aa05f6cc26
* autoglm-os initialize * clean code * chore: use proxy for download setup * feat(autoglm-os): add parameter to toggle images * fix: use temporary directory for files pulled from the vm to prevent potential collision when running multiple instances of the same task in parallel * update * add client_password * update multienv * fix * fix prompt * fix prompt * fix prompt * fix sys prompt * feat: use proxy in file evaluator * fix client_password * fix note_prompt * fix autoglm agent cmd type * fix * revert: fix: use temporary directory for files pulled from the vm to prevent potential collision when running multiple instances of the same task in parallel reverts commit bab5473eea1de0e61b0e1d68b23ce324a5b0ee57 * feat(autoglm): setup tools * fix(autoglm): remove second time of get a11y tree * add osworld server restart * Revert "add osworld server restart" This reverts commit 7bd9d84122e246ce2a26de0e49c25494244c2b3d. * fix _launch_setup * fix autoglm agent tools & xml tree * fix desktop_env * fix bug for tool name capitalization * fix: always use proxy for setup download * add fail after exceeding max turns * fix(autoglm): avoid adding image to message when screenshot is empty * fix maximize_window * fix maximize_window * fix maximize_window * fix import browsertools module bug * fix task proxy config bug * restore setup * refactor desktop env * restore image in provider * restore file.py * refactor desktop_env * quick fix * refactor desktop_env.step * fix our env reset * add max truns constraint * clean run script * clean lib_run_single.py --------- Co-authored-by: hanyullai <hanyullai@outlook.com> Co-authored-by: JingBh <jingbohao@yeah.net>
270 lines
11 KiB
Python
270 lines
11 KiB
Python
import datetime
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import json
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import logging
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import os
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import time
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from wrapt_timeout_decorator import *
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logger = logging.getLogger("desktopenv.experiment")
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def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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try:
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agent.reset(runtime_logger)
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except Exception as e:
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agent.reset()
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def setup_logger(example, example_result_dir):
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runtime_logger = logging.getLogger(f"desktopenv.example.{example['id']}")
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runtime_logger.setLevel(logging.DEBUG)
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runtime_logger.addHandler(logging.FileHandler(os.path.join(example_result_dir, "runtime.log")))
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return runtime_logger
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def run_single_example_human(env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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# Save initial screenshot
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with open(os.path.join(example_result_dir, "initial_state.png"), "wb") as _f:
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_f.write(obs['screenshot'])
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# Save trajectory information
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"instruction": instruction,
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"initial_state": "initial_state.png"
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}))
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f.write("\n")
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# Evaluate the result
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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def run_single_example_openaicua(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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done = not response.get('state_correct', False)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info, step_info = agent.step(action)
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if not done:
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if not response.get('state_correct', False):
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done = True
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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# Remove pending checks if they exist which will cause issues with json serialization
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if action.get('pending_checks', None):
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del action['pending_checks']
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_opencua(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions, info_dict = agent.predict(instruction, obs)
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logger.info(f"Got Action: {actions}")
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# Breack if no actions
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if not actions or len(actions)==0 or actions[0]=="" or actions[0].lower().startswith("error"):
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break
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info(f"Action {action} executed, reward: {reward}, done: {done}")
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_autoglm(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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try:
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agent.reset(runtime_logger)
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except Exception as e:
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agent.reset()
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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if not done: # not completed the task yet
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env.action_history.append('FAIL')
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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