Can not export to ONNX model. AttributeError: 'NoneType' object has no attribute 'shape' about yolort

[SangbumChoi]

@zhiqwang @SharynHu is right.
instead of using

yolort/yolort/runtime/ort_helper.py

Line 236 in 672ae82

self.input_sample,

try to use tuple with an empty target None

        with BytesIO() as f:
            torch.onnx.export(
                self.model,
                (self.input_sample, None),
                f,
                do_constant_folding=True,
                opset_version=self._opset_version,
                input_names=self.input_names,
                output_names=self.output_names,
                dynamic_axes=self.dynamic_axes,
                **kwargs,
            )

[SharynHu]

I reproduced this problem on my ubuntu platform with torch2.0cu117. Everything seems fine till torch.onnx.export runs the function _decide_input_format(model, args) internally. This function will change the passedargs parameter, appending a None to its end. I think it intends to match the input,target signature in the YOLO5:forward method. Since target is not provided explicitly, it adds an None to the input. However, when farwarding, that added None is not interpreted as the second parameter target but the second element of input, which produces this bug.

[zhiqwang]

Sorry for the delay in replying to you @mr-mainak , The whole workflow looks fine, the onnx we export now is too dynamic to be used by opencv.dnn, and perhaps we should need to trade off some dynamics to guarantee better generalizability.

[Wikidepia]

I got the exact same error, it will work with batch_size != 1

[willjoy]

I got the exact same error, it will work with batch_size != 1

You are right. I just tried with batch_size != 1, it worked. But still wonder if the bug can be fixed later on.

[zhiqwang]

Sorry @willjoy and @Wikidepia , I've missed this tickets, I can reproduce this problem locally. And seems that this is caused by the newer version of PyTorch (1.11-1.13), I only test this function at PyTorch 1.9-1.10, we need to fix this problem.

[mr-mainak]

However while transforming such output is coming:

/home/mainak/ms/python/onnx_env/lib/python3.8/site-packages/yolort/models/transform.py:282: 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).
  img_h, img_w = _get_shape_onnx(img)
/home/mainak/ms/python/onnx_env/lib/python3.8/site-packages/yolort/models/anchor_utils.py:46: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  anchors = torch.as_tensor(self.anchor_grids, dtype=torch.float32, device=device).to(dtype=dtype)
/home/mainak/ms/python/onnx_env/lib/python3.8/site-packages/yolort/models/anchor_utils.py:47: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  strides = torch.as_tensor(self.strides, dtype=torch.float32, device=device).to(dtype=dtype)
/home/mainak/ms/python/onnx_env/lib/python3.8/site-packages/yolort/models/box_head.py:406: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  strides = torch.as_tensor(self.strides, dtype=torch.float32, device=device).to(dtype=dtype)
/home/mainak/ms/python/onnx_env/lib/python3.8/site-packages/yolort/models/box_head.py:337: 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 head_output, grid, shift, stride in zip(head_outputs, grids, shifts, strides):

Is this normal? Then when I'm loading with opencv dnn readNetFromONNX module the following error is coming:

[ERROR:[email protected]] global onnx_importer.cpp:2588 parseShape DNN/ONNX(Shape): dynamic 'zero' shapes are not supported, input image [ 3 0 0 ]
[ERROR:[email protected]] global onnx_importer.cpp:1061 handleNode DNN/ONNX: ERROR during processing node with 1 inputs and 1 outputs: [Shape]:(onnx_node!/Shape) from domain='ai.onnx'

I am having best.pt which is obtained from training(best mAP). In order to convert this weights I'm changing the path_ultralytics_yolov5 in helper.py. Is this the correct way because when I open the onnx in Netron it's architecture is different.