navsim/agents/hydra/hydra_agent_pe_nodet.py

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_model_pe_nodet import HydraModelPENoDet
from navsim.agents.vadv2.vadv2_config import Vadv2Config
from navsim.agents.vadv2.vadv2_loss import three_to_two_classes
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
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 HydraAgentPENoDet(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 = HydraModelPENoDet(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}",
            )
        ]