try new timer

conf_my_program.py

@@ -0,0 +1,7 @@
+# conf_my_program.py:
+
+class ConfMyProgram(object):
+    def __init__(self):
+        self.name:str = 'my_var_name'
+
+conf_my_program = ConfMyProgram()

demo.py

@@ -1,24 +1,16 @@
-import concurrent.futures
+# my_program_main.py:
-import time
 
-# Define the function you want to run with a timeout
+import lib_test
-def my_task():
-    print("Task started")
-    # Simulate a long-running task
-    time.sleep(5)
-    print("Task completed")
-    return "Task result"
 
-# Main program
 def main():
-    with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+    try:
-        future = executor.submit(my_task)
+        while True:
-        try:
+            print(1)
-            # Wait for 2 seconds for my_task to complete
+            lib_test.mytest()
-            result = future.result(timeout=2)
+    # try:
-            print(f"Task completed with result: {result}")
+    #     lib_test.mytest()
-        except concurrent.futures.TimeoutError:
+    except Exception as e:
-            print("Task did not complete in time")
+        print(e)
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

lib_run_single.py

@@ -0,0 +1,60 @@
+import os
+import datetime
+import json
+from wrapt_timeout_decorator import *
+import logging
+logger = logging.getLogger("desktopenv.experiment")
+
+@timeout(60, use_signals=False)
+def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
+    agent.reset()
+    obs = env.reset(task_config=example)
+    done = False
+    step_idx = 0
+    env.controller.start_recording()
+
+    while not done and step_idx < max_steps:
+        actions = agent.predict(
+            instruction,
+            obs
+        )
+        for action in actions:
+            # Capture the timestamp before executing the action
+            action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
+            logger.info("Step %d: %s", step_idx + 1, action)
+
+            obs, reward, done, info = env.step(action, args.sleep_after_execution)
+
+            logger.info("Reward: %.2f", reward)
+            logger.info("Done: %s", done)
+            logger.info("Info: %s", info)
+
+            # Save screenshot and trajectory information
+            with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
+                        "wb") as _f:
+                with open(obs['screenshot'], "rb") as __f:
+                    screenshot = __f.read()
+                _f.write(screenshot)
+
+            with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
+                f.write(json.dumps({
+                    "step_num": step_idx + 1,
+                    "action_timestamp": action_timestamp,
+                    "action": action,
+                    "reward": reward,
+                    "done": done,
+                    "info": info,
+                    "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
+                }))
+                f.write("\n")
+
+            if done:
+                logger.info("The episode is done.")
+                break
+        step_idx += 1
+    result = env.evaluate()
+    logger.info("Result: %.2f", result)
+    scores.append(result)
+    with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
+        f.write(f"{result}\n")
+    env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))

lib_test.py

@@ -0,0 +1,15 @@
+# lib_test.py:
+
+from wrapt_timeout_decorator import *
+from time import sleep
+from conf_my_program import conf_my_program
+
+# use_signals = False is not really necessary here, it is set automatically under Windows
+# but You can force NOT to use Signals under Linux
+@timeout(5, use_signals=False)
+def mytest():
+    print("Start ", conf_my_program.name)
+    for i in range(1,10):
+        sleep(1)
+        print("{} seconds have passed".format(i))
+    return i

results/pyautogui/som/gpt-4-vision-preview/chrome/7b6c7e24-c58a-49fc-a5bb-d57b80e5b4c3/traj.jsonl

@@ -0,0 +1,3 @@
+{"step_num": 1, "action_timestamp": "20240316@115041", "action": "tag_1=(1212, 753)\ntag_2=(302, 81)\ntag_3=(541, 81)\ntag_4=(583, 81)\ntag_5=(1248, 81)\ntag_6=(156, 119)\ntag_7=(196, 119)\ntag_8=(642, 119)\ntag_9=(1090, 119)\ntag_10=(1122, 119)\ntag_11=(1162, 119)\ntag_12=(1194, 119)\ntag_13=(1226, 119)\ntag_14=(1262, 119)\ntag_15=(675, 486)\ntag_16=(147, 202)\ntag_17=(682, 201)\ntag_18=(672, 201)\ntag_19=(1078, 201)\ntag_20=(667, 638)\ntag_21=(667, 421)\ntag_22=(667, 420)\ntag_23=(667, 419)\ntag_24=(667, 798)\ntag_25=(667, 797)\npyautogui.click(tag_15)", "reward": 0, "done": false, "info": {}, "screenshot_file": "step_1_20240316@115041.png"}
+{"step_num": 2, "action_timestamp": "20240316@115102", "action": "tag_1=(1212, 753)\ntag_2=(302, 81)\ntag_3=(541, 81)\ntag_4=(583, 81)\ntag_5=(1248, 81)\ntag_6=(156, 119)\ntag_7=(196, 119)\ntag_8=(642, 119)\ntag_9=(1090, 119)\ntag_10=(1122, 119)\ntag_11=(1162, 119)\ntag_12=(1194, 119)\ntag_13=(1226, 119)\ntag_14=(1262, 119)\ntag_15=(675, 486)\ntag_16=(667, 322)\ntag_17=(688, 294)\ntag_18=(686, 291)\ntag_19=(688, 335)\ntag_20=(686, 335)\ntag_21=(667, 558)\ntag_22=(667, 545)\ntag_23=(667, 518)\ntag_24=(667, 451)\ntag_25=(654, 449)\ntag_26=(667, 509)\ntag_27=(733, 554)\ntag_28=(742, 554)\ntag_29=(742, 554)\ntag_30=(667, 579)\ntag_31=(667, 661)\ntag_32=(667, 660)\ntag_33=(667, 802)\ntag_34=(617, 801)\ntag_35=(617, 801)\ntag_36=(727, 801)\ntag_37=(727, 801)\n# Estimating the position of the browser's menu button\nmenu_x = tag_16[0] + (tag_18[0] - tag_16[0]) - 50  # 50 pixels to the left from the right end of the address bar\nmenu_y = tag_16[1] + (tag_19[1] - tag_16[1]) / 2  # Vertically centered between the top of the address bar and the bottom\npyautogui.click(menu_x, menu_y)", "reward": 0, "done": false, "info": {}, "screenshot_file": "step_2_20240316@115102.png"}
+{"Error": "Time limit exceeded in chrome/7b6c7e24-c58a-49fc-a5bb-d57b80e5b4c3"}

