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import torch
import sys
sys.argv = ['']
from sklearn.preprocessing import StandardScaler
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from pytorch_lightning.utilities.combined_loader import CombinedLoader
import laspy
import numpy as np
from scipy.spatial import cKDTree
import math
from functools import partial
from torch.utils.data import TensorDataset
from train.parsers import parse_args
args = parse_args()
class GaussianMM:
def __init__(self, mu, var):
super().__init__()
self.centers = torch.tensor(mu)
self.logstd = torch.tensor(var).log() / 2.0
self.K = self.centers.shape[0]
def logprob(self, x):
logprobs = self.normal_logprob(
x.unsqueeze(1), self.centers.unsqueeze(0), self.logstd
)
logprobs = torch.sum(logprobs, dim=2)
return torch.logsumexp(logprobs, dim=1) - math.log(self.K)
def normal_logprob(self, z, mean, log_std):
mean = mean + torch.tensor(0.0)
log_std = log_std + torch.tensor(0.0)
c = torch.tensor([math.log(2 * math.pi)]).to(z)
inv_sigma = torch.exp(-log_std)
tmp = (z - mean) * inv_sigma
return -0.5 * (tmp * tmp + 2 * log_std + c)
def __call__(self, n_samples):
idx = torch.randint(self.K, (n_samples,)).to(self.centers.device)
mean = self.centers[idx]
return torch.randn(*mean.shape).to(mean) * torch.exp(self.logstd) + mean
class LidarSingleDataModule(pl.LightningDataModule):
def __init__(self, args):
super().__init__()
self.save_hyperparameters()
self.data_path = args.data_path
self.batch_size = args.batch_size
self.max_dim = args.dim
self.whiten = args.whiten
self.p0_mu = [
[-4.5, -4.0, 0.5],
[-4.2, -3.5, 0.5],
[-4.0, -3.0, 0.5],
[-3.75, -2.5, 0.5],
]
self.p0_var = 0.02
# multiple p1 for each branch
#changed
self.p1_1_mu = [
[-2.5, -0.25, 0.5],
[-2.25, 0.675, 0.5],
[-2, 1.5, 0.5],
]
self.p1_2_mu = [
[2, -2, 0.5],
[2.6, -1.25, 0.5],
[3.2, -0.5, 0.5]
]
self.p1_var = 0.03
self.k = 20
self.n_samples = 5000
self.num_timesteps = 2
self.split_ratios = args.split_ratios
self.num_timesteps = 2
self.metric_clusters = 3
self.args = args
self._prepare_data()
def _prepare_data(self):
las = laspy.read(self.data_path)
# Extract only "ground" points.
self.mask = las.classification == 2
# Original Preprocessing
x_offset, x_scale = las.header.offsets[0], las.header.scales[0]
y_offset, y_scale = las.header.offsets[1], las.header.scales[1]
z_offset, z_scale = las.header.offsets[2], las.header.scales[2]
dataset = np.vstack(
(
las.X[self.mask] * x_scale + x_offset,
las.Y[self.mask] * y_scale + y_offset,
las.Z[self.mask] * z_scale + z_offset,
)
).transpose()
mi = dataset.min(axis=0, keepdims=True)
ma = dataset.max(axis=0, keepdims=True)
dataset = (dataset - mi) / (ma - mi) * [10.0, 10.0, 2.0] + [-5.0, -5.0, 0.0]
self.dataset = torch.tensor(dataset, dtype=torch.float32)
self.tree = cKDTree(dataset)
x0_gaussian = GaussianMM(self.p0_mu, self.p0_var)(self.n_samples)
x1_1_gaussian = GaussianMM(self.p1_1_mu, self.p1_var)(self.n_samples)
x1_2_gaussian = GaussianMM(self.p1_2_mu, self.p1_var)(self.n_samples)
x0 = self.get_tangent_proj(x0_gaussian)(x0_gaussian)
x1_1 = self.get_tangent_proj(x1_1_gaussian)(x1_1_gaussian)
x1_2 = self.get_tangent_proj(x1_2_gaussian)(x1_2_gaussian)
split_index = int(self.n_samples * self.split_ratios[0])
self.scaler = StandardScaler()
if self.whiten:
self.dataset = torch.tensor(
self.scaler.fit_transform(dataset), dtype=torch.float32
)
x0 = torch.tensor(self.scaler.transform(x0), dtype=torch.float32)
x1_1 = torch.tensor(self.scaler.transform(x1_1), dtype=torch.float32)
x1_2 = torch.tensor(self.scaler.transform(x1_2), dtype=torch.float32)
x1 = torch.cat([x1_1, x1_2], dim=0)
self.coords_t0 = x0
self.coords_t1 = x1
self.time_labels = np.concatenate([
np.zeros(len(self.coords_t0)), # t=0
np.ones(len(self.coords_t1)), # t=1
])
train_x0 = x0[:split_index]
val_x0 = x0[split_index:]
# branches
train_x1 = x1[:split_index]
val_x1 = x1[split_index:]
self.val_x0 = val_x0
train_x0_weights = torch.full((train_x0.shape[0], 1), fill_value=1.0)
train_x1_weights = torch.full((train_x1.shape[0], 1), fill_value=1.0)
val_x0_weights = torch.full((val_x0.shape[0], 1), fill_value=1.0)
val_x1_weights = torch.full((val_x1.shape[0], 1), fill_value=1.0)
# Adjust split_index to ensure minimum validation samples
if self.n_samples - split_index < self.batch_size:
split_index = self.n_samples - self.batch_size
self.train_dataloaders = {
"x0": DataLoader(TensorDataset(train_x0, train_x0_weights), batch_size=self.batch_size, shuffle=True, drop_last=True),
"x1": DataLoader(TensorDataset(train_x1, train_x1_weights), batch_size=self.batch_size, shuffle=True, drop_last=True),
}
self.val_dataloaders = {
"x0": DataLoader(TensorDataset(val_x0, val_x0_weights), batch_size=self.batch_size, shuffle=True, drop_last=True),
"x1": DataLoader(TensorDataset(val_x1, val_x1_weights), batch_size=self.batch_size, shuffle=True, drop_last=True),
