def _loss_and_grad_on_velocity_field(self, u):
u_field = self.reshape_velocity_field(u)
grad = np.zeros(u_field.shape)
nx, ny, nz = self.node_nums()
assert nx == ny
loss = 0
cnt = 0
outlet_bd = int(np.ceil(self._outlet_range * nx))
eps = 1e-8
outlet_idx = []
for j in range(outlet_bd, ny):
for k in range(nz):
outlet_idx.append((nx - 1, j, k))
for i in range(outlet_bd, nx):
for k in range(nz):
outlet_idx.append((i, ny - 1, k))
for i, j, k in outlet_idx:
cnt += 1
ux, uy, _ = u_field[i, j, k]
angle = np.arctan2(uy, ux)
loss += np.abs(angle - np.pi / 4)
dangle_ux = -uy / (ux**2 + uy**2 + eps)
dangle_uy = ux / (ux**2 + uy**2 + eps)
grad[i, j, k] += ndarray([
np.sign(angle - np.pi / 4) * dangle_ux,
np.sign(angle - np.pi / 4) * dangle_uy, 0
])
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def __init__(self, seed, folder):
np.random.seed(seed)
cell_nums = (64, 48)
E = 100
nu = 0.499
vol_tol = 1e-3
edge_sample_num = 2
EnvBase.__init__(self, cell_nums, E, nu, vol_tol, edge_sample_num,
folder)
# Initialize the parametric shapes.
self._parametric_shape_info = [('bezier', 8), ('bezier', 8),
('bezier', 8)]
# Initialize the node conditions.
self._node_boundary_info = []
inlet_velocity = 10
inlet_range = ndarray([0.45, 0.55])
inlet_lb, inlet_ub = inlet_range * cell_nums[0]
outlet_range = ndarray([0.4, 0.6])
outlet_lb, outlet_ub = outlet_range * cell_nums[1]
for i in range(cell_nums[0] + 1):
if inlet_lb < i < inlet_ub:
self._node_boundary_info.append(((i, 0, 0), 0))
self._node_boundary_info.append(((i, 0, 1), inlet_velocity))
# Initialize the interface.
self._interface_boundary_type = 'free-slip'
# Other data members.
self._inlet_velocity = inlet_velocity
self._inlet_range = inlet_range
self._outlet_range = outlet_range
def __init__(self, nu, scale):
cell_nums = (int(32 * scale), int(24 * scale))
E = 100
vol_tol = 1e-3
edge_sample_num = 2
EnvBase.__init__(self, cell_nums, E, nu, vol_tol, edge_sample_num,
None)
# Initial condition.
control_points_lower = ndarray([[32, 10], [22, 10], [12, 10], [0, 4]
]) * scale
control_points_upper = ndarray([[0, 20], [12, 14], [22, 14], [32, 14]
]) * scale
self._sample = np.concatenate(
[control_points_lower.ravel(),
control_points_upper.ravel()])
# Initialize the parametric shapes.
self._parametric_shape_info = [('bezier', 8), ('bezier', 8)]
# Initialize the node conditions.
self._node_boundary_info = []
inlet_velocity = 1.0
for j in range(cell_nums[1] + 1):
if control_points_lower[3, 1] < j < control_points_upper[0, 1]:
self._node_boundary_info.append(((0, j, 0), inlet_velocity))
self._node_boundary_info.append(((0, j, 1), 0))
# Initialize the interface.
self._interface_boundary_type = 'free-slip'
self._scale = scale
def loss_and_grad(x):
cell.Initialize(E, nu, threshold, edge_sample_num, x)
loss = ndarray(loss_func()).ravel().dot(weight)
grad = np.zeros(4)
for i in range(4):
grad[i] = ndarray(grad_func(i)).ravel().dot(weight)
return loss, grad
def __init__(
self,
cell_nums, # 2D or 3D int array.
E, # Young's modulus.
nu, # Poisson's ratio.
vol_tol, # vol_tol <= mixed cell <= 1 - vol_tol.
edge_sample_num, # The number of samples inside each cell's axis.
folder # The folder that stores temporary files.
