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import numpy as np
import torch
from torch import optim
from torch import nn
from tqdm import tqdm
from matplotlib import pyplot as plt
import torch.nn.functional as F
from collections import defaultdict
import os
import roma
class unicycle(torch.nn.Module):
def __init__(self, train_timestamp, centers, eulers, heights=None):
super(unicycle, self).__init__()
self.train_timestamp = train_timestamp
self.delta = torch.diff(self.train_timestamp)
self.input_a = centers[:, 0].clone()
self.input_b = centers[:, 1].clone()
self.a = nn.Parameter(centers[:, 0])
self.b = nn.Parameter(centers[:, 1])
diff_a = torch.diff(centers[:, 0]) / self.delta
diff_b = torch.diff(centers[:, 1]) / self.delta
v = torch.sqrt(diff_a ** 2 + diff_b**2)
self.v = nn.Parameter(F.pad(v, (0, 1), 'constant', v[-1].item()))
self.pitchroll = eulers[:, :2]
self.yaw = nn.Parameter(eulers[:, -1])
if heights is None:
self.h = torch.zeros_like(train_timestamp).float()
else:
self.h = heights
def acc_omega(self):
acc = torch.diff(self.v) / self.delta
omega = torch.diff(self.yaw) / self.delta
acc = F.pad(acc, (0, 1), 'constant', acc[-1].item())
omega = F.pad(omega, (0, 1), 'constant', omega[-1].item())
return acc, omega
def forward(self, timestamp):
if timestamp < self.train_timestamp[0]:
delta_t = self.train_timestamp[0] - timestamp
a = self.a[0] - delta_t * torch.cos(self.yaw[0]) * self.v[0]
b = self.b[0] - delta_t * torch.sin(self.yaw[0]) * self.v[0]
return a, b, self.v[0], self.pitchroll[0], self.yaw[0], self.h[0]
elif timestamp > self.train_timestamp[-1]:
delta_t = timestamp - self.train_timestamp[-1]
a = self.a[-1] + delta_t * torch.cos(self.yaw[-1]) * self.v[-1]
b = self.b[-1] + delta_t * torch.sin(self.yaw[-1]) * self.v[-1]
return a, b, self.v[-1], self.pitchroll[-1], self.yaw[-1], self.h[-1]
idx = torch.searchsorted(self.train_timestamp, timestamp, side='left')
if self.train_timestamp[idx] == timestamp:
return self.a[idx], self.b[idx], self.v[idx], self.pitchroll[idx], self.yaw[idx], self.h[idx]
else:
prev_timestamps = self.train_timestamp[idx-1]
delta_t = timestamp - prev_timestamps
prev_a, prev_b = self.a[idx-1], self.b[idx-1]
prev_v, prev_yaw = self.v[idx-1], self.yaw[idx-1]
acc, omega = self.acc_omega()
v = prev_v + acc[idx-1] * delta_t
yaw = prev_yaw + omega[idx-1] * delta_t
a = prev_a + prev_v * ((torch.sin(yaw) - torch.sin(prev_yaw)) / (omega[idx-1] + 1e-6))
b = prev_b - prev_v * ((torch.cos(yaw) - torch.cos(prev_yaw)) / (omega[idx-1] + 1e-6))
h = self.h[idx-1]
return a, b, v, self.pitchroll[idx-1], yaw, h
def capture(self):
return (
self.a,
self.b,
self.v,
self.pitchroll,
self.yaw,
self.h,
self.train_timestamp,
self.delta
)
@classmethod
def restore(cls, model_args):
(
a,
b,
v,
pitchroll,
yaw,
h,
train_timestamp,
delta
) = model_args
model = cls(train_timestamp,
torch.stack([a.clone().detach(),b.clone().detach()], dim=-1),
torch.concat([pitchroll.clone().detach(), yaw.clone().detach()[:, None]], dim=-1),
h.clone().detach())
model.a = a
model.b = b
model.v = v
model.pitchroll = pitchroll
model.yaw = yaw
model.h = h
model.train_timestamp = train_timestamp
model.delta = delta
return model
def visualize(self, save_path, noise_centers=None, gt_centers=None):
a = self.a.detach().cpu().numpy()
b = self.b.detach().cpu().numpy()
yaw = self.yaw.detach().cpu().numpy()
plt.scatter(a, b, marker='x', color='b')
plt.quiver(a, b, np.ones_like(a) * np.cos(yaw), np.ones_like(b) * np.sin(yaw), scale=20, width=0.005)
if noise_centers is not None:
noise_centers = noise_centers.detach().cpu().numpy()
plt.scatter(noise_centers[:, 0], noise_centers[:, 1], marker='o', color='gray')
if gt_centers is not None:
gt_centers = gt_centers.detach().cpu().numpy()
plt.scatter(gt_centers[:, 0], gt_centers[:, 1], marker='v', color='g')
plt.axis('equal')
plt.savefig(save_path)
plt.close()
def reg_loss(self):
reg = 0
acc, omega = self.acc_omega()
