[>>>
] 124/1991, 0.7 task/s, elapsed: 182s, ETA:
2736sTraceback (most recent call last):


 "./tools/test.py", line 153, in <module>

main()


 "./tools/test.py", line 138, in main

args.gpu_collect, efficient_test)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/apis/test.py", line 140, in multi_gpu_test

result = model(return_loss=False, rescale=True, **data)


 "/root/.local/conda/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl

result = self.forward(*input, **kwargs)


 "/root/.local/conda/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 705, in forward

output = self.module(*inputs[0], **kwargs[0])


 "/root/.local/conda/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl

result = self.forward(*input, **kwargs)


 "/root/workspace/PANDA/mmcv/mmcv/runner/fp16_utils.py", line 84, in new_func

return old_func(*args, **kwargs)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/models/segmentors/base.py", line 124, in forward

return self.forward_test(img, img_metas, **kwargs)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/models/segmentors/base.py", line 106, in forward_test

return self.simple_test(imgs[0], img_metas[0], **kwargs)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py", line 265, in simple_test

seg_logit = self.inference(img, img_meta, rescale)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py", line 250, in inference

seg_logit = self.whole_inference(img, img_meta, rescale)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py", line 224, in whole_inference

warning=False)


 "/root/workspace/semantic_seg/mmsegmentation/mmseg/ops/wrappers.py", line 29, in resize

return F.interpolate(input, size, scale_factor, mode, align_corners)


 "/root/.local/conda/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/functional.py", line 3554, in interpolate

return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)
RuntimeError: CUDA out of memory. Tried to allocate 23.53 GiB (GPU 0; 31.75 GiB total capacity; 879.06 MiB already allocated; 14.11 GiB free; 2.40 GiB reserved in total by PyTorch)

Traceback (most recent call last):


 "pytorch2onnx.py", line 154, in <module>

verify=args.verify)


 "pytorch2onnx.py", line 76, in pytorch2onnx

opset_version=12) #opset_version


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/__init__.py", line 208, in export

custom_opsets, enable_onnx_checker, use_external_data_format)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/utils.py", line 92, in export

use_external_data_format=use_external_data_format)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/utils.py", line 530, in _export

fixed_batch_size=fixed_batch_size)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/utils.py", line 384, in _model_to_graph

fixed_batch_size=fixed_batch_size, params_dict=params_dict)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/utils.py", line 188, in _optimize_graph

graph = torch._C._jit_pass_onnx(graph, operator_export_type)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/__init__.py", line 241, in _run_symbolic_function

return utils._run_symbolic_function(*args, **kwargs)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/utils.py", line 791, in _run_symbolic_function

return symbolic_fn(g, *inputs, **attrs)


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/symbolic_helper.py", line 128, in wrapper

args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)]


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/symbolic_helper.py", line 128, in <listcomp>

args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)]


 "/data1/control/software/anaconda3.8/envs/open-mmlab/lib/python3.7/site-packages/torch/onnx/symbolic_helper.py", line 81, in _parse_arg

"', since it's not constant, please try to make "
RuntimeError: Failed to export an ONNX attribute 'onnx::Gather', since it's not constant, please try to make things (e.g., kernel size) static if possible
