AN 993: Using Custom Models with Intel® FPGA AI Suite

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ID 777190
Date 5/01/2023
Public

4.2. Example 2: Customized Multilayer Perceptron (MLP) Model

This example adds layers to a simple Multilayer Perceptron (MLP) model as follows:
  • A ReLU layer was added after each linear transformation in the previous layer.
  • A Softmax layer was added at the end.

These additions are shown as an example only. The performance of this customized model has not been tested or optimized.

Model Information:
  • Model: Multilayer Perception (MLP)
  • Framework: PyTorch®/ONNX*
Figure 3. Original MLP Model Layers 
import argparse
import torch
import numpy as np
from torch import nn, onnx

class MLP(nn.Module):
	def __init__(self):
		super(MLP, self).__init__()
		self.model = nn.Sequential(
			nn.Linear(10, 128),
			nn.Linear(128, 80),
			nn.Linear(80, 10),
		)

	def forward(self, x):
		return self.model(x)
Figure 4. Modified MLP Model Layers 
import argparse
import torch
import numpy as np
from torch import nn, onnx
import os

class MLP(nn.Module):
	def __init__(self):
		super(MLP, self).__init__()
		self.model = nn.Sequential(
            nn.Linear(10, 128),
            nn.ReLU(),
            nn.Linear(128, 80),
            nn.ReLU(),
            nn.Linear(80, 10),
            nn.ReLU(),
		)

	def forward(self, x):
		return self.model(x)
This model is created with the PyTorch® framework but must be converted to ONNX* to use the model with the OpenVINO™ Model Optimizer. The following Python code example illustrates how you can convert the PyTorch® model to ONNX*: 
onnx.export(model, x, args.onnx_file, export_params=True)
For more information about converting PyTorch® to ONNX*, review the ONNX exporter documentation at the following URL:

https://pytorch.org/docs/stable/onnx.html#example-alexnet-from-pytorch-to-onnx

After the conversion is complete and the ONNX* model is saved, convert the model to OpenVINO™ IR with the following command: 
mo --input_model <path to model>.onnx
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