):
# The following data members are provided:
# - _cell_nums and _node_nums;
# - _dim;
# - _cell_options;
# - _folder;
# - _parametric_shape_info: a list of (string, int);
# - _node_boundary_info: a list of (int, float) or ((int, int, int), float) (2D) or
# ((int, int, int, int), float) (3D) that describes the boundary conditions.
# - _interface_boundary_type: either 'no-slip' or 'free-slip' (default).
# Sanity check the inputs.
cell_nums = ndarray(cell_nums).astype(np.int32)
assert cell_nums.size in [2, 3]
# The value of E does not quite matter as it simply scale the QP problem by a constant factor.
E = float(E)
assert E > 0
nu = float(nu)
assert 0 < nu < 0.5
vol_tol = float(vol_tol)
assert 0 < vol_tol < 0.5
edge_sample_num = int(edge_sample_num)
assert edge_sample_num > 1
if folder is not None:
folder = Path(folder)
create_folder(folder, exist_ok=True)
# Create data members.
self._cell_nums = np.copy(cell_nums)
self._node_nums = ndarray([n + 1 for n in cell_nums]).astype(np.int32)
self._dim = self._cell_nums.size
self._cell_options = {
'E': E,
'nu': nu,
'vol_tol': vol_tol,
'edge_sample_num': edge_sample_num
}
self._folder = folder
###########################################################################
# Derived classes should implement these data members.
###########################################################################
self._parametric_shape_info = []
self._node_boundary_info = []
self._interface_boundary_type = 'free-slip'
def loss_and_grad(x):
shape.Initialize(cell_nums, x, True)
sdf = ndarray(shape.signed_distances())
loss = sdf_weight.ravel().dot(sdf)
grad = 0
for i in range(nx):
for j in range(ny):
grad += sdf_weight[i, j] * ndarray(shape.signed_distance_gradients((i, j)))
return loss, grad
def _render_customized_2d(self, scene, ax):
nx, ny = self._node_nums
lines = []
for i in range(nx):
for j in range(ny):
v_begin = ndarray([i, j])
v_end = v_begin + ndarray(scene.GetFluidicForceDensity((i, j)))
lines.append((v_begin, v_end))
ax.add_collection(
mc.LineCollection(lines, colors='tab:red', linestyle='-'))
def sample(self):
control_points_lower = ndarray([[34, 10], [22, 10], [12, 10], [
-2, 4
]]) + np.random.normal(size=(4, 2)) * 0.01
control_points_upper = ndarray([[-2, 20], [12, 14], [22, 14], [
34, 14
]]) + np.random.normal(size=(4, 2)) * 0.01
x0 = np.concatenate(
[control_points_lower.ravel(),
control_points_upper.ravel()])
return x0
def __init__(self, seed, folder):
np.random.seed(seed)
cell_nums = (64, 64, 4)
E = 100
nu = 0.499
vol_tol = 1e-2
edge_sample_num = 2
EnvBase.__init__(self, cell_nums, E, nu, vol_tol, edge_sample_num,
folder)
# Initialize the parametric shapes.
self._parametric_shape_info = [('bezier', 11), ('bezier', 11),
('bezier', 11)]
# Initialize the node conditions.
self._node_boundary_info = []
inlet_range = ndarray([0.4, 0.6])
outlet_range = 0.8
cx, cy, _ = self.cell_nums()
assert cx == cy
nx, ny, nz = self.node_nums()
inlet_bd = inlet_range * cx
outlet_bd = outlet_range * cx
inlet_velocity = ndarray([1.0, 0.0])
for j in range(ny):
for k in range(nz):
# Set the inlet at i = 0.
if inlet_bd[0] < j < inlet_bd[1]:
self._node_boundary_info.append(
((0, j, k, 0), inlet_velocity[0]))
self._node_boundary_info.append(((0, j, k, 1), 0))
self._node_boundary_info.append(((0, j, k, 2), 0))
self._node_boundary_info.append(
((j, 0, k, 0), inlet_velocity[1]))
self._node_boundary_info.append(((j, 0, k, 1), 0))
self._node_boundary_info.append(((j, 0, k, 2), 0))
