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feat: remove deformation prediction, add selectable cardinality when sampling

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This commit is contained in:
Laurent FAINSIN 2023-04-17 16:16:11 +02:00
parent b21d3a2fe2
commit c09ff5b20f
3 changed files with 5 additions and 11 deletions
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4
compare_samples.py
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@ -10,7 +10,9 @@ nominal = pv.read(VTKFILE_NOMINAL)
# for each generated/sampled blade
gen_files = Path("./output").glob("gen*.txt")
for gen_file in gen_files:
pc_files = Path("./output").glob("pc*.txt")
files = list(gen_files) + list(pc_files)
for gen_file in files:
# load numpy txt
blade = np.loadtxt(gen_file)

7
test_generation.py
View file

@ -2,7 +2,6 @@ import argparse
from pprint import pprint
import datasets
import pyvista as pv
import torch
import torch.nn as nn
import torch.utils.data
@ -502,8 +501,6 @@ def generate(model, opt):
test_dataloader = torch.utils.data.DataLoader(
test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=int(opt.workers), drop_last=False
)
VTKFILE_NOMINAL = Path("~/data/stage-laurent-f/datasets/Rotor37/processed/nominal_blade_rotated.vtk")
nominal = pv.read(VTKFILE_NOMINAL)
with torch.no_grad():
samples = []
@ -513,7 +510,8 @@ def generate(model, opt):
x = data["positions"].transpose(1, 2)
# m, s = data["mean"].float(), data["std"].float()
gen = model.gen_samples(x.shape, "cuda", clip_denoised=False).detach().cpu()
shape = torch.Size((*x.shape[:-1], 35000))
gen = model.gen_samples(shape, "cuda", clip_denoised=False).detach().cpu()
gen = gen.transpose(1, 2).contiguous()
x = x.transpose(1, 2).contiguous()
@ -528,7 +526,6 @@ def generate(model, opt):
pc = blade
# unnormalize
pc = pc + nominal.points
pc = pc * STD + MEAN
print(f"Saving point cloud {idx}...")

5
train_generation.py
View file

@ -9,8 +9,6 @@ import torch.optim as optim
import torch.utils.data
from torch.distributions import Normal
import pyvista as pv
# from dataset.shapenet_data_pc import ShapeNet15kPointClouds
from model.pvcnn_generation import PVCNN2Base
from utils.file_utils import *
@ -652,8 +650,6 @@ def train(gpu, opt, output_dir):
""" data """
train_dataset, _ = get_dataset(opt.dataroot, opt.npoints, opt.category)
dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
VTKFILE_NOMINAL = Path("~/data/stage-laurent-f/datasets/Rotor37/processed/nominal_blade_rotated.vtk")
nominal = pv.read(VTKFILE_NOMINAL)
"""
create networks
@ -712,7 +708,6 @@ def train(gpu, opt, output_dir):
lr_scheduler.step(epoch)
for i, data in enumerate(dataloader):
x = data["positions"] - nominal.points
x = data["positions"].transpose(1, 2)
noises_batch = torch.randn_like(x)