Add files using upload-large-folder tool
Browse files- .gitattributes +2 -0
- 1731868158459906_____.png +3 -0
- ACT.zip +3 -0
- README.md +63 -0
- SVP_NNN_PC_V1.0.6.0.tgz +3 -0
- act_distill_fp32_for_mindcmd_simp_release.om +3 -0
- model-card.json +216 -0
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act_distill_fp32_for_mindcmd_simp_release.om filter=lfs diff=lfs merge=lfs -text
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1731868158459906_____.png
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Git LFS Details
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ACT.zip
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README.md
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| 1 |
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---
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| 2 |
+
language:
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| 3 |
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- zh
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| 4 |
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tags:
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| 5 |
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- hisilicon
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| 6 |
+
- hispark
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| 7 |
+
- npu
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| 8 |
+
- openharmony
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| 9 |
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- modelzoo
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| 10 |
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- pytorch
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| 11 |
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---
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| 12 |
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| 13 |
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# ACT
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| 14 |
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| 15 |
+
ACT(Action Chunking with Transformers)是面向机器人学习场景的高性能端到端动作控制模型。相比传统模块化机器人控制模型,ACT采用轻量化Transformer架构作为核心骨干进行动作表征学习,结合多模态感知融合模块和时序动作优化网络,在控制精度和实时响应速度上均有显著提升。
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| 16 |
+
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+
## Mirror Metadata
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| 18 |
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| 19 |
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- Hugging Face repo: shadow-cann/hispark-modelzoo-act
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| 20 |
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- Portal model id: ivcifqkd0400
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| 21 |
+
- Created at: 2026-03-03 10:30:33
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- Updated at: 2026-03-04 16:06:22
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- Category: 多模态
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| 24 |
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## Framework
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| 26 |
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| 27 |
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- PyTorch
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| 28 |
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## Supported OS
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| 30 |
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- OpenEuler
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## Computing Power
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| 34 |
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- Hi3403V100 SVP_NNN
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| 36 |
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## Tags
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| 38 |
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| 39 |
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- 具身智能
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| 40 |
+
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| 41 |
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## Detail Parameters
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| 42 |
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| 43 |
+
- 输入: 1 x 6;1 x 3 x 240 x 320;1 x 3 x 240 x 320
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| 44 |
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- 参数量: 87 M
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| 45 |
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- 计算量: 8.02 GFLOPs
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| 46 |
+
|
| 47 |
+
## Files In This Repo
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| 48 |
+
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| 49 |
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- ACT.zip (源模型 / 源模型下载; 源模型 / 源模型元数据)
|
| 50 |
+
- act_distill_fp32_for_mindcmd_simp_release.om (编译模型 / OM 元数据 / a16w8)
|
| 51 |
+
- SVP_NNN_PC_V1.0.6.0.tgz (附加资源 / 附加资源)
|
| 52 |
+
|
| 53 |
+
## Upstream Links
|
| 54 |
+
|
| 55 |
+
- Portal card: https://gitbubble.github.io/hisilicon-developer-portal-mirror/model-detail.html?id=ivcifqkd0400
|
| 56 |
+
- Upstream repository: https://gitee.com/HiSpark/modelzoo/blob/master/samples/contribute/ACT/README.md
|
| 57 |
+
- License reference: https://github.com/tonyzhaozh/act/blob/main/LICENSE
|
| 58 |
+
|
| 59 |
+
## Notes
|
| 60 |
+
|
| 61 |
+
- This repository was mirrored from the HiSilicon Developer Portal model card and local downloads captured on 2026-03-27.
|
| 62 |
+
- File ownership follows the portal card mapping, not just filename similarity.
