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[Caffe Frontend] supporting group > 1 cases for Deconv op #8260
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- Handling group > 1 cases, assuming group == output channels - Simply decomposed into Relay split, conv2d_transposed, and multi-leveled concatenate ops - Added some test cases Signed-off-by: zotanika <[email protected]>
mshr-h
reviewed
Jul 15, 2021
Contributor
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I want to try your PR. Is there any pretrained models which contains Deconv with group > 1? |
lixiaoquan
reviewed
Jul 19, 2021
Signed-off-by: zotanika <[email protected]>
Member
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@zotanika please resolve the conflict. |
masahi
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Jan 17, 2022
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Jan 24, 2022
* [Caffe Frontend] supporting group > 1 cases for Deconv op - Handling group > 1 cases, assuming group == output channels - Simply decomposed into Relay split, conv2d_transposed, and multi-leveled concatenate ops - Added some test cases Signed-off-by: zotanika <[email protected]> * [Caffe Frontend] amending a test case for Deconv op Signed-off-by: zotanika <[email protected]> * explicit importing tvm.testing * changing split axis to 0, according to PR apache#9336
crazydemo
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Jan 27, 2022
* [Caffe Frontend] supporting group > 1 cases for Deconv op - Handling group > 1 cases, assuming group == output channels - Simply decomposed into Relay split, conv2d_transposed, and multi-leveled concatenate ops - Added some test cases Signed-off-by: zotanika <[email protected]> * [Caffe Frontend] amending a test case for Deconv op Signed-off-by: zotanika <[email protected]> * explicit importing tvm.testing * changing split axis to 0, according to PR apache#9336
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Feb 16, 2022
* [Caffe Frontend] supporting group > 1 cases for Deconv op - Handling group > 1 cases, assuming group == output channels - Simply decomposed into Relay split, conv2d_transposed, and multi-leveled concatenate ops - Added some test cases Signed-off-by: zotanika <[email protected]> * [Caffe Frontend] amending a test case for Deconv op Signed-off-by: zotanika <[email protected]> * explicit importing tvm.testing * changing split axis to 0, according to PR apache#9336
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Signed-off-by: zotanika [email protected]
Thanks for contributing to TVM! Please refer to guideline https://tvm.apache.org/docs/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from Reviewers by @ them in the pull request thread.