[WIP]: improvement(ViT): fuse nn.Linear and convert Gather to Crop #442
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* docs(docs/zh_cn): add doc and link checker * docs(REAME): update * docs(docs/zh_cn): update * docs(benchmark): update table * docs(zh_cn/benchmark): update link * CI(docs): update link check * ci(doc): update checker * docs(zh_cn): update * style(ci): remove useless para * style(ci): update * docs(zh_cn): update * docs(benchmark.md): fix mobilnet link error * docs(docs/zh_cn): add doc and link checker * docs(REAME): update * docs(docs/zh_cn): update * docs(benchmark): update table * docs(zh_cn/benchmark): update link * CI(docs): update link check * ci(doc): update checker * docs(zh_cn): update * style(ci): remove useless para * style(ci): update * docs(zh_cn): update * docs(benchmark.md): fix mobilnet link error * docs(zh_cn/do_regression_test.md): rebase * docs(docs/zh_cn): add doc and link checker * Update README_zh-CN.md * Update README_zh-CN.md * Update index.rst * Update check-doc-link.yml * [Fix] Fix ci (open-mmlab#426) * fix ci * add nvidia key * remote torch * recover pytorch * ci(codecov): ignore ci * docs(zh_cn): add get_started.md * docs(zh_cn): fix review advice * docs(readthedocs): update * docs(zh_CN): update * docs(zh_CN): revert * fix(docs): review advices * fix(docs): review advices * fix(docs): review Co-authored-by: q.yao <[email protected]>
tpoisonooo
changed the title
grimoire
reviewed
May 12, 2022
mmdeploy/pytorch/ops/linear.py
Outdated
| backend=Backend.NCNN.value) | ||
| def linear__ncnn(ctx, g, input, weight, bias): | ||
| """Support export linear This rewrite enable export Gemm.""" | ||
| return g.op( |
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grimoire
reviewed
May 12, 2022
| def linear__ncnn(ctx, g, input, weight, bias): | ||
| """Support export linear This rewrite enable export Gemm.""" | ||
| return g.op( | ||
| 'mmdeploy::Gemm', |
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Can we remove mmdeploy domain? It looks like the same as the official one ONNX Gemm
* docs(zh_CN): fix link missing * docs(benchmark.md): fix pytorch typo
* refactor(onnx2ncnn.cpp): split it to shape_inference, pass and utils * refactor(onnx2ncnn.cpp): split it to shape_inference, pass and utils * refactor(onnx2ncnn.cpp): split code * refactor(net_module.cpp): fix build error * ci(test_onnx2ncnn.py): add generate model adn run * ci(onnx2ncnn): add ncnn backend * ci(test_onnx2ncnn): add converted onnx model` * ci(onnx2ncnn): fix ncnn tar * ci(backed-ncnn): simplify dependency install * ci(onnx2ncnn): fix apt install * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * Update backend-ncnn.yml * fix(ci): add include algorithm * Update build.yml * parent aa85760 author q.yao <[email protected]> 1651287879 +0800 committer tpoisonooo <[email protected]> 1652169959 +0800 [Fix] Fix ci (open-mmlab#426) * fix ci * add nvidia key * remote torch * recover pytorch refactor(onnx2ncnn.cpp): split it to shape_inference, pass and utils * fix(onnx2ncnn): review * fix(onnx2ncnn): build error Co-authored-by: q.yao <[email protected]>
tpoisonooo
changed the title
|
close this PR because of bad |
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Motivation
Fix review advices from Tencent/ncnn#3730
Modification
end2end.param(289 --> 195)Crop axis=0 starts=0 ends=1opr instead ofGather axis=0 indices=[0]in ViT.paramBC-breaking (Optional)
Does the modification introduce changes that break the backward-compatibility of the downstream repositories?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
Checklist