+
+ |
+ Name |
+ Description |
+ Logs |
+ Sensors |
+ Config parameters |
+
+
+ | Standard |
+ trainval |
+ Large split for training and validating agents with regular driving recordings. Corresponds to nuPlan and downsampled to 2HZ. |
+ 14GB |
+ >2000GB |
+
+ split=trainval
+ scene_filter=all_scenes
+ |
+
+
+ | test |
+ Small split for testing agents with regular driving recordings. Corresponds to nuPlan and downsampled to 2HZ. |
+ 1GB |
+ 217GB |
+
+ split=test
+ scene_filter=all_scenes
+ |
+
+
+ | mini |
+ Demo split for with regular driving recordings. Corresponds to nuPlan and downsampled to 2HZ. |
+ 1GB |
+ 151GB |
+
+ split=mini
+ scene_filter=all_scenes
+ |
+
+
+ | NAVSIM |
+ navtrain |
+ Standard split for training agents in NAVSIM with non-trivial driving scenes. Sensors available separately in download_navtrain.sh. |
+ - |
+ 445GB* |
+
+ split=trainval
+ scene_filter=navtrain
+ |
+
+
+ | navtest |
+ Standard split for testing agents in NAVSIM with non-trivial driving scenes. Available as a filter for test split. |
+ - |
+ - |
+
+ split=test
+ scene_filter=navtest
+ |
+
+
+ | Competition |
+ warmup_test_e2e |
+ Warmup test split to validate submission on hugging face. Available as a filter for mini split. |
+ - |
+ - |
+
+ split=mini
+ scene_filter=warmup_test_e2e
+ |
+
+
+ | private_test_e2e |
+ Private test split for the challenge leaderboard on hugging face. |
+ <1GB |
+ 25GB |
+
+ split=private_test_e2e
+ scene_filter=private_test_e2e
+ |
+
+
+
+(*300GB without history)
+
+## Splits
+
+The standard splits `trainval`, `test`, and `mini` are from the OpenScene dataset. Note that the data corresponds to the nuPlan dataset with a lower frequency of 2Hz. You can download all standard splits over Hugging Face with the bash scripts in [download](../download)
+
+NAVSIM provides a subset and filter of the `trainval` split, called `navtrain`. The `navtrain` split facilitates a standardized training scheme and requires significantly less sensor data storage than `travel` (445GB vs. 2100GB). If your agents don't need historical sensor inputs, you can download `navtrain` without history, which requires 300GB of storage. Note that `navtrain` can be downloaded separately via [download_navtrain.sh](https://github.com/autonomousvision/navsim/blob/main/download/download_navtrain.sh) but still requires access to the `trainval` logs. Similarly, the `navtest` split enables a standardized set for testing agents with a provided scene filter. Both `navtrain` and `navtest` are filtered to increase interesting samples in the sets.
+
+For the challenge on Hugging Face, we provide the `warmup_test_e2e` and `private_test_e2e` for the warm-up and challenge track, respectively. Note that `private_test_e2e` requires you to download the data, while `warmup_test_e2e` is a scene filter for the `mini` split.
diff --git a/docs/submission.md b/docs/submission.md
index bd8ba9f..e890abd 100644
--- a/docs/submission.md
+++ b/docs/submission.md
@@ -10,12 +10,12 @@ Follow the [submission instructions on huggingface](https://huggingface.co/space
### Warm-up track
The warm-up track evaluates your submission on a [warm-up leaderboard](https://huggingface.co/spaces/AGC2024-P/e2e-driving-warmup) based on the `mini` split. This allows you to test your method and get familiar with the devkit and the submission procedure, with a less restrictive submission budget (up to 5 submissions daily). Instructions on making a submission on HuggingFace are available in the HuggingFace space. Performance on the warm-up leaderboard is not taken into consideration for determining your team's ranking for the 2024 Autonomous Grand Challenge.
-Use the script `run_create_submission_pickle_warmup.sh` which already contains the overrides `scene_filter=warmup_test` and `split=mini` to generate the submission file for the warmup track.
+Use the script `run_create_submission_pickle_warmup.sh` which already contains the overrides `scene_filter=warmup_test_e2e` and `split=mini` to generate the submission file for the warmup track.
You should be able to obtain the same evaluation results as on the server, by running the evaluation locally.
-To do so, use the overrides `scene_filter=warmup_test` when executing the script to run the PDM scoring (e.g., `run_cv_pdm_score_evaluation.sh` for the constant-velocity agent).
+To do so, use the overrides `scene_filter=warmup_test_e2e` when executing the script to run the PDM scoring (e.g., `run_cv_pdm_score_evaluation.sh` for the constant-velocity agent).
### Formal track
This is the [official challenge leaderboard](https://huggingface.co/spaces/AGC2024-P/e2e-driving-2024), based on secret held-out test frames (see submission_test split on the install page).
-Use the script `run_create_submission_pickle.sh`. It will by default run with `scene_filter=competition_test` and `split=competition_test`.
+Use the script `run_create_submission_pickle.sh`. It will by default run with `scene_filter=private_test_e2e` and `split=private_test_e2e`.
You only need to set your own agent with the `agent` override.
diff --git a/download/download_navtrain.sh b/download/download_navtrain.sh
new file mode 100755
index 0000000..c1eaf0d
--- /dev/null
+++ b/download/download_navtrain.sh
@@ -0,0 +1,19 @@
+for split in {1..5}; do
+ wget https://s3.eu-central-1.amazonaws.com/avg-projects-2/navsim/navtrain_current_${split}.tgz
+ echo "Extracting file navtrain_current_${split}.tgz"
+ tar -xzf navtrain_current_${split}.tgz
+ rm navtrain_current_${split}.tgz
+
+ mv navtrain_current_${split} navtrain
+ rm -r navtrain_current_${split}
+done
+
+for split in {1..5}; do
+ wget https://s3.eu-central-1.amazonaws.com/avg-projects-2/navsim/navtrain_history_${split}.tgz
+ echo "Extracting file navtrain_history_${split}.tgz"
+ tar -xzf navtrain_history_${split}.tgz
+ rm navtrain_history_${split}.tgz
+
+ mv navtrain_history_${split} navtrain
+ rm -r navtrain_history_${split}
+done
diff --git a/download/download_competition_test.sh b/download/download_private_test_e2e.sh
similarity index 81%
rename from download/download_competition_test.sh
rename to download/download_private_test_e2e.sh
index 6326b44..964f011 100755
--- a/download/download_competition_test.sh
+++ b/download/download_private_test_e2e.sh
@@ -1,9 +1,9 @@
wget https://huggingface.co/datasets/OpenDriveLab/OpenScene/resolve/main/openscene-v1.1/openscene_metadata_private_test_e2e.tgz
tar -xzf openscene_metadata_private_test_e2e.tgz
rm openscene_metadata_private_test_e2e.tgz
-mv competition_test competition_test_navsim_logs
+mv private_test_e2e private_test_e2e_navsim_logs
wget https://huggingface.co/datasets/OpenDriveLab/OpenScene/resolve/main/openscene-v1.1/openscene_sensor_private_test_e2e.tgz
tar -xzf openscene_sensor_private_test_e2e.tgz
rm openscene_sensor_private_test_e2e.tgz
-mv competition_test competition_test_sensor_blobs
+mv competition_test private_test_e2e_sensor_blobs
diff --git a/navsim/agents/abstract_agent.py b/navsim/agents/abstract_agent.py
index ddda757..b140fce 100644
--- a/navsim/agents/abstract_agent.py
+++ b/navsim/agents/abstract_agent.py
@@ -1,6 +1,7 @@
from abc import abstractmethod, ABC
from typing import Dict, Union, List
import torch
+import pytorch_lightning as pl
from navsim.common.dataclasses import AgentInput, Trajectory, SensorConfig
from navsim.planning.training.abstract_feature_target_builder import AbstractFeatureBuilder, AbstractTargetBuilder
@@ -62,6 +63,7 @@ def compute_trajectory(self, agent_input: AgentInput) -> Trajectory:
:param current_input: Dataclass with agent inputs.
:return: Trajectory representing the predicted ego's position in future
"""
+ self.eval()
features : Dict[str, torch.Tensor] = {}
# build features
for builder in self.get_feature_builders():
@@ -102,4 +104,13 @@ def get_optimizers(
Returns the optimizers that are used by thy pytorch-lightning trainer.
Has to be either a single optimizer or a dict of optimizer and lr scheduler.
"""
- raise NotImplementedError("No optimizers. Agent does not support training.")
\ No newline at end of file
+ raise NotImplementedError("No optimizers. Agent does not support training.")
+
+ def get_training_callbacks(
+ self
+ ) -> List[pl.Callback]:
+ """
+ Returns a list of pytorch-lightning callbacks that are used during training.
+ See navsim.planning.training.callbacks for examples.
+ """
+ return []
\ No newline at end of file
diff --git a/navsim/agents/ego_status_mlp_agent.py b/navsim/agents/ego_status_mlp_agent.py
index 3dfd5f0..a535bae 100644
--- a/navsim/agents/ego_status_mlp_agent.py
+++ b/navsim/agents/ego_status_mlp_agent.py
@@ -6,10 +6,12 @@
from navsim.agents.abstract_agent import AbstractAgent
from navsim.common.dataclasses import AgentInput, SensorConfig
-from navsim.planning.training.abstract_feature_target_builder import AbstractFeatureBuilder, AbstractTargetBuilder
+from navsim.planning.training.abstract_feature_target_builder import (
+ AbstractFeatureBuilder,
+ AbstractTargetBuilder,
+)
from navsim.common.dataclasses import Scene
-
import torch
@@ -17,20 +19,26 @@ class EgoStatusFeatureBuilder(AbstractFeatureBuilder):
def __init__(self):
pass
+ def get_unique_name(self) -> str:
+ return "ego_status_feature"
+
def compute_features(self, agent_input: AgentInput) -> Dict[str, torch.Tensor]:
ego_status = agent_input.ego_statuses[-1]
velocity = torch.tensor(ego_status.ego_velocity)
acceleration = torch.tensor(ego_status.ego_acceleration)
driving_command = torch.tensor(ego_status.driving_command)
- ego_state_feature = torch.cat([velocity, acceleration, driving_command], dim=-1)
+ ego_status_feature = torch.cat([velocity, acceleration, driving_command], dim=-1)
- return {"ego_state": ego_state_feature}
+ return {"ego_status": ego_status_feature}
class TrajectoryTargetBuilder(AbstractTargetBuilder):
def __init__(self, trajectory_sampling: TrajectorySampling):
self._trajectory_sampling = trajectory_sampling
+ def get_unique_name(self) -> str:
+ return "trajectory_target"
+
def compute_targets(self, scene: Scene) -> Dict[str, torch.Tensor]:
future_trajectory = scene.get_future_trajectory(
num_trajectory_frames=self._trajectory_sampling.num_poses
@@ -69,27 +77,30 @@ def name(self) -> str:
def initialize(self) -> None:
"""Inherited, see superclass."""
- state_dict: Dict[str, Any] = torch.load(self._checkpoint_path)["state_dict"]
- self.load_state_dict({k.replace("agent.",""):v for k,v in state_dict.items()})
+ 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"]
+ 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_no_sensors()
-
+ return SensorConfig.build_no_sensors()
+
def get_target_builders(self) -> List[AbstractTargetBuilder]:
return [
- TrajectoryTargetBuilder(
- trajectory_sampling=self._trajectory_sampling
- ),
+ TrajectoryTargetBuilder(trajectory_sampling=self._trajectory_sampling),
]
-
+
def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
return [EgoStatusFeatureBuilder()]
-
+
def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
- poses: torch.Tensor = self._mlp(features["ego_state"])
+ poses: torch.Tensor = self._mlp(features["ego_status"])
return {"trajectory": poses.reshape(-1, self._trajectory_sampling.num_poses, 3)}
-
+
def compute_loss(
self,
features: Dict[str, torch.Tensor],
@@ -97,6 +108,6 @@ def compute_loss(
predictions: Dict[str, torch.Tensor],
) -> torch.Tensor:
return torch.nn.functional.l1_loss(predictions["trajectory"], targets["trajectory"])
-
+
def get_optimizers(self) -> Optimizer | Dict[str, Optimizer | LRScheduler]:
- return torch.optim.Adam(self._mlp.parameters(), lr=self._lr)
\ No newline at end of file
+ return torch.optim.Adam(self._mlp.parameters(), lr=self._lr)
diff --git a/navsim/agents/transfuser/transfuser_agent.py b/navsim/agents/transfuser/transfuser_agent.py
new file mode 100644
index 0000000..cef1b26
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_agent.py
@@ -0,0 +1,77 @@
+from typing import Any, List, Dict, Union
+
+import torch
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+import pytorch_lightning as pl
+
+from navsim.agents.abstract_agent import AbstractAgent
+from navsim.common.dataclasses import SensorConfig
+from navsim.planning.training.abstract_feature_target_builder import (
+ AbstractFeatureBuilder,
+ AbstractTargetBuilder,
+)
+from navsim.agents.transfuser.transfuser_config import TransfuserConfig
+from navsim.agents.transfuser.transfuser_model import TransfuserModel
+from navsim.agents.transfuser.transfuser_callback import TransfuserCallback
+from navsim.agents.transfuser.transfuser_loss import transfuser_loss
+from navsim.agents.transfuser.transfuser_features import (
+ TransfuserFeatureBuilder,
+ TransfuserTargetBuilder,
+)
+
+
+class TransfuserAgent(AbstractAgent):
+ def __init__(
+ self,
+ config: TransfuserConfig,
+ lr: float,
+ checkpoint_path: str = None,
+ ):
+ super().__init__()
+
+ self._config = config
+ self._lr = lr
+
+ self._checkpoint_path = checkpoint_path
+ self._transfuser_model = TransfuserModel(config)
+
+ 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"]
+ 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_all_sensors(include=[3])
+
+ def get_target_builders(self) -> List[AbstractTargetBuilder]:
+ return [TransfuserTargetBuilder(config=self._config)]
+
+ def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
+ return [TransfuserFeatureBuilder(config=self._config)]
+
+ def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+ return self._transfuser_model(features)
+
+ def compute_loss(
+ self,
+ features: Dict[str, torch.Tensor],
+ targets: Dict[str, torch.Tensor],
+ predictions: Dict[str, torch.Tensor],
+ ) -> torch.Tensor:
+ return transfuser_loss(targets, predictions, self._config)
+
+ def get_optimizers(self) -> Union[Optimizer, Dict[str, Union[Optimizer, LRScheduler]]]:
+ return torch.optim.Adam(self._transfuser_model.parameters(), lr=self._lr)
+
+ def get_training_callbacks(self) -> List[pl.Callback]:
+ return [TransfuserCallback(self._config)]
diff --git a/navsim/agents/transfuser/transfuser_backbone.py b/navsim/agents/transfuser/transfuser_backbone.py
new file mode 100644
index 0000000..f3bc298
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_backbone.py
@@ -0,0 +1,532 @@
+"""
+Implements the TransFuser vision backbone.
+"""
+
+import math
+import torch
+from torch import nn
+import torch.nn.functional as F
+import timm
+import copy
+
+
+class TransfuserBackbone(nn.Module):
+ """
+ Multi-scale Fusion Transformer for image + LiDAR feature fusion
+ """
+
+ def __init__(self, config):
+
+ super().__init__()
+ self.config = config
+
+ self.image_encoder = timm.create_model(
+ config.image_architecture, pretrained=True, features_only=True
+ )
+ if config.use_ground_plane:
+ in_channels = 2 * config.lidar_seq_len
+ else:
+ in_channels = config.lidar_seq_len
+
+ self.avgpool_img = nn.AdaptiveAvgPool2d(
+ (self.config.img_vert_anchors, self.config.img_horz_anchors)
+ )
+
+ self.lidar_encoder = timm.create_model(
+ config.lidar_architecture,
+ pretrained=False,
+ in_chans=in_channels,
+ features_only=True,
+ )
+ self.global_pool_lidar = nn.AdaptiveAvgPool2d(output_size=1)
+ self.avgpool_lidar = nn.AdaptiveAvgPool2d(
+ (self.config.lidar_vert_anchors, self.config.lidar_horz_anchors)
+ )
+ lidar_time_frames = [1, 1, 1, 1]
+
+ self.global_pool_img = nn.AdaptiveAvgPool2d(output_size=1)
+ start_index = 0
+ # Some networks have a stem layer
+ if len(self.image_encoder.return_layers) > 4:
+ start_index += 1
+
+ self.transformers = nn.ModuleList(
+ [
+ GPT(
+ n_embd=self.image_encoder.feature_info.info[start_index + i]["num_chs"],
+ config=config,
+ # lidar_video=self.lidar_video,
+ lidar_time_frames=lidar_time_frames[i],
+ )
+ for i in range(4)
+ ]
+ )
+ self.lidar_channel_to_img = nn.ModuleList(
+ [
+ nn.Conv2d(
+ self.lidar_encoder.feature_info.info[start_index + i]["num_chs"],
+ self.image_encoder.feature_info.info[start_index + i]["num_chs"],
+ kernel_size=1,
+ )
+ for i in range(4)
+ ]
+ )
+ self.img_channel_to_lidar = nn.ModuleList(
+ [
+ nn.Conv2d(
+ self.image_encoder.feature_info.info[start_index + i]["num_chs"],
+ self.lidar_encoder.feature_info.info[start_index + i]["num_chs"],
+ kernel_size=1,
+ )
+ for i in range(4)
+ ]
+ )
+
+ self.num_image_features = self.image_encoder.feature_info.info[start_index + 3]["num_chs"]
+ # Typical encoders down-sample by a factor of 32
+ self.perspective_upsample_factor = (
+ self.image_encoder.feature_info.info[start_index + 3]["reduction"]
+ // self.config.perspective_downsample_factor
+ )
+
+ if self.config.transformer_decoder_join:
+ self.num_features = self.lidar_encoder.feature_info.info[start_index + 3]["num_chs"]
+ else:
+ if self.config.add_features:
+ self.lidar_to_img_features_end = nn.Linear(
+ self.lidar_encoder.feature_info.info[start_index + 3]["num_chs"],
+ self.image_encoder.feature_info.info[start_index + 3]["num_chs"],
+ )
+ # Number of features the encoder produces.
