deeplearning/modulus/modulus-v2209/_modules/modulus/domain/monitor/pointwise.html

v22.09

Source code for modulus.domain.monitor.pointwise

""" Monitor for Solver class
"""

import numpy as np

from modulus.domain.monitor import Monitor
from modulus.domain.constraint import Constraint
from modulus.graph import Graph
from modulus.key import Key
from modulus.constants import TF_SUMMARY
from modulus.distributed import DistributedManager
from modulus.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
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