AssertionError: max_shape should be tensor of (h,w) for onnx for FCOS model

[sarmientoj24]

I ran this command to convert the model I trained

python mmdeploy/tools/deploy.py mmdeploy/configs/mmdet/detection/detection_onnxruntime_static.py configs/cfg.py checkpoint.pt 000544.jpg --show --log-level INFO --dump-info

Error

Process Process-2:
Traceback (most recent call last):
  File "useranaconda3/envs/yl/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "useranaconda3/envs/yl/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 105, in __call__
    ret = func(*args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/pytorch2onnx.py", line 97, in torch2onnx
    keep_initializers_as_inputs=keep_initializers_as_inputs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 354, in _wrap
    return self.call_function(func_name_, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 324, in call_function
    return self.call_function_local(func_name, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 273, in call_function_local
    return pipe_caller(*args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 105, in __call__
    ret = func(*args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/onnx/export.py", line 131, in export
    verbose=verbose)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/__init__.py", line 320, in export
    custom_opsets, enable_onnx_checker, use_external_data_format)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/utils.py", line 111, in export
    custom_opsets=custom_opsets, use_external_data_format=use_external_data_format)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/utils.py", line 729, in _export
    dynamic_axes=dynamic_axes)
  File "usermmdetection/mmdeploy/mmdeploy/core/rewriters/rewriter_utils.py", line 371, in wrapper
    return self.func(self, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/onnx/optimizer.py", line 10, in model_to_graph__custom_optimizer
    graph, params_dict, torch_out = ctx.origin_func(*args, **kwargs)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/utils.py", line 493, in _model_to_graph
    graph, params, torch_out, module = _create_jit_graph(model, args)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/utils.py", line 437, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/onnx/utils.py", line 388, in _trace_and_get_graph_from_model
    torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/jit/_trace.py", line 1166, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/jit/_trace.py", line 132, in forward
    self._force_outplace,
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "useranaconda3/envs/yl/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1090, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/core/rewriters/rewriter_utils.py", line 371, in wrapper
    return self.func(self, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/base.py", line 69, in base_detector__forward
    return __forward_impl(ctx, self, img, img_metas=img_metas, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/core/optimizers/function_marker.py", line 261, in g
    rets = f(*args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/base.py", line 28, in __forward_impl
    return self.simple_test(img, img_metas, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/core/rewriters/rewriter_utils.py", line 371, in wrapper
    return self.func(self, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/single_stage.py", line 28, in single_stage_detector__simple_test
    return self.bbox_head.simple_test(feat, img_metas, **kwargs)
  File "usermmdetection/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 360, in simple_test
    return self.simple_test_bboxes(feats, img_metas, rescale=rescale)
  File "usermmdetection/mmdetection/mmdet/models/dense_heads/dense_test_mixins.py", line 38, in simple_test_bboxes
    *outs, img_metas=img_metas, rescale=rescale)
  File "usermmdetection/mmdeploy/mmdeploy/core/rewriters/rewriter_utils.py", line 371, in wrapper
    return self.func(self, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/base_dense_head.py", line 149, in base_dense_head__get_bbox
    batch_priors, batch_mlvl_bboxes_pred, max_shape=img_shape)
  File "usermmdetection/mmdetection/mmdet/core/bbox/coder/distance_point_bbox_coder.py", line 63, in decode
    return distance2bbox(points, pred_bboxes, max_shape)
  File "usermmdetection/mmdetection/mmdet/core/bbox/transforms.py", line 170, in distance2bbox
    x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape)
  File "usermmdetection/mmdetection/mmdet/core/export/onnx_helper.py", line 24, in dynamic_clip_for_onnx
    torch.Tensor), '`max_shape` should be tensor of (h,w) for onnx'
AssertionError: `max_shape` should be tensor of (h,w) for onnx
Traceback (most recent call last):
  File "mmdeploy/tools/deploy.py", line 366, in <module>
    main()
  File "mmdeploy/tools/deploy.py", line 136, in main
    device=args.device)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 354, in _wrap
    return self.call_function(func_name_, *args, **kwargs)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 322, in call_function
    return self.get_result_sync(call_id)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 303, in get_result_sync
    ret = self.get_caller(func_name).pop_mp_output(call_id)
  File "usermmdetection/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 80, in pop_mp_output
    f'`{self._func_name}` with Call id: {call_id} failed.'
AssertionError: `mmdeploy.apis.pytorch2onnx.torch2onnx` with Call id: 0 failed.

[hanrui1sensetime]

Now you can use detection_onnxruntime_dynamic.py instead of detection_onnxruntime_static.py. And later we will fix the detection_onnxruntime_dynamic.py`