4897211a46
finished calc human evaluation updated calc configs with an extra sleep to guarantee the integrity of downloaded xlsx file
109 lines
3.3 KiB
Python
109 lines
3.3 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 sys
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import time
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from desktop_env.envs.desktop_env import DesktopEnv
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# Logger Configs {{{ #
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logger = logging.getLogger()
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logger.setLevel(logging.DEBUG)
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datetime_str: str = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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file_handler = logging.FileHandler(os.path.join("logs", "normal-{:}.log".format(datetime_str)), encoding="utf-8")
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debug_handler = logging.FileHandler(os.path.join("logs", "debug-{:}.log".format(datetime_str)), encoding="utf-8")
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stdout_handler = logging.StreamHandler(sys.stdout)
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sdebug_handler = logging.FileHandler(os.path.join("logs", "sdebug-{:}.log".format(datetime_str)), encoding="utf-8")
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file_handler.setLevel(logging.INFO)
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debug_handler.setLevel(logging.DEBUG)
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stdout_handler.setLevel(logging.INFO)
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sdebug_handler.setLevel(logging.DEBUG)
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formatter = logging.Formatter(
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fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s")
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file_handler.setFormatter(formatter)
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debug_handler.setFormatter(formatter)
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stdout_handler.setFormatter(formatter)
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sdebug_handler.setFormatter(formatter)
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stdout_handler.addFilter(logging.Filter("desktopenv"))
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sdebug_handler.addFilter(logging.Filter("desktopenv"))
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logger.addHandler(file_handler)
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logger.addHandler(debug_handler)
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logger.addHandler(stdout_handler)
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logger.addHandler(sdebug_handler)
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# }}} Logger Configs #
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logger = logging.getLogger("desktopenv.main")
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def human_agent():
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"""
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Runs the Gym environment with human input.
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"""
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with open("evaluation_examples/examples/libreoffice_calc/4f07fbe9-70de-4927-a4d5-bb28bc12c52c.json", "r") as f:
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example = json.load(f)
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example["snapshot"] = "Snapshot 30"
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#env = DesktopEnv( path_to_vm="~/vmware/Windows 10 x64/Windows 10 x64.vmx"
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env = DesktopEnv( path_to_vm="/mnt/data1/david/os-images/Ubuntu-1218/Ubuntu.vmx"
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, action_space="computer_13"
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, task_config=example
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)
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# reset the environment to certain snapshot
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observation = env.reset()
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done = False
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trajectory = [
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# {
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# "action_type": "MOVE_TO",
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# "parameters": {
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# "x": 754,
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# "y": 1057
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# }
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# },
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# {"action_type": "CLICK", "parameters": {"button": "right", "num_clicks": 1}}
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]
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for i in range(len(trajectory)):
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# action = get_human_action()
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# action = {
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# "action_type": 0,
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# "click_type": 3,
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# }
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logger.info(trajectory[i])
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observation, reward, done, info = env.step(trajectory[i])
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observation.pop("accessibility_tree")
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logger.info("Observation: %s", observation)
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logger.info("Reward: %.2f", reward)
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logger.info("Info: %s", info)
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logger.info("================================\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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input("Press Enter to start human operation...")
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human_start_time = time.time()
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input("Press Enter to finish human operation.")
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print("Time elapsed of human operation: %.2f" % (time.time() - human_start_time))
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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# env.close()
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logger.info("Environment closed.")
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if __name__ == "__main__":
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human_agent()
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