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LSM_airfoil_2d_parameterize.py
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LSM_airfoil_2d_parameterize.py
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import numpy as np
import os
import time
import torch
import sys
from network import LSM_Decoder
from data.naca_2d import naca
from data.uiuc_2d import uiuc
from data.airfoil_dataset_2d import AirfoilDataset2D
from mesh_io.naca0012_invicid_trimesh_2d import Naca0012_Invicid_Trimesh_2d
from visualize_utils import plot_airfoil_2d_details
from loss_utils import batched_chamfer_distance, reg_loss
def do_minimization_iter(
decoder,
v,
latent_z,
unique_surface_id_list,
boundary_id_list,
target_pts,
optimizer: list,
lr_scheduler: list,
regular_sampling_ratio
):
batch_size = target_pts.shape[0] if target_pts is not None else 1
v_contour = v[unique_surface_id_list].repeat(batch_size, 1, 1)
v_boundary = v[boundary_id_list].repeat(batch_size, 1, 1)
num_sampled = int(regular_sampling_ratio * v.shape[0])
rand_id = torch.randint(low=0, high=v.shape[0], device=v.device, size=[num_sampled])
combined_id = torch.cat([unique_surface_id_list, rand_id])
uniques, counts = combined_id.unique(return_counts=True)
rand_id = uniques[counts == 1]
v_sampled = v[rand_id].repeat(batch_size, 1, 1)
v_sampled.requires_grad = True
v_ = torch.cat([v_contour, v_sampled, v_boundary], dim=1)
delta = decoder(latent_z, v_.float())
delta_contour = delta[:, :v_contour.shape[1], :]
v_contour_deformed = v_contour + delta_contour
delta_sampled = delta[:, v_contour.shape[1]:v_contour.shape[1] + v_sampled.shape[1], :]
delta_boundary = delta[:, v_contour.shape[1] + v_sampled.shape[1]:, :]
loss_chamfer = batched_chamfer_distance(
v_contour_deformed, target_pts,
use_squared_loss=False,
single_sided_argmin_on_pt2=True,
single_sided_argmin_on_pt1=True
)
loss_reg = reg_loss(v_sampled, delta_sampled)
loss_boundary = ((delta_boundary ** 2).sum(dim=-1) ** 0.5).mean()
loss_code_reg = torch.mean(latent_z ** 2)
loss = loss_chamfer + loss_reg + 0.05 * loss_boundary + 1e-4 * loss_code_reg
#
# optimization
#
loss.backward()
for optim in optimizer:
optim.step()
for lr_sched in lr_scheduler:
lr_sched.step()
return loss_chamfer, loss_reg, loss_boundary
def reconstruct_latent_model(decoder, latent_dim, v, target_pts, unique_surface_id_list, boundary_id_list):
latent_z = torch.ones([1, latent_dim]).normal_(mean=0, std=1.0 / latent_dim**0.5).to(v.device)
latent_z.requires_grad = True
optimizer_z_test = torch.optim.Adam(params = [latent_z], lr = 1e-4)
lr_scheduler_z_test = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_z_test, factor = 0.3, patience = 5)
decoder.eval()
max_reconstruct_iter = 1001
for recon_iter_num in range(max_reconstruct_iter):
optimizer_z_test.zero_grad()
loss_all = do_minimization_iter(
decoder, v, latent_z,
unique_surface_id_list,
boundary_id_list,
target_pts,
optimizer = [optimizer_z_test],
lr_scheduler = [],
regular_sampling_ratio = 0.02
)
loss_chamfer = loss_all[0]
lr_scheduler_z_test.step(loss_chamfer)
if loss_chamfer < 5e-3:
break
if recon_iter_num % 50 == 0:
print(' iter {}, chamfer distance: {:.5f}, reg loss: {:.4e}'.format(recon_iter_num, loss_chamfer.item(), loss_all[1].item()))
return latent_z
#
# main
#
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-type', type=str, required=True, default='naca')
parser.add_argument('-profile', type=str, default='3413')
parser.add_argument('-workspaceDir', type=str, default='exp_dmm_2d/')
parser.add_argument('-latentModelPath', type=str, default='exp_lsm_2d/lsm_model.pth')
args = parser.parse_args()
workspace_dir = args.workspaceDir
latent_model_path = args.latentModelPath
#
# init grid
#
CFD_mesh = Naca0012_Invicid_Trimesh_2d(
cache_name = './mesh_io/naca0012_su2/init_mesh_cache.pymesh',
su2Mesh_dir = './mesh_io/naca0012_su2'
)
_ = CFD_mesh.init_mesh(use_cache = True)
v, _, edge_set, edge_index, faces, num_vertices, contour_id_list, boundary_id_list = \
CFD_mesh.parse_mesh_dict(to_tensor=True)
v = v[:,:2]
unique_contour_id_list = contour_id_list.flatten().unique()
#
# init model
#
save_dict = torch.load(latent_model_path)
decoder = save_dict['decoder']
latent_dim = save_dict['latent_z_dim']
#
# init cuda
#
use_cuda = True
if use_cuda:
unique_contour_id_list = unique_contour_id_list.cuda()
boundary_id_list = boundary_id_list.cuda()
decoder = decoder.cuda()
v = v.cuda()
#
# get target geometry
#
target_shape_str = ''
if args.type == 'naca':
target_shape_str = args.profile
airfoil_x_cos, airfoil_y_cos = naca(target_shape_str, n=300, finite_TE=False, half_cosine_spacing=True)
airfoil_pts = np.stack([airfoil_x_cos, airfoil_y_cos], axis=1)
elif args.type == 'uiuc':
target_shape_str = args.profile
airfoil_pts = uiuc('./data/UIUC_airfoils/dat', target_shape_str, 300)
else:
raise Exception('target shape type is not supported')
airfoil_pts = torch.tensor(airfoil_pts, device=v.device)[None,:]
plot_airfoil_2d_details(v, edge_index, airfoil_pts, './{}/{}_and_template_mesh'.format(workspace_dir, target_shape_str))
#
# parameterize
#
print('Parameterize target airfoil with the latent space model...')
tic = time.time()
latent_z = reconstruct_latent_model(decoder, latent_dim, v, airfoil_pts, unique_contour_id_list, boundary_id_list)
toc = time.time()
print('Done in {}s'.format(toc - tic))
# save latent code
v_ = v[None,:,:]
v_ += decoder(latent_z, v_.float())
plot_airfoil_2d_details(v_, edge_index, airfoil_pts, '{}/{}'.format(workspace_dir, target_shape_str))
torch.save({
'latent_z': latent_z.data.cpu(),
'profile': target_shape_str
}, workspace_dir + '/latent_z_{}.pth'.format(target_shape_str))