basant18/Post-Training-Quantization

Model Quantization Notebook

This notebook converts a pre-trained Keras violence detection model into TensorFlow Lite (TFLite) format using three different quantization strategies, making it suitable for deployment on edge/mobile devices.

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Overview

Property	Details
Framework	TensorFlow / TFLite
Base Model	modelv2.keras — a Keras video violence detection model
Input Shape	(1, 16, 224, 224, 3) — batch × frames × height × width × channels
Architecture	CNN + LSTM (contains dynamic LSTM loops)
Platform	Kaggle (GPU hidden to avoid CuDNN conflicts)

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Quantization Methods

A — Dynamic Range Quantization

• Output file: model_dynamic_quant.tflite
• Quantizes weights from float32 to int8 at conversion time.
• Activations are quantized dynamically at inference time.
• Fastest to convert; no calibration data required.
• Good balance between size reduction and accuracy.

B — Float16 Quantization

• Output file: model_fp16_quant.tflite
• Reduces weight precision from float32 to float16.
• Ideal for GPU-accelerated edge devices that support fp16 natively.
• Smaller model size with minimal accuracy loss.

C — Full Integer (INT8) Quantization

• Output file: model_full_int8.tflite
• Quantizes both weights and activations to int8.
• Requires a representative dataset for calibration (currently uses random dummy data — replace with real video samples for best results).
• Input and output tensors are also forced to int8.
• Smallest model size; best suited for CPU-only or microcontroller deployment.

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Requirements

tensorflow
numpy

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Usage

1. Load the Base Model

import tensorflow as tf

tf.config.set_visible_devices([], 'GPU')  # Hide GPU to avoid CuDNN issues
model = tf.keras.models.load_model('path/to/modelv2.keras')

2. Run Quantization

Open and run the notebook cells in order:

1. Cell 1–2 — Load the model
2. Cell 3–4 — Dynamic range quantization → model_dynamic_quant.tflite
3. Cell 5–6 — Float16 quantization → model_fp16_quant.tflite
4. Cell 7–8 — Full INT8 quantization → model_full_int8.tflite

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Important Notes

• Representative dataset: The INT8 quantization cell uses random dummy data for calibration. For production use, replace dummy_data in representative_data_gen() with real video frames from your training set to get accurate quantization ranges.

• LSTM compatibility flags: The model contains dynamic LSTM loops. The following flags are set in all conversion paths to prevent conversion failures:

  converter.target_spec.supported_ops = [
      tf.lite.OpsSet.TFLITE_BUILTINS,
      tf.lite.OpsSet.SELECT_TF_OPS
  ]
  converter._experimental_lower_tensor_list_ops = False
  

• Static input shape: The INT8 path uses tf.function with a tf.TensorSpec to lock the input shape to (1, 16, 224, 224, 3) before conversion — this is required for correct INT8 LSTM quantization.

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Output Files

File	Method	Precision
model_dynamic_quant.tflite	Dynamic Range	Weights: INT8, Activations: float32
model_fp16_quant.tflite	Float16	Weights & Activations: float16
model_full_int8.tflite	Full Integer	Weights & Activations: INT8