Model quantization into fp16

AI & Data Science Deep Learning (Training & Inference) TensorRT
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Description

I’m trying to quantize a model to reduce the inference time, model exists in fp32 with its layers weights in fp32 limit, during quantization in trt/onnx the output quality gets highly degraded, suggest me the best way to quantize the model with maintaining the output quality.

Environment

TensorRT Version: 10.0+
GPU Type: RTX 3090
Nvidia Driver Version:
CUDA Version: 12.4
CUDNN Version:
Operating System + Version: Ubuntu 22.04
Python Version (if applicable):
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag):

Using DEFOM-stereo model for this quantization

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Hi @sarthakgarg0303 ,
To minimize the impact of quantization on output quality, follow these best practices:

  1. Calibrate your model: Calibration is the process of collecting statistics about the activations and weights of your model. This helps the quantization algorithm to determine the optimal scaling factors for the weights and activations. Make sure to calibrate your model using a representative dataset.
  2. Use a suitable quantization scheme: There are several quantization schemes available, including:
  • Post-training quantization (PTQ): This is the simplest and fastest method, but it may not always produce the best results.
  • Quantization-aware training (QAT): This method involves training the model with simulated quantization noise, which can help the model adapt to the quantized precision.
  • Knowledge distillation: This method involves training a smaller, quantized model to mimic the behavior of the original, full-precision model.
  1. Choose the right precision: Experiment with different precisions (e.g., int8, uint8, fp16) to find the best trade-off between accuracy and performance.
  2. Use a suitable activation function: Some activation functions, like ReLU, are more robust to quantization than others.
  3. Monitor and adjust: Monitor the output quality and adjust the quantization parameters as needed.

Specific Recommendations for DEFOM-stereo Model

Since you’re using the DEFOM-stereo model, which is a stereo matching algorithm, I recommend the following:

  1. Use QAT: QAT can help the model adapt to the quantized precision, which is particularly important for stereo matching algorithms that rely on precise calculations.
  2. Calibrate using a stereo dataset: Calibrate your model using a representative stereo dataset to ensure that the quantization algorithm captures the statistics of the activations and weights accurately.
  3. Experiment with different precisions: Try different precisions (e.g., int8, uint8, fp16) to find the best trade-off between accuracy and performance.
  4. Monitor the output quality: Monitor the output quality and adjust the quantization parameters as needed.

Additional Tips

  1. Use the TensorRT optimization tools: TensorRT provides optimization tools, such as the TensorRT Optimizer, which can help you optimize your model for inference.
  2. Use the ONNX Runtime: The ONNX Runtime provides a set of tools and APIs for optimizing and deploying ONNX models.
  3. Consider using a more advanced quantization algorithm: There are several advanced quantization algorithms available, such as the TensorFlow Model Optimization Toolkit, which can provide better results than the standard quantization algorithms.