SqueezeNet Deep Compression

Question

Do you guys know where or how to obtain the 0.47MB version of SqueezeNet ?

In other words, how to make the weights bitwidth to be 6 instead of 8 ?

I cannot find the modification spot in this SqueezeNet generation code.

Answer 1

In this following method, I got 0.77 MB Model! Lets assume we have a SqueezeNet_model. We can convert SqueezeNet to Tensorflow Lite Model.

converter = tf.lite.TFLiteConverter.from_keras_model(SqueezeNet_model)

open("SqueezeNet_model.tflite", "wb").write(tflite_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quant_model = converter.convert()

Then, we can use POST quantization to decrease the size of model!

open("SqueezeNet_Quant_model.tflite", "wb").write(tflite_quant_model)
print("Quantized model in Mb:", os.path.getsize('SqueezeNet_Quant_model.tflite') / float(2**20)) // I got 0.77 MB model

Finally, we can test our model with:

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="SqueezeNet_Quant_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on some input data.
input_shape = input_details[0]['shape']
acc=0
for i in range(len(x_test)):
    input_data = np.array(x_test[i].reshape(input_shape), dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    if(np.argmax(output_data) == np.argmax(y_test[i])):
        acc+=1
acc = acc/len(x_test)
print(acc*100)