import os.path
>>>
>>> # ruff: noqa: E402
>>> import json_numpy
>>>
>>> json_numpy.patch()
>>> import json
>>> import logging
>>> import traceback
>>> from dataclasses import dataclass
>>> from pathlib import Path
>>> from typing import Any, Dict, Optional, Union
>>>
>>> import draccus
>>> import torch
>>> import uvicorn
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'uvicorn'
>>> from fastapi import FastAPI
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'fastapi'
>>> from fastapi.responses import JSONResponse
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'fastapi'
>>> from PIL import Image
>>> from transformers import AutoModelForVision2Seq, AutoProcessor
2025-06-20 14:49:54.539927: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
WARNING:tensorflow:From E:\Miniconda3\envs\openvla\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
>>>
>>> # === Utilities ===
>>> SYSTEM_PROMPT = (
... "A chat between a curious user and an artificial intelligence assistant. "
... "The assistant gives helpful, detailed, and polite answers to the user's questions."
... )
>>>
>>>
>>> def get_openvla_prompt(instruction: str, openvla_path: Union[str, Path]) -> str:
... if "v01" in openvla_path:
... return f"{SYSTEM_PROMPT} USER: What action should the robot take to {instruction.lower()}? ASSISTANT:"
... else:
... return f"In: What action should the robot take to {instruction.lower()}?\nOut:"
...
>>>
>>> # === Server Interface ===
>>> class OpenVLAServer:
... def __init__(self, openvla_path: Union[str, Path], attn_implementation: Optional[str] = "flash_attention_2") -> Path:
... """
... A simple server for OpenVLA models; exposes `/act` to predict an action for a given image + instruction.
... => Takes in {"image": np.ndarray, "instruction": str, "unnorm_key": Optional[str]}
... => Returns {"action": np.ndarray}
... """
... self.openvla_path, self.attn_implementation = openvla_path, attn_implementation
... self.device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
...
>>> # Load VLA Model using HF AutoClasses
>>> self.processor = AutoProcessor.from_pretrained(self.openvla_path, trust_remote_code=True)
File "<stdin>", line 1
self.processor = AutoProcessor.from_pretrained(self.openvla_path, trust_remote_code=True)
IndentationError: unexpected indent
>>> self.vla = AutoModelForVision2Seq.from_pretrained(
File "<stdin>", line 1
self.vla = AutoModelForVision2Seq.from_pretrained(
IndentationError: unexpected indent
>>> self.openvla_path,
File "<stdin>", line 1
self.openvla_path,
IndentationError: unexpected indent
>>> attn_implementation=attn_implementation,
File "<stdin>", line 1
attn_implementation=attn_implementation,
IndentationError: unexpected indent
>>> torch_dtype=torch.bfloat16,
File "<stdin>", line 1
torch_dtype=torch.bfloat16,
IndentationError: unexpected indent
>>> low_cpu_mem_usage=True,
File "<stdin>", line 1
low_cpu_mem_usage=True,
IndentationError: unexpected indent
>>> trust_remote_code=True,
File "<stdin>", line 1
trust_remote_code=True,
IndentationError: unexpected indent
>>> ).to(self.device)
File "<stdin>", line 1
).to(self.device)
IndentationError: unexpected indent
>>>
>>> # [Hacky] Load Dataset Statistics from Disk (if passing a path to a fine-tuned model)
>>> if os.path.isdir(self.openvla_path):
File "<stdin>", line 1
if os.path.isdir(self.openvla_path):
IndentationError: unexpected indent
>>> with open(Path(self.openvla_path) / "dataset_statistics.json", "r") as f:
File "<stdin>", line 1
with open(Path(self.openvla_path) / "dataset_statistics.json", "r") as f:
IndentationError: unexpected indent
>>> self.vla.norm_stats = json.load(f)
File "<stdin>", line 1
self.vla.norm_stats = json.load(f)
IndentationError: unexpected indent
>>>
>>> def predict_action(self, payload: Dict[str, Any]) -> str:
File "<stdin>", line 1
def predict_action(self, payload: Dict[str, Any]) -> str:
IndentationError: unexpected indent
>>> try:
File "<stdin>", line 1
try:
IndentationError: unexpected indent
>>> if double_encode := "encoded" in payload:
File "<stdin>", line 1
if double_encode := "encoded" in payload:
IndentationError: unexpected indent
>>> # Support cases where `json_numpy` is hard to install, and numpy arrays are "double-encoded" as strings
>>> assert len(payload.keys()) == 1, "Only uses encoded payload!"
File "<stdin>", line 1
assert len(payload.keys()) == 1, "Only uses encoded payload!"