results/pyautogui/som/gpt-4-vision-preview/chrome/bb5e4c0d-f964-439c-97b6-bdb9747de3f4/traj.jsonl

@@ -0,0 +1 @@
+{"Error": "Time limit exceeded in chrome/bb5e4c0d-f964-439c-97b6-bdb9747de3f4"}

run.py

@@ -8,13 +8,13 @@ import logging
 import os
 import sys
 
-from tqdm # import tqdm
+from tqdm import tqdm
 import time
-import timeout_decorator
+# import timeout_decorator
 
 from desktop_env.envs.desktop_env import DesktopEnv
 from mm_agents.agent import PromptAgent
-
+import lib_run_single
 #  Logger Configs {{{ # 
 logger = logging.getLogger()
 logger.setLevel(logging.DEBUG)
@@ -49,12 +49,6 @@ logger.addHandler(sdebug_handler)
 
 logger = logging.getLogger("desktopenv.experiment")
 
-
-# make sure each example won't exceed the time limit
-# def handler(signo, frame):
-#     raise RuntimeError("Time limit exceeded!")
-# signal.signal(signal.SIGALRM, handler)
-
 def config() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description="Run end-to-end evaluation on the benchmark"
@@ -151,80 +145,17 @@ def test(
                 example_id
             )
             os.makedirs(example_result_dir, exist_ok=True)
-
-
-            @timeout_decorator.timeout(seconds=time_limit, timeout_exception=RuntimeError, exception_message="Time limit exceeded.")
-            def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
-                agent.reset()
-                obs = env.reset(task_config=example)
-                done = False
-                step_idx = 0
-                env.controller.start_recording()
-
-                while not done and step_idx < max_steps:
-                    actions = agent.predict(
-                        instruction,
-                        obs
-                    )
-                    for action in actions:
-                        # Capture the timestamp before executing the action
-                        action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
-                        logger.info("Step %d: %s", step_idx + 1, action)
-
-                        obs, reward, done, info = env.step(action, args.sleep_after_execution)
-
-                        logger.info("Reward: %.2f", reward)
-                        logger.info("Done: %s", done)
-                        logger.info("Info: %s", info)
-
-                        # Save screenshot and trajectory information
-                        with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
-                                  "wb") as _f:
-                            with open(obs['screenshot'], "rb") as __f:
-                                screenshot = __f.read()
-                            _f.write(screenshot)
-
-                        with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
-                            f.write(json.dumps({
-                                "step_num": step_idx + 1,
-                                "action_timestamp": action_timestamp,
-                                "action": action,
-                                "reward": reward,
-                                "done": done,
-                                "info": info,
-                                "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
-                            }))
-                            f.write("\n")
-
-                        if done:
-                            logger.info("The episode is done.")
-                            break
-                    step_idx += 1
-
-                result = env.evaluate()
-                logger.info("Result: %.2f", result)
-                scores.append(result)
-                with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
-                    f.write(f"{result}\n")
-                env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
-
             # example start running
             try:
-                # signal.alarm(time_limit)
+                lib_run_single.run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores)
-                run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores)  
+            except Exception as e:
-            except RuntimeError as e:
-                logger.error(f"Error in example {domain}/{example_id}: {e}")
-                # save info of this example and then continue
                 env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
+                logger.error(f"Time limit exceeded in {domain}/{example_id}")
                 with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
                     f.write(json.dumps({
-                        "Error": f"Error in example {domain}/{example_id}: {e}"
+                        "Error": f"Time limit exceeded in {domain}/{example_id}"
                     }))
-                    f.write("\n")    
+                    f.write("\n") 
-                continue
-            except Exception as e:
-                logger.error(f"Error in example {domain}/{example_id}: {e}")
-                continue
 
     env.close()
     logger.info(f"Average score: {sum(scores) / len(scores)}")
@@ -281,5 +212,5 @@ if __name__ == '__main__':
     for domain in test_file_list:
         left_info += f"{domain}: {len(test_file_list[domain])}\n"
     logger.info(f"Left tasks:\n{left_info}")
-
+    os.environ["OPENAI_API_KEY"] = "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"
     test(args, test_all_meta)