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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/10/26 02:40:38 UTC
[GitHub] [tvm] Kyrie-Zhao opened a new issue #9368: [Bug] A list of missing op conversion (Swin Transformer)
Kyrie-Zhao opened a new issue #9368:
URL: https://github.com/apache/tvm/issues/9368
I am currently accelerating swin_transformer (ICCV2021 best paper)
https://github.com/Kyrie-Zhao/Swin-Transformer/blob/main/models/swin_transformer.py
which shows:
NotImplementedError: The following operators are not implemented: **['aten::rand', 'aten::adaptive_avg_pool1d', 'aten::roll', 'aten::floor_']**
BTW, anyone can provide some guidance regarding the procedure of implementing & registering an op by yourself? Appreciate it.
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[GitHub] [tvm] masahi commented on issue #9368: [Bug] A list of missing op conversion (Swin Transformer)
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #9368:
URL: https://github.com/apache/tvm/issues/9368#issuecomment-951537701
Can you show your script to trace the model? I got an error during tracing.
> BTW, anyone can provide some guidance regarding the procedure of implementing & registering an op by yourself?
By this, do you mean you want to work on this yourself, instead of asking us to do it?
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[GitHub] [tvm] junrushao1994 closed issue #9368: [Bug] A list of missing op conversion (Swin Transformer)
Posted by GitBox <gi...@apache.org>.
junrushao1994 closed issue #9368:
URL: https://github.com/apache/tvm/issues/9368
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[GitHub] [tvm] Kyrie-Zhao commented on issue #9368: [Bug] A list of missing op conversion (Swin Transformer)
Posted by GitBox <gi...@apache.org>.
Kyrie-Zhao commented on issue #9368:
URL: https://github.com/apache/tvm/issues/9368#issuecomment-951619728
> Can you show your script to trace the model? I got an error during tracing.
>
> > BTW, anyone can provide some guidance regarding the procedure of implementing & registering an op by yourself?
>
> By this, do you mean you want to work on this yourself, instead of asking us to do it?
Here are the codes:
...
from swin_transformer import SwinTransformer
net = SwinTransformer()
net = net.cuda()
img = torch.randn(1, 3, 224, 224)
device = torch.device("cuda")
img = img.to(device)
scripted_model = torch.jit.trace(net, img).eval()
input_name = "swin_input"
shape_list = [(input_name, img.shape)]
swin_mod, swin_params = relay.frontend.from_pytorch(scripted_model, shape_list)
_______
Nah, I am just curious about the procedure of this implementation (to help me learn more about TVM). But the answer can be yes if it is not that complicated :D
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