95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
import argparse
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import json
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import numpy as np
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import torch
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from PIL import Image
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from torchvision import transforms
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from config import Config, ModelConfig, DataConfig
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from model import ActionPredictor
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class ActionPredictorInference:
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def __init__(self, checkpoint_path: str, device: str = None):
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checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
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cfg_dict = checkpoint["config"]
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self._data_cfg = DataConfig(**cfg_dict["data"])
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self._model_cfg = ModelConfig(**cfg_dict["model"])
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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self._device = torch.device(device)
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self._model = ActionPredictor(self._model_cfg, self._data_cfg).to(self._device)
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self._model.load_state_dict(checkpoint["model_state_dict"])
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self._model.eval()
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self._transform = transforms.Compose([
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transforms.Resize((self._data_cfg.image_size, self._data_cfg.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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self._action_mean = None
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self._action_std = None
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stats_path = checkpoint_path.rsplit("/", 1)[0] + "/action_stats.npy"
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try:
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stats = np.load(stats_path)
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self._action_mean = stats[0]
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self._action_std = stats[1]
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except (FileNotFoundError, ValueError):
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pass
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@torch.no_grad()
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def predict(self, image: np.ndarray, action_history: np.ndarray) -> np.ndarray:
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img = Image.fromarray(image) if isinstance(image, np.ndarray) else image
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img_tensor = self._transform(img).unsqueeze(0).to(self._device)
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history = torch.tensor(action_history, dtype=torch.float32)
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if self._action_mean is not None:
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history = (history - self._action_mean) / self._action_std
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history = history.unsqueeze(0).to(self._device)
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pred = self._model(img_tensor, history).squeeze(0).cpu().numpy()
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if self._action_mean is not None:
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pred = pred * self._action_std + self._action_mean
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return pred
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def parse_action_history(s: str, action_dim: int) -> np.ndarray:
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steps = s.split(":")
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history = []
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for step in steps:
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values = [float(v) for v in step.split(",")]
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assert len(values) == action_dim, \
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f"Expected {action_dim} values per step, got {len(values)}"
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history.append(values)
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return np.array(history, dtype=np.float32)
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def main():
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parser = argparse.ArgumentParser(description="actpred inference")
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parser.add_argument("--model", required=True, help="checkpoint path")
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parser.add_argument("--image", required=True, help="image file path")
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parser.add_argument("--action-history", required=True,
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help="colon-separated action vectors, e.g. '45,0,45,0:45,10,45,10'")
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parser.add_argument("--action-dim", type=int, default=None,
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help="override action dim (auto-detected from checkpoint if omitted)")
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parser.add_argument("--output-format", choices=["json", "raw"], default="json")
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parser.add_argument("--device", type=str, default=None)
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args = parser.parse_args()
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predictor = ActionPredictorInference(args.model, args.device)
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action_dim = args.action_dim or predictor._data_cfg.action_dim
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image = Image.open(args.image).convert("RGB")
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history = parse_action_history(args.action_history, action_dim)
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predicted = predictor.predict(np.array(image), history)
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if args.output_format == "json":
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print(json.dumps({"predicted_action": predicted.tolist()}, indent=2))
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else:
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print(" ".join(f"{v:.4f}" for v in predicted))
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if __name__ == "__main__":
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main()
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