Comments (9)
ONNX export would be really useful. Unless there is a way of converting torchscript to ONNX.
from detr.
Hi,
Thanks for your request.
Adding support for torchscript was on our TODO list, so I've just sent a PR making the torchhub models support torchscript #51
We don't currently have plans to add support for ONNX though, but we would welcome PRs making it work (as long as the code still stays readable and simple to understand).
from detr.
@fmassa
Thanks for the link to your PR!
I'll look into adding ONNX support.
from detr.
The model detr_resnet50 can be converted into onnx, but its output is all nan
, I tested NestedTensor
in another project, its inference result in onnxruntime is also different compare to pytorch's. After removing NestedTensor
, its result is equal to pytorch's. I guess the NestedTensor
here cause this problem. Is there some suggestion to fix it?
from detr.
Hi all, I think I have fixes the above problem I mentioned. I retrieved the code in torchvision's faster-rcnn repo, and I found the purpose of this function is aimed at resolving this problem. My fix is here, I will test if it can help to resolve the onnx inference problem in DETR.
from detr.
Hi @fmassa
I take a quick fix of nested_tensor_from_tensor_list()
according to the implementation of torchvision's to make it supported by ONNX tracing, Now the inference results in onnxruntime is consistent with pytorch. And here is my modification of _onnx_nested_tensor_from_tensor_list()
.
Can you check my modification, if it is OK, I would like to submit a PR :)
from detr.
@zhiqwang your implementation looks good, can you send a PR?
from detr.
Hi @fmassa , Of course, It's my pleasure.
from detr.
So it it supported now?
from detr.
Related Issues (20)
- Question about object queries. HOT 4
- I want to train the DETR model on a CPU. How can I make it possible on a small computer, 8gb RAM HOT 3
- Why positional encoding is added to different role in encoder and decoder. HOT 1
- 🐛 Bug: Architecture diagram in README.md renders incorrectly when using dark mode
- continue training with chekckpoint
- How to finetune DETR for semantic segmentation task?
- I do not understand what the mask meaning in "samlpes"
- Process finished with exit code 137 (interrupted by signal 9: SIGKILL)Please read & provide the following
- Very low performance for segmentation task.
- box_cxcywh_to_xyxy
- ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 6 (pid: 257736) of binary: /home/public/anaconda3/envs/DL/bin/python
- Average Precision of each class for best epoch and then it's mean HOT 1
- the mAP is chage
- I think there are some errors in the posted code HOT 6
- Queries for images with low number of objects HOT 2
- RuntimeError: Error(s) in loading state_dict for DETRsegm: HOT 2
- Map metrics anomalies after backbone replacement
- when the trained model is used for inference this import error comes: RuntimeError: Failed to import transformers.models.detr.modeling_detr because of the following error (look up to see its traceback): cannot import name 'experimental_functions_run_eagerly' from 'tensorflow.python.eager.def_function' (C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py)
- Get Image masks coordinates.
- GFLOPs instead of GFLOPS?
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