reg += torch.mean(torch.abs(torch.diff(acc))) * 0.01
reg += torch.mean(torch.abs(torch.diff(omega))) * 0.1
reg_a_motion = self.v[:-1] * ((torch.sin(self.yaw[1:]) - torch.sin(self.yaw[:-1])) / (omega[:-1] + 1e-6))
reg_b_motion = -self.v[:-1] * ((torch.cos(self.yaw[1:]) - torch.cos(self.yaw[:-1])) / (omega[:-1] + 1e-6))
reg_a = self.a[:-1] + reg_a_motion
reg_b = self.b[:-1] + reg_b_motion
reg += torch.mean((reg_a - self.a[1:])**2 + (reg_b - self.b[1:])**2) * 1
return reg
def pos_loss(self):
return torch.mean((self.a - self.input_a) ** 2 + (self.b - self.input_b) ** 2) * 10
def create_unicycle_model(train_cams, model_path, opt_iter=0, opt_pos=False, data_type='kitti'):
unicycle_models = {}
if data_type == 'kitti':
cameras = [cam for cam in train_cams if 'cam_0' in cam.image_name]
elif data_type == 'waymo':
cameras = [cam for cam in train_cams if 'cam_1' in cam.image_name]
elif data_type == 'nuscenes':
cameras = [cam for cam in train_cams if (('CAM_FRONT' in cam.image_name) and ('LEFT' not in cam.image_name) and ('RIGHT' not in cam.image_name))]
elif data_type == 'pandaset':
cameras = [cam for cam in train_cams if 'front_camera' in cam.image_name]
else:
raise NotImplementedError
cameras = sorted(cameras, key=lambda x: x.timestamp)
os.makedirs(os.path.join(model_path, "unicycle"), exist_ok=True)
start_time = cameras[0].timestamp
all_centers, all_heights, all_eulers, all_timestamps = defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)
for cam in cameras:
t = cam.timestamp - start_time
for track_id, b2w in cam.dynamics.items():
all_centers[track_id].append(b2w[[0, 2], 3])
all_heights[track_id].append(b2w[1, 3])
eulers = roma.rotmat_to_euler('xzy', b2w[:3, :3])
all_eulers[track_id].append(-eulers + torch.pi / 2)
# all_eulers[track_id].append(eulers)
all_timestamps[track_id].append(t)
for track_id in all_centers.keys():
centers = torch.stack(all_centers[track_id], dim=0).cuda()
timestamps = torch.tensor(all_timestamps[track_id]).cuda()
heights = torch.tensor(all_heights[track_id]).cuda()
eulers = torch.stack(all_eulers[track_id]).cuda()
model = unicycle(timestamps, centers.clone(), eulers.clone(), heights.clone())
model.visualize(os.path.join(model_path, "unicycle", f"{track_id}_init.png"))
l = [
{'params': [model.v], 'lr': 1e-3, "name": "v"},
{'params': [model.yaw], 'lr': 1e-4, "name": "yaw"},
]
if opt_pos:
l.extend([
{'params': [model.a], 'lr': 1e-3, "name": "a"},
{'params': [model.b], 'lr': 1e-3, "name": "b"},
])
optimizer = optim.Adam(l, lr=0.0)
t_range = tqdm(range(opt_iter), desc=f"Fitting {track_id}")
for iter in t_range:
loss = 5e-3 * model.reg_loss() + 1e-3 * model.pos_loss()
t_range.set_postfix({'loss': loss.item()})
optimizer.zero_grad()
loss.backward()
optimizer.step()
unicycle_models[track_id] = {'model': model,
'optimizer': optimizer,
'input_centers': centers}
model.visualize(os.path.join(model_path, "unicycle", f"{track_id}_iter0.png"))
torch.save(model.capture(), os.path.join(model_path, f"ckpts/unicycle_{track_id}.pth"))
return unicycle_models
if __name__ == "__main__":
from scene import Scene, GaussianModel
from omegaconf import OmegaConf
from argparse import ArgumentParser
parser = ArgumentParser(description="Training script parameters")
parser.add_argument("--base_cfg", type=str, default="./configs/gs_base.yaml")
parser.add_argument("--data_cfg", type=str, default="./configs/nusc.yaml")
parser.add_argument("--source_path", type=str, default="")
parser.add_argument("--model_path", type=str, default="")
args = parser.parse_args()
cfg = OmegaConf.merge(OmegaConf.load(args.base_cfg), OmegaConf.load(args.data_cfg))
if len(args.source_path) > 0:
cfg.source_path = args.source_path
if len(args.model_path) > 0:
cfg.model_path = args.model_path
gaussians = GaussianModel(3, feat_mutable=True)
print("loading scene...")
scene = Scene(cfg, gaussians, data_type=cfg.data_type)
create_unicycle_model(scene.getTrainCameras(), cfg.model_path, 500, False, cfg.data_type) |