Organize unused code
This commit is contained in:
Lu Junjie
parent
68c3baa7fe
commit
6a7aba86dc
@ -25,7 +25,6 @@ class FlightEnvVec(VecEnv):
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self.world_box = np.zeros([6], dtype=np.float32)
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self.wrapper.getWorldBox(self.world_box) # xyz_min, xyz_max
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self.reward_names = self.wrapper.getRewardNames()
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self.pretrained = False
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# observations
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self._traj_cost = np.zeros([self.num_envs, 1], dtype=np.float32) # cost of current pred
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@ -152,7 +152,6 @@ class ReplayBuffer(BaseBuffer):
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device: Union[th.device, str] = "cpu",
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n_envs: int = 1,
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optimize_memory_usage: bool = False,
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handle_timeout_termination: bool = True,
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):
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super(ReplayBuffer, self).__init__(buffer_size, observation_dim, device, n_envs=n_envs)
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@ -165,15 +164,10 @@ class ReplayBuffer(BaseBuffer):
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self.optimize_memory_usage = optimize_memory_usage
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self.observations = np.zeros((self.buffer_size, self.n_envs) + observation_dim, dtype=np.float32)
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self.observations = np.zeros((self.buffer_size, self.n_envs, observation_dim), dtype=np.float32)
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self.goals = np.zeros((self.buffer_size, self.n_envs, 3), dtype=np.float32)
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self.depths = np.zeros((self.buffer_size, self.n_envs, 1, image_WxH[1], image_WxH[0]), dtype=np.float32)
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self.map_ids = np.zeros((self.buffer_size, self.n_envs, 1), dtype=np.float32)
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# Handle timeouts termination properly if needed
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# see https://github.com/DLR-RM/stable-baselines3/issues/284
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self.handle_timeout_termination = handle_timeout_termination
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self.timeouts = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)
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self.map_ids = np.zeros((self.buffer_size, self.n_envs, 1), dtype=np.int16)
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if psutil is not None:
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total_memory_usage = self.observations.nbytes + self.goals.nbytes + self.depths.nbytes + self.map_ids.nbytes
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@ -187,16 +181,11 @@ class ReplayBuffer(BaseBuffer):
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f"replay buffer {total_memory_usage:.2f}GB > {mem_available:.2f}GB"
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)
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def add(
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self,
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def add(self,
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obs: np.ndarray,
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goal: np.ndarray,
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depth: np.ndarray,
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map_id: int,
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infos: List[Dict[str, Any]],
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) -> None:
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# TODO: 删了obs的格式调整，检查下还能不能正常放
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map_id: int) -> None:
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# Copy to avoid modification by reference
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self.observations[self.pos] = np.array(obs).copy()
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@ -204,9 +193,6 @@ class ReplayBuffer(BaseBuffer):
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self.depths[self.pos] = np.array(depth).copy()
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self.map_ids[self.pos] = np.array(map_id).copy()
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if self.handle_timeout_termination:
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self.timeouts[self.pos] = np.array([info.get("TimeLimit.truncated", False) for info in infos])
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self.pos += 1
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if self.pos == self.buffer_size:
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self.full = True
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@ -11,7 +11,7 @@ import numpy as np
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import torch as th
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from torch.nn import functional as F
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from stable_baselines3.common.type_aliases import RolloutReturn, TrainFreq, TrainFrequencyUnit
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from stable_baselines3.common.utils import should_collect_more_steps, get_schedule_fn, configure_logger
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from stable_baselines3.common.utils import should_collect_more_steps, get_schedule_fn, configure_logger, update_learning_rate
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from stable_baselines3.common.vec_env import VecEnv
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from stable_baselines3.common.utils import get_device
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@ -119,7 +119,7 @@ class YopoAlgorithm:
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cost_losses = [] # Performance (score) of prediction
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score_losses = [] # Accuracy of the predicted score
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for step, (depth, pos, quat, obs_b, map_id) in enumerate(data_loader): # obs: body frame
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if depth.shape[0] != self.batch_size: # batch size == num of env
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if depth.shape[0] != self.batch_size: # batch size == num of env
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continue
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n_updates = n_updates + 1
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depth = depth.to(self.device)
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@ -172,82 +172,51 @@ class YopoAlgorithm:
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path = policy_path + "/epoch{}_iter{}.pth".format(epoch_, step)
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th.save({"state_dict": self.policy.state_dict(), "data": self.policy.get_constructor_parameters()}, path)
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# 模仿学习: 已弃用(暂未删除以备后续使用)
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# 0、reset_state、get_depth、reset_goal
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# 1、执行若干步（env_num * 200）
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# 2、训练若干步（batch_size = env_num, 训200次=1eposide）
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# 3、reset_state、get_depth、reset_goal
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def imitation_learning(
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self,
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total_timesteps,
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callback=None,
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log_interval=4,
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eval_env=None,
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eval_freq=-1,
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n_eval_episodes=5,
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tb_log_name="YOPO",
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eval_log_path=None,
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log_interval,
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reset_num_timesteps=True,
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):
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# 0. 初始化第一次观测
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total_timesteps, callback = self._setup_learn(
