Feature : Add ONNX conversion script

[SangbumChoi]

Hi @jozhang97

I made ONNX conversion script for this ResNet50 DETA model

There are two slight modification in original code.

1. onnx input process
2. modify boolean type of input to float in cumsum operation

If you have time, please review and merge. Also if you need further modification feel free to ask.

[SangbumChoi]

FYI, to convert this model into tensorRT, it requires different conversion script (e.g. MultiScaleDeformableAttention)

[demuxin]

Hi @SangbumChoi , I tried to convert onnx to tensorrt engine. But there is error:

[E] 2: [myelinBuilderUtils.cpp::getMyelinSupportType::1270] Error Code 2: Internal Error (ForeignNode does not support data-dependent shape for now.)
[!] Invalid Engine. Please ensure the engine was built correctly
[E] FAILED | Runtime: 22.843s | Command: /home/osmagic/anaconda3/envs/pytorch/bin/polygraphy run --trt weights/out_nms_0527_sim_san.onnx

Do you know why there's this error and how to solve it?

I'm looking forward to your reply.

[SangbumChoi]

@demuxin AFAIK, you need some custom kernel (deformable attention) to convert this model in to tensorRT. In my experience I have succeed to convert ONNX but not tensorRT.

I would happy to work and collaborate on converting tensorrt engine. Do you have any experience of writing CUDA programming?

[SangbumChoi]

@demuxin Will you like to share your email and Slack message to discuss about this?

[xinlin-xiao]

@SangbumChoi Hi,Swin-L DETA model can be conver to onnx? I use the code conver Swin-L DETA to onnx :

deta.pt2onnx(img_size=(1440,832),weights='/mnt/data1/download_new/DETA-master/exps/public/deta_swin_ft_2024.4.3/best.pt')

 def pt2onnx (self,weights,img_size,batch_size=1,device='cuda:0',export_nms=False,simplify=True):
        model=self.model
        print(img_size)
        img = torch.zeros(batch_size, 3, *img_size).to(device)
        try:
            import onnx

            print(f' starting export with onnx {onnx.__version__}...')
            f = weights.replace('.pt', '.onnx')  # filename
            torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'], output_names=['output'],
                                dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # size(1,3,640,640)
                                                'output': {0: 'batch', 2: 'y', 3: 'x'}} )
            # Checks
            model_onnx = onnx.load(f)  # load onnx model
            onnx.checker.check_model(model_onnx)  # check onnx model
            # print(onnx.helper.printable_graph(model_onnx.graph))  # print

            # Simplify
            if simplify:
                try:
                    # check_requirements(['onnx-simplifier'])
                    import onnxsim

                    print(f'simplifying with onnx-simplifier {onnxsim.__version__}...')
                    model_onnx, check = onnxsim.simplify(model_onnx,
                                                        input_shapes={'images': list(img.shape)} )
                    assert check, 'assert check failed'
                    onnx.save(model_onnx, f)
                except Exception as e:
                    print(f' simplifier failure: {e}')
            # print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        except Exception as e:
            print(f' export failure: {e}')

It return :

/mnt/data1/download_new/DETA-master/models/deformable_detr.py:243: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
  for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
 export failure: 0INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":607, please report a bug to PyTorch. We don't have an op for aten::fill_ but it isn't a special case.  Argument types: Tensor, bool, 

Candidates:
    aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> (Tensor(a!))
    aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> (Tensor(a!))

Do you have any suggestions for this mistake? thank you！！

[SangbumChoi]

@xinlin-xiao Since Swin-L can be converted into ONNX I think overall the answer might be yes