# Set the top and bottom plane.
for i in range(nx):
for j in range(ny):
for k in [0, nz - 1]:
self._node_boundary_info.append(((i, j, k, 2), 0))
# Initialize the interface.
self._interface_boundary_type = 'free-slip'
# Other data members.
self._inlet_range = inlet_range
self._outlet_range = outlet_range
self._inlet_bd = inlet_bd
self._outlet_bd = outlet_bd
self._inlet_velocity = inlet_velocity
def _loss_and_grad_on_velocity_field(self, u):
u_field = self.reshape_velocity_field(u)
grad = np.zeros(u_field.shape)
cnt = 0
loss = 0
for j, (ux, uy) in enumerate(u_field[-1]):
if ux > 0:
cnt += 1
u_diff = ndarray([ux, uy]) - ndarray(
[self._outlet_velocity, 0])
loss += u_diff.dot(u_diff)
grad[-1, j] += 2 * u_diff
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def _loss_and_grad_on_velocity_field(self, u):
u_field = self.reshape_velocity_field(u)
grad = np.zeros(u_field.shape)
nx, ny, nz = self.node_nums()
assert nx == ny
loss = 0
cnt = 0
for i in range(nx):
for j in range(ny):
if self._outlet_bezier.signed_distance((i, j)) > 0:
cnt += 1
uxy = u_field[i, j, nz - 1, :2]
ux_pos = u_field[i + 1, j, nz - 1, :2]
uy_pos = u_field[i, j + 1, nz - 1, :2]
# Compute the curl.
curl = ux_pos[1] - uy_pos[0] - uxy[1] + uxy[0]
loss += (curl - self._desired_omega)**2
# ux_pos[1]
grad[i + 1, j, nz - 1,
1] += 2 * (curl - self._desired_omega)
grad[i, j + 1, nz - 1,
0] += -2 * (curl - self._desired_omega)
grad[i, j, nz - 1, 1] += -2 * (curl - self._desired_omega)
grad[i, j, nz - 1, 0] += 2 * (curl - self._desired_omega)
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def _variables_to_shape_params(self, x):
x = ndarray(x).copy().ravel()
assert x.size == 26
cx, cy, _ = self._cell_nums
assert cx == cy
# Since there are two modes, we have two sets of parameters.
def get_params_and_grads(rotation_angle_idx):
params = np.concatenate([
x[:24] * cx,
ndarray([0.6 * cx, 0.5 * cy, x[rotation_angle_idx]]),
(self._upper * cx).ravel(),
ndarray([0.0, self._dir_offset, 1.0]),
(self._lower * cx).ravel(),
ndarray([0.0, -self._dir_offset, 1.0]),
(self._right * cx).ravel(),
ndarray([self._dir_offset, 0.0, 1.0]),
])
grads = np.zeros((params.size, x.size))
for i in range(24):
grads[i, i] = cx
grads[26, rotation_angle_idx] = 1
grads = ndarray(grads)
return params, grads
params_on, grads_on = get_params_and_grads(24)
params_off, grads_off = get_params_and_grads(25)
return [(params_on, grads_on), (params_off, grads_off)]
def test_shape_composition_2d_single(shape_info, verbose):
np.random.seed(42)
cell_nums = (32, 24)
shape = ShapeComposition2d()
params = []
for name, param in shape_info:
shape.AddParametricShape(name, param.size)
params.append(ndarray(param).ravel())
params = np.concatenate(params)
params += np.random.normal(size=params.size) * 0.01
shape.Initialize(cell_nums, params, True)
if verbose:
visualize_level_set(shape)
