File size: 5,551 Bytes
da2e2ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import os
import pickle
from typing import Any, Union
import numpy as np
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from navsim.agents.hydra.hydra_config import HydraConfig
from navsim.agents.hydra.hydra_features import HydraFeatureBuilder, HydraTargetBuilder
from navsim.agents.hydra.hydra_loss_fn import hydra_kd_imi_agent_loss
from navsim.agents.hydra.hydra_model import HydraModel
from navsim.common.dataclasses import SensorConfig
from navsim.planning.training.abstract_feature_target_builder import (
AbstractFeatureBuilder,
AbstractTargetBuilder,
)
DEVKIT_ROOT = os.getenv('NAVSIM_DEVKIT_ROOT')
TRAJ_PDM_ROOT = os.getenv('NAVSIM_TRAJPDM_ROOT')
from typing import Dict, List
import pytorch_lightning as pl
import torch
from nuplan.planning.simulation.trajectory.trajectory_sampling import TrajectorySampling
from navsim.agents.abstract_agent import AbstractAgent
from navsim.common.dataclasses import Trajectory
class HydraAgent(AbstractAgent):
def __init__(
self,
config: HydraConfig,
lr: float,
checkpoint_path: str = None,
pdm_split=None,
metrics=None,
):
super().__init__()
config.trajectory_pdm_weight = {
'noc': 3.0,
'da': 3.0,
'ttc': 2.0,
'progress': config.progress_weight,
'comfort': 1.0,
'ddc': 1.0,
'lk': config.progress_weight,
'tl': 3.0,
}
self._config = config
self._lr = lr
self.metrics = metrics
self._checkpoint_path = checkpoint_path
self.vadv2_model = HydraModel(config)
self.vocab_size = config.vocab_size
self.backbone_wd = config.backbone_wd
new_pkl_dir = f'vocab_score_full_{self.vocab_size}_navtrain'
self.vocab_pdm_score_full = pickle.load(
open(f'{TRAJ_PDM_ROOT}/{new_pkl_dir}/{pdm_split}.pkl', 'rb'))
# todo
self.vocab_pdm_score_expansion = pickle.load(
open(f'{xxx}/{xxx}/{xxx}.pkl', 'rb'))
def name(self) -> str:
"""Inherited, see superclass."""
return self.__class__.__name__
def initialize(self) -> None:
"""Inherited, see superclass."""
state_dict: Dict[str, Any] = torch.load(self._checkpoint_path, map_location=torch.device("cpu"))["state_dict"]
self.load_state_dict({k.replace("agent.", ""): v for k, v in state_dict.items()})
def get_sensor_config(self) -> SensorConfig:
"""Inherited, see superclass."""
return SensorConfig(
cam_f0=[6],
cam_l0=[6],
cam_l1=[6],
cam_l2=[6],
cam_r0=[6],
cam_r1=[6],
cam_r2=[6],
cam_b0=[6],
lidar_pc=[],
)
def get_target_builders(self) -> List[AbstractTargetBuilder]:
return [HydraTargetBuilder(config=self._config)]
def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
return [HydraFeatureBuilder(config=self._config)]
def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
return self.vadv2_model(features)
def forward_train(self, features, interpolated_traj):
return self.vadv2_model(features, interpolated_traj)
def compute_loss(
self,
features: Dict[str, torch.Tensor],
targets: Dict[str, torch.Tensor],
predictions: Dict[str, torch.Tensor],
tokens=None
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
# get the pdm score by tokens
scores = {}
for k in self.metrics:
if k == 'tl' or k == 'lk' or k == 'ddc':
tmp = [self.vocab_pdm_score_expansion[token][k][None] for token in tokens]
scores[k] = (torch.from_numpy(np.concatenate(tmp, axis=0))
.to(predictions['trajectory'].device))
else:
tmp = [self.vocab_pdm_score_full[token][k][None] for token in tokens]
scores[k] = (torch.from_numpy(np.concatenate(tmp, axis=0))
.to(predictions['trajectory'].device))
return hydra_kd_imi_agent_loss(targets, predictions, self._config, scores)
def get_optimizers(self) -> Union[Optimizer, Dict[str, Union[Optimizer, LRScheduler]]]:
backbone_params_name = '_backbone.image_encoder'
img_backbone_params = list(
filter(lambda kv: backbone_params_name in kv[0], self.vadv2_model.named_parameters()))
default_params = list(filter(lambda kv: backbone_params_name not in kv[0], self.vadv2_model.named_parameters()))
params_lr_dict = [
{'params': [tmp[1] for tmp in default_params]},
{
'params': [tmp[1] for tmp in img_backbone_params],
'lr': self._lr * self._config.lr_mult_backbone,
'weight_decay': self.backbone_wd
}
]
return torch.optim.Adam(params_lr_dict, lr=self._lr)
def get_training_callbacks(self) -> List[pl.Callback]:
return [
# TransfuserCallback(self._config),
ModelCheckpoint(
save_top_k=30,
monitor="val/loss_epoch",
mode="min",
dirpath=f"{os.environ.get('NAVSIM_EXP_ROOT')}/{self._config.ckpt_path}/",
filename="{epoch:02d}-{step:04d}",
)
] |