Use correct device for prior generation

[jakubhejhal]

Motivation

I was trying to export RTMDet model to ONNX using the torch2onnx function:

mmdeploy/mmdeploy/apis/pytorch2onnx.py

Line 11 in bc75c9d

def torch2onnx(img: Any,

Which has a device parameter. But even though I set it to cpu, I was still getting cuda errors:

Process Process-2:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/core/pipeline_manager.py", line 107, in __call__
    ret = func(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/pytorch2onnx.py", line 99, in torch2onnx
    export(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/core/pipeline_manager.py", line 356, in _wrap
    return self.call_function(func_name_, *args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/core/pipeline_manager.py", line 326, in call_function
    return self.call_function_local(func_name, *args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/core/pipeline_manager.py", line 275, in call_function_local
    return pipe_caller(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/core/pipeline_manager.py", line 107, in __call__
    ret = func(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/onnx/export.py", line 138, in export
    torch.onnx.export(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/onnx/utils.py", line 506, in export
    _export(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/onnx/utils.py", line 1548, in _export
    graph, params_dict, torch_out = _model_to_graph(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/onnx/optimizer.py", line 27, in model_to_graph__custom_optimizer
    graph, params_dict, torch_out = ctx.origin_func(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/onnx/utils.py", line 1113, in _model_to_graph
    graph, params, torch_out, module = _create_jit_graph(model, args)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/onnx/utils.py", line 989, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/onnx/utils.py", line 893, in _trace_and_get_graph_from_model
    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/jit/_trace.py", line 1268, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/jit/_trace.py", line 127, in forward
    graph, out = torch._C._create_graph_by_tracing(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1488, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/apis/onnx/export.py", line 123, in wrapper
    return forward(*arg, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/codebase/mmdet/models/detectors/single_stage.py", line 85, in single_stage_detector__forward
    return __forward_impl(self, batch_inputs, data_samples=data_samples)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/core/optimizers/function_marker.py", line 266, in g
    rets = f(*args, **kwargs)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/codebase/mmdet/models/detectors/single_stage.py", line 23, in __forward_impl
    output = self.bbox_head.predict(x, data_samples, rescale=False)
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 197, in predict
    predictions = self.predict_by_feat(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 98, in rtmdet_ins_head__predict_by_feat
    return _nms_with_mask_static(self, priors, bboxes, scores,
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 151, in _nms_with_mask_static
    mask_logits = _mask_predict_by_feat_single(self, mask_feats, kernels,
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 165, in _mask_predict_by_feat_single
    coord = self.prior_generator.single_level_grid_priors(
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/mmdet/models/task_modules/prior_generators/point_generator.py", line 206, in single_level_grid_priors
    shift_x = (torch.arange(0, feat_w, device=device) +
  File "/home/kubik/work/.venv/lib/python3.10/site-packages/torch/cuda/__init__.py", line 247, in _lazy_init
    torch._C._cuda_init()
RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx

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[Priyanshu88]

Hi @jakubhejhal, I am facing below problem with rtmdet-ins-head. Any suggestion on how to resolve this?

    nms_with_mask_static_fn = mmdeploy_codebase_mmdet_models_dense_heads_rtmdet_ins_head_nms_with_mask_static_fn(bbox_head, cat_17, stack_3, sigmoid, cat_16, bbox_head_mask_head_projection, 100, 0.6, 0.05, 1000, 100, 0.5);  bbox_head = cat_17 = stack_3 = sigmoid = cat_16 = bbox_head_mask_head_projection = None
  File "/opt/conda/lib/python3.9/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 42, in nms_with_mask_static_fn
    mask_logits = _mask_predict_by_feat_single(self, mask_feats, kernels,
  File "/opt/conda/lib/python3.9/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 215, in _mask_predict_by_feat_single
    return mask_predict_by_feat_single_fn(self, mask_feat, kernels,priors)
  File "/opt/conda/lib/python3.9/site-packages/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 57, in mask_predict_by_feat_single_fn
    coord = self.prior_generator.single_level_grid_priors(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1265, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".forma