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total_timesteps,
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eval_env,
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callback,
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eval_freq,
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n_eval_episodes,
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eval_log_path,
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reset_num_timesteps,
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tb_log_name,
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)
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self.pretrained = self.env.pretrained
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callback.on_training_start(locals(), globals())
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# 0. setup learn and init the first observation
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self._setup_learn(total_timesteps, reset_num_timesteps)
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while self.num_timesteps < total_timesteps:
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# 1. 数据收集
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# 1. Rollout and Collect Data into Buffer
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rollout = self.collect_rollouts(
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self.env,
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train_freq=self.train_freq,
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action_noise=self.action_noise,
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callback=callback,
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replay_buffer=self.replay_buffer,
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log_interval=log_interval,
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replay_buffer=self.replay_buffer
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)
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if rollout.continue_training is False:
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break
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# 2. 训练模型
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# 2. Train the Policy
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if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
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# If no `gradient_steps` is specified,
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# do as many gradients steps as steps performed during the rollout
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# If no `gradient_steps` is specified, do as many gradients steps as steps performed during the rollout
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gradient_steps = self.gradient_steps if self.gradient_steps >= 0 else rollout.episode_timesteps
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# Special case when the user passes `gradient_steps=0`
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if gradient_steps > 0:
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if gradient_steps > 0: # Special case when the user passes `gradient_steps=0`
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self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)
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self.reset_state()
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iteration = int(self.num_timesteps / (self.train_freq.frequency * self.env.num_envs))
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# 3. 重置环境
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# 3. reset the environment
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if self.change_env_freq > 0 and iteration % self.change_env_freq == 0:
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self.env.spawnTreesAndSavePointcloud()
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self._map_id = self._map_id + 1
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self.reset_state()
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# 4. 终端打印log
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# 4. print the log and save weight
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if log_interval is not None and iteration % log_interval[0] == 0:
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self._dump_logs()
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if log_interval is not None and iteration % log_interval[1] == 0:
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policy_path = self.logger.get_dir() + "/Policy"
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os.makedirs(policy_path, exist_ok=True)
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path = policy_path + "/epoch0_iter{}.pth".format(iteration)
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th.save({"state_dict": self.policy.state_dict(), "data": self.policy.get_constructor_parameters()}, path)
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callback.on_training_end()
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def test_policy(self, num_rollouts: int = 10):
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max_ep_length = 400
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self.policy.set_training_mode(False)
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@ -294,19 +263,16 @@ class YopoAlgorithm:
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def train(self, gradient_steps: int, batch_size: int) -> None:
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"""
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Sample the replay buffer and do the updates
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(gradient descent and update target networks)
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Imitation learning: sample data from the replay buffer and train the Policy
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"""
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# Switch to train mode (this affects batch norm / dropout)
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self.policy.set_training_mode(True)
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# Update learning rate according to schedule (TODO in supervised learning)
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self._update_learning_rate(self.policy.optimizer)
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self.policy.set_training_mode(True) # Switch to train mode (this affects batch norm / dropout)
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update_learning_rate(self.policy.optimizer, self.lr_schedule(self._current_progress_remaining))
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cost_losses = []
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score_losses = [] # dy, dz, r, p, vx, vy, vz
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for _ in range(gradient_steps):
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# Sample replay buffer
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replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
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replay_data = self.replay_buffer.sample(batch_size)
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depth = th.from_numpy(replay_data.depths).to(self.device)
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pos = replay_data.observations[:, 0:3]
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vel_acc_b = replay_data.observations[:, 3:9]
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@ -354,69 +320,43 @@ class YopoAlgorithm:
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def collect_rollouts(
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self,
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env,
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callback,
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train_freq,
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replay_buffer,
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action_noise=None,
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log_interval=None,
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) -> RolloutReturn:
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self.policy.set_training_mode(False)
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num_collected_steps, num_collected_episodes = 0, 0
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assert isinstance(env, VecEnv), "You must pass a VecEnv"
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assert train_freq.frequency > 0, "Should at least collect one step or episode."
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if env.num_envs > 1:
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assert train_freq.unit == TrainFrequencyUnit.STEP, "You must use only one env when doing episodic training."
