Function Decomposition: The argument parsing logic was moved to a separate function parse_args() to improve readability and maintainability. This function encapsulates the logic related to parsing command-line arguments.
Input Validation: Added input validation to ensure that the chosen protocol (-i/--protocol) is either "http" or "grpc". This prevents unexpected behavior due to invalid protocol values.
Code Organization: The code was organized into distinct sections corresponding to different model executions (preprocessing, tensorrt_llm, postprocessing, ensemble). This separation enhances clarity and makes it easier to understand the flow of the script.
Reduced Redundancy: Reused the same create_inference_server_client method for establishing connections with the inference server, avoiding redundancy in code and potential inconsistencies.
Improved Exception Handling: Added exception handling to catch and print any exceptions that occur during model inference, providing better error messages for debugging and troubleshooting.
Variable Reuse: Reused the input0 variable when defining input data for the ensemble model, enhancing code readability and reducing redundant variable definitions.
Consistent Naming: Ensured consistent naming conventions for variables and flags (FLAGS) throughout the script, improving code clarity and maintainability.
Overall, these changes aim to make the code more robust, readable, and efficient, leading to better maintainability and easier debugging in the future.
Function Decomposition: The argument parsing logic was moved to a separate function parse_args() to improve readability and maintainability. This function encapsulates the logic related to parsing command-line arguments.
Input Validation: Added input validation to ensure that the chosen protocol (-i/--protocol) is either "http" or "grpc". This prevents unexpected behavior due to invalid protocol values.
Code Organization: The code was organized into distinct sections corresponding to different model executions (preprocessing, tensorrt_llm, postprocessing, ensemble). This separation enhances clarity and makes it easier to understand the flow of the script.
Reduced Redundancy: Reused the same create_inference_server_client method for establishing connections with the inference server, avoiding redundancy in code and potential inconsistencies.
Improved Exception Handling: Added exception handling to catch and print any exceptions that occur during model inference, providing better error messages for debugging and troubleshooting.
Variable Reuse: Reused the input0 variable when defining input data for the ensemble model, enhancing code readability and reducing redundant variable definitions.
Consistent Naming: Ensured consistent naming conventions for variables and flags (FLAGS) throughout the script, improving code clarity and maintainability.
Overall, these changes aim to make the code more robust, readable, and efficient, leading to better maintainability and easier debugging in the future.