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--- |
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license: apache-2.0 |
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tags: |
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- RyzenAI |
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- image-classification |
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- onnx |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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--- |
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## MobileNetV2 |
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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](https://arxiv.org/abs/1801.04381) by Mark Sandler et al. and first released in [this repository](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet). |
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We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/). |
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## Model description |
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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. |
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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. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co./models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you. |
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## How to use |
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### Installation |
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1. Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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2. Run the following script to install pre-requisites for this model. |
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```shell |
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pip install -r requirements.txt |
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``` |
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### Test & Evaluation |
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- Inference one image (Image Classification): |
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```python |
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import sys |
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import onnxruntime |
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import torch |
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import torchvision.transforms as transforms |
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from PIL import Image |
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image_path = sys.argv[1] |
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onnx_model = sys.argv[2] |
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normalize = transforms.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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img_transformer = transforms.Compose([ |
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transforms.Resize(256), |
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transforms.CenterCrop(224), |
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transforms.ToTensor(), |
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normalize]) |
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img_tensor = img_transformer(Image.open(image_path)).unsqueeze(0) |
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img_tensor = torch.permute(img_tensor, (0, 2, 3, 1)) |
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so = onnxruntime.SessionOptions() |
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ort_session = onnxruntime.InferenceSession( |
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onnx_model, so, |
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providers=['CPUExecutionProvider'], |
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provider_options=None) |
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input = img_tensor.numpy() |
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ort_input = {ort_session.get_inputs()[0].name: input} |
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output = ort_session.run(None, ort_input) |
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top5_probabilities, top5_class_indices = torch.topk(torch.nn.functional.softmax(torch.tensor(output[0])), k=5) |
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``` |
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- Evaluate ImageNet validation dataset (50,000 Images), using `eval_onnx.py` . |
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- Test accuracy of the quantized model on CPU. |
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```shell |
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python eval_onnx.py --onnx_model=./mobilenetv2_int8.onnx --data_dir=./{DATA_PATH} |
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``` |
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- Test accuracy of the quantized model on IPU. |
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```shell |
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python eval_onnx.py --onnx_model=./mobilenetv2_int8.onnx --data_dir=./{DATA_PATH} --ipu --provider_config Path\To\vaip_config.json |
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``` |
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- Users can use `vaip_config.json` in folder `voe-4.0-win_amd64` of `ryzen-ai-sw-1.0.zip` file. |
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`DATA_PATH`: Path to ImageNet dataset where contains the `validation` folder. |
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### Performance |
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Dataset: ImageNet validation dataset (50,000 images). |
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| Metric | Accuracy on IPU | |
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| :-----------------: | :-------------: | |
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| top1& top5 accuracy | 75.62% / 92.52% | |
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## Citation |
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```bibtex |
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@article{MobileNet v2, |
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author = {Mark Sandler and |
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Andrew G. Howard and |
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Menglong Zhu and |
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Andrey Zhmoginov and |
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Liang{-}Chieh Chen}, |
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title = {MobileNetV2: Inverted Residuals and Linear Bottlenecks}, |
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year = {2018}, |
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url = {http://arxiv.org/abs/1801.04381}, |
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} |
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``` |