# Verify the gradients.
nx = shape.node_num(0)
ny = shape.node_num(1)
sdf_weight = np.random.normal(size=(nx, ny))
def loss_and_grad(x):
shape.Initialize(cell_nums, x, True)
sdf = ndarray(shape.signed_distances())
loss = sdf_weight.ravel().dot(sdf)
grad = 0
for i in range(nx):
for j in range(ny):
grad += sdf_weight[i, j] * ndarray(shape.signed_distance_gradients((i, j)))
return loss, grad
from py_diff_stokes_flow.common.grad_check import check_gradients
return check_gradients(loss_and_grad, params.ravel(), verbose=verbose)
def test_shape_composition_3d(verbose):
return test_shape_composition_3d_single(
[('polar_bezier3',
ndarray([
1, 4, 1, 4, 1, 4, 1, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 1, 4, 1, 4, 1,
4, 1, 5, 5, np.pi / 3
]))], verbose)
def get_params_and_grads(rotation_angle_idx):
params = np.concatenate([
x[:24] * cx,
ndarray([0.6 * cx, 0.5 * cy, x[rotation_angle_idx]]),
(self._upper * cx).ravel(),
ndarray([0.0, self._dir_offset, 1.0]),
(self._lower * cx).ravel(),
ndarray([0.0, -self._dir_offset, 1.0]),
(self._right * cx).ravel(),
ndarray([self._dir_offset, 0.0, 1.0]),
])
grads = np.zeros((params.size, x.size))
for i in range(24):
grads[i, i] = cx
grads[26, rotation_angle_idx] = 1
grads = ndarray(grads)
return params, grads
def _loss_and_grad_on_velocity_field(self, u):
u_field = self.reshape_velocity_field(u)
grad = np.zeros(u_field.shape)
nx, ny, nz = self.node_nums()
loss = 0
cnt = 0
for j in range(ny):
for k in range(nz):
if self._outlet_bd[0, 0] < j < self._outlet_bd[0, 1] or \
self._outlet_bd[1, 0] < j < self._outlet_bd[1, 1]:
cnt += 1
u_diff = u_field[nx - 1, j, k] - ndarray([0.5, 0, 0])
loss += u_diff.dot(u_diff)
grad[nx - 1, j, k] += 2 * u_diff
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def get_bezier(radius):
bezier = ShapeComposition2d()
params = np.concatenate(
[np.full(8, radius) * cx,
ndarray([0.5 * cx, 0.5 * cy, 0])])
bezier.AddParametricShape('polar_bezier', params.size)
cxy = StdIntArray2d((int(cx), int(cy)))
bezier.Initialize(cxy, params, True)
return bezier
def reshape_velocity_field(self, u):
u_array = ndarray(u)
assert len(u_array.shape) in [1, self._dim + 1]
assert u_array.size == np.prod(self._node_nums) * self._dim
if len(u_array.shape) == 1:
return np.copy(u_array).reshape(
tuple(self._node_nums) + (self._dim, ))
else:
return np.copy(u_array).ravel()
def plot_velocity_field(ax, u_field, s, u_max):
x_size = ndarray(np.arange(u_field.shape[0])) / s
y_size = ndarray(np.arange(u_field.shape[1])) / s
X, Y = np.meshgrid(x_size, y_size)
u_norm = np.sqrt(np.sum(u_field**2, axis=(2, )))
strm = ax.streamplot(X,
Y,
u_field[:, :, 0].T,
u_field[:, :, 1].T,
density=(cell_nums[0] / cell_nums[1] * 3, 3),
color=u_norm.T,
norm=matplotlib.colors.Normalize(0, u_max),
arrowstyle='->',
linewidth=1.5,
cmap='coolwarm')
ax.set_xlim([0, cell_nums[0]])
ax.set_ylim([0, cell_nums[1]])
ax.set_xticks([])
ax.set_yticks([])
def _variables_to_shape_params(self, x):
x = ndarray(x).copy().ravel()
assert x.size == 5
cx, cy = self._cell_nums
# Convert x to the shape parameters.
lower = ndarray([
[1, x[4]],
x[2:4],
x[:2],
[0, self._inlet_range[0]],
])
lower[:, 0] *= cx
lower[:, 1] *= cy
upper = ndarray([
[0, self._inlet_range[1]],
[x[0], 1 - x[1]],
[x[2], 1 - x[3]],
[1, 1 - x[4]],
])
upper[:, 0] *= cx
upper[:, 1] *= cy
params = np.concatenate([lower.ravel(), upper.ravel()])
