[Bug] RuntimeError: invalid unordered_map<K, T> key #1448
Description
Checklist
- I have searched related issues but cannot get the expected help.
- 2. I have read the FAQ documentation but cannot get the expected help.
- 3. The bug has not been fixed in the latest version.
Describe the bug
RuntimeError: invalid unordered_map<K, T> key error
Reproduction
when i run deply.py in mmdeploy-dev-1.x, report a RuntimeError: invalid unordered_map<K, T> key error,
the model is retinanet
Environment
win10 TensorRT-8.5.1.7 cuda11.7 mmdeploy-dev-1.xError traceback
Traceback (most recent call last):
File "D:\anaconda3\envs\mmcv\lib\multiprocessing\process.py", line 315, in _bootstrap
self.run()
File "D:\anaconda3\envs\mmcv\lib\multiprocessing\process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\core\pipeline_manager.py", line 107, in __call__
ret = func(*args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\pytorch2onnx.py", line 96, in torch2onnx
export(
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\core\pipeline_manager.py", line 356, in _wrap
return self.call_function(func_name_, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\core\pipeline_manager.py", line 326, in call_function
return self.call_function_local(func_name, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\core\pipeline_manager.py", line 275, in call_function_local
return pipe_caller(*args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\core\pipeline_manager.py", line 107, in __call__
ret = func(*args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\onnx\export.py", line 122, in export
torch.onnx.export(
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\onnx\utils.py", line 504, in export
_export(
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\onnx\utils.py", line 1529, in _export
graph, params_dict, torch_out = _model_to_graph(
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\core\rewriters\rewriter_utils.py", line 379, in wrapper
return self.func(self, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\apis\onnx\optimizer.py", line 10, in model_to_graph__custom_optimizer
graph, params_dict, torch_out = ctx.origin_func(*args, **kwargs)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\onnx\utils.py", line 1111, in _model_to_graph
graph, params, torch_out, module = _create_jit_graph(model, args)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\onnx\utils.py", line 987, in _create_jit_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\onnx\utils.py", line 891, in _trace_and_get_graph_from_model
trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\jit\_trace.py", line 1184, in _get_trace_graph
outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\jit\_trace.py", line 127, in forward
graph, out = torch._C._create_graph_by_tracing(
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\jit\_trace.py", line 118, in wrapper
outs.append(self.inner(*trace_inputs))
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\anaconda3\envs\mmcv\lib\site-packages\torch\nn\modules\module.py", line 1178, in _slow_forward
result = self.forward(*input, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\core\rewriters\rewriter_utils.py", line 379, in wrapper
return self.func(self, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\codebase\mmdet\models\detectors\single_stage.py", line 74, in single_stage_detector__forward
return __forward_impl(
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\core\optimizers\function_marker.py", line 261, in g
rets = f(*args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\codebase\mmdet\models\detectors\single_stage.py", line 24, in __forward_impl
output = self.bbox_head.predict(x, data_samples, rescale=False)
File "D:\anaconda3\envs\mmcv\lib\site-packages\mmdet\models\dense_heads\base_dense_head.py", line 197, in predict
predictions = self.predict_by_feat(
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\core\rewriters\rewriter_utils.py", line 379, in wrapper
return self.func(self, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\codebase\mmdet\models\dense_heads\base_dense_head.py", line 73, in base_dense_head__predict_by_feat
mlvl_priors = self.prior_generator.grid_priors(
File "D:\anaconda3\envs\mmcv\lib\site-packages\mmdet\models\task_modules\prior_generators\anchor_generator.py", line 242, in grid_priors
anchors = self.single_level_grid_priors(
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\core\rewriters\rewriter_utils.py", line 379, in wrapper
return self.func(self, *args, **kwargs)
File "e:\dnntrain\test\mmdeploy-dev-1.x\mmdeploy\codebase\mmdet\models\task_modules\prior_generators\anchor.py", line 100, in anchorgenerator__single_level_grid_priors__trt
return grid_priors_trt(base_anchors, feat_h, feat_w, stride_h, stride_w)
RuntimeError: invalid unordered_map<K, T> key