PyTorch 模型转换为CoreML 模型 2024年7月12日
AI
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To convert a PyTorch model (with a .pth file) to a format supported by iOS, you need to convert it to Core ML format (.mlmodel). Here's a step-by-step guide to accomplish this:

1. Install the Required Libraries

You need to install torch, coremltools, and onnx if you haven't already:

pip install torch coremltools onnx

2. Convert PyTorch Model to ONNX

First, convert the PyTorch model to ONNX format:

import torch
import torchvision.models as models

# Load your PyTorch model
model = models.resnet18(pretrained=True)
model.eval()

# Dummy input for the model
dummy_input = torch.randn(1, 3, 224, 224)

# Export the model to ONNX
torch.onnx.export(model, dummy_input, "model.onnx", opset_version=11)

3. Convert ONNX Model to Core ML

Use coremltools to convert the ONNX model to Core ML format:

import coremltools as ct

# Load the ONNX model
onnx_model_path = "model.onnx"
onnx_model = ct.converters.onnx.convert(model=onnx_model_path)

# Save the Core ML model
onnx_model.save("model.mlmodel")

4. Integrate Core ML Model into iOS Application

You can now integrate the generated .mlmodel file into your iOS application. Here's a brief overview:

  1. Add the Model to Your Xcode Project: Drag and drop the .mlmodel file into your Xcode project.
  2. Create a Swift Class for the Model: Xcode will automatically generate a Swift class for the model.
  3. Use the Model in Your Code:
import CoreML
import Vision
import UIKit

func predict(image: UIImage) -> [VNClassificationObservation]? {
    guard let model = try? VNCoreMLModel(for: Model().model) else { return nil }
    let request = VNCoreMLRequest(model: model) { (request, error) in
        guard let results = request.results as? [VNClassificationObservation] else { return }
        // Process results
    }

    guard let ciImage = CIImage(image: image) else { return nil }
    let handler = VNImageRequestHandler(ciImage: ciImage, options: [:])
    try? handler.perform([request])

    return request.results as? [VNClassificationObservation]
}

// Usage
if let image = UIImage(named: "example.jpg") {
    if let results = predict(image: image) {
        for result in results {
            print("\(result.identifier): \(result.confidence)")
        }
    }
}

Additional Considerations

  • Model Optimization: Consider optimizing the model for better performance on mobile devices using techniques like quantization.
  • Validation: After conversion, validate the model to ensure its accuracy and performance are as expected.

References