# Jacobian.
J = np.zeros((params.size, x.size))
J[1, 4] = 1
J[2, 2] = 1
J[3, 3] = 1
J[4, 0] = 1
J[5, 1] = 1
J[10, 0] = 1
J[11, 1] = -1
J[12, 2] = 1
J[13, 3] = -1
J[15, 4] = -1
# Scale it by cx and cy.
J[:, 0] *= cx
J[:, 2] *= cx
J[:, 1] *= cy
J[:, 3] *= cy
J[:, 4] *= cy
return ndarray(params).copy(), ndarray(J).copy()
def _variables_to_shape_params(self, x):
x = ndarray(x).copy().ravel()
assert x.size == 32
cx, cy, _ = self._cell_nums
assert cx == cy
params = np.concatenate([
np.full(8, self._inlet_radius),
x,
np.full(8, self._outlet_radius),
ndarray([0.5, 0.5, 0]),
])
params[:-1] *= cx
# Jacobian.
J = np.zeros((params.size, x.size))
for i in range(x.size):
J[8 + i, i] = cx
return ndarray(params).copy(), ndarray(J).copy()
def test_bezier_2d(verbose):
np.random.seed(42)
folder = Path('bezier_2d')
cell_nums = (32, 24)
control_points = ndarray([[32, 12], [22, 6], [12, 18], [0, 12]])
control_points[:, 1] += np.random.normal(size=4)
bezier = Bezier2d()
bezier.Initialize(cell_nums, control_points.ravel(), True)
sdf = ndarray(bezier.signed_distances())
sdf_master = np.load(folder / 'sdf_master.npy')
if np.max(np.abs(sdf - sdf_master)) > 0:
if verbose:
print_error('Incorrect signed distance function.')
return False
if verbose:
visualize_level_set(bezier)
# Verify the gradients.
nx = bezier.node_num(0)
ny = bezier.node_num(1)
sdf_weight = np.random.normal(size=(nx, ny))
def loss_and_grad(x):
bezier = Bezier2d()
bezier.Initialize(cell_nums, x.ravel(), True)
sdf = ndarray(bezier.signed_distances())
loss = sdf_weight.ravel().dot(sdf)
grad = 0
for i in range(nx):
for j in range(ny):
grad += sdf_weight[i, j] * ndarray(
bezier.signed_distance_gradients((i, j)))
return loss, grad
from py_diff_stokes_flow.common.grad_check import check_gradients
return check_gradients(loss_and_grad,
control_points.ravel(),
verbose=verbose)
def test_shape_composition_2d(verbose):
flag = test_shape_composition_2d_single([
( 'bezier',
ndarray([
[32, 10],
[22, 10],
[12, 4],
[0, 4]
])
),
( 'bezier',
ndarray([
[0, 20],
[12, 20],
[22, 14],
[32, 14]
])
),
], verbose)
if not flag: return False
flag = test_shape_composition_2d_single([
( 'plane', ndarray([1, 0.1, -16]) ),
( 'sphere', ndarray([16, 12, 8]) ),
], verbose)
if not flag: return False
flag = test_shape_composition_2d_single([
( 'polar_bezier',
ndarray([
4, 8, 4, 8, 4, 8, 4, 8,
16, 12,
np.pi / 3
])
),
], verbose)
if not flag: return False
return True
def _loss_and_grad_on_velocity_field(self, u):
u_field = self.reshape_velocity_field(u)
grad = np.zeros(u_field.shape)
nx, ny, nz = self.node_nums()
assert nx == ny
loss = 0
cnt = 0
target_velocity = ndarray([self._inlet_velocity[0], self._inlet_velocity[1], 0.])