+ self.num_features = self.image_encoder.feature_info.info[start_index + 3]["num_chs"]
+ else:
+ # Number of features the encoder produces.
+ self.num_features = (
+ self.image_encoder.feature_info.info[start_index + 3]["num_chs"]
+ + self.lidar_encoder.feature_info.info[start_index + 3]["num_chs"]
+ )
+
+ # FPN fusion
+ channel = self.config.bev_features_channels
+ self.relu = nn.ReLU(inplace=True)
+ # top down
+ if self.config.detect_boxes or self.config.use_bev_semantic:
+ self.upsample = nn.Upsample(
+ scale_factor=self.config.bev_upsample_factor, mode="bilinear", align_corners=False
+ )
+ self.upsample2 = nn.Upsample(
+ size=(
+ self.config.lidar_resolution_height // self.config.bev_down_sample_factor,
+ self.config.lidar_resolution_width // self.config.bev_down_sample_factor,
+ ),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ self.up_conv5 = nn.Conv2d(channel, channel, (3, 3), padding=1)
+ self.up_conv4 = nn.Conv2d(channel, channel, (3, 3), padding=1)
+
+ # lateral
+ self.c5_conv = nn.Conv2d(
+ self.lidar_encoder.feature_info.info[start_index + 3]["num_chs"], channel, (1, 1)
+ )
+
+ def top_down(self, x):
+
+ p5 = self.relu(self.c5_conv(x))
+ p4 = self.relu(self.up_conv5(self.upsample(p5)))
+ p3 = self.relu(self.up_conv4(self.upsample2(p4)))
+
+ return p3
+
+ def forward(self, image, lidar):
+ """
+ Image + LiDAR feature fusion using transformers
+ Args:
+ image_list (list): list of input images
+ lidar_list (list): list of input LiDAR BEV
+ """
+ image_features, lidar_features = image, lidar
+
+ # Generate an iterator for all the layers in the network that one can loop through.
+ image_layers = iter(self.image_encoder.items())
+ lidar_layers = iter(self.lidar_encoder.items())
+
+ # Stem layer.
+ # In some architectures the stem is not a return layer, so we need to skip it.
+ if len(self.image_encoder.return_layers) > 4:
+ image_features = self.forward_layer_block(
+ image_layers, self.image_encoder.return_layers, image_features
+ )
+ if len(self.lidar_encoder.return_layers) > 4:
+ lidar_features = self.forward_layer_block(
+ lidar_layers, self.lidar_encoder.return_layers, lidar_features
+ )
+
+ # Loop through the 4 blocks of the network.
+ for i in range(4):
+ image_features = self.forward_layer_block(
+ image_layers, self.image_encoder.return_layers, image_features
+ )
+ lidar_features = self.forward_layer_block(
+ lidar_layers, self.lidar_encoder.return_layers, lidar_features
+ )
+
+ image_features, lidar_features = self.fuse_features(image_features, lidar_features, i)
+
+ if self.config.detect_boxes or self.config.use_bev_semantic:
+ x4 = lidar_features
+
+ image_feature_grid = None
+ if self.config.use_semantic or self.config.use_depth:
+ image_feature_grid = image_features
+
+ if self.config.transformer_decoder_join:
+ fused_features = lidar_features
+ else:
+ image_features = self.global_pool_img(image_features)
+ image_features = torch.flatten(image_features, 1)
+ lidar_features = self.global_pool_lidar(lidar_features)
+ lidar_features = torch.flatten(lidar_features, 1)
+
+ if self.config.add_features:
+ lidar_features = self.lidar_to_img_features_end(lidar_features)
+ fused_features = image_features + lidar_features
+ else:
+ fused_features = torch.cat((image_features, lidar_features), dim=1)
+
+ if self.config.detect_boxes or self.config.use_bev_semantic:
+ features = self.top_down(x4)
+ else:
+ features = None
+
+ return features, fused_features, image_feature_grid
+
+ def forward_layer_block(self, layers, return_layers, features):
+ """
+ Run one forward pass to a block of layers from a TIMM neural network and returns the result.
+ Advances the whole network by just one block
+ :param layers: Iterator starting at the current layer block
+ :param return_layers: TIMM dictionary describing at which intermediate layers features are returned.
+ :param features: Input features
+ :return: Processed features
+ """
+ for name, module in layers:
+ features = module(features)
+ if name in return_layers:
+ break
+ return features
+
+ def fuse_features(self, image_features, lidar_features, layer_idx):
+ """
+ Perform a TransFuser feature fusion block using a Transformer module.
+ :param image_features: Features from the image branch
+ :param lidar_features: Features from the LiDAR branch
+ :param layer_idx: Transformer layer index.
+ :return: image_features and lidar_features with added features from the other branch.
+ """
+ image_embd_layer = self.avgpool_img(image_features)
+ lidar_embd_layer = self.avgpool_lidar(lidar_features)
+
+ lidar_embd_layer = self.lidar_channel_to_img[layer_idx](lidar_embd_layer)
+
+ image_features_layer, lidar_features_layer = self.transformers[layer_idx](
+ image_embd_layer, lidar_embd_layer
+ )
+ lidar_features_layer = self.img_channel_to_lidar[layer_idx](lidar_features_layer)
+
+ image_features_layer = F.interpolate(
+ image_features_layer,
+ size=(image_features.shape[2], image_features.shape[3]),
+ mode="bilinear",
+ align_corners=False,
+ )
+ lidar_features_layer = F.interpolate(
+ lidar_features_layer,
+ size=(lidar_features.shape[2], lidar_features.shape[3]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ image_features = image_features + image_features_layer
+ lidar_features = lidar_features + lidar_features_layer
+
+ return image_features, lidar_features
+
+
+class GPT(nn.Module):
+ """the full GPT language backbone, with a context size of block_size"""
+
+ # def __init__(self, n_embd, config, lidar_video, lidar_time_frames):
+ def __init__(self, n_embd, config, lidar_time_frames):
+ super().__init__()
+ self.n_embd = n_embd
+ # We currently only support seq len 1
+ self.seq_len = 1
+ self.lidar_seq_len = config.lidar_seq_len
+ self.config = config
+ self.lidar_time_frames = lidar_time_frames
+
+ # positional embedding parameter (learnable), image + lidar
+ self.pos_emb = nn.Parameter(
+ torch.zeros(
+ 1,
+ self.seq_len * self.config.img_vert_anchors * self.config.img_horz_anchors
+ + lidar_time_frames
+ * self.config.lidar_vert_anchors
+ * self.config.lidar_horz_anchors,
+ self.n_embd,
+ )
+ )
+
+ self.drop = nn.Dropout(config.embd_pdrop)
+
+ # transformer
+ self.blocks = nn.Sequential(
+ *[
+ Block(
+ n_embd, config.n_head, config.block_exp, config.attn_pdrop, config.resid_pdrop
+ )
+ for layer in range(config.n_layer)
+ ]
+ )
+
+ # decoder head
+ self.ln_f = nn.LayerNorm(n_embd)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, module):
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(
+ mean=self.config.gpt_linear_layer_init_mean,
+ std=self.config.gpt_linear_layer_init_std,
+ )
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(self.config.gpt_layer_norm_init_weight)
+
+ def forward(self, image_tensor, lidar_tensor):
+ """
+ Args:
+ image_tensor (tensor): B*4*seq_len, C, H, W
+ lidar_tensor (tensor): B*seq_len, C, H, W
+ """
+
+ bz = lidar_tensor.shape[0]
+ lidar_h, lidar_w = lidar_tensor.shape[2:4]
+
+ img_h, img_w = image_tensor.shape[2:4]
+
+ assert self.seq_len == 1
+ image_tensor = image_tensor.permute(0, 2, 3, 1).contiguous().view(bz, -1, self.n_embd)
+ lidar_tensor = lidar_tensor.permute(0, 2, 3, 1).contiguous().view(bz, -1, self.n_embd)
+
+ token_embeddings = torch.cat((image_tensor, lidar_tensor), dim=1)
+
+ x = self.drop(self.pos_emb + token_embeddings)
+ x = self.blocks(x) # (B, an * T, C)
+ x = self.ln_f(x) # (B, an * T, C)
+
+ image_tensor_out = (
+ x[:, : self.seq_len * self.config.img_vert_anchors * self.config.img_horz_anchors, :]
+ .view(bz * self.seq_len, img_h, img_w, -1)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+ lidar_tensor_out = (
+ x[
+ :,
+ self.seq_len * self.config.img_vert_anchors * self.config.img_horz_anchors :,
+ :,
+ ]
+ .view(bz, lidar_h, lidar_w, -1)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+
+ return image_tensor_out, lidar_tensor_out
+
+
+class SelfAttention(nn.Module):
+ """
+ A vanilla multi-head masked self-attention layer with a projection at the
+ end.
+ """
+
+ def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop):
+ super().__init__()
+ assert n_embd % n_head == 0
+ # key, query, value projections for all heads
+ self.key = nn.Linear(n_embd, n_embd)
+ self.query = nn.Linear(n_embd, n_embd)
+ self.value = nn.Linear(n_embd, n_embd)
+ # regularization
+ self.attn_drop = nn.Dropout(attn_pdrop)
+ self.resid_drop = nn.Dropout(resid_pdrop)
+ # output projection
+ self.proj = nn.Linear(n_embd, n_embd)
+ self.n_head = n_head
+
+ def forward(self, x):
+ b, t, c = x.size()
+
+ # calculate query, key, values for all heads in batch and move head
+ # forward to be the batch dim
+ k = self.key(x).view(b, t, self.n_head, c // self.n_head).transpose(1, 2) # (b, nh, t, hs)
+ q = (
+ self.query(x).view(b, t, self.n_head, c // self.n_head).transpose(1, 2)
+ ) # (b, nh, t, hs)
+ v = (
+ self.value(x).view(b, t, self.n_head, c // self.n_head).transpose(1, 2)
+ ) # (b, nh, t, hs)
+
+ # self-attend: (b, nh, t, hs) x (b, nh, hs, t) -> (b, nh, t, t)
+ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+ att = F.softmax(att, dim=-1)
+ att = self.attn_drop(att)
+ y = att @ v # (b, nh, t, t) x (b, nh, t, hs) -> (b, nh, t, hs)
+ y = (
+ y.transpose(1, 2).contiguous().view(b, t, c)
+ ) # re-assemble all head outputs side by side
+
+ # output projection
+ y = self.resid_drop(self.proj(y))
+ return y
+
+
+class Block(nn.Module):
+ """an unassuming Transformer block"""
+
+ def __init__(self, n_embd, n_head, block_exp, attn_pdrop, resid_pdrop):
+ super().__init__()
+ self.ln1 = nn.LayerNorm(n_embd)
+ self.ln2 = nn.LayerNorm(n_embd)
+ self.attn = SelfAttention(n_embd, n_head, attn_pdrop, resid_pdrop)
+ self.mlp = nn.Sequential(
+ nn.Linear(n_embd, block_exp * n_embd),
+ nn.ReLU(True), # changed from GELU
+ nn.Linear(block_exp * n_embd, n_embd),
+ nn.Dropout(resid_pdrop),
+ )
+
+ def forward(self, x):
+ x = x + self.attn(self.ln1(x))
+ x = x + self.mlp(self.ln2(x))
+
+ return x
+
+
+class MultiheadAttentionWithAttention(nn.Module):
+ """
+ MultiheadAttention that also return attention weights
+ """
+
+ def __init__(self, n_embd, n_head, pdrop):
+ super().__init__()
+ assert n_embd % n_head == 0
+ # key, query, value projections for all heads
+ self.key = nn.Linear(n_embd, n_embd)
+ self.query = nn.Linear(n_embd, n_embd)
+ self.value = nn.Linear(n_embd, n_embd)
+ # regularization
+ self.attn_drop = nn.Dropout(pdrop)
+ self.resid_drop = nn.Dropout(pdrop)
+ # output projection
+ self.proj = nn.Linear(n_embd, n_embd)
+ self.n_head = n_head
+
+ def forward(self, q_in, k_in, v_in):
+ b, t, c = q_in.size()
+ _, t_mem, _ = k_in.size()
+
+ # calculate query, key, values for all heads in batch and move head
+ # forward to be the batch dim
+ q = (
+ self.query(q_in).view(b, t, self.n_head, c // self.n_head).transpose(1, 2)
+ ) # (b, nh, t, hs)
+ k = (
+ self.key(k_in).view(b, t_mem, self.n_head, c // self.n_head).transpose(1, 2)
+ ) # (b, nh, t, hs)
+ v = (
+ self.value(v_in).view(b, t_mem, self.n_head, c // self.n_head).transpose(1, 2)
+ ) # (b, nh, t, hs)
+
+ # self-attend: (b, nh, t, hs) x (b, nh, hs, t) -> (b, nh, t, t)
+ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+ att = F.softmax(att, dim=-1)
+ att = self.attn_drop(att)
+ y = att @ v # (b, nh, t, t) x (b, nh, t, hs) -> (b, nh, t, hs)
+ y = (
+ y.transpose(1, 2).contiguous().view(b, t, c)
+ ) # re-assemble all head outputs side by side
+
+ # output projection
+ y = self.resid_drop(self.proj(y))
+ attention = torch.mean(att, dim=1) # Average attention over heads
+ return y, attention
+
+
+class TransformerDecoderLayerWithAttention(nn.Module):
+ """A Transformer decoder that returns the attentions."""
+
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation=F.relu,
+ layer_norm_eps=1e-5,
+ ):
+ super().__init__()
+ self.self_attn = MultiheadAttentionWithAttention(d_model, nhead, dropout)
+ self.multihead_attn = MultiheadAttentionWithAttention(d_model, nhead, dropout)
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+ self.dropout3 = nn.Dropout(dropout)
+
+ self.activation = activation
+
+ def forward(self, tgt, memory):
+ x = tgt
+ tmp, _ = self.self_attn(x, x, x)
+ x = self.norm1(x + self.dropout1(tmp))
+ tmp, attention = self.multihead_attn(x, memory, memory)
+ x = self.norm2(x + self.dropout2(tmp))
+ tmp = self.linear2(self.dropout(self.activation(self.linear1(x))))
+ x = self.norm3(x + self.dropout3(tmp))
+
+ return x, attention
+
+
+class TransformerDecoderWithAttention(nn.Module):
+ """A Transformer decoder that returns the attentions."""
