Source code for modulus.sym.domain.inferencer.pointwise

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, List, Union, Callable from pathlib import Path import inspect import torch import numpy as np from modulus.sym.domain.inferencer import Inferencer from modulus.sym.domain.constraint import Constraint from modulus.sym.graph import Graph from modulus.sym.key import Key from modulus.sym.node import Node from modulus.sym.distributed import DistributedManager from modulus.sym.utils.io import InferencerPlotter from modulus.sym.utils.io.vtk import var_to_polyvtk, VTKBase, VTKUniformGrid from modulus.sym.dataset import DictInferencePointwiseDataset [docs]class PointwiseInferencer(Inferencer):  """  Pointwise Inferencer that allows inferencing on pointwise data  Parameters  ----------  nodes : List[Node]  List of Modulus Nodes to unroll graph with.  invar : Dict[str, np.ndarray (N, 1)]  Dictionary of numpy arrays as input.  output_names : List[str]  List of desired outputs.  batch_size : int, optional  Batch size used when running validation, by default 1024  plotter : InferencerPlotter  Modulus plotter for showing results in tensorboard.  requires_grad : bool = False  If automatic differentiation is needed for computing results.  """ def __init__( self, nodes: List[Node], invar: Dict[str, np.array], output_names: List[str], batch_size: int = 1024, plotter: InferencerPlotter = None, requires_grad: bool = False, model=None, ): # get dataset and dataloader self.dataset = DictInferencePointwiseDataset( invar=invar, output_names=output_names ) self.dataloader = Constraint.get_dataloader( dataset=self.dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=0, distributed=False, infinite=False, ) # construct model from nodes if model is None: self.model = Graph( nodes, Key.convert_list(self.dataset.invar_keys), Key.convert_list(self.dataset.outvar_keys), ) else: self.model = model self.manager = DistributedManager() self.device = self.manager.device self.model.to(self.device) # set foward method self.requires_grad = requires_grad self.forward = self.forward_grad if requires_grad else self.forward_nograd # set plotter self.plotter = plotter def eval_epoch(self): invar_cpu = {key: [] for key in self.dataset.invar_keys} predvar_cpu = {key: [] for key in self.dataset.outvar_keys} # Loop through mini-batches for i, (invar0,) in enumerate(self.dataloader): # Move data to device invar = Constraint._set_device( invar0, device=self.device, requires_grad=self.requires_grad ) pred_outvar = self.forward(invar) invar_cpu = {key: value + [invar0[key]] for key, value in invar_cpu.items()} predvar_cpu = { key: value + [pred_outvar[key].cpu().detach().numpy()] for key, value in predvar_cpu.items() } # Concat mini-batch arrays invar = {key: np.concatenate(value) for key, value in invar_cpu.items()} predvar = {key: np.concatenate(value) for key, value in predvar_cpu.items()} return invar, predvar def save_results(self, name, results_dir, writer, save_filetypes, step): # evaluate on entire dataset invar, predvar = self.eval_epoch() # save batch to vtk/np files TODO clean this up after graph unroll stuff if “np” in save_filetypes: np.savez(results_dir + name, {**invar, **predvar}) if “vtk” in save_filetypes: var_to_polyvtk({**invar, **predvar}, results_dir + name) # add tensorboard plots if self.plotter is not None: self.plotter._add_figures( “Inferencers”, name, results_dir, writer, step, invar, predvar )