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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.vadv2.vadv2_config import Vadv2Config
from navsim.agents.vadv2.vadv2_features import (
Vadv2FeatureBuilder,
Vadv2TargetBuilder,
)
from navsim.agents.vadv2.vadv2_loss import vadv2_loss_pdm_w_progress
from navsim.agents.vadv2.vadv2_pdm_model_progress import Vadv2ModelPDMProgress
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 Vadv2AgentPDMProgress(AbstractAgent):
def __init__(
self,
config: Vadv2Config,
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,
}
self._config = config
self._lr = lr
self.metrics = metrics
self._checkpoint_path = checkpoint_path
self.vadv2_model = Vadv2ModelPDMProgress(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."""
# if torch.cuda.is_available():
# state_dict: Dict[str, Any] = torch.load(self._checkpoint_path)["state_dict"]
# else:
# state_dict: Dict[str, Any] = torch.load(self._checkpoint_path, map_location=torch.device("cpu"))[
# "state_dict"]
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.build_mm_sensors()
def get_target_builders(self) -> List[AbstractTargetBuilder]:
return [Vadv2TargetBuilder(config=self._config)]
def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
return [Vadv2FeatureBuilder(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 vadv2_loss_pdm_w_progress(targets, predictions, self._config, scores)
def get_optimizers(self) -> Union[Optimizer, Dict[str, Union[Optimizer, LRScheduler]]]:
if self._config.backbone_type == 'moe':
backbone_params_eva = '_backbone.image_encoder.eva'
backbone_params_da = '_backbone.image_encoder.davit'
img_backbone_params = list(
filter(lambda kv: backbone_params_eva in kv[0] or backbone_params_da in kv[0], self.vadv2_model.named_parameters())
)
default_params = list(filter(lambda kv: backbone_params_da not in kv[0] and backbone_params_eva 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)
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}",
)
]
def compute_trajectory(self, agent_input):
"""
Submission
"""
self.eval()
features: Dict[str, torch.Tensor] = {}
# build features
for builder in self.get_feature_builders():
features.update(builder.compute_features(agent_input))
# add batch dimension
features = {k: v.unsqueeze(0).cuda() for k, v in features.items()}
vocab = self.vadv2_model._trajectory_head.vocab
self.vadv2_model = self.vadv2_model.cuda()
# forward pass
with torch.no_grad():
predictions = self.vadv2_model(features)
imis = predictions["imi"].softmax(-1).log().cpu().numpy()
nocs = predictions["noc"].log().cpu().numpy()
das = predictions["da"].log().cpu().numpy()
ttcs = predictions["ttc"].log().cpu().numpy()
comforts = predictions["comfort"].log().cpu().numpy()
progresses = predictions["progress"].log().cpu().numpy()
imi_weight = 0.1
noc_weight = 0.25
da_weight = 2.0
ttc_weight = 3.0
progress_weight = 5.0
comfort_weight = 1.0
tpc_weight = 2.25
# A temporary trajectory for choosing the best epoch -> for grid search
score = (
imi_weight * imis +
noc_weight * nocs +
da_weight * das +
tpc_weight * (
ttc_weight * ttcs +
comfort_weight * comforts +
progress_weight * progresses
)
)[0].argmax(0)
traj = vocab[score].cpu().numpy()
return Trajectory(traj,
TrajectorySampling(time_horizon=4, interval_length=0.1))
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