105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import os
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import numpy as np
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from pathlib import Path
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from PIL import Image
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import torch
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from torch.utils.data import Dataset
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from torchvision import transforms
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class ActionDataset(Dataset):
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def __init__(self, data_dir: str, history_len: int, action_dim: int,
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image_size: int = 64, normalize_actions: bool = False,
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action_stats_path: str = None, episode_indices: list = None):
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self.history_len = history_len
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self.action_dim = action_dim
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self.image_size = image_size
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self.transform = transforms.Compose([
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transforms.Resize((image_size, image_size)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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data_path = Path(data_dir)
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episodes = sorted([d for d in data_path.iterdir() if d.is_dir()])
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if episode_indices is not None:
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episodes = [episodes[i] for i in episode_indices]
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self.samples = []
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for ep_dir in episodes:
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actions_path = ep_dir / "actions.npy"
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images_dir = ep_dir / "images"
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if not actions_path.exists() or not images_dir.is_dir():
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continue
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actions = np.load(actions_path).astype(np.float32)
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n_frames = len(actions)
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image_files = sorted(images_dir.glob("*.jpg")) + sorted(images_dir.glob("*.png"))
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n_images = len(image_files)
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n = min(n_frames, n_images)
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for t in range(history_len, n):
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self.samples.append((image_files[t], actions[t - history_len:t], actions[t]))
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self._action_mean = None
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self._action_std = None
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if normalize_actions:
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if action_stats_path and os.path.exists(action_stats_path):
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stats = np.load(action_stats_path)
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self._action_mean = stats[0]
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self._action_std = stats[1]
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else:
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all_actions = np.stack([s[2] for s in self.samples])
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self._action_mean = all_actions.mean(axis=0)
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self._action_std = all_actions.std(axis=0) + 1e-8
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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image_path, action_history, target = self.samples[idx]
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image = Image.open(image_path).convert("RGB")
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image = self.transform(image)
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action_history = torch.tensor(action_history, dtype=torch.float32)
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target = torch.tensor(target, dtype=torch.float32)
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if self._action_mean is not None:
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action_history = (action_history - self._action_mean) / self._action_std
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target = (target - self._action_mean) / self._action_std
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return {"image": image, "action_history": action_history, "target": target}
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def get_action_stats(self):
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if self._action_mean is not None:
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return np.stack([self._action_mean, self._action_std])
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return None
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@staticmethod
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def get_episode_count(data_dir: str) -> int:
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data_path = Path(data_dir)
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return len([d for d in data_path.iterdir() if d.is_dir()])
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class DataRecorder:
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def __init__(self, output_dir: str, action_dim: int):
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self.output_dir = Path(output_dir)
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self.action_dim = action_dim
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self._frame_idx = 0
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self._actions = []
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ep_idx = 0
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while (self.output_dir / f"episode_{ep_idx:04d}").exists():
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ep_idx += 1
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self.episode_dir = self.output_dir / f"episode_{ep_idx:04d}"
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self.images_dir = self.episode_dir / "images"
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self.images_dir.mkdir(parents=True, exist_ok=True)
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def record(self, image: np.ndarray, action: np.ndarray):
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img = Image.fromarray(image)
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img.save(self.images_dir / f"{self._frame_idx:04d}.jpg", quality=90)
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self._actions.append(action.astype(np.float32))
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self._frame_idx += 1
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def flush(self):
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if self._actions:
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actions = np.array(self._actions, dtype=np.float32)
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np.save(self.episode_dir / "actions.npy", actions)
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return str(self.episode_dir)
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