|
| 63 |
+
- Cover image: 1731868158459906_____.png
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SVP_NNN_PC_V1.0.6.0.tgz
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version https://git-lfs.github.com/spec/v1
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oid sha256:536b103d08e9490f968207326798e1fc50ea4234888b73db729cd6e6a04c5d8b
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| 3 |
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size 31072256
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act_distill_fp32_for_mindcmd_simp_release.om
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version https://git-lfs.github.com/spec/v1
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size 72787553
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model-card.json
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| 1 |
+
{
|
| 2 |
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"name": "ACT",
|
| 3 |
+
"id": "ivcifqkd0400",
|
| 4 |
+
"description": "ACT(Action Chunking with Transformers)是面向机器人学习场景的高性能端到端动作控制模型。相比传统模块化机器人控制模型,ACT采用轻量化Transformer架构作为核心骨干进行动作表征学习,结合多模态感知融合模块和时序动作优化网络,在控制精度和实时响应速度上均有显著提升。",
|
| 5 |
+
"category": "多模态",
|
| 6 |
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"framework": [
|
| 7 |
+
"PyTorch"
|
| 8 |
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],
|
| 9 |
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"supportOs": [
|
| 10 |
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"OpenEuler"
|
| 11 |
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],
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| 12 |
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"computingPower": [
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| 13 |
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"Hi3403V100 SVP_NNN"
|
| 14 |
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],
|
| 15 |
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"tags": [
|
| 16 |
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"具身智能"
|
| 17 |
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],
|
| 18 |
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"repositoryUrl": "https://gitee.com/HiSpark/modelzoo/blob/master/samples/contribute/ACT/README.md",
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"licenseUrl": "https://github.com/tonyzhaozh/act/blob/main/LICENSE",
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"downloads": [
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{
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"源模型 / 源模型下载",
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"源模型 / 源模型元数据"
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| 27 |
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"fileName": "act_distill_fp32_for_mindcmd_simp_release.om",
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"apiDetail": {
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"createdBy": 140009447602724,
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"creationDate": "2026-03-03 10:30:33",
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"creationUserCN": "liuweihong",
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"lastUpdateDate": "2026-03-04 16:06:22",
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"rowIdx": -1,
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"id": "ivcifqkd0400",
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"name": "ACT",
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"isBeta": 0,
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"betaVersionDesc": "",
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"description": "ACT(Action Chunking with Transformers)是面向机器人学习场景的高性能端到端动作控制模型。相比传统模块化机器人控制模型,ACT采用轻量化Transformer架构作为核心骨干进行动作表征学习,结合多模态感知融合模块和时序动作优化网络,在控制精度和实时响应速度上均有显著提升。",
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"parentId": "ivcifqkd0400",
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"coverImageId": 1731868158459906,
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"coverImageUrl": "https://openxinhuo-board-image.obs.cn-east-3.myhuaweicloud.com/1731868158459906%2F%E6%A8%A1%E5%9E%8B%E5%B0%81%E9%9D%A2.png",
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"modelEffectId": 1731868175171585,
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"modelEffectUrl": "https://openxinhuo-board-image.obs.cn-east-3.myhuaweicloud.com/1731868175171585%2F3403_act.mp4",
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"computerVersion": [],
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"naturalLanguageProcess": [],
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"multimodal": [
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"具身智能"
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],
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"video": [],
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"framework": [
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"PyTorch"
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"modelRepository": "https://gitee.com/HiSpark/modelzoo/blob/master/samples/contribute/ACT/README.md",
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"originModelLink": "",
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"dataSet": "https://huggingface.co/datasets/lwh2017/grab_banana/tree/main/banana_grasp_100_320x240",
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"modelLicense": "https://github.com/tonyzhaozh/act/blob/main/LICENSE",
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"detailParams": [
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{
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"name": "输入",
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"value": "1 x 6;1 x 3 x 240 x 320;1 x 3 x 240 x 320"
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},
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{
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"name": "参数量",
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"value": "87 M"
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| 88 |
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},
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| 89 |
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{
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| 90 |
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"name": "计算量",
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| 91 |
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"value": "8.02 GFLOPs"
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| 92 |
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}
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| 93 |
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],
