.Variable(dY_val)
X_deformed = etf.deform_grid(X,
displacement,
order=order,
crop=crop,
mode=mode,
axis=axis)
[dX] = tf.gradients(X_deformed, X, dY)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
X_deformed_val, dX_val = sess.run([X_deformed, dX])
np.testing.assert_almost_equal(X_deformed_ref, X_deformed_val)
np.testing.assert_almost_equal(dX_ref, dX_val)
def backward(ctx, *dys):
displacement, = ctx.saved_tensors
deform_args = ctx.deform_args
deform_kwargs = ctx.deform_kwargs
x_shapes = ctx.x_shapes
dys_numpy = [dy.detach().cpu().numpy() for dy in dys]
displacement = displacement.detach().cpu().numpy()
dxs = elasticdeform.deform_grid_gradient(dys_numpy, displacement,
*deform_args, X_shape=x_shapes, **deform_kwargs)
return (None, None, None) + tuple(torch.tensor(dx, device=dy.device) for dx, dy in zip(dxs, dys))
def deform_grid_gradient_c(X_in,
displacement,
order=3,
mode='constant',
cval=0.0,
crop=None,
prefilter=True,
axis=None,
X_shape=None):
return elasticdeform.deform_grid_gradient(X_in, displacement, order, mode,
cval, crop, prefilter, axis,
X_shape)
def grad(*dys_disp_xs):
dys = list(dys_disp_xs[:len(xs)])
displacement = dys_disp_xs[len(xs)]
X_shape = [x.shape for x in dys_disp_xs[len(xs) + 1:]]
if not use_tf_v1:
dys = [dy.numpy() for dy in dys]
displacement = displacement.numpy()
dXs = elasticdeform.deform_grid_gradient(dys,
displacement,
*args,
X_shape=X_shape,
**kwargs)
return [numpy.nan * displacement] + dXs
def run_comparison_tensorflow_multi(self, shape, points, order=3, sigma=25, crop=None, mode='constant', axis=None):
if tf is None or not hasattr(tf, 'py_function') or hasattr(tf, 'py_func'):
raise unittest.SkipTest("TensorFlow 2 was not loaded.")
# generate random displacement vector
displacement = np.random.randn(len(shape) if axis is None else len(axis), *points) * sigma
# generate random data
X_val = np.random.rand(*shape)
# generate more random data
Y_val = np.random.rand(*shape)
# compute forward reference value
X_deformed_ref, Y_deformed_ref = elasticdeform.deform_grid([X_val, Y_val],
displacement, order=order, crop=crop, mode=mode, axis=axis)
# generate gradient
dX_deformed_val = np.random.rand(*X_deformed_ref.shape)
dY_deformed_val = np.random.rand(*Y_deformed_ref.shape)
# compute backward reference value
dX_ref, dY_ref = elasticdeform.deform_grid_gradient([dX_deformed_val, dY_deformed_val],
displacement, order=order, crop=crop, mode=mode, axis=axis, X_shape=[shape, shape])
# compute tensorflow output
X = tf.Variable(X_val)
Y = tf.Variable(Y_val)
dX_deformed = tf.Variable(dX_deformed_val)
dY_deformed = tf.Variable(dY_deformed_val)
with tf.GradientTape(persistent=True) as g:
g.watch(X)
g.watch(Y)
X_deformed, Y_deformed = etf.deform_grid([X, Y], displacement, order=order, crop=crop, mode=mode, axis=axis)
dX = g.gradient(X_deformed, X, dX_deformed)
dY = g.gradient(Y_deformed, Y, dY_deformed)
np.testing.assert_almost_equal(X_deformed_ref, X_deformed)
np.testing.assert_almost_equal(Y_deformed_ref, Y_deformed)
np.testing.assert_almost_equal(dX_ref, dX)
np.testing.assert_almost_equal(dY_ref, dY)