+
+ def __init__(self, layers, num_layers, norm=None):
+ super().__init__()
+ self.layers = nn.ModuleList([copy.deepcopy(layers) for i in range(num_layers)])
+ self.num_layers = num_layers
+ self.norm = norm
+
+ def forward(self, queries, memory):
+ output = queries
+ attentions = []
+ for mod in self.layers:
+ output, attention = mod(output, memory)
+ attentions.append(attention)
+
+ if self.norm is not None:
+ output = self.norm(output)
+
+ avg_attention = torch.mean(torch.stack(attentions), dim=0)
+ return output, avg_attention
diff --git a/navsim/agents/transfuser/transfuser_callback.py b/navsim/agents/transfuser/transfuser_callback.py
new file mode 100644
index 0000000..7be4b39
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_callback.py
@@ -0,0 +1,212 @@
+import time
+from typing import Any, Dict, Optional, Union
+from PIL import ImageColor
+
+import cv2
+import numpy as np
+import numpy.typing as npt
+
+import pytorch_lightning as pl
+import torch
+import torchvision.utils as vutils
+
+from nuplan.common.maps.abstract_map import SemanticMapLayer
+from nuplan.common.actor_state.oriented_box import OrientedBox
+from nuplan.common.actor_state.state_representation import StateSE2
+
+from navsim.visualization.config import TAB_10, MAP_LAYER_CONFIG, AGENT_CONFIG
+from navsim.agents.transfuser.transfuser_features import BoundingBox2DIndex
+from navsim.agents.transfuser.transfuser_config import TransfuserConfig
+
+
+class TransfuserCallback(pl.Callback):
+ def __init__(
+ self, config: TransfuserConfig, num_plots: int = 3, num_rows: int = 2, num_columns: int = 2
+ ) -> None:
+
+ self._config = config
+
+ self._num_plots = num_plots
+ self._num_rows = num_rows
+ self._num_columns = num_columns
+
+ def on_validation_epoch_start(
+ self, trainer: pl.Trainer, lightning_module: pl.LightningModule
+ ) -> None:
+ pass
+
+ def on_validation_epoch_end(
+ self, trainer: pl.Trainer, lightning_module: pl.LightningModule
+ ) -> None:
+ device = lightning_module.device
+ for idx_plot in range(self._num_plots):
+ features, targets = next(iter(trainer.val_dataloaders))
+ features, targets = dict_to_device(features, device), dict_to_device(targets, device)
+ with torch.no_grad():
+ predictions = lightning_module.agent.forward(features)
+
+ features, targets, predictions = (
+ dict_to_device(features, "cpu"),
+ dict_to_device(targets, "cpu"),
+ dict_to_device(predictions, "cpu"),
+ )
+ grid = self._visualize_model(features, targets, predictions)
+ trainer.logger.experiment.add_image(
+ f"val_plot_{idx_plot}", grid, global_step=trainer.current_epoch
+ )
+
+ def on_test_epoch_start(
+ self, trainer: pl.Trainer, lightning_module: pl.LightningModule
+ ) -> None:
+ pass
+
+ def on_test_epoch_end(self, trainer: pl.Trainer, lightning_module: pl.LightningModule) -> None:
+ pass
+
+ def on_train_epoch_start(
+ self, trainer: pl.Trainer, lightning_module: pl.LightningModule
+ ) -> None:
+ pass
+
+ def on_train_epoch_end(
+ self,
+ trainer: pl.Trainer,
+ lightning_module: pl.LightningModule,
+ unused: Optional[Any] = None,
+ ) -> None:
+ device = lightning_module.device
+ for idx_plot in range(self._num_plots):
+ features, targets = next(iter(trainer.train_dataloader))
+ features, targets = dict_to_device(features, device), dict_to_device(targets, device)
+ with torch.no_grad():
+ predictions = lightning_module.agent.forward(features)
+
+ features, targets, predictions = (
+ dict_to_device(features, "cpu"),
+ dict_to_device(targets, "cpu"),
+ dict_to_device(predictions, "cpu"),
+ )
+ grid = self._visualize_model(features, targets, predictions)
+ trainer.logger.experiment.add_image(
+ f"train_plot_{idx_plot}", grid, global_step=trainer.current_epoch
+ )
+
+ def _visualize_model(
+ self,
+ features: Dict[str, torch.Tensor],
+ targets: Dict[str, torch.Tensor],
+ predictions: Dict[str, torch.Tensor],
+ ) -> torch.Tensor:
+
+ camera = features["camera_feature"].permute(0, 2, 3, 1).numpy()
+ bev = targets["bev_semantic_map"].numpy()
+ lidar_map = features["lidar_feature"].squeeze(1).numpy()
+ agent_labels = targets["agent_labels"].numpy()
+ agent_states = targets["agent_states"].numpy()
+ trajectory = targets["trajectory"].numpy()
+
+ pred_bev = predictions["bev_semantic_map"].argmax(1).numpy()
+ pred_agent_labels = predictions["agent_labels"].sigmoid().numpy()
+ pred_agent_states = predictions["agent_states"].numpy()
+ pred_trajectory = predictions["trajectory"].numpy()
+
+ plots = []
+ for sample_idx in range(self._num_rows * self._num_columns):
+ plot = np.zeros((256, 768, 3), dtype=np.uint8)
+ plot[:128, :512] = (camera[sample_idx] * 255).astype(np.uint8)[::2, ::2]
+
+ plot[128:, :256] = semantic_map_to_rgb(bev[sample_idx], self._config)
+ plot[128:, 256:512] = semantic_map_to_rgb(pred_bev[sample_idx], self._config)
+
+ agent_states_ = agent_states[sample_idx][agent_labels[sample_idx]]
+ pred_agent_states_ = pred_agent_states[sample_idx][pred_agent_labels[sample_idx] > 0.5]
+ plot[:, 512:] = lidar_map_to_rgb(
+ lidar_map[sample_idx],
+ agent_states_,
+ pred_agent_states_,
+ trajectory[sample_idx],
+ pred_trajectory[sample_idx],
+ self._config,
+ )
+
+ plots.append(torch.tensor(plot).permute(2, 0, 1))
+
+ return vutils.make_grid(plots, normalize=False, nrow=self._num_rows)
+
+
+def dict_to_device(
+ dict: Dict[str, torch.Tensor], device: Union[torch.device, str]
+) -> Dict[str, torch.Tensor]:
+ for key in dict.keys():
+ dict[key] = dict[key].to(device)
+ return dict
+
+
+def semantic_map_to_rgb(
+ semantic_map: npt.NDArray[np.int64], config: TransfuserConfig
+) -> npt.NDArray[np.uint8]:
+
+ height, width = semantic_map.shape[:2]
+ rgb_map = np.ones((height, width, 3), dtype=np.uint8) * 255
+
+ for label in range(1, config.num_bev_classes):
+
+ if config.bev_semantic_classes[label][0] == "linestring":
+ hex_color = MAP_LAYER_CONFIG[SemanticMapLayer.BASELINE_PATHS]["line_color"]
+ else:
+ layer = config.bev_semantic_classes[label][-1][0] # take color of first element
+ hex_color = (
+ AGENT_CONFIG[layer]["fill_color"]
+ if layer in AGENT_CONFIG.keys()
+ else MAP_LAYER_CONFIG[layer]["fill_color"]
+ )
+
+ rgb_map[semantic_map == label] = ImageColor.getcolor(hex_color, "RGB")
+ return rgb_map[::-1, ::-1]
+
+
+def lidar_map_to_rgb(
+ lidar_map: npt.NDArray[np.int64],
+ agent_states: npt.NDArray[np.float32],
+ pred_agent_states: npt.NDArray[np.float32],
+ trajectory: npt.NDArray[np.float32],
+ pred_trajectory: npt.NDArray[np.float32],
+ config: TransfuserConfig,
+) -> npt.NDArray[np.uint8]:
+
+ gt_color, pred_color = (0, 255, 0), (255, 0, 0)
+ point_size = 4
+
+ height, width = lidar_map.shape[:2]
+
+ def coords_to_pixel(coords):
+ pixel_center = np.array([[height / 2.0, width / 2.0]])
+ coords_idcs = (coords / config.bev_pixel_size) + pixel_center
+ return coords_idcs.astype(np.int32)
+
+ rgb_map = (lidar_map * 255).astype(np.uint8)
+ rgb_map = 255 - rgb_map[..., None].repeat(3, axis=-1)
+
+ for color, agent_state_array in zip(
+ [gt_color, pred_color], [agent_states, pred_agent_states]
+ ):
+ for agent_state in agent_state_array:
+ agent_box = OrientedBox(
+ StateSE2(*agent_state[BoundingBox2DIndex.STATE_SE2]),
+ agent_state[BoundingBox2DIndex.LENGTH],
+ agent_state[BoundingBox2DIndex.WIDTH],
+ 1.0,
+ )
+ exterior = np.array(agent_box.geometry.exterior.coords).reshape((-1, 1, 2))
+ exterior = coords_to_pixel(exterior)
+ exterior = np.flip(exterior, axis=-1)
+ cv2.polylines(rgb_map, [exterior], isClosed=True, color=color, thickness=2)
+
+ for color, traj in zip(
+ [gt_color, pred_color], [trajectory, pred_trajectory]
+ ):
+ trajectory_indices = coords_to_pixel(traj[:,:2])
+ for x, y in trajectory_indices:
+ cv2.circle(rgb_map, (y, x), point_size, color, -1) # -1 fills the circle
+
+ return rgb_map[::-1, ::-1]
diff --git a/navsim/agents/transfuser/transfuser_config.py b/navsim/agents/transfuser/transfuser_config.py
new file mode 100644
index 0000000..868a67f
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_config.py
@@ -0,0 +1,116 @@
+from dataclasses import dataclass
+from typing import Any, List, Tuple, Dict
+
+from nuplan.common.maps.abstract_map import SemanticMapLayer
+from nuplan.common.actor_state.tracked_objects_types import TrackedObjectType
+from nuplan.planning.simulation.trajectory.trajectory_sampling import TrajectorySampling
+
+
+@dataclass
+class TransfuserConfig:
+
+ trajectory_sampling: TrajectorySampling = TrajectorySampling(
+ time_horizon=4, interval_length=0.5
+ )
+
+ image_architecture: str = "resnet34"
+ lidar_architecture: str = "resnet34"
+
+ max_height_lidar: float = 100.0
+ pixels_per_meter: float = 4.0
+ hist_max_per_pixel: int = 5
+
+ lidar_min_x: float = -32
+ lidar_max_x: float = 32
+ lidar_min_y: float = -32
+ lidar_max_y: float = 32
+
+ lidar_split_height: float = 0.2
+ use_ground_plane: bool = False
+
+ # new
+ lidar_seq_len: int = 1
+
+ camera_width: int = 1024
+ camera_height: int = 256
+ lidar_resolution_width = 256
+ lidar_resolution_height = 256
+
+ img_vert_anchors: int = 256 // 32
+ img_horz_anchors: int = 1024 // 32
+ lidar_vert_anchors: int = 256 // 32
+ lidar_horz_anchors: int = 256 // 32
+
+ block_exp = 4
+ n_layer = 2 # Number of transformer layers used in the vision backbone
+ n_head = 4
+ n_scale = 4
+ embd_pdrop = 0.1
+ resid_pdrop = 0.1
+ attn_pdrop = 0.1
+ # Mean of the normal distribution initialization for linear layers in the GPT
+ gpt_linear_layer_init_mean = 0.0
+ # Std of the normal distribution initialization for linear layers in the GPT
+ gpt_linear_layer_init_std = 0.02
+ # Initial weight of the layer norms in the gpt.
+ gpt_layer_norm_init_weight = 1.0
+
+ perspective_downsample_factor = 1
+ transformer_decoder_join = True
+ detect_boxes = True
+ use_bev_semantic = True
+ use_semantic = False
+ use_depth = False
+ add_features = True
+
+ # Transformer
+ tf_d_model: int = 256
+ tf_d_ffn: int = 1024
+ tf_num_layers: int = 3
+ tf_num_head: int = 8
+ tf_dropout: float = 0.0
+
+ # detection
+ num_bounding_boxes: int = 30
+
+ # loss weights
+ trajectory_weight: float = 10.0
+ agent_class_weight: float = 10.0
+ agent_box_weight: float = 1.0
+ bev_semantic_weight: float = 10.0
+
+ # BEV mapping
+ bev_semantic_classes = {
+ 1: ("polygon", [SemanticMapLayer.LANE, SemanticMapLayer.INTERSECTION]), # road
+ 2: ("polygon", [SemanticMapLayer.WALKWAYS]), # walkways
+ 3: ("linestring", [SemanticMapLayer.LANE, SemanticMapLayer.LANE_CONNECTOR]), # centerline
+ 4: (
+ "box",
+ [
+ TrackedObjectType.CZONE_SIGN,
+ TrackedObjectType.BARRIER,
+ TrackedObjectType.TRAFFIC_CONE,
+ TrackedObjectType.GENERIC_OBJECT,
+ ],
+ ), # static_objects
+ 5: ("box", [TrackedObjectType.VEHICLE]), # vehicles
+ 6: ("box", [TrackedObjectType.PEDESTRIAN]), # pedestrians
+ }
+
+ bev_pixel_width: int = lidar_resolution_width
+ bev_pixel_height: int = lidar_resolution_height // 2
+ bev_pixel_size: float = 0.25
+
+ num_bev_classes = 7
+ bev_features_channels: int = 64
+ bev_down_sample_factor: int = 4
+ bev_upsample_factor: int = 2
+
+ @property
+ def bev_semantic_frame(self) -> Tuple[int, int]:
+ return (self.bev_pixel_height, self.bev_pixel_width)
+
+ @property
+ def bev_radius(self) -> float:
+ values = [self.lidar_min_x, self.lidar_max_x, self.lidar_min_y, self.lidar_max_y]
+ return max([abs(value) for value in values])
diff --git a/navsim/agents/transfuser/transfuser_features.py b/navsim/agents/transfuser/transfuser_features.py
new file mode 100644
index 0000000..5a2e285
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_features.py
@@ -0,0 +1,405 @@
+from enum import IntEnum
+from typing import Any, Dict, List, Tuple
+import cv2
+import numpy as np
+import numpy.typing as npt
+
+import torch
+from torchvision import transforms
+
+from shapely import affinity
+from shapely.geometry import Polygon, LineString
+
+from nuplan.common.maps.abstract_map import AbstractMap, SemanticMapLayer, MapObject
+from nuplan.common.actor_state.oriented_box import OrientedBox
+from nuplan.common.actor_state.state_representation import StateSE2
+from nuplan.common.actor_state.tracked_objects_types import TrackedObjectType
+
+from navsim.agents.transfuser.transfuser_config import TransfuserConfig
+from navsim.common.dataclasses import AgentInput, Scene, Annotations
+from navsim.common.enums import BoundingBoxIndex, LidarIndex
+from navsim.planning.scenario_builder.navsim_scenario_utils import tracked_object_types
+from navsim.planning.training.abstract_feature_target_builder import (
+ AbstractFeatureBuilder,
+ AbstractTargetBuilder,
+)
+
+
+class TransfuserFeatureBuilder(AbstractFeatureBuilder):
+ def __init__(self, config: TransfuserConfig):
+ self._config = config
+
+ def get_unique_name(self) -> str:
+ """Inherited, see superclass."""
+ return "transfuser_feature"
+
+ def compute_features(self, agent_input: AgentInput) -> Dict[str, torch.Tensor]:
+ """Inherited, see superclass."""
+ features = {}
+
+ features["camera_feature"] = self._get_camera_feature(agent_input)
+ features["lidar_feature"] = self._get_lidar_feature(agent_input)
+ features["status_feature"] = torch.concatenate(
+ [
+ torch.tensor(agent_input.ego_statuses[-1].driving_command, dtype=torch.float32),
+ torch.tensor(agent_input.ego_statuses[-1].ego_velocity, dtype=torch.float32),
+ torch.tensor(agent_input.ego_statuses[-1].ego_acceleration, dtype=torch.float32),
+ ],
+ )
+
+ return features
+
+ def _get_camera_feature(self, agent_input: AgentInput) -> torch.Tensor:
+ """
+ Extract stitched camera from AgentInput
+ :param agent_input: input dataclass
+ :return: stitched front view image as torch tensor
+ """
+
+ cameras = agent_input.cameras[-1]
+
+ # Crop to ensure 4:1 aspect ratio
+ l0 = cameras.cam_l0.image[28:-28, 416:-416]
+ f0 = cameras.cam_f0.image[28:-28]
+ r0 = cameras.cam_r0.image[28:-28, 416:-416]
+
+ # stitch l0, f0, r0 images
+ stitched_image = np.concatenate([l0, f0, r0], axis=1)
+ resized_image = cv2.resize(stitched_image, (1024, 256))
+ tensor_image = transforms.ToTensor()(resized_image)
+
+ return tensor_image
+
+ def _get_lidar_feature(self, agent_input: AgentInput) -> torch.Tensor:
+ """
+ Compute LiDAR feature as 2D histogram, according to Transfuser
+ :param agent_input: input dataclass
+ :return: LiDAR histogram as torch tensors
+ """
+
+ # only consider (x,y,z) & swap axes for (N,3) numpy array
+ lidar_pc = agent_input.lidars[-1].lidar_pc[LidarIndex.POSITION].T
+
+ # NOTE: Code from
+ # https://github.com/autonomousvision/carla_garage/blob/main/team_code/data.py#L873
+ def splat_points(point_cloud):
+ # 256 x 256 grid
+ xbins = np.linspace(
+ self._config.lidar_min_x,
+ self._config.lidar_max_x,
+ (self._config.lidar_max_x - self._config.lidar_min_x)
+ * int(self._config.pixels_per_meter)
+ + 1,
+ )
+ ybins = np.linspace(
+ self._config.lidar_min_y,
+ self._config.lidar_max_y,
+ (self._config.lidar_max_y - self._config.lidar_min_y)
+ * int(self._config.pixels_per_meter)
+ + 1,
+ )
+ hist = np.histogramdd(point_cloud[:, :2], bins=(xbins, ybins))[0]
+ hist[hist > self._config.hist_max_per_pixel] = self._config.hist_max_per_pixel
+ overhead_splat = hist / self._config.hist_max_per_pixel
+ return overhead_splat
+
+ # Remove points above the vehicle
+ lidar_pc = lidar_pc[lidar_pc[..., 2] < self._config.max_height_lidar]
+ below = lidar_pc[lidar_pc[..., 2] <= self._config.lidar_split_height]
+ above = lidar_pc[lidar_pc[..., 2] > self._config.lidar_split_height]
+ above_features = splat_points(above)
+ if self._config.use_ground_plane:
+ below_features = splat_points(below)
+ features = np.stack([below_features, above_features], axis=-1)
+ else:
+ features = np.stack([above_features], axis=-1)
+ features = np.transpose(features, (2, 0, 1)).astype(np.float32)
+
+ return torch.tensor(features)
+
+
+class TransfuserTargetBuilder(AbstractTargetBuilder):
+ def __init__(self, config: TransfuserConfig):
+ self._config = config
+
+ def get_unique_name(self) -> str:
+ """Inherited, see superclass."""
+ return "transfuser_target"
+
+ def compute_targets(self, scene: Scene) -> Dict[str, torch.Tensor]:
+ """Inherited, see superclass."""
