RADIO-L Ascend NPU 部署指南

/data/ysws/agentsp/RADIO-L-ascend/
├── inference.py          # 精度测试脚本
├── log.txt               # 测试日志
├── README.md             # 本文档
├── test_image.pt        # 测试图像 (3x224x224)
├── test_image_448.pt    # 测试图像 (3x448x448)
├── cpu_output.pt        # CPU输出参考
└── npu_output.pt         # NPU输出参考

docker exec -it test-modelagent bash

source /usr/local/Ascend/ascend-toolkit/set_env.sh

cd /data/ysws/agentsp/RADIO-L-ascend/
python3 inference.py

output = model(pixel_values)
# output.summary: torch.Size([B, 3072]) - 全局图像特征
# output.features: torch.Size([B, 196, 1024]) - 空间特征

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("/data/ysws/agentsp/RADIO-L", trust_remote_code=True)
model = model.to("npu:0")

pixel_values = torch.randn(1, 3, 224, 224).to("npu:0")
output = model(pixel_values)

print(f"Summary: {output.summary.shape}")     # [1, 3072]
print(f"Features: {output.features.shape}")   # [1, 196, 1024]

from einops import rearrange

spatial = rearrange(
    output.features,
    'b (h w) d -> b d h w',
    h=14, w=14
)
# spatial shape: [1, 1024, 14, 14]