First diffusion

1.csv

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1.py

@@ -0,0 +1,8 @@
+# /data/hdd01/dingzx/dataset/private_test_hard_two_stage/openscene_meta_datas/0a79e71ca7aa8e249.pkl
+# 1d70d69255aef8709
+import pickle
+
+with open('/data/hdd01/dingzx/dataset/private_test_hard_two_stage/openscene_meta_datas/0a79e71ca7aa8e249.pkl', 'rb') as f:
+    data = pickle.load(f)
+
+print(data)  # 打印文件内容

1/1.py

@@ -0,0 +1,4 @@
+print("<<")
+print("<<")
+print("<<")
+print("<<")

navsim/agents/abstract_agent.py

@@ -68,18 +68,23 @@ def compute_trajectory(self, agent_input: AgentInput) -> Trajectory:
         """
         self.eval()
         features: Dict[str, torch.Tensor] = {}
+        # targets: Dict[str, torch.Tensor] = {}
         # build features
         for builder in self.get_feature_builders():
             features.update(builder.compute_features(agent_input))
 
+        # build targets
+        # for builder in self.get_target_builders():
+        #     targets.update(builder.compute_targets(agent_input))
+
         # add batch dimension
         features = {k: v.unsqueeze(0) for k, v in features.items()}
-
+        # targets = {k: v.unsqueeze(0) for k, v in targets.items()}
         # forward pass
         with torch.no_grad():
             predictions = self.forward(features)
             poses = predictions["trajectory"].squeeze(0).numpy()
-
+        # print("huatu")
         # extract trajectory
         return Trajectory(poses, self._trajectory_sampling)
 

navsim/agents/abstract_agent_diffusiondrive.py

@@ -0,0 +1,127 @@
+from abc import abstractmethod, ABC
+from typing import Dict, Union, List
+import torch
+import pytorch_lightning as pl
+from typing import Tuple
+import numpy as np
+from navsim.common.dataclasses import AgentInput, Trajectory, SensorConfig, Scene
+from navsim.planning.training.abstract_feature_target_builder import AbstractFeatureBuilder, AbstractTargetBuilder
+
+
+class AbstractAgent(torch.nn.Module, ABC):
+    """Interface for an agent in NAVSIM."""
+
+    def __init__(
+        self,
+        requires_scene: bool = False,
+    ):
+        super().__init__()
+        self.requires_scene = requires_scene
+
+    @abstractmethod
+    def name(self) -> str:
+        """
+        :return: string describing name of this agent.
+        """
+        pass
+
+    @abstractmethod
+    def get_sensor_config(self) -> SensorConfig:
+        """
+        :return: Dataclass defining the sensor configuration for lidar and cameras.
+        """
+        pass
+
+    @abstractmethod
+    def initialize(self) -> None:
+        """
+        Initialize agent
+        :param initialization: Initialization class.
+        """
+        pass
+
+    def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass of the agent.
+        :param features: Dictionary of features.
+        :return: Dictionary of predictions.
+        """
+        raise NotImplementedError
+
+    def get_feature_builders(self) -> List[AbstractFeatureBuilder]:
+        """
+        :return: List of target builders.
+        """
+        raise NotImplementedError("No feature builders. Agent does not support training.")
+
+    def get_target_builders(self) -> List[AbstractTargetBuilder]:
+        """
+        :return: List of feature builders.
+        """
+        raise NotImplementedError("No target builders. Agent does not support training.")
+
+    def compute_trajectory(self, agent_input: AgentInput) -> Tuple[np.ndarray,np.ndarray]:
+        """
+        Computes the ego vehicle 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] = {}
+        # targets: Dict[str, torch.Tensor] = {}
+        # build features
+        for builder in self.get_feature_builders():
+            features.update(builder.compute_features(agent_input))
+
+        # for builder in self.get_target_builders():
+            # targets.update(builder.compute_targets(scene))
+
+
+        # "trajectory": trajectory,
+        # "agent_states": agent_states,
+        # "agent_labels": agent_labels,
+        # "bev_semantic_map": bev_semantic_map,`
+
+        # add batch dimension
+        features = {k: v.unsqueeze(0) for k, v in features.items()}
+        # targets = {k: v.unsqueeze(0) for k, v in targets.items()}
+        # print(targets)
+        # print('<<<<<<<<<<<<<<<')
+        # poses1 = targets["trajectory"].squeeze(0).numpy()
+        # forward pass
+        with torch.no_grad():
+            predictions = self.forward(features)
+            poses = predictions['trajectory'].squeeze(0).numpy()# 20 8 3     20 64 8 3
+            anchor_poses = predictions['anchor_trajectories'].squeeze(0).numpy()  # 确保转换为numpy
+        # extract trajectory
+        # print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
+        # print(anchor_trajectories.shape)
+        return poses,anchor_poses
+        # return Trajectory(poses)
+
+    def compute_loss(
+        self,
+        features: Dict[str, torch.Tensor],
+        targets: Dict[str, torch.Tensor],
+        predictions: Dict[str, torch.Tensor],
+    ) -> torch.Tensor:
+        """
+        Computes the loss used for backpropagation based on the features, targets and model predictions.
+        """
+        raise NotImplementedError("No loss. Agent does not support training.")
+
+    def get_optimizers(
+        self,
+    ) -> Union[torch.optim.Optimizer, Dict[str, Union[torch.optim.Optimizer, torch.optim.lr_scheduler.LRScheduler]]]:
+        """
+        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.")
+
+    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 []