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| 94 |
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"quickStart": {
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"url": "https://gitee.com/HiSpark/modelzoo/blob/master/samples/contribute/ACT/SVP_NNN/src/main.cpp",
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"markDownUrl": "",
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"developLanguage": [
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{
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"language": "C++",
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"context": "{\"ops\":[{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#333333\"},\"insert\":\"模型可以通过以下代码生成可执行文件,并开放接口由python调用执行推理,以 SVP_NNN 推理引擎为例。\"},{\"attributes\":{\"text-indent\":\"0px\"},\"insert\":\"\\n\"},{\"insert\":\"#include <fstream>\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include <iostream>\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include \\\"sample_process.h\\\"\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include \\\"utils.h\\\"\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include <vector>\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"#include <string>\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"using namespace std;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\"int main() {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" // 初始化推理环境(只执行一次)\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" SampleProcess sample;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (sample.InitResource() != SUCCESS) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Init resource failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 加载模型(只执行一次)\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (sample.LoadModel() != SUCCESS) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Load model failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" sample.DestroyResource();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" return -1;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 循环处理多次输入\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" while (true) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" vector<const void*> input_datas;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" vector<size_t> input_sizes;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" const int INPUT_COUNT = 3;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 读取输入数据(保持原有逻辑)\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" bool readSuccess = true;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" for (int i = 0; i < INPUT_COUNT; ++i) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" uint32_t data_size;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cin.read(reinterpret_cast<char*>(&data_size), sizeof(data_size));\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (!cin.good()) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Read input \\\" << i << \\\" size failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" readSuccess = false;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" break;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" void* data = nullptr;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" svp_acl_error ret = svp_acl_rt_malloc(&data, data_size, SVP_ACL_MEM_MALLOC_NORMAL_ONLY);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (ret != SVP_ACL_SUCCESS || data == nullptr) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Malloc buffer for input \\\" << i << \\\" failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" readSuccess = false;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" break;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" cin.read(reinterpret_cast<char*>(data), data_size);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (!cin.good()) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Read input \\\" << i << \\\" data failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" svp_acl_rt_free(data);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" readSuccess = false;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" break;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" input_datas.push_back(data);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" input_sizes.push_back(data_size);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 检查是否读取失败(比如到达输入末尾)\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (!readSuccess) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" // 释放已分配的内存\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" for (auto ptr : input_datas) svp_acl_rt_free(ptr);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" break;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 设置输入并执行推理\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" sample.SetInputDatas(input_datas, input_sizes);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" if (sample.Process() != SUCCESS) {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cerr << \\\"Inference failed\\\" << endl;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" } else {\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" cout << \\\"3-input inference success\\\" << endl; // 注意这里修正了原代码的数字错误(5->3)\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 释放当前批次的输入内存\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" for (auto data : input_datas) svp_acl_rt_free(data);\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" }\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\\n\"},{\"insert\":\" // 最后释放所有资源\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" sample.DestroyResource();\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\" return 0;\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"insert\":\"}\"},{\"attributes\":{\"code-block\":\"plain\"},\"insert\":\"\\n\"},{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#40485b\",\"line-height\":\"1.6\"},\"insert\":\"备注:上述C++代码依赖的动态库与头文件位于\"},{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#40485b\",\"line-height\":\"1.6\",\"link\":\"https://gitee.com/HiSpark/modelzoo/tree/master/samples/contribute/ACT/SVP_NNN/src\"},\"insert\":\"samples/contribute/ACT/SVP_NNN/src\"},{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#40485b\",\"line-height\":\"1.6\"},\"insert\":\"目录下,编译相关配置参考\"},{\"attributes\":{\"background\":\"transparent\",\"size\":\"16px\",\"color\":\"#095eab\",\"line-height\":\"1.6\",\"link\":\"https://gitee.com/HiSpark/modelzoo/blob/master/samples/contribute/ACT/SVP_NNN/src/CMakeLists.txt\"},\"insert\":\"CMakeLists.txt\"},{\"attributes\":{\"background\":\"#ffffff\",\"size\":\"16px\",\"color\":\"#40485b\",\"line-height\":\"1.6\"},\"insert\":\"。\"},{\"insert\":\"\\n\"}]}"
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