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eval_gmeshdiffusion_generated_samples.py
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eval_gmeshdiffusion_generated_samples.py
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# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import argparse
import json
import numpy as np
import torch
# Import topology / geometry trainers
from geometry.gshell_tets_geometry import GShellTetsGeometry
from render import texture
import pymeshlab
from pytorch3d.io import save_obj
import tqdm
RADIUS = 4.0
# RADIUS = 2.5
# Enable to debug back-prop anomalies
# torch.autograd.set_detect_anomaly(True)
#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='nvdiffrec')
parser.add_argument('--config', type=str, default=None, help='Config file')
parser.add_argument('-i', '--iter', type=int, default=5000)
parser.add_argument('-b', '--batch', type=int, default=1)
parser.add_argument('-s', '--spp', type=int, default=1)
parser.add_argument('-l', '--layers', type=int, default=1)
parser.add_argument('-r', '--train-res', nargs=2, type=int, default=[512, 512])
parser.add_argument('-dr', '--display-res', type=int, default=None)
parser.add_argument('-tr', '--texture-res', nargs=2, type=int, default=[1024, 1024])
parser.add_argument('-di', '--display-interval', type=int, default=0)
parser.add_argument('-si', '--save-interval', type=int, default=1000)
parser.add_argument('-lr', '--learning-rate', type=float, default=0.01)
parser.add_argument('-mr', '--min-roughness', type=float, default=0.08)
parser.add_argument('-mip', '--custom-mip', action='store_true', default=False)
parser.add_argument('-rt', '--random-textures', action='store_true', default=False)
parser.add_argument('-bg', '--background', default='checker', choices=['black', 'white', 'checker', 'reference'])
parser.add_argument('--loss', default='logl1', choices=['logl1', 'logl2', 'mse', 'smape', 'relmse'])
parser.add_argument('-o', '--out-dir', type=str, default=None)
parser.add_argument('-rm', '--ref_mesh', type=str)
parser.add_argument('-bm', '--base-mesh', type=str, default=None)
parser.add_argument('--validate', type=bool, default=True)
parser.add_argument('--grid_root', type=str)
FLAGS = parser.parse_args()
FLAGS.mtl_override = None # Override material of model
FLAGS.dmtet_grid = 64 # Resolution of initial tet grid. We provide 64 and 128 resolution grids. Other resolutions can be generated with https://github.com/crawforddoran/quartet
FLAGS.mesh_scale = 2.3 # Scale of tet grid box. Adjust to cover the model
FLAGS.env_scale = 1.0 # Env map intensity multiplier
FLAGS.envmap = None # HDR environment probe
FLAGS.display = None # Conf validation window/display. E.g. [{"relight" : <path to envlight>}]
FLAGS.camera_space_light = False # Fixed light in camera space. This is needed for setups like ethiopian head where the scanned object rotates on a stand.
FLAGS.lock_light = False # Disable light optimization in the second pass
FLAGS.lock_pos = False # Disable vertex position optimization in the second pass
FLAGS.sdf_regularizer = 0.2 # Weight for sdf regularizer (see paper for details)
FLAGS.laplace = "relative" # Mesh Laplacian ["absolute", "relative"]
FLAGS.laplace_scale = 10000.0 # Weight for Laplacian regularizer. Default is relative with large weight
FLAGS.pre_load = True # Pre-load entire dataset into memory for faster training
FLAGS.kd_min = [ 0.0, 0.0, 0.0, 0.0] # Limits for kd
FLAGS.kd_max = [ 1.0, 1.0, 1.0, 1.0]
FLAGS.ks_min = [ 0.0, 0.08, 0.0] # Limits for ks
FLAGS.ks_max = [ 1.0, 1.0, 1.0]
FLAGS.nrm_min = [-1.0, -1.0, 0.0] # Limits for normal map
FLAGS.nrm_max = [ 1.0, 1.0, 1.0]
FLAGS.cam_near_far = [0.1, 1000.0]
FLAGS.use_tanh_deform = False