+
+ trajectory = torch.tensor(
+ scene.get_future_trajectory(
+ num_trajectory_frames=self._config.trajectory_sampling.num_poses
+ ).poses
+ )
+ frame_idx = scene.scene_metadata.num_history_frames - 1
+ annotations = scene.frames[frame_idx].annotations
+ ego_pose = StateSE2(*scene.frames[frame_idx].ego_status.ego_pose)
+
+ agent_states, agent_labels = self._compute_agent_targets(annotations)
+ bev_semantic_map = self._compute_bev_semantic_map(annotations, scene.map_api, ego_pose)
+
+ return {
+ "trajectory": trajectory,
+ "agent_states": agent_states,
+ "agent_labels": agent_labels,
+ "bev_semantic_map": bev_semantic_map,
+ }
+
+ def _compute_agent_targets(self, annotations: Annotations) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Extracts 2D agent bounding boxes in ego coordinates
+ :param annotations: annotation dataclass
+ :return: tuple of bounding box values and labels (binary)
+ """
+
+ max_agents = self._config.num_bounding_boxes
+ agent_states_list: List[npt.NDArray[np.float32]] = []
+
+ def _xy_in_lidar(x: float, y: float, config: TransfuserConfig) -> bool:
+ return (config.lidar_min_x <= x <= config.lidar_max_x) and (
+ config.lidar_min_y <= y <= config.lidar_max_y
+ )
+
+ for box, name in zip(annotations.boxes, annotations.names):
+ box_x, box_y, box_heading, box_length, box_width = (
+ box[BoundingBoxIndex.X],
+ box[BoundingBoxIndex.Y],
+ box[BoundingBoxIndex.HEADING],
+ box[BoundingBoxIndex.LENGTH],
+ box[BoundingBoxIndex.WIDTH],
+ )
+
+ if name == "vehicle" and _xy_in_lidar(box_x, box_y, self._config):
+ agent_states_list.append(
+ np.array([box_x, box_y, box_heading, box_length, box_width], dtype=np.float32)
+ )
+
+ agents_states_arr = np.array(agent_states_list)
+
+ # filter num_instances nearest
+ agent_states = np.zeros((max_agents, BoundingBox2DIndex.size()), dtype=np.float32)
+ agent_labels = np.zeros(max_agents, dtype=bool)
+
+ if len(agents_states_arr) > 0:
+ distances = np.linalg.norm(agents_states_arr[..., BoundingBox2DIndex.POINT], axis=-1)
+ argsort = np.argsort(distances)[:max_agents]
+
+ # filter detections
+ agents_states_arr = agents_states_arr[argsort]
+ agent_states[: len(agents_states_arr)] = agents_states_arr
+ agent_labels[: len(agents_states_arr)] = True
+
+ return torch.tensor(agent_states), torch.tensor(agent_labels)
+
+ def _compute_bev_semantic_map(
+ self, annotations: Annotations, map_api: AbstractMap, ego_pose: StateSE2
+ ) -> torch.Tensor:
+ """
+ Creates sematic map in BEV
+ :param annotations: annotation dataclass
+ :param map_api: map interface of nuPlan
+ :param ego_pose: ego pose in global frame
+ :return: 2D torch tensor of semantic labels
+ """
+
+ bev_semantic_map = np.zeros(self._config.bev_semantic_frame, dtype=np.int64)
+ for label, (entity_type, layers) in self._config.bev_semantic_classes.items():
+ if entity_type == "polygon":
+ entity_mask = self._compute_map_polygon_mask(map_api, ego_pose, layers)
+ elif entity_type == "linestring":
+ entity_mask = self._compute_map_linestring_mask(map_api, ego_pose, layers)
+ else:
+ entity_mask = self._compute_box_mask(annotations, layers)
+ bev_semantic_map[entity_mask] = label
+
+ return torch.Tensor(bev_semantic_map)
+
+ def _compute_map_polygon_mask(
+ self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer]
+ ) -> npt.NDArray[np.bool_]:
+ """
+ Compute binary mask given a map layer class
+ :param map_api: map interface of nuPlan
+ :param ego_pose: ego pose in global frame
+ :param layers: map layers
+ :return: binary mask as numpy array
+ """
+
+ map_object_dict = map_api.get_proximal_map_objects(
+ point=ego_pose.point, radius=self._config.bev_radius, layers=layers
+ )
+ map_polygon_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8)
+ for layer in layers:
+ for map_object in map_object_dict[layer]:
+ polygon: Polygon = self._geometry_local_coords(map_object.polygon, ego_pose)
+ exterior = np.array(polygon.exterior.coords).reshape((-1, 1, 2))
+ exterior = self._coords_to_pixel(exterior)
+ cv2.fillPoly(map_polygon_mask, [exterior], color=255)
+ # OpenCV has origin on top-left corner
+ map_polygon_mask = np.rot90(map_polygon_mask)[::-1]
+ return map_polygon_mask > 0
+
+ def _compute_map_linestring_mask(
+ self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer]
+ ) -> npt.NDArray[np.bool_]:
+ """
+ Compute binary of linestring given a map layer class
+ :param map_api: map interface of nuPlan
+ :param ego_pose: ego pose in global frame
+ :param layers: map layers
+ :return: binary mask as numpy array
+ """
+ map_object_dict = map_api.get_proximal_map_objects(
+ point=ego_pose.point, radius=self._config.bev_radius, layers=layers
+ )
+ map_linestring_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8)
+ for layer in layers:
+ for map_object in map_object_dict[layer]:
+ linestring: LineString = self._geometry_local_coords(
+ map_object.baseline_path.linestring, ego_pose
+ )
+ points = np.array(linestring.coords).reshape((-1, 1, 2))
+ points = self._coords_to_pixel(points)
+ cv2.polylines(map_linestring_mask, [points], isClosed=False, color=255, thickness=2)
+ # OpenCV has origin on top-left corner
+ map_linestring_mask = np.rot90(map_linestring_mask)[::-1]
+ return map_linestring_mask > 0
+
+ def _compute_box_mask(
+ self, annotations: Annotations, layers: TrackedObjectType
+ ) -> npt.NDArray[np.bool_]:
+ """
+ Compute binary of bounding boxes in BEV space
+ :param annotations: annotation dataclass
+ :param layers: bounding box labels to include
+ :return: binary mask as numpy array
+ """
+ box_polygon_mask = np.zeros(self._config.bev_semantic_frame[::-1], dtype=np.uint8)
+ for name_value, box_value in zip(annotations.names, annotations.boxes):
+ agent_type = tracked_object_types[name_value]
+ if agent_type in layers:
+ # box_value = (x, y, z, length, width, height, yaw) TODO: add intenum
+ x, y, heading = box_value[0], box_value[1], box_value[-1]
+ box_length, box_width, box_height = box_value[3], box_value[4], box_value[5]
+ agent_box = OrientedBox(StateSE2(x, y, heading), box_length, box_width, box_height)
+ exterior = np.array(agent_box.geometry.exterior.coords).reshape((-1, 1, 2))
+ exterior = self._coords_to_pixel(exterior)
+ cv2.fillPoly(box_polygon_mask, [exterior], color=255)
+ # OpenCV has origin on top-left corner
+ box_polygon_mask = np.rot90(box_polygon_mask)[::-1]
+ return box_polygon_mask > 0
+
+ @staticmethod
+ def _query_map_objects(
+ self, map_api: AbstractMap, ego_pose: StateSE2, layers: List[SemanticMapLayer]
+ ) -> List[MapObject]:
+ """
+ Queries map objects
+ :param map_api: map interface of nuPlan
+ :param ego_pose: ego pose in global frame
+ :param layers: map layers
+ :return: list of map objects
+ """
+
+ # query map api with interesting layers
+ map_object_dict = map_api.get_proximal_map_objects(
+ point=ego_pose.point, radius=self, layers=layers
+ )
+ map_objects: List[MapObject] = []
+ for layer in layers:
+ map_objects += map_object_dict[layer]
+ return map_objects
+
+ @staticmethod
+ def _geometry_local_coords(geometry: Any, origin: StateSE2) -> Any:
+ """
+ Transform shapely geometry in local coordinates of origin.
+ :param geometry: shapely geometry
+ :param origin: pose dataclass
+ :return: shapely geometry
+ """
+
+ a = np.cos(origin.heading)
+ b = np.sin(origin.heading)
+ d = -np.sin(origin.heading)
+ e = np.cos(origin.heading)
+ xoff = -origin.x
+ yoff = -origin.y
+
+ translated_geometry = affinity.affine_transform(geometry, [1, 0, 0, 1, xoff, yoff])
+ rotated_geometry = affinity.affine_transform(translated_geometry, [a, b, d, e, 0, 0])
+
+ return rotated_geometry
+
+ def _coords_to_pixel(self, coords):
+ """
+ Transform local coordinates in pixel indices of BEV map
+ :param coords: _description_
+ :return: _description_
+ """
+
+ # NOTE: remove half in backward direction
+ pixel_center = np.array([[0, self._config.bev_pixel_width / 2.0]])
+ coords_idcs = (coords / self._config.bev_pixel_size) + pixel_center
+
+ return coords_idcs.astype(np.int32)
+
+
+class BoundingBox2DIndex(IntEnum):
+
+ _X = 0
+ _Y = 1
+ _HEADING = 2
+ _LENGTH = 3
+ _WIDTH = 4
+
+ @classmethod
+ def size(cls):
+ valid_attributes = [
+ attribute
+ for attribute in dir(cls)
+ if attribute.startswith("_")
+ and not attribute.startswith("__")
+ and not callable(getattr(cls, attribute))
+ ]
+ return len(valid_attributes)
+
+ @classmethod
+ @property
+ def X(cls):
+ return cls._X
+
+ @classmethod
+ @property
+ def Y(cls):
+ return cls._Y
+
+ @classmethod
+ @property
+ def HEADING(cls):
+ return cls._HEADING
+
+ @classmethod
+ @property
+ def LENGTH(cls):
+ return cls._LENGTH
+
+ @classmethod
+ @property
+ def WIDTH(cls):
+ return cls._WIDTH
+
+ @classmethod
+ @property
+ def POINT(cls):
+ # assumes X, Y have subsequent indices
+ return slice(cls._X, cls._Y + 1)
+
+ @classmethod
+ @property
+ def STATE_SE2(cls):
+ # assumes X, Y, HEADING have subsequent indices
+ return slice(cls._X, cls._HEADING + 1)
diff --git a/navsim/agents/transfuser/transfuser_loss.py b/navsim/agents/transfuser/transfuser_loss.py
new file mode 100644
index 0000000..40dd9b3
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_loss.py
@@ -0,0 +1,137 @@
+from typing import Dict
+from scipy.optimize import linear_sum_assignment
+
+import torch
+import torch.nn.functional as F
+
+from navsim.agents.transfuser.transfuser_config import TransfuserConfig
+
+
+def transfuser_loss(
+ targets: Dict[str, torch.Tensor], predictions: Dict[str, torch.Tensor], config: TransfuserConfig
+):
+ """
+ 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
+ """
+
+ trajectory_loss = F.l1_loss(predictions["trajectory"], targets["trajectory"])
+ agent_class_loss, agent_box_loss = _agent_loss(targets, predictions, config)
+ bev_semantic_loss = F.cross_entropy(
+ predictions["bev_semantic_map"], targets["bev_semantic_map"].long()
+ )
+ loss = (
+ config.trajectory_weight * trajectory_loss
+ + config.agent_class_weight * agent_class_loss
+ + config.agent_box_weight * agent_box_loss
+ + config.bev_semantic_weight * bev_semantic_loss
+ )
+ return loss
+
+
+def _agent_loss(
+ targets: Dict[str, torch.Tensor], predictions: Dict[str, torch.Tensor], config: TransfuserConfig
+):
+ """
+ Hungarian matching loss for agent detection
+ :param targets: dictionary of name tensor pairings
+ :param predictions: dictionary of name tensor pairings
+ :param config: global Transfuser config
+ :return: detection loss
+ """
+
+ gt_states, gt_valid = targets["agent_states"], targets["agent_labels"]
+ pred_states, pred_logits = predictions["agent_states"], predictions["agent_labels"]
+
+ # save constants
+ batch_dim, num_instances = pred_states.shape[:2]
+ num_gt_instances = gt_valid.sum()
+ num_gt_instances = num_gt_instances if num_gt_instances > 0 else num_gt_instances + 1
+
+ ce_cost = _get_ce_cost(gt_valid, pred_logits)
+ l1_cost = _get_l1_cost(gt_states, pred_states, gt_valid)
+
+ cost = config.agent_class_weight * ce_cost + config.agent_box_weight * l1_cost
+ cost = cost.cpu()
+
+ indices = [linear_sum_assignment(c) for i, c in enumerate(cost)]
+ matching = [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
+ for i, j in indices
+ ]
+ idx = _get_src_permutation_idx(matching)
+
+ pred_states_idx = pred_states[idx]
+ gt_states_idx = torch.cat([t[i] for t, (_, i) in zip(gt_states, indices)], dim=0)
+
+ pred_valid_idx = pred_logits[idx]
+ gt_valid_idx = torch.cat([t[i] for t, (_, i) in zip(gt_valid, indices)], dim=0).float()
+
+ l1_loss = F.l1_loss(pred_states_idx, gt_states_idx, reduction="none")
+ l1_loss = l1_loss.sum(-1) * gt_valid_idx
+ l1_loss = l1_loss.view(batch_dim, -1).sum() / num_gt_instances
+
+ ce_loss = F.binary_cross_entropy_with_logits(pred_valid_idx, gt_valid_idx, reduction="none")
+ ce_loss = ce_loss.view(batch_dim, -1).mean()
+
+ return ce_loss, l1_loss
+
+
+@torch.no_grad()
+def _get_ce_cost(gt_valid: torch.Tensor, pred_logits: torch.Tensor) -> torch.Tensor:
+ """
+ Function to calculate cross-entropy cost for cost matrix.
+ :param gt_valid: tensor of binary ground-truth labels
+ :param pred_logits: tensor of predicted logits of neural net
+ :return: bce cost matrix as tensor
+ """
+
+ # NOTE: numerically stable BCE with logits
+ # https://github.com/pytorch/pytorch/blob/c64e006fc399d528bb812ae589789d0365f3daf4/aten/src/ATen/native/Loss.cpp#L214
+ gt_valid_expanded = gt_valid[:, :, None].detach().float() # (b, n, 1)
+ pred_logits_expanded = pred_logits[:, None, :].detach() # (b, 1, n)
+
+ max_val = torch.relu(-pred_logits_expanded)
+ helper_term = max_val + torch.log(
+ torch.exp(-max_val) + torch.exp(-pred_logits_expanded - max_val)
+ )
+ ce_cost = (1 - gt_valid_expanded) * pred_logits_expanded + helper_term # (b, n, n)
+ ce_cost = ce_cost.permute(0, 2, 1)
+
+ return ce_cost
+
+
+@torch.no_grad()
+def _get_l1_cost(
+ gt_states: torch.Tensor, pred_states: torch.Tensor, gt_valid: torch.Tensor
+) -> torch.Tensor:
+ """
+ Function to calculate L1 cost for cost matrix.
+ :param gt_states: tensor of ground-truth bounding boxes
+ :param pred_states: tensor of predicted bounding boxes
+ :param gt_valid: mask of binary ground-truth labels
+ :return: l1 cost matrix as tensor
+ """
+
+ gt_states_expanded = gt_states[:, :, None, :2].detach() # (b, n, 1, 2)
+ pred_states_expanded = pred_states[:, None, :, :2].detach() # (b, 1, n, 2)
+ l1_cost = gt_valid[..., None].float() * (gt_states_expanded - pred_states_expanded).abs().sum(
+ dim=-1
+ )
+ l1_cost = l1_cost.permute(0, 2, 1)
+ return l1_cost
+
+
+def _get_src_permutation_idx(indices):
+ """
+ Helper function to align indices after matching
+ :param indices: matched indices
+ :return: permuted indices
+ """
+ # permute predictions following indices
+ batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ src_idx = torch.cat([src for (src, _) in indices])
+ return batch_idx, src_idx
diff --git a/navsim/agents/transfuser/transfuser_model.py b/navsim/agents/transfuser/transfuser_model.py
new file mode 100644
index 0000000..fcdafe6
--- /dev/null
+++ b/navsim/agents/transfuser/transfuser_model.py
@@ -0,0 +1,168 @@
+from typing import Dict
+import numpy as np
+import torch
+import torch.nn as nn
+
+from navsim.agents.transfuser.transfuser_config import TransfuserConfig
+from navsim.agents.transfuser.transfuser_backbone import TransfuserBackbone
+from navsim.common.enums import StateSE2Index
+from navsim.agents.transfuser.transfuser_features import BoundingBox2DIndex
+
+
+class TransfuserModel(nn.Module):
+ def __init__(self, config: TransfuserConfig):
+
+ super().__init__()
+
+ self._query_splits = [
+ 1,
+ config.num_bounding_boxes,
+ ]
+
+ self._config = config
+ self._backbone = TransfuserBackbone(config)
+
+ self._keyval_embedding = nn.Embedding(
+ 8**2 + 1, config.tf_d_model
+ ) # 8x8 feature grid + trajectory
+ self._query_embedding = nn.Embedding(sum(self._query_splits), config.tf_d_model)
+
+ # usually, the BEV features are variable in size.
