How can I get the prediction result as bounding box coordinates?

[yassiney]

Hello, @hasanirtiza
I used your suggestion in #127 but i didn't get bounding box coordinates.
For each image I need to have the prediction result as bounding box coordinates.
I will use it to test your pre-trained model on my own data (calculate average precision, recall and precision )

Thank you in advance.

[hasanirtiza]

What do you mean by, you did not get bounding box coordinates ? There were no detections on your demo images (result_demo folder was empty)? Or there were detections, but you also want to see the values of the bounding box ?

[yassiney]

I copied my images to the demo folder

I run :
cd /content/Pedestron/
conda run python tools/demo.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_16.pth.stu demo/ result_demo/

I get :
ERROR conda.cli.main_run:execute(34): Subprocess for 'conda run ['python', 'tools/demo.py', 'configs/elephant/cityperson/cascade_hrnet.py', './models_pretrained/epoch_16.pth.stu', 'demo/', 'result_demo/']' command failed. (See above for error)
demo/
['demo/crop001145.png', 'demo/crop001581.png', 'demo/person_143.png', 'demo/crop001809.png', 'demo/crop001095.png', 'demo/crop001712.png', 'demo/crop001110.png', 'demo/crop001707.png', 'demo/crop001642.png', 'demo/crop001817.png', 'demo/crop001569.png', 'demo/person_224.png', 'demo/person_and_bike_129.png', 'demo/person_395.png', 'demo/crop001579.png', 'demo/crop_000603.png', 'demo/crop001830.png', 'demo/person_175.png', 'demo/crop001842.png', 'demo/person_201.png', 'demo/person_319.png', 'demo/person_295.png', 'demo/crop001714.png', 'demo/crop001079.png', 'demo/person_259.png', 'demo/person_and_bike_016.png', 'demo/crop001144.png', 'demo/person_102.png', 'demo/crop001608.png', 'demo/crop001635.png', 'demo/crop001012.png', 'demo/person_179.png', 'demo/person_091.png', 'demo/crop001002.png', 'demo/crop001057.png', 'demo/crop001665.png', 'demo/person_178.png', 'demo/person_and_bike_118.png', 'demo/crop001526.png', 'demo/person_283.png', 'demo/crop001271.png', 'demo/person_223.png', 'demo/crop001637.png', 'demo/crop001253.png', 'demo/person_119.png', 'demo/crop001690.png', 'demo/crop001505.png', 'demo/person_348.png', 'demo/crop001694.png', 'demo/person_298.png', 'demo/person_231.png', 'demo/crop001611.png', 'demo/crop001629.png', 'demo/person_333.png', 'demo/crop001059.png', 'demo/person_177.png', 'demo/person_and_bike_010.png', 'demo/crop001037.png', 'demo/crop001264.png', 'demo/person_378.png', 'demo/crop001256.png']
The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.features.0.0.weight, backbone.features.0.1.weight, backbone.features.0.1.bias, backbone.features.0.1.running_mean, backbone.features.0.1.running_var, backbone.features.0.1.num_batches_tracked, backbone.features.1.conv.0.0.weight, backbone.features.1.conv.0.1.weight, backbone.features.1.conv.0.1.bias, backbone.features.1.conv.0.1.running_mean, backbone.features.1.conv.0.1.running_var, backbone.features.1.conv.0.1.num_batches_tracked, backbone.features.1.conv.1.weight, backbone.features.1.conv.2.weight, backbone.features.1.conv.2.bias, backbone.features.1.conv.2.running_mean, backbone.features.1.conv.2.running_var, backbone.features.1.conv.2.num_batches_tracked, backbone.features.2.conv.0.0.weight, backbone.features.2.conv.0.1.weight, backbone.features.2.conv.0.1.bias, backbone.features.2.conv.0.1.running_mean, backbone.features.2.conv.0.1.running_var, backbone.features.2.conv.0.1.num_batches_tracked, backbone.features.2.conv.1.0.weight, 