}
# to edit?
self.test_dataloaders = {
"x0": DataLoader(TensorDataset(val_x0, val_x0_weights), batch_size=self.val_x0.shape[0], shuffle=True, drop_last=False),
"x1": DataLoader(TensorDataset(val_x1, val_x1_weights), batch_size=self.val_x0.shape[0], shuffle=True, drop_last=True),
"dataset": DataLoader(TensorDataset(self.dataset), batch_size=self.dataset.shape[0], shuffle=True, drop_last=False),
}
points = self.dataset.cpu().numpy()
x, y = points[:, 0], points[:, 1]
# Diagonal-based coordinates (rotated 45°)
u = (x + y) / np.sqrt(2) # along x=y
# start region (A) using u
u_thresh = np.percentile(u, 30) # tweak this threshold to control size
mask_A = u <= u_thresh
# among the rest, split by x=y diagonal
remaining = ~mask_A
mask_B = remaining & (x < y) # left of diagonal
mask_C = remaining & (x >= y) # right of diagonal
# Assign dataloaders
self.metric_samples_dataloaders = [
DataLoader(torch.tensor(points[mask_A], dtype=torch.float32), batch_size=points[mask_A].shape[0], shuffle=False),
DataLoader(torch.tensor(points[remaining], dtype=torch.float32), batch_size=points[remaining].shape[0], shuffle=False),
]
def train_dataloader(self):
combined_loaders = {
"train_samples": CombinedLoader(self.train_dataloaders, mode="min_size"),
"metric_samples": CombinedLoader(
self.metric_samples_dataloaders, mode="min_size"
),
}
return CombinedLoader(combined_loaders, mode="max_size_cycle")
def val_dataloader(self):
combined_loaders = {
"val_samples": CombinedLoader(self.val_dataloaders, mode="min_size"),
"metric_samples": CombinedLoader(
self.metric_samples_dataloaders, mode="min_size"
),
}
return CombinedLoader(combined_loaders, mode="max_size_cycle")
def test_dataloader(self):
combined_loaders = {
"test_samples": CombinedLoader(self.test_dataloaders, mode="min_size"),
"metric_samples": CombinedLoader(
self.metric_samples_dataloaders, mode="min_size"
),
}
return CombinedLoader(combined_loaders, mode="max_size_cycle")
def get_tangent_proj(self, points):
w = self.get_tangent_plane(points)
return partial(LidarSingleDataModule.projection_op, w=w)
def get_tangent_plane(self, points, temp=1e-3):
points_np = points.detach().cpu().numpy()
_, idx = self.tree.query(points_np, k=self.k)
nearest_pts = self.dataset[idx]
nearest_pts = torch.tensor(nearest_pts).to(points)
dists = (points.unsqueeze(1) - nearest_pts).pow(2).sum(-1, keepdim=True)
weights = torch.exp(-dists / temp)
# Fits plane with least vertical distance.
w = LidarSingleDataModule.fit_plane(nearest_pts, weights)
return w
@staticmethod
def fit_plane(points, weights=None):
"""Expects points to be of shape (..., 3).
Returns [a, b, c] such that the plane is defined as
ax + by + c = z
"""
D = torch.cat([points[..., :2], torch.ones_like(points[..., 2:3])], dim=-1)
z = points[..., 2]
if weights is not None:
Dtrans = D.transpose(-1, -2)
else:
DW = D * weights
Dtrans = DW.transpose(-1, -2)
w = torch.linalg.solve(
torch.matmul(Dtrans, D), torch.matmul(Dtrans, z.unsqueeze(-1))
).squeeze(-1)
return w
@staticmethod
def projection_op(x, w):
"""Projects points to a plane defined by w."""
# Normal vector to the tangent plane.
n = torch.cat([w[..., :2], -torch.ones_like(w[..., 2:3])], dim=1)
pn = torch.sum(x * n, dim=-1, keepdim=True)
nn = torch.sum(n * n, dim=-1, keepdim=True)
# Offset.
d = w[..., 2:3]
# Projection of x onto n.
projn_x = ((pn + d) / nn) * n
# Remove component in the normal direction.
return x - projn_x
def get_timepoint_data(self):
"""Return data organized by timepoints for visualization"""
return {
't0': self.coords_t0,
't1': self.coords_t1,
'time_labels': self.time_labels
}
def get_datamodule():
datamodule = LidarSingleDataModule(args)
datamodule.setup(stage="fit")
return datamodule |