for j in range(ny):
for k in range(nz):
if j > self._outlet_bd:
cnt += 1
u_diff = u_field[nx - 1, j, k] - target_velocity
loss += u_diff.dot(u_diff)
grad[nx - 1, j, k] += 2 * u_diff
cnt += 1
u_diff = u_field[j, ny - 1, k] - target_velocity
loss += u_diff.dot(u_diff)
grad[j, ny - 1, k] += 2 * u_diff
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def loss_and_grad(u_single, target_field):
u_field = self.reshape_velocity_field(u_single)
loss = 0
grad = np.zeros(u_field.shape)
cnt = 0
for j in range(ny):
for k in range(nz):
if self._boundary_bezier.signed_distance(
(int(nx - 1), int(j), int(k))) < 0:
uxyz = u_field[nx - 1, j, k]
u_diff = uxyz - target_field[j, k]
loss += u_diff.dot(u_diff)
grad[nx - 1, j, k] += 2 * u_diff
cnt += 1
loss /= cnt
grad /= cnt
return loss, ndarray(grad).ravel()
def _variables_to_shape_params(self, x):
x = ndarray(x).copy().ravel()
assert x.size == 5
cx, cy, _ = self._cell_nums
assert cx == cy
lower_left = ndarray([
[self._inlet_range[0], 0],
[x[4], x[0]],
[x[0], x[4]],
[0, self._inlet_range[0]]
])
right = ndarray([
[1., self._outlet_range],
[x[2], x[3]],
[self._inlet_range[1], x[1]],
[self._inlet_range[1], 0]
])
upper = ndarray([
[0, self._inlet_range[1]],
[x[1], self._inlet_range[1]],
[x[3], x[2]],
[self._outlet_range, 1.]
])
lower_left *= cx
right *= cx
upper *= cx
params = np.concatenate([lower_left.ravel(),
[-0.01, -0.01, 1],
right.ravel(),
[0.01, 0, 1],
upper.ravel(),
[0, 0.01, 1],
])
# Jacobian.
J = np.zeros((params.size, x.size))
J[2, 4] = J[3, 0] = 1
J[4, 0] = J[5, 4] = 1
J[13, 2] = J[14, 3] = 1
J[16, 1] = 1
J[24, 1] = J[26, 3] = J[27, 2] = 1
J *= cx
return ndarray(params).copy(), ndarray(J).copy()
def test_cell_2d(verbose):
np.random.seed(42)
cell = Cell2d()
E = 1e5
nu = 0.45
threshold = 1e-3
edge_sample_num = 3
# Consider a line that passes (0.5, 0.5) with a slope between 1/3 and 1.
p = ndarray([0.5, 0.5])
k = np.random.uniform(low=1 / 3, high=1)
# Line equation: (y - p[1]) / (x - p[0]) = k.
# y - p[1] = kx - kp[0].
# kx - y + p[1] - kp[0].
line_eq = ndarray([k, -1, p[1] - k * p[0]])
# Solid area: line_eq >= 0.
# So, the lower part is the solid area.
# This means corner distance from [0, 0] and [1, 0] are positive.
sdf_at_corners = []
for c in [(0, 0), (0, 1), (1, 0), (1, 1)]:
sdf_at_corners.append(
(line_eq[0] * c[0] + line_eq[1] * c[1] + line_eq[2]) /
np.linalg.norm(line_eq[:2]))
cell.Initialize(E, nu, threshold, edge_sample_num, sdf_at_corners)
# Check if all areas are correct.
dx = 1 / 3
x_intercept = (-line_eq[1] * dx - line_eq[2]) / line_eq[0]
area_00 = x_intercept * x_intercept * k * 0.5
area_01 = dx**2 - (dx - x_intercept)**2 * k * 0.5
area_02 = dx**2
area_10 = 0
area_11 = dx**2 * 0.5
area_12 = dx**2
area_20 = 0
area_21 = dx**2 - area_01
area_22 = dx**2 - area_00
area = ndarray([
area_00, area_01, area_02, area_10, area_11, area_12, area_20, area_21,
area_22
])
area_from_cell = ndarray(cell.sample_areas())
if not np.allclose(area, area_from_cell):
if verbose:
print_error('area is inconsistent.')
