Got it, let's break down three reliable ways to get your .pkl model working in MATLAB—depending on your workflow and model type, one of these should fit perfectly.

Method 1: Convert .pkl to MATLAB-Compatible .mat File (Best for Standard ML Models) #

If your model is from scikit-learn, a custom classical ML pipeline, or other simple Python objects, converting it to a .mat file first is the most straightforward approach.

Step 1: Use Python to Convert the .pkl File #

First, make sure you have scipy installed (if not, run pip install scipy in your Python environment). Then run this script:

import pickle
from scipy.io import savemat

# Load your trained model from the .pkl file
with open('your_trained_model.pkl', 'rb') as pkl_file:
    model = pickle.load(pkl_file)

# Save the model as a .mat file (MATLAB reads this natively)
savemat('model_for_matlab.mat', {'trained_model': model})

Step 2: Load the .mat File in MATLAB #

In MATLAB, just use the built-in load function:

% Load the converted .mat file
loaded_data = load('model_for_matlab.mat');
trained_model = loaded_data.trained_model;

% Example: Use a scikit-learn model's predict method
test_input = [1.2, 3.4, 5.6];
prediction = trained_model.predict(test_input);

Note: This works best for non-framework-specific objects. If your model is from PyTorch/TensorFlow, MATLAB won't recognize the framework-specific layers—skip to Method 3 for those cases.

Method 2: Directly Call Python from MATLAB (Flexible for All Model Types) #

MATLAB can interface directly with Python, so you can use Python's pickle module to load the model right inside MATLAB without conversion.

Step 1: Verify MATLAB-Python Compatibility #

First, check which Python version MATLAB is using (make sure it's the same environment you used to train the model):

pyversion % Displays the Python path and version MATLAB is linked to

If it's not the right environment, use pyversion('path/to/your/python.exe') to switch to your training environment.

Step 2: Load the .pkl Model in MATLAB #

Run this MATLAB code to load the model via Python:

% Import Python's pickle module
py.importlib.import_module('pickle');

% Open and load the .pkl file
pkl_file = py.open('your_trained_model.pkl', 'rb');
trained_model = py.pickle.load(pkl_file);
pkl_file.close();

% Use the model (mirror Python syntax, convert outputs to MATLAB types if needed)
% Example: Predict with a scikit-learn model
test_data = py.list([[0.1, 0.2], [0.3, 0.4]]); % Convert MATLAB array to Python list
python_pred = trained_model.predict(test_data);
matlab_pred = double(python_pred); % Convert Python array to MATLAB double array

Key Notes for This Method: #

• Ensure the Python environment linked to MATLAB has all dependencies your model needs (e.g., scikit-learn, torch, tensorflow).
• For deep learning models, you can still run predictions this way—just call the model's forward/predict method as you would in Python.

Method 3: For Deep Learning Models (ONNX Conversion) #

If your model is a PyTorch/TensorFlow deep learning model, using ONNX is the most robust approach (MATLAB has native ONNX support for seamless integration).

Step 1: Export Model to ONNX from Python #

For PyTorch:

import torch
model = torch.load('your_pytorch_model.pkl') # Load your trained model
model.eval()

# Create a dummy input matching your model's input shape
dummy_input = torch.randn(1, 3, 224, 224) # Example: 1 image, 3 channels, 224x224

# Export to ONNX
torch.onnx.export(model, dummy_input, 'model.onnx', opset_version=12)

For TensorFlow/Keras:

import tensorflow as tf
model = tf.keras.models.load_model('your_keras_model.h5') # Or load via pickle if needed

# Export to ONNX
tf.saved_model.save(model, 'temp_saved_model')
!python -m tf2onnx.convert --saved-model temp_saved_model --output model.onnx

Step 2: Load ONNX Model in MATLAB #

% Load the ONNX model
net = importONNXModel('model.onnx');

% Prepare input (match the dummy input shape you used for export)
input_data = randn(1, 224, 224, 3); % Adjust dimensions to fit your model

% Run prediction
prediction = predict(net, input_data);

内容的提问来源于stack exchange，提问作者Raj Shrivastava