def get_batch_transform_displacement_map(displacement, interpolation_order = 3):
"""
Apply a displacement map using elasticdeform library.
"""
fn = lambda x: (etf.deform_grid(x[0], x[1], order = interpolation_order, axis = (1, 2)), x[1])
return lambda x: tf.map_fn(fn, elems = (x, displacement))[0]
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') or hasattr(tf, 'py_function')):
raise unittest.SkipTest("TensorFlow 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 tensorflow output
if hasattr(tf, 'py_func'):
# TensorFlow 1
# build tensorflow graph
X = tf.Variable(X_val)
dX_deformed = tf.Variable(dX_deformed_val)
X_deformed = etf.deform_grid(X, displacement, order=order, crop=crop, mode=mode, axis=axis)
[dX] = tf.gradients(X_deformed, X, dX_deformed)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
X_deformed_val, dX_val = sess.run([X_deformed, dX])
X_deformed = X_deformed_val
dX = dX_val
else:
# TensorFlow 2
X = tf.Variable(X_val)
dX_deformed = tf.Variable(dX_deformed_val)
with tf.GradientTape() as g:
g.watch(X)
X_deformed = etf.deform_grid(X, displacement, order=order, crop=crop, mode=mode, axis=axis)
dX = g.gradient(X_deformed, X, dX_deformed)
np.testing.assert_almost_equal(X_deformed_ref, X_deformed)
np.testing.assert_almost_equal(dX_ref, dX)
def get_batch_transform_displacement_rotation_map(displacement, interpolation_order = 3):
"""
Apply a displacement map using elasticdeform library.
"""
fn = lambda x: (etf.deform_grid(x[0], x[1], order = interpolation_order,rotate=np.random.uniform(low=-45, high=45, size=(1)), axis = (1, 2)), x[1])
return lambda x: tf.map_fn(fn, elems = (x, displacement))[0]
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)
