AURORA / eq-kubric /create_scene.py
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import subprocess
def create_scene(index: int, dataset_type="location", max_num_objects=1, retries=10, log_file="logs/error_log.txt", verbose=False) -> int:
"""
Attempts to create a data entry point in a dataset by running a Docker container that executes a Python script.
This function tries to execute a command to create a scene up to a specified number of retries. It logs the attempts and errors to a specified file. If successful, it returns 1; if it fails after all retries, it returns 0.
Args:
index (int): The index of the scene to generate, which affects the generate_idx parameter in the command.
dataset_type (str): The type of the dataset, which customizes the Python script to be executed.
max_num_objects (int): The maximum number of objects to include in the scene.
retries (int): The number of attempts to make in case of failure.
log_file (str): Path to the file where error logs are appended.
Returns:
int: 0 if the scene was successfully created, 1 otherwise.
Raises:
None directly by the function, but subprocess.run may raise an exception if the command execution fails critically.
Side Effects:
Writes to a log file at `log_file` path if any errors occur during the execution.
Tries to execute a Docker command which can affect system resources and Docker state.
"""
command = f"""
sudo docker run --rm --interactive \
--user $(id -u):$(id -g) \
--volume "$(pwd):/kubric" kubricdockerhub/kubruntu \
/usr/bin/python3 eq-kubric/my_kubric_twoframe_{dataset_type}.py \
--sub_outputdir {dataset_type} \
--generate_idx {index+1} \
--max_num_objects {max_num_objects}
"""
attempt = 0
while attempt < retries:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode == 0 and ("INFO:root:Done!" in result.stderr or "INFO:root:Done!" in result.stdout):
return 0
attempt += 1
if verbose:
print(f"Attempt {attempt} failed with return code {result.returncode}.")
print(result.stderr)
error_message = f"Failed to create a scene after {attempt} attempts: Index: {index+1}, Dataset Type: '{dataset_type}'."
with open(log_file, "a") as file:
file.write(f"{error_message}\n")
return 1