IndentationError: unexpected indent
>>> payload = json.loads(payload["encoded"])
File "<stdin>", line 1
payload = json.loads(payload["encoded"])
IndentationError: unexpected indent
>>>
>>> # Parse payload components
>>> image, instruction = payload["image"], payload["instruction"]
File "<stdin>", line 1
image, instruction = payload["image"], payload["instruction"]
IndentationError: unexpected indent
>>> unnorm_key = payload.get("unnorm_key", None)
File "<stdin>", line 1
unnorm_key = payload.get("unnorm_key", None)
IndentationError: unexpected indent
>>>
>>> # Run VLA Inference
>>> prompt = get_openvla_prompt(instruction, self.openvla_path)
File "<stdin>", line 1
prompt = get_openvla_prompt(instruction, self.openvla_path)
IndentationError: unexpected indent
>>> inputs = self.processor(prompt, Image.fromarray(image).convert("RGB")).to(self.device, dtype=torch.bfloat16)
File "<stdin>", line 1
inputs = self.processor(prompt, Image.fromarray(image).convert("RGB")).to(self.device, dtype=torch.bfloat16)
IndentationError: unexpected indent
>>> action = self.vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
File "<stdin>", line 1
action = self.vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
IndentationError: unexpected indent
>>> if double_encode:
File "<stdin>", line 1
if double_encode:
IndentationError: unexpected indent
>>> return JSONResponse(json_numpy.dumps(action))
File "<stdin>", line 1
return JSONResponse(json_numpy.dumps(action))
IndentationError: unexpected indent
>>> else:
File "<stdin>", line 1
else:
IndentationError: unexpected indent
>>> return JSONResponse(action)
File "<stdin>", line 1
return JSONResponse(action)
IndentationError: unexpected indent
>>> except: # noqa: E722
File "<stdin>", line 1
except: # noqa: E722
IndentationError: unexpected indent
>>> logging.error(traceback.format_exc())
File "<stdin>", line 1
logging.error(traceback.format_exc())
IndentationError: unexpected indent
>>> logging.warning(
File "<stdin>", line 1
logging.warning(
IndentationError: unexpected indent
>>> "Your request threw an error; make sure your request complies with the expected format:\n"
File "<stdin>", line 1
"Your request threw an error; make sure your request complies with the expected format:\n"
IndentationError: unexpected indent
>>> "{'image': np.ndarray, 'instruction': str}\n"
File "<stdin>", line 1
"{'image': np.ndarray, 'instruction': str}\n"
IndentationError: unexpected indent
>>> "You can optionally an `unnorm_key: str` to specific the dataset statistics you want to use for "
File "<stdin>", line 1
"You can optionally an `unnorm_key: str` to specific the dataset statistics you want to use for "
IndentationError: unexpected indent
>>> "de-normalizing the output actions."
File "<stdin>", line 1
"de-normalizing the output actions."
IndentationError: unexpected indent
>>> )
File "<stdin>", line 1
)
IndentationError: unexpected indent
>>> return "error"
File "<stdin>", line 1
return "error"
IndentationError: unexpected indent
>>>
>>> def run(self, host: str = "0.0.0.0", port: int = 8000) -> None:
File "<stdin>", line 1
def run(self, host: str = "0.0.0.0", port: int = 8000) -> None:
IndentationError: unexpected indent
>>> self.app = FastAPI()
File "<stdin>", line 1
self.app = FastAPI()
IndentationError: unexpected indent
>>> self.app.post("/act")(self.predict_action)
File "<stdin>", line 1
self.app.post("/act")(self.predict_action)
IndentationError: unexpected indent
>>> uvicorn.run(self.app, host=host, port=port)
File "<stdin>", line 1
uvicorn.run(self.app, host=host, port=port)
IndentationError: unexpected indent
>>>
>>>
>>> @dataclass
... class DeployConfig:
... # fmt: off
... openvla_path: Union[str, Path] = "openvla/openvla-7b" # HF Hub Path (or path to local run directory)
...
>>> # Server Configuration
>>> host: str = "0.0.0.0" # Host IP Address
File "<stdin>", line 1
host: str = "0.0.0.0" # Host IP Address
IndentationError: unexpected indent
>>> port: int = 8000 # Host Port
File "<stdin>", line 1
port: int = 8000 # Host Port
IndentationError: unexpected indent
>>>
>>> # fmt: on
>>>
>>>
>>> @draccus.wrap()
... def deploy(cfg: DeployConfig) -> None:
... server = OpenVLAServer(cfg.openvla_path)
... server.run(cfg.host, port=cfg.port)
...
>>>
>>> if __name__ == "__main__":
... deploy()
...
本文介绍了一种常见的Python脚本运行错误，并给出了正确的运行方式。作者通过一个简单的hello.py脚本示例，展示了如何区分Python命令行与shell命令的区别。
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