+ self._bev_downscale = nn.Conv2d(512, config.tf_d_model, kernel_size=1)
+ self._status_encoding = nn.Linear(4 + 2 + 2, config.tf_d_model)
+
+ self._bev_semantic_head = nn.Sequential(
+ nn.Conv2d(
+ config.bev_features_channels,
+ config.bev_features_channels,
+ kernel_size=(3, 3),
+ stride=1,
+ padding=(1, 1),
+ bias=True,
+ ),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(
+ config.bev_features_channels,
+ config.num_bev_classes,
+ kernel_size=(1, 1),
+ stride=1,
+ padding=0,
+ bias=True,
+ ),
+ nn.Upsample(
+ size=(config.lidar_resolution_height // 2, config.lidar_resolution_width),
+ mode="bilinear",
+ align_corners=False,
+ ),
+ )
+
+ tf_decoder_layer = nn.TransformerDecoderLayer(
+ d_model=config.tf_d_model,
+ nhead=config.tf_num_head,
+ dim_feedforward=config.tf_d_ffn,
+ dropout=config.tf_dropout,
+ batch_first=True,
+ )
+
+ self._tf_decoder = nn.TransformerDecoder(tf_decoder_layer, config.tf_num_layers)
+ self._agent_head = AgentHead(
+ num_agents=config.num_bounding_boxes,
+ d_ffn=config.tf_d_ffn,
+ d_model=config.tf_d_model,
+ )
+
+ self._trajectory_head = TrajectoryHead(
+ num_poses=config.trajectory_sampling.num_poses,
+ d_ffn=config.tf_d_ffn,
+ d_model=config.tf_d_model,
+ )
+
+ def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+
+ camera_feature: torch.Tensor = features["camera_feature"]
+ lidar_feature: torch.Tensor = features["lidar_feature"]
+ status_feature: torch.Tensor = features["status_feature"]
+
+ batch_size = status_feature.shape[0]
+
+ bev_feature_upscale, bev_feature, _ = self._backbone(camera_feature, lidar_feature)
+
+ bev_feature = self._bev_downscale(bev_feature).flatten(-2, -1)
+ bev_feature = bev_feature.permute(0, 2, 1)
+ status_encoding = self._status_encoding(status_feature)
+
+ keyval = torch.concatenate([bev_feature, status_encoding[:, None]], dim=1)
+ keyval += self._keyval_embedding.weight[None, ...]
+
+ query = self._query_embedding.weight[None, ...].repeat(batch_size, 1, 1)
+ query_out = self._tf_decoder(query, keyval)
+
+ bev_semantic_map = self._bev_semantic_head(bev_feature_upscale)
+ trajectory_query, agents_query = query_out.split(self._query_splits, dim=1)
+
+ output: Dict[str, torch.Tensor] = {"bev_semantic_map": bev_semantic_map}
+ trajectory = self._trajectory_head(trajectory_query)
+ output.update(trajectory)
+
+ agents = self._agent_head(agents_query)
+ output.update(agents)
+
+ return output
+
+
+class AgentHead(nn.Module):
+ def __init__(
+ self,
+ num_agents: int,
+ d_ffn: int,
+ d_model: int,
+ ):
+ super(AgentHead, self).__init__()
+
+ self._num_objects = num_agents
+ self._d_model = d_model
+ self._d_ffn = d_ffn
+
+ self._mlp_states = nn.Sequential(
+ nn.Linear(self._d_model, self._d_ffn),
+ nn.ReLU(),
+ nn.Linear(self._d_ffn, BoundingBox2DIndex.size()),
+ )
+
+ self._mlp_label = nn.Sequential(
+ nn.Linear(self._d_model, 1),
+ )
+
+ def forward(self, agent_queries) -> Dict[str, torch.Tensor]:
+
+ agent_states = self._mlp_states(agent_queries)
+ agent_states[..., BoundingBox2DIndex.POINT] = (
+ agent_states[..., BoundingBox2DIndex.POINT].tanh() * 32
+ )
+ agent_states[..., BoundingBox2DIndex.HEADING] = (
+ agent_states[..., BoundingBox2DIndex.HEADING].tanh() * np.pi
+ )
+
+ agent_labels = self._mlp_label(agent_queries).squeeze(dim=-1)
+
+ return {"agent_states": agent_states, "agent_labels": agent_labels}
+
+
+class TrajectoryHead(nn.Module):
+ def __init__(self, num_poses: int, d_ffn: int, d_model: int):
+ super(TrajectoryHead, self).__init__()
+
+ self._num_poses = num_poses
+ self._d_model = d_model
+ self._d_ffn = d_ffn
+
+ self._mlp = nn.Sequential(
+ nn.Linear(self._d_model, self._d_ffn),
+ nn.ReLU(),
+ nn.Linear(self._d_ffn, num_poses * StateSE2Index.size()),
+ )
+
+ def forward(self, object_queries) -> Dict[str, torch.Tensor]:
+ poses = self._mlp(object_queries).reshape(-1, self._num_poses, StateSE2Index.size())
+ poses[..., StateSE2Index.HEADING] = poses[..., StateSE2Index.HEADING].tanh() * np.pi
+ return {"trajectory": poses}
diff --git a/navsim/common/dataclasses.py b/navsim/common/dataclasses.py
index 963174c..1c0de53 100644
--- a/navsim/common/dataclasses.py
+++ b/navsim/common/dataclasses.py
@@ -14,6 +14,9 @@
from nuplan.planning.simulation.trajectory.trajectory_sampling import TrajectorySampling
from nuplan.common.actor_state.state_representation import StateSE2
+from nuplan.common.maps.abstract_map import AbstractMap
+from nuplan.common.maps.nuplan_map.map_factory import get_maps_api
+from nuplan.database.maps_db.gpkg_mapsdb import MAP_LOCATIONS
from nuplan.database.utils.pointclouds.lidar import LidarPointCloud
from pyquaternion import Quaternion
@@ -23,6 +26,7 @@
NAVSIM_INTERVAL_LENGTH: float = 0.5
OPENSCENE_DATA_ROOT = os.environ.get("OPENSCENE_DATA_ROOT")
+NUPLAN_MAPS_ROOT = os.environ.get("NUPLAN_MAPS_ROOT")
@dataclass
@@ -85,8 +89,9 @@ def from_camera_dict(
@dataclass
class Lidar:
+ # NOTE:
# merged lidar point cloud as (6,n) float32 array with n points
- # first axis: (x, y, z, intensity, ring, lidar_id)
+ # first axis: (x, y, z, intensity, ring, lidar_id), see LidarIndex
lidar_pc: Optional[npt.NDArray[np.float32]] = None
@staticmethod
@@ -127,10 +132,6 @@ class AgentInput:
cameras: List[Cameras]
lidars: List[Lidar]
- def __post_init__(self):
- # TODO: add assertions
- pass
-
@classmethod
def from_scene_dict_list(
cls,
@@ -255,6 +256,7 @@ class Scene:
# Ground truth information
scene_metadata: SceneMetadata
+ map_api: AbstractMap
frames: List[Frame]
def get_future_trajectory(self, num_trajectory_frames: Optional[int] = None) -> Trajectory:
@@ -303,10 +305,8 @@ def get_history_trajectory(self, num_trajectory_frames: Optional[int] = None) ->
)
def get_agent_input(self) -> AgentInput:
- # NOTE: this function is unused and might be removed.
local_ego_poses = self.get_history_trajectory().poses
-
ego_statuses: List[EgoStatus] = []
cameras: List[Cameras] = []
lidars: List[Lidar] = []
@@ -327,6 +327,13 @@ def get_agent_input(self) -> AgentInput:
return AgentInput(ego_statuses, cameras, lidars)
+ @classmethod
+ def _build_map_api(cls, map_name: str) -> AbstractMap:
+ assert (
+ map_name in MAP_LOCATIONS
+ ), f"The map name {map_name} is invalid, must be in {MAP_LOCATIONS}"
+ return get_maps_api(NUPLAN_MAPS_ROOT, "nuplan-maps-v1.0", map_name)
+
@classmethod
def _build_annotations(
cls,
@@ -379,6 +386,7 @@ def from_scene_dict_list(
num_history_frames=num_history_frames,
num_future_frames=num_future_frames,
)
+ map_api = cls._build_map_api(scene_metadata.map_name)
frames: List[Frame] = []
for frame_idx in range(len(scene_dict_list)):
@@ -411,7 +419,7 @@ def from_scene_dict_list(
)
frames.append(frame)
- return Scene(scene_metadata=scene_metadata, frames=frames)
+ return Scene(scene_metadata=scene_metadata, map_api=map_api, frames=frames)
@dataclass
@@ -419,6 +427,7 @@ class SceneFilter:
num_history_frames: int = 4
num_future_frames: int = 10
+ frame_interval: Optional[int] = None
has_route: bool = True
max_scenes: Optional[int] = None
@@ -426,6 +435,19 @@ class SceneFilter:
tokens: Optional[List[str]] = None
# TODO: expand filter options
+ def __post_init__(self):
+
+ if self.frame_interval is None:
+ self.frame_interval = self.num_frames
+
+ assert (
+ self.num_history_frames >= 1
+ ), "SceneFilter: num_history_frames must greater equal one."
+ assert (
+ self.num_future_frames >= 0
+ ), "SceneFilter: num_future_frames must greater equal zero."
+ assert self.frame_interval >= 1, "SceneFilter: frame_interval must greater equal one."
+
@property
def num_frames(self) -> int:
return self.num_history_frames + self.num_future_frames
diff --git a/navsim/common/dataloader.py b/navsim/common/dataloader.py
index 9e06124..09fe8e2 100644
--- a/navsim/common/dataloader.py
+++ b/navsim/common/dataloader.py
@@ -13,8 +13,8 @@
def filter_scenes(data_path: Path, scene_filter: SceneFilter) -> Dict[str, List[Dict[str, Any]]]:
- def split_list(input_list: List[Any], n: int) -> List[List[Any]]:
- return [input_list[i : i + n] for i in range(0, len(input_list), n)]
+ def split_list(input_list: List[Any], num_frames: int, frame_interval: int) -> List[List[Any]]:
+ return [input_list[i : i + num_frames] for i in range(0, len(input_list), frame_interval)]
filtered_scenes: Dict[str, Scene] = {}
stop_loading: bool = False
@@ -28,10 +28,18 @@ def split_list(input_list: List[Any], n: int) -> List[List[Any]]:
if log_file.name.replace(".pkl", "") in scene_filter.log_names
]
+ if scene_filter.tokens is not None:
+ filter_tokens = True
+ tokens = set(scene_filter.tokens)
+ else:
+ filter_tokens = False
+
for log_pickle_path in tqdm(log_files, desc="Loading logs"):
scene_dict_list = pickle.load(open(log_pickle_path, "rb"))
- for frame_list in split_list(scene_dict_list, scene_filter.num_frames):
+ for frame_list in split_list(
+ scene_dict_list, scene_filter.num_frames, scene_filter.frame_interval
+ ):
# Filter scenes which are too short
if len(frame_list) < scene_filter.num_frames:
continue
@@ -44,14 +52,10 @@ def split_list(input_list: List[Any], n: int) -> List[List[Any]]:
continue
# Filter by token
- if (
- scene_filter.tokens is not None
- and frame_list[scene_filter.num_history_frames - 1]["token"]
- not in scene_filter.tokens
- ):
+ token = frame_list[scene_filter.num_history_frames - 1]["token"]
+ if filter_tokens and token not in tokens:
continue
- token = frame_list[scene_filter.num_history_frames - 1]["token"]
filtered_scenes[token] = frame_list
if (scene_filter.max_scenes is not None) and (
@@ -76,14 +80,14 @@ def __init__(
sensor_config: SensorConfig = SensorConfig.build_no_sensors(),
):
- self._filtered_tokens = filter_scenes(data_path, scene_filter)
+ self.scene_frames_dicts = filter_scenes(data_path, scene_filter)
self._sensor_blobs_path = sensor_blobs_path
self._scene_filter = scene_filter
self._sensor_config = sensor_config
@property
def tokens(self) -> List[str]:
- return list(self._filtered_tokens.keys())
+ return list(self.scene_frames_dicts.keys())
def __len__(self):
return len(self.tokens)
@@ -94,7 +98,7 @@ def __getitem__(self, idx) -> str:
def get_scene_from_token(self, token: str) -> Scene:
assert token in self.tokens
return Scene.from_scene_dict_list(
- self._filtered_tokens[token],
+ self.scene_frames_dicts[token],
self._sensor_blobs_path,
num_history_frames=self._scene_filter.num_history_frames,
num_future_frames=self._scene_filter.num_future_frames,
@@ -104,12 +108,22 @@ def get_scene_from_token(self, token: str) -> Scene:
def get_agent_input_from_token(self, token: str) -> AgentInput:
assert token in self.tokens
return AgentInput.from_scene_dict_list(
- self._filtered_tokens[token],
+ self.scene_frames_dicts[token],
self._sensor_blobs_path,
num_history_frames=self._scene_filter.num_history_frames,
sensor_config=self._sensor_config,
)
+ def get_tokens_list_per_log(self) -> Dict[str, List[str]]:
+ # generate a dict that contains a list of tokens for each log-name
+ tokens_per_logs: Dict[str, List[str]] = {}
+ for token, scene_dict_list in self.scene_frames_dicts.items():
+ log_name = scene_dict_list[0]["log_name"]
+ if tokens_per_logs.get(log_name):
+ tokens_per_logs[log_name].append(token)
+ else:
+ tokens_per_logs.update({log_name: [token]})
+ return tokens_per_logs
class MetricCacheLoader:
@@ -120,39 +134,32 @@ def __init__(
):
self._file_name = file_name
- self._metric_cache_paths = self._load_metric_cache_paths(cache_path)
+ self.metric_cache_paths = self._load_metric_cache_paths(cache_path)
def _load_metric_cache_paths(self, cache_path: Path) -> Dict[str, Path]:
-
- # This is ugly lol
- metric_cache_dict: Dict[str, Path] = {}
- for log_path in cache_path.iterdir():
- if "metadata" in str(log_path):
- continue
- for scenario_path in log_path.iterdir():
- for token_path in scenario_path.iterdir():
- metric_cache_path = token_path / self._file_name
- assert (
- metric_cache_path.is_file()
- ), f"Metric cache at {metric_cache_path} is missing!"
