| from .coefficient import * | |
| class FunctionCoefficient(Coefficient): | |
| DRJIT_STRUCT = { | |
| 'parameters' : dict, | |
| 'function_generator' : callable, | |
| } | |
| def __init__(self, name: str, parameters: dict, function_generator: callable, | |
| grad_generator: callable = None, laplacian_generator: callable = None): | |
| self.is_zero = False | |
| self.parameters = parameters | |
| self.function_generator = function_generator | |
| self.grad_generator = grad_generator | |
| self.laplacian_generator = laplacian_generator | |
| self.name = name | |
| self.type = "CustomFunction" | |
| for key in parameters.keys(): | |
| dr.make_opaque(parameters[key]) | |
| self.update_function() | |
| def update_function(self): | |
| self.function = lambda points : self.function_generator(points, self.parameters) | |
| if ((self.grad_generator is not None) & (self.laplacian_generator is not None)): | |
| self.grad = lambda points : self.grad_generator(points, self.parameters) | |
| self.laplacian = lambda points : self.laplacian_generator(points, self.parameters) | |
| else: | |
| self.grad = None | |
| self.laplacian = None | |
| def get_value(self, points): | |
| return self.function(points) | |
| def get_grad_laplacian(self, points): | |
| if ((self.grad is not None) & (self.laplacian is not None)): | |
| return self.grad(points), self.laplacian(points) | |
| else: | |
| raise Exception( | |
| f"Laplacian or gradient is not defined for the function coefficient\"{self.name}\"!") | |
| def get_opt_params(self, param_dict: dict, opt_params: list): | |
| for i in opt_params: | |
| param_exists = False | |
| for j in self.parameters.keys(): | |
| if i == j: | |
| param_dict[f"{self.name}.{self.type}.{i}"] = self.parameters[i] | |
| param_exists = True | |
| if not param_exists: | |
| raise Exception( | |
| f"Function coefficient \"{self.name}\" of type \"{self.type}\" does not have parameter called \"{i}\".") | |
| def update(self, optimizer): | |
| param_exists = False | |
| for key in optimizer.keys(): | |
| vals = key.split(".") | |
| name = vals[0] | |
| type = vals[1] | |
| param = vals[2] | |
| if (name == self.name) & (type == self.type): | |
| for p in self.parameters.keys(): | |
| if p == param: | |
| param_exists = True | |
| self.parameters[p] = optimizer[key] | |
| if param_exists: | |
| self.update_function() | |
| def zero_grad(self): | |
| for key in self.parameters.keys(): | |
| if dr.grad_enabled(self.parameters[key]): | |
| dr.set_grad(self.parameters[key], 0.0) | |
| def copy(self): | |
| new = FunctionCoefficient(self.name, self.parameters, self.function_generator, | |
| self.grad_generator, self.laplacian_generator) | |
| return new | |