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update pretrained model

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TJU-Lu 2025-12-05 23:09:22 +08:00
parent 39cece7fac
commit b92ca64731
6 changed files with 4 additions and 5 deletions
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2
README.md
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@ -101,7 +101,7 @@ You can refer to [config.yaml](Simulator/src/config/config.yaml) for modificatio
**3. Start the YOPO Planner** **3. Start the YOPO Planner**
You can refer to [traj_opt.yaml](YOPO/config/traj_opt.yaml) for modification of the flight speed (The given weights are pretrained at 6 m/s and perform smoothly at speeds between 0 - 6 m/s). You can refer to [traj_opt.yaml](YOPO/config/traj_opt.yaml) for modification of the flight speed (The given weights are pretrained at 6 m/s and perform smoothly at speeds between 0 - 6 m/s, and more pretrained models are available at [Releases](https://github.com/TJU-Aerial-Robotics/YOPO/releases)).
``` ```
cd YOPO cd YOPO

2
YOPO/loss/guidance_loss.py
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@ -66,7 +66,7 @@ class GuidanceLoss(nn.Module):
goal_length = goal_dir.norm(dim=1) # [B] goal_length = goal_dir.norm(dim=1) # [B]
# length difference along goal direction (cosine similarity) # length difference along goal direction (cosine similarity)
parallel_diff = (goal_length - traj_along).abs() # [B] parallel_diff = F.smooth_l1_loss(goal_length, traj_along, reduction='none')
# length perpendicular to goal direction # length perpendicular to goal direction
traj_perp = traj_dir - traj_along.unsqueeze(1) * goal_dir_norm # [B, 3] traj_perp = traj_dir - traj_along.unsqueeze(1) * goal_dir_norm # [B, 3]

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YOPO/saved/YOPO_1/epoch50.pth Executable file → Normal file
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YOPO/saved/YOPO_1/events.out.tfevents.1749801999.610.2449545.0
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YOPO/saved/YOPO_1/events.out.tfevents.1763990916.610.3199703.0 Normal file
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5
YOPO/test_yopo_ros.py
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@ -140,7 +140,7 @@ class YopoNet:
obs = np.concatenate((vel_c, acc_c, goal_c), axis=0).astype(np.float32) obs = np.concatenate((vel_c, acc_c, goal_c), axis=0).astype(np.float32)
obs_norm = self.state_transform.normalize_obs(torch.from_numpy(obs[None, :])) obs_norm = self.state_transform.normalize_obs(torch.from_numpy(obs[None, :]))
return obs_norm.to(self.device, non_blocking=True) return obs_norm
@torch.inference_mode() @torch.inference_mode()
def callback_depth(self, data): def callback_depth(self, data):
@ -171,9 +171,8 @@ class YopoNet:
# input prepare # input prepare
time1 = time.time() time1 = time.time()
depth_input = torch.from_numpy(depth).to(self.device, non_blocking=True) # (non_blocking: copying speed 3x) depth_input = torch.from_numpy(depth).to(self.device, non_blocking=True) # (non_blocking: copying speed 3x)
obs_norm = self.process_odom() obs_norm = self.process_odom().to(self.device, non_blocking=True)
obs_input = self.state_transform.prepare_input(obs_norm) obs_input = self.state_transform.prepare_input(obs_norm)
obs_input = obs_input.to(self.device, non_blocking=True)
# torch.cuda.synchronize() # torch.cuda.synchronize()
time2 = time.time() time2 = time.time()