FLAGS.use_sdf_mlp = False
FLAGS.force_default_mtl = True
FLAGS.twosided_texture = True
FLAGS.random_lgt = False
FLAGS.sphere_init = False
FLAGS.num_smooth_steps = 3
FLAGS.use_msdf_mlp = False
if FLAGS.config is not None:
data = json.load(open(FLAGS.config, 'r'))
for key in data:
FLAGS.__dict__[key] = data[key]
os.makedirs(FLAGS.out_dir, exist_ok=True)
mtl_default_diffuse = {
'name' : '_default_mat',
'bsdf': 'diffuse',
'uniform': True,
'kd' : texture.Texture2D(torch.tensor([0.75, 0.3, 0.6], dtype=torch.float32, device='cuda')),
'ks' : texture.Texture2D(torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32, device='cuda'))
}
if FLAGS.force_default_mtl:
mtl_default = mtl_default_diffuse
else:
mtl_default = None
tet_path = './data/tets/64_tets_cropped_rectified.npz'
tet = np.load(tet_path)
vertices = torch.tensor(tet['vertices'])
edges = torch.tensor(tet['edges']).long()
vertices_unique = vertices[:].unique()
dx = (vertices_unique[1] - vertices_unique[0]) / 2.0
vertices_discretized = (torch.round(
(vertices - vertices.min()) / dx)
).long()
midpoints = (vertices[edges[:, 0]] + vertices[edges[:, 1]]) / 2.0
midpoints_dicretized = (torch.round(
(midpoints - vertices.min()) / dx)
).long()
aabb = torch.tensor(FLAGS.aabb, dtype=torch.float).cuda().view(2, 3)
center = aabb.mean(0, keepdim=True) / 2.0
mesh_scale = 3.8
mesh_scale = mesh_scale / torch.max(aabb[1] - aabb[0]).item()
count = 0
grid_root = FLAGS.grid_root
geometry = GShellTetsGeometry(FLAGS.dmtet_grid, FLAGS.mesh_scale, FLAGS, tet_init_file=tet_path, extract_from_generative=True)
with torch.no_grad():
for grid_name in tqdm.tqdm(sorted(list(os.listdir(grid_root)))):
if '_occ' in grid_name:
continue
grid_all = torch.load(
os.path.join(grid_root, grid_name), map_location='cuda'
)
occgrid_all = torch.load(
os.path.join(grid_root, grid_name).replace('.pt', '_occ.pt'), map_location='cuda'
)[:, 0]
for i in tqdm.trange(grid_all.size(0), leave=False):
mesh_path = FLAGS.out_dir
os.makedirs(mesh_path, exist_ok=True)
mesh_savepath = os.path.join(mesh_path, '{:06d}.obj'.format(count))
if os.path.exists(mesh_savepath):
count += 1
continue
grid = grid_all[i]
occgrid = occgrid_all[i]
sdf_sign = (
grid[0, vertices_discretized[:, 0], vertices_discretized[:, 1], vertices_discretized[:, 2]]
).cuda().float()
geometry.deform.data[:] = (
grid[1:4, vertices_discretized[:, 0], vertices_discretized[:, 1], vertices_discretized[:, 2]]
).cuda().transpose(0, 1).float().clamp(-1, 1)
sdf_coeff = torch.ones(128, 128, 128).float().cuda() * 0.5
msdf_sign = torch.zeros(128, 128, 128).float().cuda()
msdf_sign[midpoints_dicretized[:, 0], midpoints_dicretized[:, 1], midpoints_dicretized[:, 2]] = torch.sign(
grid[0, midpoints_dicretized[:, 0], midpoints_dicretized[:, 1], midpoints_dicretized[:, 2]].cuda()
).float()
geometry.deform.data[:] = geometry.deform.data[:].clip(-1.0, 1.0)
geometry.deform_scale = 2.0
base_mesh = geometry.getMesh_from_augmented_grid_withocc(mtl_default, torch.sign(sdf_sign), sdf_coeff, msdf_sign, occgrid=occgrid)['imesh']
### rescale and translate back to align with the dataset
base_mesh.v_pos = (base_mesh.v_pos / mesh_scale) + center
### save post-processed mesh
save_obj(
verts=base_mesh.v_pos,
faces=base_mesh.t_pos_idx,
f=mesh_savepath
)
ms = pymeshlab.MeshSet()
ms.load_new_mesh(mesh_savepath)
ms.meshing_remove_unreferenced_vertices()
ms.meshing_isotropic_explicit_remeshing()
ms.apply_coord_laplacian_smoothing(stepsmoothnum=FLAGS.num_smooth_steps, cotangentweight=True)
# ms.apply_coord_hc_laplacian_smoothing()
# ms.apply_coord_laplacian_smoothing(stepsmoothnum=3, cotangentweight=True) ## for smoother surface
ms.meshing_isotropic_explicit_remeshing()
ms.apply_filter_script()
ms.save_current_mesh(mesh_savepath)
count += 1