- token = str(token_path).split("/")[-1]
- metric_cache_dict[token] = metric_cache_path
-
+ metadata_dir = cache_path / "metadata"
+ metadata_file = [file for file in metadata_dir.iterdir() if ".csv" in str(file)][0]
+ with open(str(metadata_file), "r") as f:
+ cache_paths=f.read().splitlines()[1:]
+ metric_cache_dict = {
+ cache_path.split("/")[-2]: cache_path
+ for cache_path in cache_paths
+ }
return metric_cache_dict
@property
def tokens(self) -> List[str]:
- return list(self._metric_cache_paths.keys())
+ return list(self.metric_cache_paths.keys())
def __len__(self):
- return len(self._metric_cache_paths)
+ return len(self.metric_cache_paths)
def __getitem__(self, idx: int) -> MetricCache:
return self.get_from_token(self.tokens[idx])
def get_from_token(self, token: str) -> MetricCache:
- with lzma.open(self._metric_cache_paths[token], "rb") as f:
+ with lzma.open(self.metric_cache_paths[token], "rb") as f:
metric_cache: MetricCache = pickle.load(f)
return metric_cache
diff --git a/navsim/common/enums.py b/navsim/common/enums.py
new file mode 100644
index 0000000..8cb44be
--- /dev/null
+++ b/navsim/common/enums.py
@@ -0,0 +1,184 @@
+from enum import IntEnum
+
+
+class StateSE2Index(IntEnum):
+
+ _X = 0
+ _Y = 1
+ _HEADING = 2
+
+ @classmethod
+ def size(cls):
+ valid_attributes = [
+ attribute
+ for attribute in dir(cls)
+ if attribute.startswith("_")
+ and not attribute.startswith("__")
+ and not callable(getattr(cls, attribute))
+ ]
+ return len(valid_attributes)
+
+ @classmethod
+ @property
+ def X(cls):
+ return cls._X
+
+ @classmethod
+ @property
+ def Y(cls):
+ return cls._Y
+
+ @classmethod
+ @property
+ def HEADING(cls):
+ return cls._HEADING
+
+ @classmethod
+ @property
+ def POINT(cls):
+ # assumes X, Y have subsequent indices
+ return slice(cls._X, cls._Y + 1)
+
+ @classmethod
+ @property
+ def STATE_SE2(cls):
+ # assumes X, Y, HEADING have subsequent indices
+ return slice(cls._X, cls._HEADING + 1)
+
+
+class BoundingBoxIndex(IntEnum):
+
+ _X = 0
+ _Y = 1
+ _Z = 2
+ _LENGTH = 3
+ _WIDTH = 4
+ _HEIGHT = 5
+ _HEADING = 6
+
+ @classmethod
+ def size(cls):
+ valid_attributes = [
+ attribute
+ for attribute in dir(cls)
+ if attribute.startswith("_")
+ and not attribute.startswith("__")
+ and not callable(getattr(cls, attribute))
+ ]
+ return len(valid_attributes)
+
+ @classmethod
+ @property
+ def X(cls):
+ return cls._X
+
+ @classmethod
+ @property
+ def Y(cls):
+ return cls._Y
+
+ @classmethod
+ @property
+ def Z(cls):
+ return cls._Z
+
+ @classmethod
+ @property
+ def LENGTH(cls):
+ return cls._LENGTH
+
+ @classmethod
+ @property
+ def WIDTH(cls):
+ return cls._WIDTH
+
+ @classmethod
+ @property
+ def HEIGHT(cls):
+ return cls._HEIGHT
+
+ @classmethod
+ @property
+ def HEADING(cls):
+ return cls._HEADING
+
+ @classmethod
+ @property
+ def POINT2D(cls):
+ # assumes X, Y have subsequent indices
+ return slice(cls._X, cls._Y + 1)
+
+ @classmethod
+ @property
+ def POSITION(cls):
+ # assumes X, Y, Z have subsequent indices
+ return slice(cls._X, cls._Z + 1)
+
+ @classmethod
+ @property
+ def DIMENSION(cls):
+ # assumes LENGTH, WIDTH, HEIGHT have subsequent indices
+ return slice(cls._LENGTH, cls._HEIGHT + 1)
+
+
+class LidarIndex(IntEnum):
+
+ _X = 0
+ _Y = 1
+ _Z = 2
+ _INTENSITY = 3
+ _RING = 4
+ _ID = 5
+
+ @classmethod
+ def size(cls):
+ valid_attributes = [
+ attribute
+ for attribute in dir(cls)
+ if attribute.startswith("_")
+ and not attribute.startswith("__")
+ and not callable(getattr(cls, attribute))
+ ]
+ return len(valid_attributes)
+
+ @classmethod
+ @property
+ def X(cls):
+ return cls._X
+
+ @classmethod
+ @property
+ def Y(cls):
+ return cls._Y
+
+ @classmethod
+ @property
+ def Z(cls):
+ return cls._Z
+
+ @classmethod
+ @property
+ def INTENSITY(cls):
+ return cls._INTENSITY
+
+ @classmethod
+ @property
+ def RING(cls):
+ return cls._RING
+
+ @classmethod
+ @property
+ def ID(cls):
+ return cls._ID
+
+ @classmethod
+ @property
+ def POINT2D(cls):
+ # assumes X, Y have subsequent indices
+ return slice(cls._X, cls._Y + 1)
+
+ @classmethod
+ @property
+ def POSITION(cls):
+ # assumes X, Y, Z have subsequent indices
+ return slice(cls._X, cls._Z + 1)
diff --git a/navsim/planning/metric_caching/caching.py b/navsim/planning/metric_caching/caching.py
index 9c2ce82..1a1ed2d 100644
--- a/navsim/planning/metric_caching/caching.py
+++ b/navsim/planning/metric_caching/caching.py
@@ -3,11 +3,10 @@
import os
import uuid
from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
from hydra.utils import instantiate
from omegaconf import DictConfig
-from nuplan.planning.scenario_builder.abstract_scenario import AbstractScenario
from nuplan.planning.training.experiments.cache_metadata_entry import (
CacheMetadataEntry,
CacheResult,
@@ -18,8 +17,8 @@
from navsim.planning.metric_caching.metric_cache_processor import MetricCacheProcessor
from navsim.planning.scenario_builder.navsim_scenario import NavSimScenario
-from navsim.common.dataloader import SceneLoader
-from navsim.common.dataclasses import SensorConfig
+from navsim.common.dataloader import SceneLoader, SceneFilter
+from navsim.common.dataclasses import SensorConfig, Scene
logger = logging.getLogger(__name__)
@@ -40,14 +39,40 @@ def cache_scenarios(args: List[Dict[str, Union[List[str], DictConfig]]]) -> List
#
# This is necessary to save memory when running on large datasets.
def cache_scenarios_internal(
- args: List[Dict[str, Union[List[AbstractScenario], DictConfig]]]
+ args: List[Dict[str, Union[Path, DictConfig]]]
) -> List[CacheResult]:
+
+ def cache_single_scenario(scene_dict: Dict[str, Any], processor: MetricCacheProcessor) -> Optional[CacheMetadataEntry]:
+ scene = Scene.from_scene_dict_list(
+ scene_dict,
+ None,
+ num_history_frames=cfg.scene_filter.num_history_frames,
+ num_future_frames=cfg.scene_filter.num_future_frames,
+ sensor_config=SensorConfig.build_no_sensors(),
+ )
+ scenario = NavSimScenario(
+ scene, map_root=os.environ["NUPLAN_MAPS_ROOT"], map_version="nuplan-maps-v1.0"
+ )
+
+ return processor.compute_metric_cache(scenario)
+
node_id = int(os.environ.get("NODE_RANK", 0))
thread_id = str(uuid.uuid4())
- scenarios: List[AbstractScenario] = [a["scenario"] for a in args]
+ log_names = [a["log_file"] for a in args]
+ tokens = [t for a in args for t in a["tokens"]]
cfg: DictConfig = args[0]["cfg"]
+ scene_filter: SceneFilter =instantiate(cfg.scene_filter)
+ scene_filter.log_names = log_names
+ scene_filter.tokens = tokens
+ scene_loader = SceneLoader(
+ sensor_blobs_path=None,
+ data_path=Path(cfg.navsim_log_path),
+ scene_filter=scene_filter,
+ sensor_config=SensorConfig.build_no_sensors(),
+ )
+
# Create feature preprocessor
assert (
cfg.cache.cache_path is not None
@@ -59,17 +84,17 @@ def cache_scenarios_internal(
)
logger.info(
- f"Extracted {len(scenarios)} scenarios for thread_id={thread_id}, node_id={node_id}."
+ f"Extracted {len(scene_loader)} scenarios for thread_id={thread_id}, node_id={node_id}."
)
num_failures = 0
num_successes = 0
all_file_cache_metadata: List[Optional[CacheMetadataEntry]] = []
- for idx, scenario in enumerate(scenarios):
+ for idx, scene_dict in enumerate(scene_loader.scene_frames_dicts.values()):
logger.info(
- f"Processing scenario {idx + 1} / {len(scenarios)} in thread_id={thread_id}, node_id={node_id}"
+ f"Processing scenario {idx + 1} / {len(scene_loader)} in thread_id={thread_id}, node_id={node_id}"
)
-
- file_cache_metadata = processor.compute_metric_cache(scenario)
+ file_cache_metadata = cache_single_scenario(scene_dict, processor)
+ gc.collect()
num_failures += 0 if file_cache_metadata else 1
num_successes += 1 if file_cache_metadata else 0
@@ -102,12 +127,8 @@ def cache_data(cfg: DictConfig, worker: WorkerPool) -> None:
cfg.cache.cache_path is not None
), f"Cache path cannot be None when caching, got {cfg.cache.cache_path}"
- NUPLAN_MAPS_ROOT = os.environ["NUPLAN_MAPS_ROOT"]
- NUPLAN_MAP_VERSION = "nuplan-maps-v1.0"
-
- scenarios: List[AbstractScenario] = []
-
- # TODO: add scene filter settings to config
+ # Extract scenes based on scene-loader to know which tokens to distribute across workers
+ # TODO: infer the tokens per log from metadata, to not have to load metric cache and scenes here
scene_loader = SceneLoader(
sensor_blobs_path=None,
data_path=Path(cfg.navsim_log_path),
@@ -115,15 +136,14 @@ def cache_data(cfg: DictConfig, worker: WorkerPool) -> None:
sensor_config=SensorConfig.build_no_sensors(),
)
- for token in scene_loader:
- scene = scene_loader.get_scene_from_token(token)
-
- scenario = NavSimScenario(
- scene, map_root=NUPLAN_MAPS_ROOT, map_version=NUPLAN_MAP_VERSION
- )
- scenarios.append(scenario)
-
- data_points = [{"scenario": scenario, "cfg": cfg} for scenario in scenarios]
+ data_points = [
+ {
+ "cfg": cfg,
+ "log_file": log_file,
+ "tokens": tokens_list,
+ }
+ for log_file, tokens_list in scene_loader.get_tokens_list_per_log().items()
+ ]
logger.info("Starting metric caching of %s files...", str(len(data_points)))
cache_results = worker_map(worker, cache_scenarios, data_points)
diff --git a/navsim/planning/metric_caching/metric_cache_processor.py b/navsim/planning/metric_caching/metric_cache_processor.py
index 99bd556..9f2a945 100644
--- a/navsim/planning/metric_caching/metric_cache_processor.py
+++ b/navsim/planning/metric_caching/metric_cache_processor.py
@@ -243,6 +243,9 @@ def compute_metric_cache(self, scenario: AbstractScenario) -> Optional[CacheMeta
/ "metric_cache.pkl"
)
+ if file_name.exists() and not self._force_feature_computation:
+ return CacheMetadataEntry(file_name)
+
# init and run PDM-Closed
planner_input, planner_initialization = self._get_planner_inputs(scenario)
self._pdm_closed.initialize(planner_initialization)
diff --git a/navsim/planning/scenario_builder/navsim_scenario.py b/navsim/planning/scenario_builder/navsim_scenario.py
index c80cf98..3e7bd7a 100644
--- a/navsim/planning/scenario_builder/navsim_scenario.py
+++ b/navsim/planning/scenario_builder/navsim_scenario.py
@@ -336,4 +336,4 @@ def get_future_traffic_light_status_history(
def get_scenario_tokens(self) -> List[str]:
"""Return the list of lidarpc tokens from the DB that are contained in the scenario."""
- raise NotImplementedError
+ raise NotImplementedError
\ No newline at end of file
diff --git a/navsim/planning/scenario_builder/navsim_scenario_utils.py b/navsim/planning/scenario_builder/navsim_scenario_utils.py
index 247b880..2d12174 100644
--- a/navsim/planning/scenario_builder/navsim_scenario_utils.py
+++ b/navsim/planning/scenario_builder/navsim_scenario_utils.py
@@ -20,10 +20,9 @@
from nuplan.planning.simulation.trajectory.trajectory_sampling import TrajectorySampling
from navsim.common.dataclasses import Annotations
+from navsim.common.enums import BoundingBoxIndex
# TODO: Refactor this file
-
-# TODO: should be available somewhere in the nuplan-devkit
tracked_object_types: Dict[str, TrackedObjectType] = {
"vehicle": TrackedObjectType.VEHICLE,
"pedestrian": TrackedObjectType.PEDESTRIAN,
@@ -93,7 +92,11 @@ def gt_boxes_oriented_box(
oriented_boxes: List[OrientedBox] = []
for gt_box in gt_boxes:
# gt_box = (x, y, z, length, width, height, yaw) TODO: add intenum
- local_box_x, local_box_y, local_box_heading = gt_box[0], gt_box[1], gt_box[-1]
+ local_box_x, local_box_y, local_box_heading = (
+ gt_box[BoundingBoxIndex.X],
+ gt_box[BoundingBoxIndex.Y],
+ gt_box[BoundingBoxIndex.HEADING],
+ )
local_box_se2 = rotate_state_se2(
StateSE2(local_box_x, local_box_y, local_box_heading),
angle=ego_state.rear_axle.heading,
diff --git a/navsim/planning/script/config/__init__.py b/navsim/planning/script/config/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/pdm_scoring/agent/constant_velocity_agent.yaml b/navsim/planning/script/config/common/agent/constant_velocity_agent.yaml
similarity index 100%
rename from navsim/planning/script/config/pdm_scoring/agent/constant_velocity_agent.yaml
rename to navsim/planning/script/config/common/agent/constant_velocity_agent.yaml
diff --git a/navsim/planning/script/config/pdm_scoring/agent/ego_status_mlp_agent.yaml b/navsim/planning/script/config/common/agent/ego_status_mlp_agent.yaml
similarity index 97%
rename from navsim/planning/script/config/pdm_scoring/agent/ego_status_mlp_agent.yaml
rename to navsim/planning/script/config/common/agent/ego_status_mlp_agent.yaml
index 7634ac8..28dc9d6 100644
--- a/navsim/planning/script/config/pdm_scoring/agent/ego_status_mlp_agent.yaml
+++ b/navsim/planning/script/config/common/agent/ego_status_mlp_agent.yaml
@@ -8,4 +8,4 @@ trajectory_sampling:
interval_length: 0.5
checkpoint_path: null
hidden_layer_dim: 512
-lr: 1e-4
\ No newline at end of file
+lr: 1e-4
diff --git a/navsim/planning/script/config/pdm_scoring/agent/human_agent.yaml b/navsim/planning/script/config/common/agent/human_agent.yaml
similarity index 100%
rename from navsim/planning/script/config/pdm_scoring/agent/human_agent.yaml
rename to navsim/planning/script/config/common/agent/human_agent.yaml
diff --git a/navsim/planning/script/config/common/agent/transfuser_agent.yaml b/navsim/planning/script/config/common/agent/transfuser_agent.yaml
new file mode 100644
index 0000000..7363b88
--- /dev/null
+++ b/navsim/planning/script/config/common/agent/transfuser_agent.yaml
@@ -0,0 +1,15 @@
+_target_: navsim.agents.transfuser.transfuser_agent.TransfuserAgent
+_convert_: 'all'
+
+config:
+ _target_: navsim.agents.transfuser.transfuser_config.TransfuserConfig
+ _convert_: 'all'
+
+ trajectory_sampling:
+ _target_: nuplan.planning.simulation.trajectory.trajectory_sampling.TrajectorySampling
+ _convert_: 'all'
+ time_horizon: 4
+ interval_length: 0.5
+
+checkpoint_path: null
+lr: 1e-4
diff --git a/navsim/planning/script/config/common/scene_filter/__init__.py b/navsim/planning/script/config/common/scene_filter/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/common/scene_filter/all_scenes.yaml b/navsim/planning/script/config/common/scene_filter/all_scenes.yaml
