File size: 8,699 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
import os
import pickle
from typing import Any, Union, List
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_pe import HydraModelPE
from navsim.agents.hydra.hydra_model_pe_det import HydraDetModelPE
from navsim.agents.hydra.hydra_model_pe_temporal import HydraModelTemporalPE
from navsim.agents.vadv2.vadv2_config import Vadv2Config
from navsim.agents.vadv2.vadv2_loss import three_to_two_classes
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
from typing import Dict, List
try:
from navsim.agents.utils.positional_encoding import SinePositionalEncoding3D
except:
print('sine pe not registered')
pass
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from navsim.agents.abstract_agent import AbstractAgent
def hydra_nodet_loss(
targets: Dict[str, torch.Tensor], predictions: Dict[str, torch.Tensor], config: Vadv2Config,
vocab_pdm_score
):
"""
Helper function calculating complete loss of Transfuser
:param targets: dictionary of name tensor pairings
:param predictions: dictionary of name tensor pairings
:param config: global Transfuser config
:return: combined loss value
"""
noc, da, ttc, comfort, progress = (predictions['noc'], predictions['da'],
predictions['ttc'],
predictions['comfort'], predictions['progress'])
imi = predictions['imi']
# 2 cls
da_loss = F.binary_cross_entropy(da, vocab_pdm_score['da'].to(da.dtype))
ttc_loss = F.binary_cross_entropy(ttc, vocab_pdm_score['ttc'].to(da.dtype))
comfort_loss = F.binary_cross_entropy(comfort, vocab_pdm_score['comfort'].to(da.dtype))
noc_loss = F.binary_cross_entropy(noc, three_to_two_classes(vocab_pdm_score['noc'].to(da.dtype)))
progress_loss = F.binary_cross_entropy(progress, vocab_pdm_score['progress'].to(progress.dtype))
vocab = predictions["trajectory_vocab"]
# B, 8 (4 secs, 0.5Hz), 3
target_traj = targets["trajectory"]
# 4, 9, ..., 39
sampled_timepoints = [5 * k - 1 for k in range(1, 9)]
B = target_traj.shape[0]
l2_distance = -((vocab[:, sampled_timepoints][None].repeat(B, 1, 1, 1) - target_traj[:, None]) ** 2) / config.sigma
imi_loss = F.cross_entropy(imi, l2_distance.sum((-2, -1)).softmax(1))
imi_loss_final = config.trajectory_imi_weight * imi_loss
noc_loss_final = config.trajectory_pdm_weight['noc'] * noc_loss
da_loss_final = config.trajectory_pdm_weight['da'] * da_loss
ttc_loss_final = config.trajectory_pdm_weight['ttc'] * ttc_loss
progress_loss_final = config.trajectory_pdm_weight['progress'] * progress_loss
comfort_loss_final = config.trajectory_pdm_weight['comfort'] * comfort_loss
loss = (
imi_loss_final
+ noc_loss_final
+ da_loss_final
+ ttc_loss_final
+ progress_loss_final
+ comfort_loss_final
)
return loss, {
'imi_loss': imi_loss_final,
'pdm_noc_loss': noc_loss_final,
'pdm_da_loss': da_loss_final,
'pdm_ttc_loss': ttc_loss_final,
'pdm_progress_loss': progress_loss_final,
'pdm_comfort_loss': comfort_loss_final
}
class HydraAgentTemporalPE(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': config.ttc_weight,
'progress': config.progress_weight,
'comfort': 1.0,
}
self._config = config
self._lr = lr
self.metrics = metrics
self._checkpoint_path = checkpoint_path
self.vadv2_model = HydraModelTemporalPE(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(
cam_f0=[0, 1, 2, 3],
cam_l0=[0, 1, 2, 3],
cam_l1=[0, 1, 2, 3],
cam_l2=[0, 1, 2, 3],
cam_r0=[0, 1, 2, 3],
cam_r1=[0, 1, 2, 3],
cam_r2=[0, 1, 2, 3],
cam_b0=[0, 1, 2, 3],
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:
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_nodet_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}",
)
]
|