return False
# Check if all line segments are correct.
line_00 = np.sqrt(1 + k**2) * x_intercept
line_01 = np.sqrt(1 + k**2) * (dx - x_intercept)
line_02 = 0
line_10 = 0
line_11 = np.sqrt(1 + k**2) * dx
line_12 = 0
line_20 = 0
line_21 = line_01
line_22 = line_00
line = ndarray([
line_00, line_01, line_02, line_10, line_11, line_12, line_20, line_21,
line_22
])
line_from_cell = ndarray(cell.sample_boundary_areas())
if not np.allclose(line, line_from_cell):
if verbose:
print_error('boundary area is inconsistent.')
return False
# Test the gradients.
for loss_func, grad_func, name in [
(cell.py_normal, cell.py_normal_gradient, 'normal'),
(cell.offset, cell.py_offset_gradient, 'offset'),
(cell.sample_areas, cell.py_sample_areas_gradient, 'sample_areas'),
(cell.sample_boundary_areas, cell.py_sample_boundary_areas_gradient,
'sample_boundary_areas'), (cell.area, cell.py_area_gradient, 'area'),
(cell.py_energy_matrix, cell.py_energy_matrix_gradient,
'energy_matrix'),
(cell.py_dirichlet_vector, cell.py_dirichlet_vector_gradient,
'dirichlet_vector')
]:
if verbose:
print_info('Checking loss and gradient:', name)
dim = ndarray(loss_func()).size
weight = np.random.normal(size=dim)
def loss_and_grad(x):
cell.Initialize(E, nu, threshold, edge_sample_num, x)
loss = ndarray(loss_func()).ravel().dot(weight)
grad = np.zeros(4)
for i in range(4):
grad[i] = ndarray(grad_func(i)).ravel().dot(weight)
return loss, grad
if not check_gradients(
loss_and_grad, ndarray(sdf_at_corners), verbose=verbose):
if verbose:
print_error('Gradient check failed.')
return False
return True
def _variables_to_shape_params(self, x):
x = ndarray(x).copy().ravel()
assert x.size == 8
cx, cy, _ = self._cell_nums
lower = ndarray([
[1, self._outlet_range[0, 0]],
x[2:4],
x[:2],
[0, self._inlet_range[0, 0]],
])
right = ndarray([
[1, self._outlet_range[1, 0]],
[x[4], 1 - x[5]],
x[4:6],
[1, self._outlet_range[0, 1]],
])
upper = ndarray([
[0, self._inlet_range[1, 1]],
[x[0], 1 - x[1]],
[x[2], 1 - x[3]],
[1, self._outlet_range[1, 1]],
])
left = ndarray([
[0, self._inlet_range[0, 1]],
x[6:8],
[x[6], 1 - x[7]],
[0, self._inlet_range[1, 0]],
])
cxy = ndarray([cx, cy])
lower *= cxy
right *= cxy
upper *= cxy
left *= cxy
params = np.concatenate([lower.ravel(),
[0, -0.01, 1],
right.ravel(),
[0.01, 0, 1],
upper.ravel(),
[0, 0.01, 1],
left.ravel(),
[-0.01, 0, 1]
])
# Jacobian.
J = np.zeros((params.size, x.size))
J[2, 2] = J[3, 3] = 1
J[4, 0] = J[5, 1] = 1
J[13, 4] = 1
J[14, 5] = -1
J[15, 4] = J[16, 5] = 1
J[24, 0] = 1
J[25, 1] = -1
J[26, 2] = 1
J[27, 3] = -1
J[35, 6] = J[36, 7] = 1
J[37, 6] = 1
J[38, 7] = -1
J[:, ::2] *= cx
J[:, 1::2] *= cy
return ndarray(params).copy(), ndarray(J).copy()
def upper_bound(self):
return ndarray([.49, .49, .99, .49, .99, .49, .49, .49])
def lower_bound(self):
return ndarray([.01, .01, .49, .01, .49, .01, .01, .01])
