# 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.
import numpy as np
import torch
from typing import List, Dict
from pathlib import Path
from modulus.sym.domain.validator import Validator
from modulus.sym.domain.constraint import Constraint
from modulus.sym.utils.io.vtk import var_to_polyvtk, VTKBase
from modulus.sym.utils.io import ValidatorPlotter
from modulus.sym.graph import Graph
from modulus.sym.key import Key
from modulus.sym.node import Node
from modulus.sym.constants import TF_SUMMARY
from modulus.sym.dataset import DictPointwiseDataset
from modulus.sym.distributed import DistributedManager
[docs]class PointwiseValidator(Validator):
"""
Pointwise Validator that allows walidating 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.
true_outvar : Dict[str, np.ndarray (N, 1)]
Dictionary of numpy arrays used to validate against validation.
batch_size : int, optional
Batch size used when running validation, by default 1024
plotter : ValidatorPlotter
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],
true_outvar: Dict[str, np.array],
batch_size: int = 1024,
plotter: ValidatorPlotter = None,
requires_grad: bool = False,
):
# TODO: add support for other datasets?
# get dataset and dataloader
self.dataset = DictPointwiseDataset(invar=invar, outvar=true_outvar)
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
self.model = Graph(
nodes,
Key.convert_list(self.dataset.invar_keys),
Key.convert_list(self.dataset.outvar_keys),
)
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 save_results(self, name, results_dir, writer, save_filetypes, step):
invar_cpu = {key: [] for key in self.dataset.invar_keys}
true_outvar_cpu = {key: [] for key in self.dataset.outvar_keys}
pred_outvar_cpu = {key: [] for key in self.dataset.outvar_keys}
# Loop through mini-batches
for i, (invar0, true_outvar0, lambda_weighting) in enumerate(self.dataloader):
# Move data to device (may need gradients in future, if so requires_grad=True)
invar = Constraint._set_device(
invar0, device=self.device, requires_grad=self.requires_grad
)
true_outvar = Constraint._set_device(
true_outvar0, device=self.device, requires_grad=self.requires_grad
)
pred_outvar = self.forward(invar)
# Collect minibatch info into cpu dictionaries
invar_cpu = {
key: value + [invar[key].cpu().detach()]
for key, value in invar_cpu.items()
}
true_outvar_cpu = {
key: value + [true_outvar[key].cpu().detach()]
for key, value in true_outvar_cpu.items()
}
pred_outvar_cpu = {
key: value + [pred_outvar[key].cpu().detach()]
for key, value in pred_outvar_cpu.items()
}
# Concat mini-batch tensors
invar_cpu = {key: torch.cat(value) for key, value in invar_cpu.items()}
true_outvar_cpu = {
key: torch.cat(value) for key, value in true_outvar_cpu.items()
}
pred_outvar_cpu = {
key: torch.cat(value) for key, value in pred_outvar_cpu.items()
}
# compute losses on cpu
# TODO add metrics specific for validation
# TODO: add potential support for lambda_weighting
losses = PointwiseValidator._l2_relative_error(true_outvar_cpu, pred_outvar_cpu)
# convert to numpy arrays
invar = {k: v.numpy() for k, v in invar_cpu.items()}
true_outvar = {k: v.numpy() for k, v in true_outvar_cpu.items()}
pred_outvar = {k: v.numpy() for k, v in pred_outvar_cpu.items()}
# save batch to vtk file TODO clean this up after graph unroll stuff
named_true_outvar = {"true_" + k: v for k, v in true_outvar.items()}
named_pred_outvar = {"pred_" + k: v for k, v in pred_outvar.items()}
# save batch to vtk/npz file TODO clean this up after graph unroll stuff
if "np" in save_filetypes:
np.savez(
results_dir + name, {**invar, **named_true_outvar, **named_pred_outvar}
)
if "vtk" in save_filetypes:
var_to_polyvtk(
{**invar, **named_true_outvar, **named_pred_outvar}, results_dir + name
)
# add tensorboard plots
if self.plotter is not None:
self.plotter._add_figures(
"Validators",
name,
results_dir,
writer,
step,
invar,
true_outvar,
pred_outvar,
)
# add tensorboard scalars
for k, loss in losses.items():
if TF_SUMMARY:
writer.add_scalar("val/" + name + "/" + k, loss, step, new_style=True)
else:
writer.add_scalar(
"Validators/" + name + "/" + k, loss, step, new_style=True
)
return losses
[docs]class PointVTKValidator(PointwiseValidator):
"""
Pointwise validator using mesh points of VTK object
Parameters
----------
vtk_obj : VTKBase
Modulus VTK object to use point locations from
nodes : List[Node]
List of Modulus Nodes to unroll graph with.
input_vtk_map : Dict[str, List[str]]
Dictionary mapping from Modulus input variables to VTK variable names {"modulus.sym.name": ["vtk name"]}.
