deeplearning/modulus/modulus-sym-v100/_modules/modulus/sym/domain/monitor/pointwise.html

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

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""" Monitor for Solver class
"""

import numpy as np

from modulus.sym.domain.monitor import Monitor
from modulus.sym.domain.constraint import Constraint
from modulus.sym.graph import Graph
from modulus.sym.key import Key
from modulus.sym.constants import TF_SUMMARY
from modulus.sym.distributed import DistributedManager
from modulus.sym.utils.io import dict_to_csv, csv_to_dict


[docs]class PointwiseMonitor(Monitor): """ Pointwise Inferencer that allows inferencing on pointwise data Parameters ---------- invar : Dict[str, np.ndarray (N, 1)] Dictionary of numpy arrays as input. output_names : List[str] List of outputs needed for metric. metrics : Dict[str, Callable] Dictionary of pytorch functions whose input is a dictionary torch tensors whose keys are the `output_names`. The keys to `metrics` will be used to label the metrics in tensorboard/csv outputs. nodes : List[Node] List of Modulus Nodes to unroll graph with. requires_grad : bool = False If automatic differentiation is needed for computing results. """ def __init__(self, invar, output_names, metrics, nodes, requires_grad=False): # construct model from nodes self.requires_grad = requires_grad self.model = Graph( nodes, Key.convert_list(invar.keys()), Key.convert_list(output_names) ) self.manager = DistributedManager() self.device = self.manager.device self.model.to(self.device) # set metrics self.metrics = metrics self.monitor_outvar_store = {} # set invar self.invar = Constraint._set_device(invar, device=self.device) def save_results(self, name, writer, step, data_dir): # run forward inference invar = Constraint._set_device( self.invar, device=self.device, requires_grad=self.requires_grad ) outvar = self.model(invar) metrics = {key: func({**invar, **outvar}) for key, func in self.metrics.items()} for k, m in metrics.items(): # add tensorboard scalars if TF_SUMMARY: writer.add_scalar("monitor/" + name + "/" + k, m, step, new_style=True) else: writer.add_scalar("Monitors/" + name + "/" + k, m, step, new_style=True) # write csv files if k not in self.monitor_outvar_store.keys(): try: self.monitor_outvar_store[k] = csv_to_dict(data_dir + k + ".csv") except: self.monitor_outvar_store[k] = { "step": np.array([[step]]), k: m.detach().cpu().numpy().reshape(-1, 1), } else: monitor_outvar = { "step": np.array([[step]]), k: m.detach().cpu().numpy().reshape(-1, 1), } self.monitor_outvar_store[k] = { key: np.concatenate([value_1, value_2], axis=0) for (key, value_1), (key, value_2) in zip( self.monitor_outvar_store[k].items(), monitor_outvar.items() ) } dict_to_csv(self.monitor_outvar_store[k], filename=data_dir + k + ".csv") return metrics
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.