from onnx.optimizer import optimize # 加载ONNX模型 model = onnx.load('model.onnx') # 优化模型 passes = ["fuse_bn_into_conv"] model = optimize(model, passes) # 保存优化后的模型 onnx.save(model, 'model_optimized.onnx') 3. 使用
passes = ['fuse_bn_into_conv'] # Apply the optimization on the original model optimized_model = optimizer.optimize(onnx_model, passes) 1. 2. 3. 将mobile net v2应用fuse_bn_into_conv之后,BatchNormalization的参数合并到了Conv的weight和bias参数中,如下图所示: 三. ONNX Runtime计算ONNX模型 on...
optimized_model = onnxoptimizer.optimize(model_onnx_simplify, passes) onnx.save(optimized_model, onnxSimplifySavePath) index: 0 Got: 10 Expected: 20 Please fix either the inputs or the model.
其optimize函数定义如下(可以简单看下目前支持的一些优化方法): def optimize(model: onnx.ModelProto, skip_fuse_bn: bool, skipped_optimizers: Optional[Sequence[str]]) -> onnx.ModelProto: """ :model参数: 待优化的ONXX模型. :return: 优化之后的ONNX模型. 简化之前, 使用这个方法产生会在'forward_al...
model = onnx.load("model.onnx") optimized_model = optimizer.optimize(model) ``` 经过优化的模型可以提高推理效率,减少模型运行所需的计算资源,适合部署在嵌入式设备和移动端等资源受限的环境中。 2. 深度学习库的优化支持 除了ONNX库提供的优化方法外,许多深度学习库也提供了针对ONNX模型的优化支持。PyTorch...
Learn how use of the Open Neural Network Exchange (ONNX) can help to optimize the inference of your machine learning model. Inference or model scoring, is the process of using a deployed model to generate predictions on production data.
In this post, I discuss how to use ONNX Runtime at a high level. I also go into more depth about how to optimize your models. Figure 1. ONNX Runtime high-level architecture Run a model with ONNX Runtime ONNX Runtime is compatible with most programming languages. As in the other ...
Journey to optimize large scale transformer model inference with ONNX Runtime Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, questio...
optimized_model=optimizer.optimize(original_model, passes) ## Save the optimized ONNX model. onnx.save(optimized_model,'path/to/the/optimized_model.onnx') A model is loaded from path/to/the/model.onnx, optimized with thefuse_bn_into_convpass, and then saved to path/to/the/optimized_mod...
A deep CNN model for real-time object detection that detects 80 different classes. A little bigger than YOLOv2 but still very fast. As accurate as SSD but 3 times faster. Tiny YOLOv3 Redmon et al. A smaller version of YOLOv3 model. YOLOv4 Bochkovskiy et al. Optimizes the speed and...