Use colons to denote components of multi-dimensional VTK arrays ("name":# )
true_vtk_map : Dict[str, List[str]]
Dictionary mapping from Modulus target variables to VTK variable names {"modulus.sym.name": ["vtk name"]}.
invar : Dict[str, np.array], optional
Dictionary of additional numpy arrays as input, by default {}
true_outvar : Dict[str, np.array], optional
Dictionary of additional numpy arrays used to validate against validation, by default {}
batch_size : int
Batch size used when running validation.
plotter : ValidatorPlotter
Modulus plotter for showing results in tensorboard.
requires_grad : bool, optional
If automatic differentiation is needed for computing results., by default True
log_iter : bool, optional
Save results to different file each call, by default False
"""
def __init__(
self,
vtk_obj: VTKBase,
nodes: List[Node],
input_vtk_map: Dict[str, List[str]],
true_vtk_map: Dict[str, List[str]],
invar: Dict[str, np.array] = {}, # Additional inputs
true_outvar: Dict[str, np.array] = {}, # Additional targets
batch_size: int = 1024,
plotter: ValidatorPlotter = None,
requires_grad: bool = False,
log_iter: bool = False,
):
# Set VTK file save dir and file name
self.vtk_obj = vtk_obj
self.vtk_obj.file_dir = "./validators"
self.vtk_obj.file_name = "validator"
# Set up input/output names
invar_vtk = self.vtk_obj.get_data_from_map(input_vtk_map)
invar.update(invar_vtk)
# Extract true vars from VTK
true_vtk = self.vtk_obj.get_data_from_map(true_vtk_map)
true_outvar.update(true_vtk)
# set plotter
self.plotter = plotter
self.log_iter = log_iter
# initialize inferencer
super().__init__(
nodes=nodes,
invar=invar,
true_outvar=true_outvar,
batch_size=batch_size,
plotter=plotter,
requires_grad=requires_grad,
)
def save_results(self, name, results_dir, writer, save_filetypes, step):
invar_cpu = {key: [] for key in self.dataset.invar_keys}
true_outvar_cpu = {key: [] for key in self.dataset.outvar_keys}
pred_outvar_cpu = {key: [] for key in self.dataset.outvar_keys}
# Loop through mini-batches
for i, (invar0, true_outvar0, lambda_weighting) in enumerate(self.dataloader):
# Move data to device (may need gradients in future, if so requires_grad=True)
invar = Constraint._set_device(
invar0, device=self.device, requires_grad=self.requires_grad
)
true_outvar = Constraint._set_device(
true_outvar0, device=self.device, requires_grad=self.requires_grad
)
pred_outvar = self.forward(invar)
# Collect minibatch info into cpu dictionaries
invar_cpu = {
key: value + [invar[key].cpu().detach()]
for key, value in invar_cpu.items()
}
true_outvar_cpu = {
key: value + [true_outvar[key].cpu().detach()]
for key, value in true_outvar_cpu.items()
}
pred_outvar_cpu = {
key: value + [pred_outvar[key].cpu().detach()]
for key, value in pred_outvar_cpu.items()
}
# Concat mini-batch tensors
invar_cpu = {key: torch.cat(value) for key, value in invar_cpu.items()}
true_outvar_cpu = {
key: torch.cat(value) for key, value in true_outvar_cpu.items()
}
pred_outvar_cpu = {
key: torch.cat(value) for key, value in pred_outvar_cpu.items()
}
# compute losses on cpu
# TODO add metrics specific for validation
# TODO: add potential support for lambda_weighting
losses = PointwiseValidator._l2_relative_error(true_outvar_cpu, pred_outvar_cpu)
# convert to numpy arrays
invar = {k: v.numpy() for k, v in invar_cpu.items()}
true_outvar = {k: v.numpy() for k, v in true_outvar_cpu.items()}
pred_outvar = {k: v.numpy() for k, v in pred_outvar_cpu.items()}
# save batch to vtk file TODO clean this up after graph unroll stuff
named_true_outvar = {"true_" + k: v for k, v in true_outvar.items()}
named_pred_outvar = {"pred_" + k: v for k, v in pred_outvar.items()}
# save batch to vtk/npz file TODO clean this up after graph unroll stuff
self.vtk_obj.file_dir = Path(results_dir)
self.vtk_obj.file_name = Path(name).stem
if "np" in save_filetypes:
np.savez(
results_dir + name, {**invar, **named_true_outvar, **named_pred_outvar}
)
if "vtk" in save_filetypes:
if self.log_iter:
self.vtk_obj.var_to_vtk(data_vars={**pred_outvar}, step=step)
else:
self.vtk_obj.var_to_vtk(data_vars={**pred_outvar})
# add tensorboard plots
if self.plotter is not None:
self.plotter._add_figures(
"Validators",
name,
results_dir,
writer,
step,
invar,
true_outvar,
pred_outvar,
)
# add tensorboard scalars
for k, loss in losses.items():
if TF_SUMMARY:
writer.add_scalar("val/" + name + "/" + k, loss, step, new_style=True)
else:
writer.add_scalar(
"Validators/" + name + "/" + k, loss, step, new_style=True
)
return losses
