RegNet-X-080 on Ascend NPU

pip install torch torch_npu torchvision -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install timm pillow -i https://pypi.tuna.tsinghua.edu.cn/simple/
export HF_ENDPOINT=https://hf-mirror.com

import torch
import torch_npu
import timm
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from PIL import Image

# 加载模型
model = timm.create_model("regnetx_080.tv2_in1k", pretrained=True)
model = model.npu()
model.eval()
print(f"参数量: {sum(p.numel() for p in model.parameters())/1e6:.1f}M")

# 数据预处理
config = resolve_data_config({}, model="regnetx_080.tv2_in1k")
transform = create_transform(**config)

# 加载并预处理图像
img = Image.open("image.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0).npu()

# 推理
with torch.no_grad():
    output = model(input_tensor)

# Top-5 预测
probs = torch.nn.functional.softmax(output, dim=1)
top5_probs, top5_indices = torch.topk(probs, k=5, dim=1)
print("Top-5 predictions:", top5_indices.tolist())

# 使用随机输入推理（不需要图片）
python inference.py

# 使用指定图片推理
python inference.py --image cat.jpg

# 强制 CPU 推理
python inference.py --cpu

python accuracy_eval.py

python accuracy_eval.py

.
├── inference.py            # NPU 推理脚本
├── accuracy_eval.py        # 精度验证与性能基准脚本
├── accuracy_report.json    # 精度验证报告 (运行后生成)
├── perf_report.json        # 性能测试报告 (运行后生成)
└── readme.md               # 本文档

@article{radosavovic2020designing,
  title={Designing Network Design Spaces},
  author={Radosavovic, Ilija and Kosaraju, Raj Prateek and Girshick, Ross and He, Kaiming and Doll{'a}r, Piotr},
  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

@article{goyal2021seer,
  title={Self-Supervised Pretraining of Visual Features in the Wild},
  author={Goyal, Priya and Caron, Mathilde and Lefaudeux, Benjamin and Xu, Ishan and Wang, Pengchuan and Pai, Vivek and Singh, Mannat and Li, Vitaliy and Lubin, Mikhail and Bhojanapalli, Sergey and others},
  journal={arXiv preprint arXiv:2103.01988},
  year={2021}
}