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herehttps://drive.google.com/drive/folders/1gCOdkzwMQ-Gvi_E9osiWoQFVQOJo2eKs?usp=sharing

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Loading torch model /tmp/models/Ketchup.pth /usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( /usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum orNonefor 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passingweights=None`. warnings.warn(msg) Model loaded in 2.19 seconds Traceback (most recent call last): File "/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py", line 230, in main(args) File "/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py", line 211, in main save_onnx(net, x, output_file, args.input_name, args.output_name) File "/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py", line 168, in save_onnx torch.onnx.export( File "/usr/local/lib/python3.8/dist-packages/torch/onnx/init.py", line 350, in export return utils.export( File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 163, in export _export( File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 1074, in _export graph, params_dict, torch_out = _model_to_graph( File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 727, in _model_to_graph graph, params, torch_out, module = _create_jit_graph(model, args) File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 602, in _create_jit_graph graph, torch_out = _trace_and_get_graph_from_model(model, args) File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 517, in _trace_and_get_graph_from_model trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph( File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 1175, in _get_trace_graph outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(*input, kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 127, in forward graph, out = torch._C._create_graph_by_tracing( File "/usr/local/lib/python3.8/dist-packages/torch/jit/_trace.py", line 118, in wrapper outs.append(self.inner(trace_inputs)) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(input, kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1118, in _slow_forward result = self.forward(*input, kwargs) File "/workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py", line 94, in forward out4_2 = self.m4_2(out4) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(*input, kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1118, in _slow_forward result = self.forward(*input, *kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py", line 139, in forward input = module(input) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(input, kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1118, in _slow_forward result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 457, in forward return self._conv_forward(input, self.weight, self.bias) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 453, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.92 GiB total capacity; 926.51 MiB already allocated; 22.19 MiB free; 960.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF'