Deep Learning Toolbox Converter for PyTorch Models

作者: MathWorks Deep Learning Toolbox Team
Import pretrained PyTorch models into MATLAB
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更新时间 2024/3/20
The converter for PyTorch models enables you to import pretrained PyTorch models and weights into MATLAB.
To import a PyTorch network in MATLAB, please refer to importNetworkFromPyTorch.
Note: the model must be traced in PyTorch before importing into MATLAB. See below for an example:
# This example loads a pretrained PyTorch model from torchvision,
# traces it with example inputs, and saves the trace as a .pt file.
import torch
from torchvision import models
# Load the model with pretrained weights
model = models.mobilenet_v2(pretrained=True)
# Call "eval" to ensure that layers like batch norm and dropout are set to
# inference mode
model.eval()
# Move the model to the CPU
model.to("cpu")
# Create example inputs
X = torch.rand(1, 3, 224, 224)
# Trace model with the example input
traced_model = torch.jit.trace(model.forward, X)
# Save the traced model to a .pt file
traced_model.save('traced_mnasnet.pt')
The initial release in R2022b supports importing image classification models. Support for other model types will be added in future updates.
MATLAB 版本兼容性
创建方式 R2022b
兼容 R2022b 到 R2024a 的版本
平台兼容性
Windows macOS (Apple 芯片) macOS (Intel) Linux

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