| import mitsuba as mi |
| mi.set_variant("cuda_ad_rgb_double") |
| import drjit as dr |
| import matplotlib.pyplot as plt |
| import sys |
| from PDE2D.Coefficient import * |
| from PDE2D.utils import * |
| from PDE2D.BoundaryShape import * |
| from PDE2D.Solver import * |
| from PDE2D import PATH |
| import argparse |
| import os |
|
|
| root_directory = os.path.join(PATH, "output2D", "finite_differences", "discrete-sdf") |
|
|
| def create_path(path): |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
| parser = argparse.ArgumentParser(description='''Forward mode grad computation (translation)''') |
| parser.add_argument('--spe', default = 25, type=int) |
| parser.add_argument("--seednormal", default = 0, type = int) |
| parser.add_argument("--iternormal", default = 256, type = int) |
| parser.add_argument("--resnormal", default = 1024, type = int) |
| parser.add_argument("--distance", default = 0.01, type = float) |
| parser.add_argument("--upsample", default = 1, type = int) |
| parser.add_argument("--epsilon", default = 5e-6, type = float) |
| args = parser.parse_args() |
|
|
| distance = args.distance |
| spe = 2 ** args.spe |
|
|
| sdf_array = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1,-1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1,-1, 1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) |
|
|
| sdf_array = sdf_array.repeat(args.upsample, axis = 0).repeat(args.upsample, axis = 1) |
| box_length = 2.1 |
| box_center = [0,0] |
| in_boundary = SDFGrid(tensor_np= sdf_array, box_length=box_length, box_center=box_center, epsilon = args.epsilon, redistance = True) |
|
|
| out_boundary = CircleWithElectrodes(injection_confs = [[0,10]], is_delta = True, epsilon=args.epsilon) |
| shape = BoundaryWithDirichlets(out_boundary, [in_boundary], dirichlet_values = [[0]], epsilon = args.epsilon) |
| data_holder = DataHolder(shape) |
| opt_params = ["inboundary.dirichlet.translation_x", "inboundary.dirichlet.translation_y"] |
| wos = WostConstant(data_holder, opt_params = opt_params) |
|
|
|
|
| normalder_file = os.path.join(root_directory, "normalder") |
| normal_der = [] |
|
|
| for i in range(args.seednormal, args.seednormal + args.iternormal): |
| normal_der.append(np.load(os.path.join(normalder_file, f"res{args.resnormal}-d{distance}-{i}.npy"))) |
| normal_der = np.array(normal_der).sum(axis = 0) / args.iternormal |
|
|
| num_conf = out_boundary.num_confs |
| normal_der = mi.TensorXf(normal_der) |
|
|
| print(normal_der.shape) |
| wos.input.shape.in_boundaries[0].set_normal_derivative(normal_der) |
|
|
| conf_numbers = [dr.opaque(UInt32, i, shape = (1)) for i in range(num_conf)] |
|
|
| path = os.path.join(root_directory, "prb") |
| create_path(path) |
|
|
|
|
| points, active_conf, electrode_nums = out_boundary.create_electrode_points(spe, conf_numbers=conf_numbers) |
| grad_x = mi.Float(0) |
| grad_y = mi.Float(0) |
|
|
| dr.disable_grad(wos.input.shape.in_boundaries[0].translation_y) |
| dr.enable_grad(wos.input.shape.in_boundaries[0].translation_x) |
| dr.forward(wos.input.shape.in_boundaries[0].translation_x) |
|
|
| dL_x, _ = wos.solve(points, active_conf, conf_numbers=conf_numbers, all_inside = True, |
| normal_derivative_dist=distance, mode = dr.ADMode.Forward) |
|
|
| grad_x += create_electrode_result(dL_x, spe, electrode_nums, apply_normalization = True) |
|
|
| dr.disable_grad(wos.input.shape.in_boundaries[0].translation_x) |
| dr.enable_grad(wos.input.shape.in_boundaries[0].translation_y) |
| dr.forward(wos.input.shape.in_boundaries[0].translation_y) |
|
|
| dL_y, _ = wos.solve(points, active_conf, conf_numbers=conf_numbers, |
| all_inside = True, normal_derivative_dist=distance, mode = dr.ADMode.Forward) |
|
|
| grad_y += create_electrode_result(dL_y, spe, electrode_nums, apply_normalization = True) |
|
|
| np.save(os.path.join(path, f"gradx-d{distance}.npy"), grad_x.numpy()) |
| np.save(os.path.join(path, f"grady-d{distance}.npy"), grad_y.numpy()) |
|
|
| fig, ax = plt.subplots(layout='constrained', figsize = (12,5)) |
| plot_primals(ax, grad_x[0], np.zeros_like(grad_x[0]), electrode_nums, 16, name1 = "grad_x", name2 = "-") |
| fig.savefig(f"{path}/grad_x-d{distance}.pdf", bbox_inches = "tight", dpi = 300) |
| plt.close(fig) |
|
|
| fig, ax = plt.subplots(layout='constrained', figsize = (12,5)) |
| plot_primals(ax, grad_y[0], np.zeros_like(grad_y[0]), electrode_nums, 16, name1 = "grad_y", name2 = "-") |
| fig.savefig(f"{path}/grad_y-d{distance}.pdf", bbox_inches = "tight", dpi = 300) |
| plt.close(fig) |
|
|