index 6eb9c58..2a1f495 100644
--- a/navsim/planning/script/config/common/scene_filter/all_scenes.yaml
+++ b/navsim/planning/script/config/common/scene_filter/all_scenes.yaml
@@ -1,11 +1,12 @@
_target_: navsim.common.dataclasses.SceneFilter
_convert_: 'all'
-num_history_frames: 4
-num_future_frames: 10
-has_route: True
+num_history_frames: 4 # number of past frames to be extracted, frames are at 2Hz (1=ony current frame, 2=1 second)
+num_future_frames: 10 # number of future frames to be extracted, frames are at 2Hz (10=5 seconds)
+frame_interval: null # number of frames to skip between each scene, if null, extracted scenes are non-overlapping
-max_scenes: Null
-log_names: Null
+has_route: true # only extract scenes with valid route information
-tokens: Null
\ No newline at end of file
+max_scenes: null # maximum number of scenes to extract, if null, all scenes are extracted. If integer, scene loading stops when reaching it
+log_names: null # list of log names to extract scenes from, if null, all logs are extracted
+tokens: null # list of tokens to extract scenes from, if null, all tokens are extracted
\ No newline at end of file
diff --git a/navsim/planning/script/config/common/scene_filter/navtest.yaml b/navsim/planning/script/config/common/scene_filter/navtest.yaml
new file mode 100644
index 0000000..be360b9
--- /dev/null
+++ b/navsim/planning/script/config/common/scene_filter/navtest.yaml
@@ -0,0 +1,12294 @@
+_target_: navsim.common.dataclasses.SceneFilter
+_convert_: 'all'
+
+num_history_frames: 4
+num_future_frames: 10
+frame_interval: 1
+has_route: true
+
+max_scenes: null
+log_names:
+ - '2021.06.03.12.02.06_veh-35_01100_01227'
+ - '2021.09.09.17.18.51_veh-48_01462_01552'
+ - '2021.06.28.15.02.02_veh-38_02398_02848'
+ - '2021.06.03.12.02.06_veh-35_00233_00609'
+ - '2021.05.25.15.59.03_veh-30_04027_04200'
+ - '2021.06.03.13.55.17_veh-35_02572_02855'
+ - '2021.09.16.13.53.10_veh-42_00180_00342'
+ - '2021.05.25.14.16.10_veh-35_02482_02649'
+ - '2021.09.29.14.44.26_veh-28_00238_00320'
+ - '2021.05.25.14.24.08_veh-25_04059_04203'
+ - '2021.05.25.17.54.41_veh-35_01654_01850'
+ - '2021.09.16.15.47.30_veh-45_01199_01391'
+ - '2021.05.25.14.24.08_veh-25_00934_01067'
+ - '2021.09.09.18.29.25_veh-39_00969_01184'
+ - '2021.10.06.08.16.17_veh-52_00922_01296'
+ - '2021.05.25.16.37.23_veh-25_00005_00217'
+ - '2021.06.03.17.06.58_veh-35_03860_03992'
+ - '2021.09.16.13.53.10_veh-42_00860_01069'
+ - '2021.06.28.18.03.27_veh-14_00620_01581'
+ - '2021.09.16.16.20.27_veh-08_02435_02525'
+ - '2021.05.25.18.38.25_veh-25_04058_04186'
+ - '2021.09.09.17.18.51_veh-48_00574_00646'
+ - '2021.06.03.17.06.58_veh-35_00712_00855'
+ - '2021.06.03.13.55.17_veh-35_00073_00426'
+ - '2021.09.16.14.39.34_veh-42_01609_01687'
+ - '2021.09.09.17.18.51_veh-48_01173_01237'
+ - '2021.09.09.18.29.25_veh-39_01622_01766'
+ - '2021.09.29.18.19.40_veh-28_00844_01218'
+ - '2021.10.06.08.16.17_veh-52_00181_00574'
+ - '2021.10.06.07.26.10_veh-52_00772_00917'
+ - '2021.09.09.18.29.25_veh-39_00569_00903'
+ - '2021.10.06.08.16.17_veh-52_00032_00170'
+ - '2021.06.03.18.47.39_veh-35_00503_00777'
+ - '2021.05.25.14.16.10_veh-35_01100_01664'
+ - '2021.10.06.08.16.17_veh-52_01590_01725'
+ - '2021.06.28.20.24.43_veh-38_00369_00601'
+ - '2021.09.29.14.44.26_veh-28_00528_00992'
+ - '2021.06.28.15.10.57_veh-16_02438_02580'
+ - '2021.10.06.07.26.10_veh-52_00953_01126'
+ - '2021.10.06.07.26.10_veh-52_01245_02064'
+ - '2021.09.16.19.49.00_veh-42_00990_01609'
+ - '2021.09.29.15.23.04_veh-28_00601_00802'
+ - '2021.06.03.13.55.17_veh-35_02419_02561'
+ - '2021.09.29.18.19.40_veh-28_00331_00426'
+ - '2021.09.16.19.12.04_veh-42_01438_01677'
+ - '2021.08.30.13.45.25_veh-40_01116_01336'
+ - '2021.09.09.18.29.25_veh-39_00427_00556'
+ - '2021.09.16.14.39.34_veh-42_01111_01448'
+ - '2021.06.03.17.06.58_veh-35_02943_03220'
+ - '2021.09.29.19.02.14_veh-28_00540_00917'
+ - '2021.06.28.16.29.11_veh-38_01415_01821'
+ - '2021.09.09.17.18.51_veh-48_00657_00876'
+ - '2021.09.16.19.27.01_veh-45_01749_03230'
+ - '2021.05.25.14.16.10_veh-35_04097_04328'
+ - '2021.09.16.19.27.01_veh-45_00472_00711'
+ - '2021.05.25.15.59.03_veh-30_03499_03671'
+ - '2021.08.30.16.16.44_veh-40_01099_01351'
+ - '2021.09.29.19.02.14_veh-28_02911_03005'
+ - '2021.08.30.13.45.25_veh-40_00878_01104'
+ - '2021.09.16.19.12.04_veh-42_00289_00398'
+ - '2021.05.25.14.16.10_veh-35_00083_00485'
+ - '2021.09.29.18.19.40_veh-28_01727_01833'
+ - '2021.09.09.17.18.51_veh-48_00098_00328'
+ - '2021.09.09.14.18.22_veh-48_00221_00299'
+ - '2021.09.09.18.04.06_veh-40_00555_00731'
+ - '2021.09.16.15.12.03_veh-42_01037_01434'
+ - '2021.06.03.13.55.17_veh-35_00789_00999'
+ - '2021.06.03.18.47.39_veh-35_00257_00492'
+ - '2021.09.09.17.18.51_veh-48_01248_01450'
+ - '2021.09.09.14.18.22_veh-48_01298_01492'
+ - '2021.06.28.13.53.26_veh-26_00492_00696'
+ - '2021.05.25.15.59.03_veh-30_04463_04606'
+ - '2021.08.30.16.16.44_veh-40_00779_01088'
+ - '2021.06.28.16.29.11_veh-38_03263_03766'
+ - '2021.09.16.14.39.34_veh-42_00297_00935'
+ - '2021.09.16.13.53.10_veh-42_00077_00153'
+ - '2021.10.06.08.16.17_veh-52_01949_02501'
+ - '2021.05.25.16.37.23_veh-25_03311_03550'
+ - '2021.06.28.20.24.43_veh-38_03385_04952'
+ - '2021.09.29.19.02.14_veh-28_00964_01689'
+ - '2021.09.29.14.44.26_veh-28_01331_01485'
+ - '2021.09.16.13.53.10_veh-42_01510_01591'
+ - '2021.06.03.18.47.39_veh-35_00123_00246'
+ - '2021.10.06.08.16.17_veh-52_01430_01579'
+ - '2021.09.29.19.02.14_veh-28_00273_00514'
+ - '2021.09.29.19.02.14_veh-28_02451_02708'
+ - '2021.10.06.07.26.10_veh-52_00422_00728'
+ - '2021.09.16.13.53.10_veh-42_00630_00818'
+ - '2021.08.16.14.23.37_veh-45_00015_00132'
+ - '2021.08.30.13.45.25_veh-40_00784_00867'
+ - '2021.09.16.19.12.04_veh-42_01088_01192'
+ - '2021.08.30.14.54.34_veh-40_00439_00835'
+ - '2021.09.09.14.18.22_veh-48_01503_01761'
+ - '2021.06.28.16.57.59_veh-26_00016_00484'
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+ - '2021.10.06.07.26.10_veh-52_00006_00398'
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+ - '2021.05.25.17.54.41_veh-35_04967_05098'
+ - '2021.09.09.18.04.06_veh-40_00743_01071'
+ - '2021.05.25.17.54.41_veh-35_02723_02902'
+ - '2021.08.30.14.54.34_veh-40_00885_00986'
+ - '2021.05.25.15.59.03_veh-30_01478_01643'
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+ - '2021.09.09.14.18.22_veh-48_00322_00895'
+ - '2021.06.28.16.29.11_veh-38_00022_00368'
+ - '2021.09.16.19.12.04_veh-42_01221_01380'
+ - '2021.08.30.13.45.25_veh-40_00610_00771'
+ - '2021.09.29.14.44.26_veh-28_01059_01191'
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+ - '2021.08.30.14.54.34_veh-40_00334_00419'
+ - '2021.09.16.14.39.34_veh-42_00032_00186'
+ - '2021.09.29.14.44.26_veh-28_00337_00504'
+ - '2021.06.03.13.55.17_veh-35_02866_03582'
+ - '2021.06.03.17.06.58_veh-35_02571_02742'
+ - '2021.10.06.08.16.17_veh-52_00612_00782'
+ - '2021.09.29.19.02.14_veh-28_01717_01824'
+ - '2021.06.28.21.16.05_veh-14_00957_01198'
+ - '2021.09.29.18.19.40_veh-28_01268_01685'
+ - '2021.09.16.17.40.09_veh-45_02539_02745'
+ - '2021.09.09.14.18.22_veh-48_00960_01115'
+ - '2021.09.29.14.44.26_veh-28_01202_01296'
+ - '2021.10.06.07.26.10_veh-52_02208_02394'
+ - '2021.09.29.18.19.40_veh-28_00438_00833'
+ - '2021.06.03.12.02.06_veh-35_03526_03712'
+ - '2021.08.30.16.16.44_veh-40_00256_00716'
+ - '2021.09.16.21.13.37_veh-42_00172_00347'
+ - '2021.05.25.17.54.41_veh-35_04111_04288'
+ - '2021.05.25.14.16.10_veh-35_03373_03550'
+
+tokens:
+ - '5798a6e25f2553e4'
+ - '3dc6b109b480502f'
+ - 'a4f2f32aa59f53b9'
+ - '9164913b818a58b1'
+ - '713c5a168c2452dc'
+ - '1975d15fde2955ff'
+ - 'b27bfe84cc545daa'
+ - '57730912486a5853'
+ - 'b111bb8716b756d2'
+ - 'e11f2ff7ffba5b45'
+ - 'b7ee1af184835990'
+ - '3789d42ea83f569d'
+ - 'e9bb1dab462252e9'
+ - '5ad18b7f7b2a52c7'
+ - 'e0bb2b04e9455fa6'
+ - '1ebfee14fc925d6e'
+ - 'd2789ec30aaa5d6a'
+ - '0d5b8da00d505be0'
+ - '8be5c83488ff5980'
+ - '871164a7e40652b5'
+ - '8a8bf2275db85432'
+ - '9bdf0d338d625e2a'
+ - '43cc094e7af0518e'
+ - '21747e180852591a'
+ - '440364f63ee95043'
+ - '82e565141eb55f88'
+ - 'd7ff11915cdf538e'
+ - '23d0ae8aedf8537b'
+ - 'afe269451bc757e1'
+ - '0f3d66a03c5e500b'
+ - 'c49e7550cee75722'
+ - 'db817c126780531f'
+ - '3ec484ccad125e99'
+ - '7602906d3c785da3'
+ - '8cafe946e9c752fc'
+ - '10209c0e885f5162'
+ - '586c5b66ad8158ee'
+ - '8c2e3aa977d95da5'
+ - 'b2f320b5d94753f9'
+ - 'ce3ff067f10b526b'
+ - 'b86152f6ab29580e'
+ - '7bbb6cfc4f135e50'
+ - '5a06f03022ec5b67'
+ - '662212c87d0d5b09'
+ - 'dc952092ef6a5243'
+ - '6227e5b0f0d4592e'
+ - '4f21925f1a7c5983'
+ - 'c5e8daf69c6552a7'
+ - '627ed4c9950753e9'
+ - '8781cde1032354cb'
+ - '7444ae8aea51554e'
+ - '2035406a3a6955be'
+ - '263367576c285a2a'
+ - '475a45837ca75030'
+ - 'd5cf4595a3435917'
+ - '7135ce0e5eea5907'
+ - '17ecb960d15d5b5f'
+ - 'a1e689b17ca65053'
+ - '3b185264ec565b67'
+ - '69616d6f715256dc'
+ - 'd792515ce4b257a4'
+ - '0f206a62842b59b2'
+ - '19cee80b71525f86'
+ - '96a0bbef8c335c3e'
+ - '3ea2b59016145c63'
+ - '8072e0c324205e72'
+ - '0dfcc232f8445d3f'
+ - '7e3ae733f7ef5c8f'
+ - 'cab75d9e74eb52a1'
+ - '9ecb516bac035ce4'
+ - '77ee60147e0e55b7'
+ - '7186e23637965344'
+ - '484b6d9a28f350bd'
+ - 'a82ce9edf79d56a6'
+ - '81d6a870aab05080'
+ - '5b61b9072cad5dbc'
+ - '33aa90792d5855a7'
+ - '13fcbcdacbe35755'
+ - 'b39df49b8b565ac3'
+ - 'd90d4f05ebde5bed'
+ - 'ed1a1174e6aa5270'
+ - '09779eb483435cab'
+ - '7be2f6436ce95042'
+ - '099ff4835ee45eb6'
+ - '76dd2e9ad6f753b8'
+ - 'a4c23fa815945fe3'
+ - '50e871f60296566b'
+ - '007845701f635565'
+ - 'f3521da962a75e3d'
+ - 'ce2de8af70e05a57'
+ - 'fc1785d55b675c6c'
+ - 'e3418fb50fa55320'
+ - '5d058c203f765173'
+ - '18a85f2812b45525'
+ - '385e44b3382350f1'
+ - 'a366d90f10ca5866'
+ - '01b9125ad835592f'
+ - 'e52c457d50455d0c'
+ - '603d4a913f315a41'
+ - '599e006109385ae5'
+ - '683e518fbfa650d5'
+ - '26e6818bd14d52a5'
+ - '59159c92e5d9571d'
+ - '09c56525e8e853a8'
+ - 'a197927702b850f4'
+ - 'd4849a52aaa55a9f'
+ - 'b39406677030585e'
+ - '8569121235665be9'
+ - 'b3509fd31f495cfb'
+ - 'eeb4b973450f50e9'
+ - '137f854539c45fe1'
+ - '62d339c4086d51ba'
+ - 'fc2a5f9f60965ab9'
+ - 'bdffdc5fc2fa542b'
+ - '8bacfc35148b5dad'
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diff --git a/navsim/planning/script/config/common/scene_filter/navtrain.yaml b/navsim/planning/script/config/common/scene_filter/navtrain.yaml
new file mode 100644
index 0000000..345bf21
--- /dev/null
+++ b/navsim/planning/script/config/common/scene_filter/navtrain.yaml
@@ -0,0 +1,104490 @@
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+ - '836c9e38856b554e'
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+ - '0edf88e96df55dfc'
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+ - '58c2223f618a53a7'
+ - 'd7cbf37d1c5c5dd0'
+ - '7911dc9f5ad958cd'
+ - 'abaa17110d005ff4'
+ - '4da51b3f3cb053c0'
+ - 'fa1fe0df56585b2d'
+ - '553043286de55254'
+ - '2f98c6a9ea055559'
+ - 'f21d1b5285275aee'
+ - '0c6a6826288c5c06'
+ - '79131da2d9ea5cfe'
+ - 'a6510270439c58cf'
+ - '650ed51ae6b459a2'
+ - '3e4048255a7e5be6'
+ - '1b06f10b020e5295'
+ - '1b1ab513bca4556f'
+ - 'f6a7286724265868'
+ - '9abcfe87763c5c4b'
+ - '93d9a170881f5b57'
+ - '7b0494858dc55b99'
+ - 'ac6a782dc3aa57be'
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+ - '94019dcb637a5939'
+ - '65b2dacfef3a554d'
+ - 'c7129af1e4455742'
+ - '3763b9a05b475d6a'
+ - '8ed0ddb59f0750ad'
+ - '49b0a4b42d6e5999'
+ - '8275b67ecf785ff7'
+ - 'b25cfa1c48335c0f'
+ - '258acc8edefd564f'
+ - '5c278717cf4e5b6d'
+ - 'e8061888da7c54a6'
+ - '880219f6e70956d7'
+ - '31897ce73ae2590c'
+ - '771723bc1fbf5ad6'
+ - '9998239c558552c7'
+ - 'edc5a6868f245d3f'
+ - 'b1751763d28e5f0b'
+ - '9dda26e32bde52a0'
+ - 'd1f4496facb7596b'
+ - '39df0240ad9156c2'
+ - 'd2e81c3f25e050e5'
+ - '726feffe1f755640'
+ - '36ea1c34a0755c21'
+ - 'ed3db31882d35ab9'
+ - '5619738e78cc5e04'
+ - '4204b1bf7df850fd'
+ - '9a78a9b3ea4e5e8e'
+ - '55d1606b2a2d5531'
+ - '7782261d63ba557d'
+ - '63a6dd2bda8d5148'
+ - 'b1ecc5d6b6a55958'
+ - '82ea963843ef5356'
+ - 'a83e983fb7365f31'
+ - '89cefa2b381551e2'
+ - '4aa385519611532e'
+ - 'b9be65e7f62756df'
+ - 'db79847ee5f65406'
+ - 'daf2972b1f8b5cdd'
+ - 'c5f463eac4265290'
+ - 'fa8c0162bd935c33'
+ - 'c155ebfa01985d01'
+ - 'c96514e369e95589'
+ - 'af937631321a5e25'
+ - '3b4e8eadeac554c2'
+ - '6f4643f4c727531f'
+ - '8ff15ec8ba0e52ef'
+ - 'c570e4d1ec57590e'
+ - 'ee75ad6bc935524e'
+ - '8a509bf3b9c35bf0'
+ - '8a86b47a339c5663'
+ - '8a3386edad6c5ac8'
+ - '31aac2f7818d562a'
+ - 'cb9ab4af251c5731'
+ - '9ed4e5793b675f2c'
+ - '677557e87bb252b4'
+ - '07e3ee56d347531d'
+ - '287bb427e26651a0'
+ - '67434942b6e75bab'
+ - '0a21d7cd30b45d42'
+ - '418e0a4583df5b99'
+ - 'ba95ce344c1f545a'
+ - '9d992e04cab65040'
+ - 'c10eea2e235c5845'
+ - '56a5020a987956e8'
+ - '16d47ad1390e5327'
+ - 'cbb66c905bb15b0d'
+ - 'fc5c9e4541bc5fbe'
+ - 'f6a3e6a2214e5013'
+ - 'ab82940ecc575181'
+ - '6a9b4054a0be50af'
+ - '0a8a8ec5514c55ce'
+ - '3d2a2ef84d78504a'
+ - '56b3a90e8afe5490'
+ - '7189336fa20f5268'
+ - '119155d285af5920'
+ - '3b301ce063a753e8'
+ - '348fb0c377d65741'
+ - 'c38b25cfca4f562e'
+ - 'ae51c5b8a2be5d79'
+ - '8e9bd116b09159ca'
+ - 'be41cfe468a550f9'
+ - '61547703eee25ebd'
+ - '40db8085d8035ec9'
+ - '6623d3a734ac5ad3'
+ - '166a36d8d3895bb1'
+ - 'c495ae8c3567571d'
+ - '4ede4e5d5bf558ae'
+ - '01bf673b5065536a'
+ - '484ebebfe1045171'
+ - '3ca4ed9cdfde5db1'
+ - '099731abee545aad'
+ - '025c9ade3c0b53fb'
+ - '6ae3dc7e01be5889'
+ - 'ac0e90d20c4156fb'
+ - '576e3d90901e5a48'
+ - '1f58d71a76525927'
+ - '7368eea9970e5dd0'
+ - '0a5c5e4bd7b55078'
+ - '81aba3b2156e5469'
+ - '576e5310acf457b9'
+ - '5a922922cbf05d3d'
+ - '42d438f463055b4e'
+ - 'cc1e1b5fae2a501d'
+ - '5785313a42705302'
+ - 'b2b8d00ac29754ad'
+ - '186bbfa59a9d54f2'
+ - '5abbdaac06cb52bd'
+ - 'e4274af8f96e5360'
+ - '4c6ef6409a945ec8'
+ - '3a0353fe1c715c0b'
+ - 'e5171c3d66355075'
+ - '8e02e758465e571f'
+ - '4d05fa6758d35052'
+ - '6314b4d7e5cb5749'
+ - 'e3b6232564f759c2'