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backbone.features.7.conv.3.running_var, backbone.features.7.conv.3.num_batches_tracked, backbone.features.8.conv.0.0.weight, backbone.features.8.conv.0.1.weight, backbone.features.8.conv.0.1.bias, backbone.features.8.conv.0.1.running_mean, backbone.features.8.conv.0.1.running_var, backbone.features.8.conv.0.1.num_batches_tracked, backbone.features.8.conv.1.0.weight, backbone.features.8.conv.1.1.weight, backbone.features.8.conv.1.1.bias, backbone.features.8.conv.1.1.running_mean, backbone.features.8.conv.1.1.running_var, backbone.features.8.conv.1.1.num_batches_tracked, backbone.features.8.conv.2.weight, backbone.features.8.conv.3.weight, backbone.features.8.conv.3.bias, backbone.features.8.conv.3.running_mean, backbone.features.8.conv.3.running_var, backbone.features.8.conv.3.num_batches_tracked, backbone.features.9.conv.0.0.weight, backbone.features.9.conv.0.1.weight, backbone.features.9.conv.0.1.bias, backbone.features.9.conv.0.1.running_mean, 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backbone.features.11.conv.3.running_var, backbone.features.11.conv.3.num_batches_tracked, backbone.features.12.conv.0.0.weight, backbone.features.12.conv.0.1.weight, backbone.features.12.conv.0.1.bias, backbone.features.12.conv.0.1.running_mean, backbone.features.12.conv.0.1.running_var, backbone.features.12.conv.0.1.num_batches_tracked, backbone.features.12.conv.1.0.weight, backbone.features.12.conv.1.1.weight, backbone.features.12.conv.1.1.bias, backbone.features.12.conv.1.1.running_mean, backbone.features.12.conv.1.1.running_var, backbone.features.12.conv.1.1.num_batches_tracked, backbone.features.12.conv.2.weight, backbone.features.12.conv.3.weight, backbone.features.12.conv.3.bias, backbone.features.12.conv.3.running_mean, backbone.features.12.conv.3.running_var, backbone.features.12.conv.3.num_batches_tracked, backbone.features.13.conv.0.0.weight, backbone.features.13.conv.0.1.weight, backbone.features.13.conv.0.1.bias, backbone.features.13.conv.0.1.running_mean, 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backbone.features.15.conv.3.running_var, backbone.features.15.conv.3.num_batches_tracked, backbone.features.16.conv.0.0.weight, backbone.features.16.conv.0.1.weight, backbone.features.16.conv.0.1.bias, backbone.features.16.conv.0.1.running_mean, backbone.features.16.conv.0.1.running_var, backbone.features.16.conv.0.1.num_batches_tracked, backbone.features.16.conv.1.0.weight, backbone.features.16.conv.1.1.weight, backbone.features.16.conv.1.1.bias, backbone.features.16.conv.1.1.running_mean, backbone.features.16.conv.1.1.running_var, backbone.features.16.conv.1.1.num_batches_tracked, backbone.features.16.conv.2.weight, backbone.features.16.conv.3.weight, backbone.features.16.conv.3.bias, backbone.features.16.conv.3.running_mean, backbone.features.16.conv.3.running_var, backbone.features.16.conv.3.num_batches_tracked, backbone.features.17.conv.0.0.weight, backbone.features.17.conv.0.1.weight, backbone.features.17.conv.0.1.bias, backbone.features.17.conv.0.1.running_mean, 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neck.lateral_convs.3.conv.weight, neck.lateral_convs.3.conv.bias