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callback.on_rollout_start()
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continue_training = True
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"""
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1、pred endstate
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2、get obs: self._last_obs = env.step(endstate)
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3、get depth: self._last_depth = env.getDepthImage()
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4、record to buffer and back to 1
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"""
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while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):
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# 1. pred endstate used latest policy or pre-trained policy
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sampled_endstate = self._sample_action(action_noise, env.num_envs)
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# 1. pred endstate used latest policy
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sampled_endstate = self._sample_action()
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# 2. perform action
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# 2. perform action and get new observation
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new_obs, rewards, dones = env.step(sampled_endstate)
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self.num_timesteps += env.num_envs
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num_collected_steps += 1
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# Give access to local variables
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callback.update_locals(locals())
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# Only stop training if return value is False, not when it is None.
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if callback.on_step() is False:
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return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes,
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continue_training=False)
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# 3. store the last obs, depth, and goal
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# self._update_info_buffer(infos, dones)
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self._store_transition(replay_buffer)
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self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)
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self._current_progress_remaining = 1.0 - float(self.num_timesteps) / float(self._total_timesteps)
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# 4. update the obs, depth, goal, and reset the goal for the done-env
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# 4. update the obs, depth, and reset the goal for the done-env
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self._last_obs = new_obs
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self._last_depth = env.getDepthImage()
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for idx, done in enumerate(dones):
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if done:
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# Update stats
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num_collected_episodes += 1
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self._episode_num += 1
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# reset goal for the 'done' env
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self._last_goal[idx] = self.get_random_goal(self._last_obs[idx])
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callback.on_rollout_end()
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return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training)
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def prapare_input_observation(self, obs):
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@ -468,38 +408,23 @@ class YopoAlgorithm:
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costs_[i][indices] = 0.0
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return costs_
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def _setup_learn(
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self,
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total_timesteps,
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eval_env=None,
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callback=None,
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eval_freq=10000,
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n_eval_episodes=5,
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log_path=None,
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reset_num_timesteps=True,
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tb_log_name="run",
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):
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def _setup_learn(self, total_timesteps, reset_num_timesteps=True):
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# reset the time info
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self.start_time = time.time()
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if reset_num_timesteps:
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self.num_timesteps = 0 # steps of sampling
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self._n_updates = 0 # steps of policy updating
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self._total_timesteps = total_timesteps
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self._num_timesteps_at_start = self.num_timesteps
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# ----------------- Init the First Observation -----------------
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# super()._setup_learn() 中： self._last_obs = self.env.reset()
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total_timesteps_, callback_ = super()._setup_learn(
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total_timesteps,
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eval_env,
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callback,
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eval_freq,
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n_eval_episodes,
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log_path,
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reset_num_timesteps,
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tb_log_name,
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)
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self._last_obs = self.env.reset()
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self._last_depth = self.env.getDepthImage()
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self._last_goal = np.zeros([self.env.num_envs, 3], dtype=np.float32)
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for i in range(0, self.env.num_envs):
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self._last_goal[i] = self.get_random_goal(self._last_obs[i])
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self._map_id = np.zeros((self.env.num_envs, 1), dtype=np.float32)
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return total_timesteps_, callback_
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def _sample_action(self) -> np.ndarray:
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"""
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use pretrained model or current model to sample the actions (endstate)
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@ -509,7 +434,7 @@ class YopoAlgorithm:
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obs = self._last_obs.copy()
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goal_w = self._last_goal.copy()
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depth = th.from_numpy(self._last_depth).to(self.device)
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# wxyz 四元数的逆[w, -x, -y, -z]
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# [w, x, y, z] inv() of quat: [w, -x, -y, -z]
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quat_bw = -obs[:, 9:13]
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quat_bw[:, 0] = -quat_bw[:, 0]
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vel_acc_norm_b = self.normalize_obs(obs[:, 3:9])
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@ -546,12 +471,7 @@ class YopoAlgorithm:
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depth = deepcopy(self._last_depth)
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map_id = deepcopy(self._map_id)
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replay_buffer.add(
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obs,
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goal,
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depth,
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map_id
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)
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replay_buffer.add(obs, goal, depth, map_id)
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def get_random_goal(self, uav_state=None):
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world = self.env.world_box
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@ -564,8 +484,8 @@ class YopoAlgorithm:
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random_goal = random_numbers * world_scale + world_center
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# 2. Use goal in front of the UAV (for better imitation learning)
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else:
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q_wb = uav_state[9:]
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p_wb = uav_state[0:3]
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q_wb = uav_state[9:].copy()
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p_wb = uav_state[0:3].copy()
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goal = np.random.randn(3) + np.array([2, 0, 0])
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goal_dir = goal / np.linalg.norm(goal)
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random_goal_b = 50 * goal_dir
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