zhengrongzhang
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Parent(s):
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init model
Browse files- README.md +72 -0
- eval_onnx.py +164 -0
- mnasnet_b1_int.onnx +3 -0
- requirements.txt +8 -0
README.md
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---
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license: apache-2.0
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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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tags:
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- RyzenAI
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- vision
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- classification
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- pytorch
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- timm
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---
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# MNASNet_b1
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Quantized MNASNet_b1 model 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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MNASNet was first introduced in the paper [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626).
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The model implementation is from [timm](https://huggingface.co/timm/mnasnet_100.rmsp_in1k).
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## How to use
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### Installation
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
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Run the following script to install pre-requisites for this model.
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```bash
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pip install -r requirements.txt
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```
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### Data Preparation
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Follow [ImageNet](https://huggingface.co/datasets/imagenet-1k) to prepare dataset.
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### Model Evaluation
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```python
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python eval_onnx.py --onnx_model mnasnet_b1_int.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset
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```
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### Performance
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|Metric |Accuracy on IPU|
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| :----: | :----: |
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|Top1/Top5| 73.85% / 91.82% |
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```bibtex
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@misc{rw2019timm,
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author = {Ross Wightman},
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title = {PyTorch Image Models},
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year = {2019},
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publisher = {GitHub},
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journal = {GitHub repository},
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doi = {10.5281/zenodo.4414861},
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howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
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}
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```
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```bibtex
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@inproceedings{tan2019mnasnet,
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title={Mnasnet: Platform-aware neural architecture search for mobile},
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author={Tan, Mingxing and Chen, Bo and Pang, Ruoming and Vasudevan, Vijay and Sandler, Mark and Howard, Andrew and Le, Quoc V},
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booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
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pages={2820--2828},
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year={2019}
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}
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```
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eval_onnx.py
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#!/usr/bin/env python
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from typing import Tuple
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import argparse
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import onnxruntime
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import os
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import sys
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import time
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import torch
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import torchvision.datasets as datasets
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--onnx_model", default="model.onnx", help="Input onnx model")
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parser.add_argument(
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"--data_dir",
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default="/workspace/dataset/imagenet",
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help="Directory of dataset")
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parser.add_argument(
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"--batch_size", default=1, type=int, help="Evaluation batch size")
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parser.add_argument(
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"--ipu",
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action="store_true",
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help="Use IPU for inference.",
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)
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parser.add_argument(
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"--provider_config",
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type=str,
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default="vaip_config.json",
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help="Path of the config file for seting provider_options.",
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)
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args = parser.parse_args()
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self, name, fmt=':f'):
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self.name = name
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self.fmt = fmt
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def __str__(self):
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fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
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return fmtstr.format(**self.__dict__)
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def accuracy(output: torch.Tensor,
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target: torch.Tensor,
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topk: Tuple[int] = (1,)) -> Tuple[float]:
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"""Computes the accuracy over the k top predictions for the specified values of k.
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Args:
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output: Prediction of the model.
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target: Ground truth labels.
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topk: Topk accuracy to compute.
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Returns:
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Accuracy results according to 'topk'.
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"""
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with torch.no_grad():
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
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res.append(correct_k.mul_(100.0 / batch_size))
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return res
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def prepare_data_loader(data_dir: str,
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batch_size: int = 100,
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workers: int = 8) -> torch.utils.data.DataLoader:
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"""Returns a validation data loader of ImageNet by given `data_dir`.
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Args:
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data_dir: Directory where images stores. There must be a subdirectory named
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'validation' that stores the validation set of ImageNet.
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batch_size: Batch size of data loader.
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workers: How many subprocesses to use for data loading.
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Returns:
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An object of torch.utils.data.DataLoader.
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"""
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valdir = os.path.join(data_dir, 'validation')
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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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val_dataset = datasets.ImageFolder(
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valdir,
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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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]))
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return torch.utils.data.DataLoader(
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val_dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True)
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def val_imagenet():
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"""Validate ONNX model on ImageNet dataset."""
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print(f'Current onnx model: {args.onnx_model}')
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if args.ipu:
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providers = ["VitisAIExecutionProvider"]
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provider_options = [{"config_file": args.provider_config}]
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else:
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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provider_options = None
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ort_session = onnxruntime.InferenceSession(
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args.onnx_model, providers=providers, provider_options=provider_options)
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val_loader = prepare_data_loader(args.data_dir, args.batch_size)
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top1 = AverageMeter('Acc@1', ':6.2f')
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top5 = AverageMeter('Acc@5', ':6.2f')
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start_time = time.time()
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val_loader = tqdm(val_loader, file=sys.stdout)
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with torch.no_grad():
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for batch_idx, (images, targets) in enumerate(val_loader):
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inputs, targets = images.numpy(), targets
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ort_inputs = {ort_session.get_inputs()[0].name: inputs}
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outputs = ort_session.run(None, ort_inputs)
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outputs = torch.from_numpy(outputs[0])
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acc1, acc5 = accuracy(outputs, targets, topk=(1, 5))
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top1.update(acc1, images.size(0))
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top5.update(acc5, images.size(0))
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current_time = time.time()
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print('Test Top1 {:.2f}%\tTop5 {:.2f}%\tTime {:.2f}s\n'.format(
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float(top1.avg), float(top5.avg), (current_time - start_time)))
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return top1.avg, top5.avg
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if __name__ == '__main__':
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val_imagenet()
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mnasnet_b1_int.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:a4784fa94c18ad2cb8c91081663cbdc825ba7bc61d1d913abd50d0fe0ff84949
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size 17571696
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requirements.txt
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torch==1.12.0
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torchsummary==1.5.1
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torchvision==0.13.0
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Pillow==9.4.0
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onnx==1.14.0
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# onnxruntime-gpu==1.14.1
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timm==0.9.8
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tqdm==4.64.0
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