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MobileNetV2

MobileNetV2 is an image classification model pre-trained on ImageNet-1k dataset at resolution 224x224. It was introduced in the paper MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler et al. and first released in this repository.

We develop a modified version that could be supported by AMD Ryzen AI.

Model description

MobileNetV2 is a simple network architecture that allows to build a family of highly efficient mobile models. It allows memory-efficient inference. MobileNetV2 is a model typically used for image classification tasks. And also can be used for object detection and image segmentation tasks. All tasks show competitive results.

The model is named mobilenet_v2_depth_size, for example, mobilenet_v2_1.4_224, where 1.4 is the depth multiplier and 224 is the resolution of the input images the model was trained on.

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

How to use

Installation

  1. Follow Ryzen AI Installation to prepare the environment for Ryzen AI.

  2. Run the following script to install pre-requisites for this model.

    pip install -r requirements.txt 
    

Test & Evaluation

  • Inference one image (Image Classification):

    import sys
    import onnxruntime
    import torch
    import torchvision.transforms as transforms 
    from PIL import Image
    
    image_path = sys.argv[1]
    onnx_model = sys.argv[2]
    
    normalize = transforms.Normalize(
      mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    img_transformer = transforms.Compose([
                    transforms.Resize(256),
                    transforms.CenterCrop(224),
                    transforms.ToTensor(),  
                    normalize])
    img_tensor = img_transformer(Image.open(image_path)).unsqueeze(0)
    img_tensor = torch.permute(img_tensor, (0, 2, 3, 1))
    so = onnxruntime.SessionOptions()
    ort_session = onnxruntime.InferenceSession(
            onnx_model, so, 
            providers=['CPUExecutionProvider'], 
            provider_options=None)
    input = img_tensor.numpy()
    ort_input = {ort_session.get_inputs()[0].name: input}
    
    output = ort_session.run(None, ort_input)
    top5_probabilities, top5_class_indices = torch.topk(torch.nn.functional.softmax(torch.tensor(output[0])), k=5)
    
  • Evaluate ImageNet validation dataset (50,000 Images), using eval_onnx.py .

    • Test accuracy of the quantized model on CPU.

      python eval_onnx.py  --onnx_model=./mobilenetv2_int8.onnx --data_dir=./{DATA_PATH}
      
    • Test accuracy of the quantized model on IPU.

      python eval_onnx.py  --onnx_model=./mobilenetv2_int8.onnx --data_dir=./{DATA_PATH} --ipu --provider_config Path\To\vaip_config.json
      
    • Users can use vaip_config.json in folder voe-4.0-win_amd64 of ryzen-ai-sw-1.0.zip file.

​ DATA_PATH: Path to ImageNet dataset where contains the validation folder.

Performance

Dataset: ImageNet validation dataset (50,000 images).

Metric Accuracy on IPU
top1& top5 accuracy 75.62% / 92.52%

Citation

@article{MobileNet v2,
  author       = {Mark Sandler and
                  Andrew G. Howard and
                  Menglong Zhu and
                  Andrey Zhmoginov and
                  Liang{-}Chieh Chen},
  title        = {MobileNetV2: Inverted Residuals and Linear Bottlenecks},
  year         = {2018},
  url          = {http://arxiv.org/abs/1801.04381},
}
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Dataset used to train amd/mobilenet_v2_1.0_224