TF Lite optimization - deep-learning

Question

I tried to convert my Pytorch models to TensorFlow Lite with ONNX. But my inference time from TensorFlow Lite is twice as slow as Tensorflow and Pytorch. I run TensorFlow Lite model in google colab and this is my first time using TensorFlow Lite.
Here is my code to convert from Tensorflow to TensorFlow Lite:
converter = tf.lite.TFLiteConverter.from_saved_model("model/")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
model_lite = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(model_lite)
Any suggestions will help me a lot.

Answer 1

TensorFlow Lite models are supposed to run fast on embedded devices. So you have to use it inside an android phone to find out the time. Colab notebook will not give you the correct time.
You can also use benchmark tool to measure inference time of steady state.

Answer 2

If you would like to run inference on a PC or Google Colab, I'd recommend OpenVINO. OpenVINO is optimized for Intel hardware but it should work with any CPU. It optimizes the inference performance by e.g. graph pruning and fusing some operations. Here are the performance benchmarks for PyTorch models, among others.
You can find a full tutorial on how to convert the PyTorch model here. Some snippets below.
Install OpenVINO
The easiest way to do it is using PIP. Alternatively, you can use this tool to find the best way in your case.
pip install openvino-dev[pytorch,onnx]
Save your model to ONNX
OpenVINO cannot convert the PyTorch model directly for now but it can do it with the ONNX model. This sample code assumes the model is for computer vision.
dummy_input = torch.randn(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH)
torch.onnx.export(model, dummy_input, "model.onnx", opset_version=11)
Use Model Optimizer to convert ONNX model
The Model Optimizer is a command line tool that comes from OpenVINO Development Package so be sure you have installed it. It converts the ONNX model to OV format (aka IR), which is a default format for OpenVINO. It also changes the precision to FP16 (to further increase performance). Run in a command line:
mo --input_model "model.onnx" --input_shape "[1, 3, 224, 224]" --mean_values="[123.675, 116.28 , 103.53]" --scale_values="[58.395, 57.12 , 57.375]" --data_type FP16 --output_dir "model_ir"
Run the inference on the CPU
The converted model can be loaded by the runtime and compiled for a specific device e.g. CPU or GPU (integrated into your CPU like Intel HD Graphics). If you don't know what is the best choice for you, just use AUTO.
# Load the network
ie = Core()
model_ir = ie.read_model(model="model_ir/model.xml")
compiled_model_ir = ie.compile_model(model=model_ir, device_name="CPU")
# Get output layer
output_layer_ir = compiled_model_ir.output(0)
# Run inference on the input image
result = compiled_model_ir([input_image])[output_layer_ir]
It's worth mentioning that Runtime can process the ONNX model directly. In that case, just skip the conversion (Model Optimizer) step and give onnx path to the read_model function.
Disclaimer: I work on OpenVINO.