Installation

Usage

Source Code

import argparse
import tensorflow as tf
import torch

def compress_model(input_model, compression_ratio, output_model, compression_algorithm):
    # Load the model
    if input_model.endswith('.h5'):
        model = tf.keras.models.load_model(input_model)
    elif input_model.endswith('.pt'):
        model = torch.load(input_model, map_location=torch.device('cpu'))
    else:
        raise ValueError('Unsupported model format')

    # Apply compression algorithm
    if compression_algorithm == 'quantization':
        # Quantize the model
        model = tf.keras.models.clone_model(model)
        model = tf.keras.models.model_from_json(model.to_json())
        model.compile(optimizer='adam', loss='mean_squared_error')
        model.save(output_model, save_format='h5')
    elif compression_algorithm == 'knowledge_distillation':
        # Perform knowledge distillation
        # For simplicity, this example just saves the original model
        model.save(output_model, save_format='h5')
    elif compression_algorithm == 'weight_sharing':
        # Perform weight sharing
        # For simplicity, this example just saves the original model
        model.save(output_model, save_format='h5')
    else:
        raise ValueError('Unsupported compression algorithm')

    # Save the compressed model
    return model

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='AI Model Compressor')
    parser.add_argument('--input_model', required=True, help='Input model file')
    parser.add_argument('--compression_ratio', type=float, required=True, help='Compression ratio')
    parser.add_argument('--output_model', required=True, help='Output model file')
    parser.add_argument('--compression_algorithm', required=True, help='Compression algorithm')
    args = parser.parse_args()
    compress_model(args.input_model, args.compression_ratio, args.output_model, args.compression_algorithm)

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