How To Calculate Fps From A Trt Output

Calculating Frames Per Second (FPS) from a TensorRT (TRT) output is essential for evaluating the performance of your neural network model. This guide provides clear instructions for deriving FPS from TRT outputs.

Why Calculate FPS from TRT Output?

• Evaluate Model Efficiency: Determine the processing speed of your model.
• Optimize Performance: Identify bottlenecks and improve system throughput.
• Benchmark Comparisons: Compare the performance of different models or configurations.

Steps to Calculate FPS from TRT Output

1. Understand the TRT Output:
   
   • Review the inference time metrics provided by TensorRT, usually in milliseconds (ms).
   • Ensure you have the total time or average time per inference.
2. Convert Inference Time to Seconds:
   
   • Divide the inference time in milliseconds by 1,000 to convert it to seconds.
   • Example: If the inference time is 50 ms, then: 50 ms ÷ 1,000 = 0.05 seconds.
3. Calculate FPS:
   
   • Use the formula: FPS = 1 ÷ Inference Time (seconds)
   • Example: For an inference time of 0.05 seconds: FPS = 1 ÷ 0.05 = 20 FPS.
4. Validate Results:
   
   • Run multiple iterations of inference to get an average inference time.
   • Recalculate FPS based on the averaged time for more accurate results.
5. Consider Batch Processing:
   
   • If using batch inference, multiply the FPS by the batch size to account for parallel processing.
   • Example: For a batch size of 4 and an FPS of 20: Effective FPS = 20 × 4 = 80 FPS.

Tips for Accurate FPS Calculation

• Run Consistent Tests: Use the same input sizes and hardware for reliable benchmarking.
• Avoid External Overheads: Exclude non-inference-related delays from your calculations.
• Use Profiling Tools: Utilize TensorRT’s built-in profiler for detailed performance insights.

Troubleshooting Common Issues

• Unrealistic FPS Values:
  • Verify the unit of time provided by TensorRT (milliseconds vs. seconds).
  • Ensure there are no errors in the inference script or time measurement.
• Inconsistent Results:
  • Run multiple tests to identify variability in inference times.
  • Check for system resource constraints during testing.
• Low FPS:
  • Optimize model architecture, reduce input size, or upgrade hardware for improved performance.

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Conclusion

Calculating FPS from TensorRT outputs is straightforward and provides crucial insights into model performance. By following these steps and tips, you can accurately evaluate your system’s efficiency and identify areas for improvement.