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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.dreamer.hydra_dreamer_config import HydraDreamerConfig
from navsim.agents.dreamer.hydra_dreamer_planning_model import HydraDreamerPlanningModel
from navsim.agents.dreamer.hydra_dreamer_wm_features import HydraDreamerWmFeatureBuilder
from navsim.agents.hydra.hydra_features import HydraFeatureBuilder, HydraTargetBuilder
from navsim.agents.hydra.hydra_loss_fn import hydra_kd_imi_agent_loss
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 navsim.agents.abstract_agent import AbstractAgent
class HydraDreamerPlanningAgent(AbstractAgent):
def __init__(
self,
config: HydraDreamerConfig,
lr: float,
checkpoint_path: str = None,
dreamer_ckpt_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,
}
self._config = config
self._lr = lr
self.metrics = metrics
self._checkpoint_path = checkpoint_path
self.dreamer_ckpt_path = dreamer_ckpt_path
self.vadv2_model = HydraDreamerPlanningModel(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'))
def name(self) -> str:
"""Inherited, see superclass."""
return self.__class__.__name__
def initialize(self) -> None:
"""Inherited, see superclass."""
planner_state_dict: Dict[str, Any] = torch.load(
self._checkpoint_path,
map_location=torch.device("cpu")
)["state_dict"]
dreamer_state_dict: Dict[str, Any] = torch.load(
self.dreamer_ckpt_path,
map_location=torch.device("cpu")
)["state_dict"]
state_dict = {}
for k, v in planner_state_dict.items():
# ignore backbone
if '_backbone' not in k:
state_dict[k] = v
for k, v in dreamer_state_dict.items():
new_k = k.replace('agent.', 'agent.vadv2_model.')
state_dict[new_k] = v
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=True,
cam_l0=True,
cam_l1=True,
cam_l2=True,
cam_r0=True,
cam_r1=True,
cam_r2=True,
cam_b0=True,
lidar_pc=[],
)
def get_target_builders(self) -> List[AbstractTargetBuilder]:
return [HydraTargetBuilder(config=self._config)]
def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
return [HydraDreamerWmFeatureBuilder(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:
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}",
)
]
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