missing keys in source state_dict: backbone.stage2.0.branches.1.3.bn2.bias, backbone.stage3.1.branches.2.1.bn1.running_var, backbone.stage4.1.branches.0.1.bn1.bias, backbone.bn1.weight, backbone.stage3.2.branches.2.3.bn1.running_mean, backbone.stage3.2.branches.0.3.conv2.weight, backbone.stage3.3.branches.1.0.bn1.weight, backbone.stage4.2.branches.1.1.bn1.bias, backbone.stage3.3.branches.1.3.bn1.weight, backbone.stage4.1.branches.0.1.bn1.running_var, backbone.stage3.3.fuse_layers.1.2.0.weight, backbone.stage4.0.branches.2.3.bn1.bias, backbone.stage4.2.branches.0.2.bn2.weight, backbone.layer1.0.bn2.running_mean, backbone.stage3.3.branches.2.1.bn1.running_var, backbone.stage3.1.branches.0.0.bn1.running_var, backbone.stage2.0.fuse_layers.0.1.1.bias, backbone.stage4.0.branches.0.0.bn1.bias, backbone.stage4.2.fuse_layers.3.0.1.0.weight, backbone.stage2.0.branches.0.3.bn1.running_mean, backbone.stage3.0.branches.1.0.bn2.bias, backbone.stage4.0.fuse_layers.1.0.0.1.bias, 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[ ] 0/61, elapsed: 0s, ETA:
/content/Pedestron/tools/../mmdet/apis/inference.py:39: UserWarning: Class names are not saved in the checkpoint's meta data, use COCO classes by default.
warnings.warn('Class names are not saved in the checkpoint's '
/usr/local/lib/python3.7/site-packages/torch/nn/functional.py:2479: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
Traceback (most recent call last):
File "tools/demo.py", line 67, in
run_detector_on_dataset()
File "tools/demo.py", line 63, in run_detector_on_dataset
detections = mock_detector(model, im, output_dir)
File "tools/demo.py", line 37, in mock_detector
results = inference_detector(model, image)
File "/content/Pedestron/tools/../mmdet/apis/inference.py", line 66, in inference_detector
return _inference_single(model, imgs, img_transform, device)
File "/content/Pedestron/tools/../mmdet/apis/inference.py", line 93, in _inference_single
result = model(return_loss=False, rescale=True, **data)
File "/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "/content/Pedestron/tools/../mmdet/core/fp16/decorators.py", line 49, in new_func
return old_func(*args, **kwargs)
File "/content/Pedestron/tools/../mmdet/models/detectors/base.py", line 88, in forward
return self.forward_test(img, img_meta, **kwargs)
File "/content/Pedestron/tools/../mmdet/models/detectors/base.py", line 79, in forward_test
return self.simple_test(imgs[0], img_metas[0], **kwargs)
File "/content/Pedestron/tools/../mmdet/models/detectors/cascade_rcnn.py", line 243, in simple_test
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
File "/content/Pedestron/tools/../mmdet/models/detectors/test_mixins.py", line 10, in simple_test_rpn
proposal_list = self.rpn_head.get_bboxes(proposal_inputs)
File "/content/Pedestron/tools/../mmdet/core/fp16/decorators.py", line 127, in new_func
return old_func(args, **kwargs)
File "/content/Pedestron/tools/../mmdet/models/anchor_heads/anchor_head.py", line 221, in get_bboxes
scale_factor, cfg, rescale)
File "/content/Pedestron/tools/../mmdet/models/anchor_heads/rpn_head.py", line 92, in get_bboxes_single
proposals, _ = nms(proposals, cfg.nms_thr)
File "/content/Pedestron/tools/../mmdet/ops/nms/nms_wrapper.py", line 43, in nms
inds = nms_cuda.nms(dets_th, iou_thr)
RuntimeError: CUDA error: no kernel image is available for execution on the device (launch_kernel at /opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/cuda/Loops.cuh:102)
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x47 (0x7f67ac410e37 in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: void at::native::gpu_index_kernel<__nv_dl_wrapper_t<__nv_dl_tag<void ()(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef), &(void at::native::index_kernel_impl<at::native::OpaqueType<8> >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)), 1u>> >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef, __nv_dl_wrapper_t<__nv_dl_tag<void ()(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef), &(void at::native::index_kernel_impl<at::native::OpaqueType<8> >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)), 1u>> const&) + 0x79f (0x7f67b38bdb0f in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: + 0x54ce442 (0x7f67b38b7442 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: + 0x54ce7a8 (0x7f67b38b77a8 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: + 0x16293bb (0x7f67afa123bb in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #5: at::native::index(at::Tensor const&, c10::ArrayRefat::Tensor) + 0x417 (0x7f67afa0f887 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #6: at::TypeDefault::index(at::Tensor const&, c10::ArrayRefat::Tensor) + 0x74 (0x7f67afefdbc4 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #7: torch::autograd::VariableType::index(at::Tensor const&, c10::ArrayRefat::Tensor) + 0x836 (0x7f67b176e1d6 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #8: at::Tensor::index(c10::ArrayRefat::Tensor) const + 0x70 (0x7f67b159a840 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #9: nms_cuda(at::Tensor, float) + 0x79a (0x7f6793f64110 in /content/Pedestron/tools/../mmdet/ops/nms/nms_cuda.cpython-37m-x86_64-linux-gnu.so)
frame #10: nms(at::Tensor const&, float) + 0x129 (0x7f6793f53a99 in /content/Pedestron/tools/../mmdet/ops/nms/nms_cuda.cpython-37m-x86_64-linux-gnu.so)
frame #11: + 0x28f38 (0x7f6793f61f38 in /content/Pedestron/tools/../mmdet/ops/nms/nms_cuda.cpython-37m-x86_64-linux-gnu.so)
frame #12: + 0x25b0f (0x7f6793f5eb0f in /content/Pedestron/tools/../mmdet/ops/nms/nms_cuda.cpython-37m-x86_64-linux-gnu.so)

---

CalledProcessError Traceback (most recent call last)
in ()
----> 1 get_ipython().run_cell_magic('shell', '', '\ncd /content/Pedestron/\nconda run python tools/demo.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_16.pth.stu demo/ result_demo/')

2 frames
/usr/local/lib/python3.7/dist-packages/google/colab/_system_commands.py in check_returncode(self)
137 if self.returncode:
138 raise subprocess.CalledProcessError(
--> 139 returncode=self.returncode, cmd=self.args, output=self.output)
140
141 def repr_pretty(self, p, cycle): # pylint:disable=unused-argument

CalledProcessError: Command '
cd /content/Pedestron/
conda run python tools/demo.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_16.pth.stu demo/ result_demo/' returned non-zero exit status 1.