+ - '36cdbf9d50a95de2'
+ - 'ed43216096395bcc'
+ - '85622eb3359d50e0'
+ - '43dabd93665a5f38'
+ - 'a2bb8053c05057fe'
+ - 'f30bda4a0afb5f49'
+ - '119b525b616c5e96'
+ - '1deb2f173e225cb1'
+ - 'f7edbcf3fb9e535b'
+ - 'eb4f0c07577951c6'
+ - 'fabbc7a621d35bb3'
+ - '28163dcb3d3754f0'
+ - 'c51028f4fadd5bf8'
+ - '6ecc7a486cea57bd'
+ - '147e344e7f6f563b'
+ - '73449cb1c63b5e0f'
+ - '690bbede42a8560a'
+ - 'aa61c27978275516'
+ - 'd97b1927ef195035'
+ - '4ea73d0306ec5486'
+ - '2442efa3f0c555a9'
+ - '5439a694bff25479'
+ - '6557c71ff9a65f33'
+ - '8cabef1235cb5228'
+ - '15272c348ed15559'
+ - 'fc83faa47bc8595a'
+ - 'b9d773146a70516d'
+ - '67a99af851475e10'
+ - 'f1994af0bd595b7f'
+ - '61d93811ee8956c7'
+ - '2292ec8113a35d62'
+ - 'a78b05fdbe775c42'
+ - '6cbcf12324535e9e'
+ - '367c1e24b9305213'
+ - '4bfdf8eb90445b5a'
+ - 'cb27bc7ac8565ea0'
+ - 'b6c6a72f278653ef'
+ - '0cd6fa515d405315'
+ - 'dc722692270d5d13'
+ - '6244a789f919560d'
+ - 'd11ab3888d6455fd'
+ - 'd322b8b15a1451f7'
+ - '030dc8080285527b'
+ - '1a15af177a285453'
+ - 'b3c2bd6aaf0e52e5'
+ - 'ea546f170065528b'
+ - '03ccf4fb5064520c'
+ - '9f36ab257feb536c'
+ - '9b49483c408a5b76'
+ - '55179d69cfc95bd7'
+ - 'dd20b8e05e0e5010'
+ - 'caeeed286f12520a'
+ - 'ef2ab4cafadd5a54'
+ - '1ca24362fa475959'
+ - '03af459c0ccb53d6'
+ - '9a9229370ad8524d'
+ - '543020339e8a577d'
+ - 'e9f7c995c1465175'
+ - 'e0c5036f61a4537d'
+ - '7ecc880a7111558d'
+ - '5c6c2e6695f15e42'
+ - '80e99b5c9dc95f54'
+ - 'ae7a329c1fd8557e'
+ - 'd1728f1833805fc9'
+ - 'b2cd630b16ed59e7'
+ - '11c58a1c12985533'
+ - '8b1fe8ee3eeb549c'
+ - '30a729aa0eaa5e80'
+ - 'eb972ef0ae8d5772'
+ - '62666579e34a5136'
+ - 'b3b9a3413f3c50ae'
+ - 'f429e6a02a7353a2'
+ - '1fad4a83e64b51b3'
+ - '8bd1a27cae685393'
+ - 'aa1a5302fb585cea'
+ - '27d29b6274745319'
+ - '169eaa9c3b8b5255'
+ - 'b79abcf5d2c35080'
+ - 'b5c906cdb5fd5cd4'
+ - 'a76cca3715d45ecc'
+ - 'a8178e8d04275c3e'
+ - '6a0bdbc2e8a25d06'
+ - '33e6cebc700b5bb7'
+ - '782973aaebb65b46'
+ - '399ff77884e35ebe'
+ - '430843d30f9258ed'
+ - 'ff1fc4f3cd385cd7'
+ - 'd94dfc22b06e5117'
+ - 'da35866213c45620'
+ - '513aee9f6e4f590c'
+ - 'db012665680258d4'
+ - 'b44da409d5255a6a'
+ - '7e4efb4690175510'
+ - 'c6bd186817bd553e'
+ - '2fb2fc7a6fec5bc2'
+ - 'e4dc93c1e2095f89'
+ - 'e7461b36d515584e'
+ - '1061433656085b89'
+ - 'b67b88fa9fc851ba'
+ - 'faa4ce03e9535803'
+ - 'dce72a2b17b85b3b'
+ - '9f7c0124dbe25aeb'
+ - 'baa15a0bb0305c89'
+ - 'f97db0d3a2015bbb'
+ - '7456db0ddb7550b6'
+ - '499218023770519d'
+ - 'c19615de32245f3c'
+ - '18c5f41085ec56c8'
+ - 'd8bd70c1a40654f9'
+ - '381b24176b85561a'
+ - '7d21da1e1f1d5588'
+ - '5e8943de6e075343'
+ - '320ecf1800375b02'
+ - '10f931d5837c5871'
+ - '5a59d9cd37d45046'
+ - '5df387fd1a9f568b'
+ - '4c5d0e59dbcd5674'
+ - '5d82280a3e77589d'
+ - '6eb683206f12502f'
+ - 'feb584561a655213'
+ - '2fc820a5dab05ae2'
+ - 'c5e2591b0c825f45'
+ - 'c75894b604935cee'
+ - 'eb94b0a52a7e5691'
+ - 'cdab920104f757c2'
+ - '8a1e571ae13b5e5a'
+ - 'a98abc8530645df3'
+ - 'b43bf5b6fdee57f3'
+ - '873c22a4a020555f'
+ - 'db575d6d1c3f5e28'
+ - '8eecc2c210f15f05'
+ - 'cc76b5eca4fe5196'
+ - '685fc03bf7b5564e'
+ - '48c3726ca5f052a5'
+ - '2b37322cb8c85817'
+ - '751938cacf8855ad'
+ - 'e193d05e9c945308'
+ - '979928c056005ac6'
+ - 'b9ee86725b005bc8'
+ - 'd0fbb1a2a6135728'
+ - 'b7b9f31751e459f0'
+ - '3209aaa0c32a585a'
+ - '9125d73c00235223'
+ - '886df3cce3a95a83'
+ - '87bcfe31169f5528'
+ - '00508bc3b05d50a6'
+ - '9467a992f7775e2a'
+ - '1f854f3b70f35ba9'
+ - '372d22a9c2d65224'
+ - '19407cb6c22a58c7'
+ - 'f0a9d7e133715acd'
+ - 'd842b0bddf335eb0'
+ - '58b5de041ba35d55'
+ - 'c8ade9e4082d540f'
+ - 'c2be1f3b37bd5cb4'
+ - '6861dc17f93153ab'
+ - '2fc3b9bc4ba85c4b'
+ - '30a4bb243a2a5ce6'
+ - 'eefcab176b8b5bf1'
+ - '1edb16d927ad5344'
+ - 'd2a2439560b55b5e'
+ - '2081e251e1345dae'
+ - '909752b1ce9756d0'
+ - '669a42e4039b581a'
+ - '36be05ef71005428'
+ - '4f4aeb0560035ec9'
+ - 'caa0b0e5c82f5f81'
+ - 'd8f9c97356bd59aa'
+ - '7a92477e48a254c1'
+ - 'e827758c9a4d5610'
+ - 'd6aa4ba9d0d651c8'
+ - '7bd35dd3cd735885'
+ - '5e7f016d3da25c49'
+ - 'a24251d000005d71'
+ - 'c403f53058695f04'
+ - '8f303260e1ab51c2'
+ - 'a275151b2d7757f9'
+ - '7a69b8395942567f'
\ No newline at end of file
diff --git a/navsim/planning/script/config/common/scene_filter/competition_test.yaml b/navsim/planning/script/config/common/scene_filter/private_test_e2e.yaml
similarity index 99%
rename from navsim/planning/script/config/common/scene_filter/competition_test.yaml
rename to navsim/planning/script/config/common/scene_filter/private_test_e2e.yaml
index ed8fcbf..50a5cb0 100644
--- a/navsim/planning/script/config/common/scene_filter/competition_test.yaml
+++ b/navsim/planning/script/config/common/scene_filter/private_test_e2e.yaml
@@ -3,11 +3,11 @@ _convert_: 'all'
num_history_frames: 4
num_future_frames: 0
-has_route: False
-
-max_scenes: Null
-log_names: Null
+frame_interval: null
+has_route: false
+max_scenes: null
+log_names: null
tokens:
- "4adc4d31c899590a"
- "b781a9dd05b35138"
diff --git a/navsim/planning/script/config/common/scene_filter/warmup_test.yaml b/navsim/planning/script/config/common/scene_filter/warmup_test_e2e.yaml
similarity index 99%
rename from navsim/planning/script/config/common/scene_filter/warmup_test.yaml
rename to navsim/planning/script/config/common/scene_filter/warmup_test_e2e.yaml
index 2f898f2..cf55a4f 100644
--- a/navsim/planning/script/config/common/scene_filter/warmup_test.yaml
+++ b/navsim/planning/script/config/common/scene_filter/warmup_test_e2e.yaml
@@ -3,9 +3,10 @@ _convert_: 'all'
num_history_frames: 4
num_future_frames: 10
-has_route: True
+frame_interval: null
+has_route: true
-max_scenes: Null
+max_scenes: null
log_names:
- "2021.07.24.20.37.45_veh-17_00015_00375"
- "2021.06.08.12.54.54_veh-26_04262_04732"
diff --git a/navsim/planning/script/config/common/worker/__init__.py b/navsim/planning/script/config/common/worker/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/metric_caching/__init__.py b/navsim/planning/script/config/metric_caching/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/pdm_scoring/__init__.py b/navsim/planning/script/config/pdm_scoring/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/pdm_scoring/default_run_create_submission_pickle.yaml b/navsim/planning/script/config/pdm_scoring/default_run_create_submission_pickle.yaml
index 7d5cc52..4ea6e59 100644
--- a/navsim/planning/script/config/pdm_scoring/default_run_create_submission_pickle.yaml
+++ b/navsim/planning/script/config/pdm_scoring/default_run_create_submission_pickle.yaml
@@ -9,9 +9,9 @@ defaults:
- default_common
- default_evaluation
- agent: constant_velocity_agent
- - override scene_filter: competition_test
+ - override scene_filter: private_test_e2e
-split: competition_test
+split: private_test_e2e
"team_name": ??? # The team name
"authors": ??? # The team members
diff --git a/navsim/planning/script/config/training/__init__.py b/navsim/planning/script/config/training/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/navsim/planning/script/config/training/default_training.yaml b/navsim/planning/script/config/training/default_training.yaml
index ed624e6..d333c13 100644
--- a/navsim/planning/script/config/training/default_training.yaml
+++ b/navsim/planning/script/config/training/default_training.yaml
@@ -4,7 +4,7 @@ hydra:
output_subdir: ${output_dir}/code/hydra # Store hydra's config breakdown here for debugging
searchpath: # Only 
+![\"drawing\"](\"https://raw.githubusercontent.com/autonomousvision/navsim/main/assets/navsim_transparent.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NAVSIM Visualization Tutorial\n",
+ "\n",
+ "This notebook will introduce some basic plots to visualize the driving scenes in NAVSIM. All plots are created with `matplotlib` and are easy to customize for your application.\n",
+ "\n",
+ "## Table of Contents\n",
+ "1. [Config](#config)\n",
+ "2. [Birds-Eye-View](#bev)\n",
+ "3. [Cameras](#camera)\n",
+ "4. [Creating custom plots](#custom)\n",
+ "5. [Creating GIFs](#gifs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Config \n",
+ "\n",
+ "NAVSIM offers two types of plots: \n",
+ "- Birds-Eye-View (BEV) plots or \n",
+ "- Camera plots. \n",
+ "\n",
+ "The LiDAR sensor can be visualized either in BEV or in camera images. All plots have a global configuration in [`navsim/visualization/config.py`](https://github.com/autonomousvision/navsim/blob/main/navsim/navsim/visualization/config.py). In this Python file, you can configure all colors or dimensions. The LiDAR point cloud can be colored in any colormap, showing the distance to the ego vehicle or the height of each point. In this tutorial, we first instantiate a `SceneFilter` and `SceneLoader` from the mini split."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import hydra\n",
+ "from hydra.utils import instantiate\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from navsim.common.dataloader import SceneLoader\n",
+ "from navsim.common.dataclasses import SceneFilter, SensorConfig\n",
+ "\n",
+ "SPLIT = \"mini\" # [\"mini\", \"test\", \"trainval\"]\n",
+ "FILTER = \"all_scenes\"\n",
+ "\n",
+ "hydra.initialize(config_path=\"../navsim/planning/script/config/common/scene_filter\")\n",
+ "cfg = hydra.compose(config_name=FILTER)\n",
+ "scene_filter: SceneFilter = instantiate(cfg)\n",
+ "openscene_data_root = Path(os.getenv(\"OPENSCENE_DATA_ROOT\"))\n",
+ "\n",
+ "scene_loader = SceneLoader(\n",
+ " openscene_data_root / f\"navsim_logs/{SPLIT}\",\n",
+ " openscene_data_root / f\"sensor_blobs/{SPLIT}\",\n",
+ " scene_filter,\n",
+ " sensor_config=SensorConfig.build_all_sensors(),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Birds-Eye-View \n",
+ "\n",
+ "The Birds-Eye-View (BEV) visualization in NAVSIM is useful for overviewing the map, bounding-box annotations, or the LiDAR point cloud. In standard setting, the BEV plot includes a 64m $\\times$ 64m frame centered at the rear axle of the ego vehicle (excluding LiDAR for simplicity). First, we take a random token and load a scene to visualize."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "token = np.random.choice(scene_loader.tokens)\n",
+ "scene = scene_loader.get_scene_from_token(token)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function `plot_bev_frame` takes a `Scene` and index of the step to visualize (history or future). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import plot_bev_frame\n",
+ "\n",
+ "frame_idx = scene.scene_metadata.num_history_frames - 1 # current frame\n",
+ "fig, ax = plot_bev_frame(scene, frame_idx)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function `plot_bev_with_agent` visualizes the trajectory of an agent in comparison to the human vehicle operator at the current frame. This notebook shows an example of the naive `ConstantVelocityAgent`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import plot_bev_with_agent\n",
+ "from navsim.agents.constant_velocity_agent import ConstantVelocityAgent\n",
+ "\n",
+ "agent = ConstantVelocityAgent()\n",
+ "fig, ax = plot_bev_with_agent(scene, agent)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cameras \n",
+ "\n",
+ "The agents in NAVSIM have access to eight cameras surrounding the vehicle. The function `plot_cameras_frame` shows the cameras in a 3 $\\times$ 3 grid with cameras in each direction of the ego-vehicle and the BEV plot in the center. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import plot_cameras_frame\n",
+ "\n",
+ "fig, ax = plot_cameras_frame(scene, frame_idx)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With `plot_cameras_frame_with_annotations`, you can visualize the bounding-box annotations in the camera images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import plot_cameras_frame_with_annotations\n",
+ "\n",
+ "fig, ax = plot_cameras_frame_with_annotations(scene, frame_idx)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With `plot_cameras_frame_with_lidar`, you can visualize the LiDAR point cloud in the camera images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import plot_cameras_frame_with_lidar\n",
+ "\n",
+ "fig, ax = plot_cameras_frame_with_lidar(scene, frame_idx)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating custom plots \n",
+ "\n",
+ "The plots in NAVSIM use `matplotlib` and either add elements to a `plt.Axes` object or return the full `plt.Figure`. Functions in [`navsim/visualization/`](https://github.com/autonomousvision/navsim/blob/main/navsim/navsim/visualization) can be re-used to create custom plots. In this example, we create a plot for the bounding-box annotations and the LiDAR point cloud."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import configure_bev_ax\n",
+ "from navsim.visualization.bev import add_annotations_to_bev_ax, add_lidar_to_bev_ax\n",
+ "\n",
+ "\n",
+ "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
+ "\n",
+ "ax.set_title(\"Custom plot\")\n",
+ "\n",
+ "add_annotations_to_bev_ax(ax, scene.frames[frame_idx].annotations)\n",
+ "add_lidar_to_bev_ax(ax, scene.frames[frame_idx].lidar)\n",
+ "\n",
+ "# configures frame to BEV view\n",
+ "configure_bev_ax(ax)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating GIFs \n",
+ "\n",
+ "You can transform frame-wise plots into short animated GIFs. Give any function to `frame_plot_to_gif`, which takes a `Scene` and `frame_idx` as input (ie. `plot_cameras_frame_with_annotations`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from navsim.visualization.plots import frame_plot_to_gif\n",
+ "\n",
+ "frame_indices = [idx for idx in range(len(scene.frames))] # all frames in scene\n",
+ "file_name = f\"./{token}.gif\"\n",
+ "images = frame_plot_to_gif(file_name, plot_cameras_frame_with_annotations, scene, frame_indices)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "navsim",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.19"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}