~/projects/condlanenet/conditional-lane-detection$ python tools/condlanenet/culane/test_culane.py Traceback (most recent call last): File "tools/condlanenet/culane/test_culane.py", line 21, in from mmdet.models.detectors.condlanenet import CondLanePostProcessor File "/home/yjx/projects/condlanenet/conditional-lane-detection/mmdet/models/init.py", line 1, in from .backbones import * # noqa: F401,F403 File "/home/yjx/projects/condlanenet/conditional-lane-detection/mmdet/models/backbones/init.py", line 1, in from .hrnet import HRNet File "/home/yjx/projects/condlanenet/conditional-lane-detection/mmdet/models/backbones/hrnet.py", line 7, in from mmdet.utils import get_root_logger ImportError: cannot import name 'get_root_logger' from 'mmdet.utils' (/home/yjx/projects/condlanenet/conditional-lane-detection/mmdet/utils/init.py)

Hi, thank you for your great work! I have run it on my own datasets (different view with culane), and it predicts very well. but I have problem in how to export .pth to onnx.

I have tried : python tools/pytorch2onnx.py configs/condlanenet/culane/culane_small_test.py ./culane_small.pth --out ./condlanenet.onnx --shape 320 800

and it did save a ONNX file(44.7MB) in my path. but when I open ONNX，I don't understand the output.

can anyone here help me out? thanks!

I tested the model provided by the authors and the results were a bit different from what the authors described. Do you have the same experience? model: large. result: [{"name":"Accuracy","value":0.9636818107904509,"order":"desc"},{"name":"FP","value":0.021141864366163457,"order":"asc"},{"name":"FN","value":0.03633477114785525,"order":"asc"},{"name":"F1","value":0.9712022686934042,"order":"asc"}]

Hi, great work !! I am trying to run inference on some of my own images, but the output is incorrect. Here are the steps I followed:-

1. Loaded the small model trained on curvelanes dataset
2. Resized the image to (800,320) [I want the output size to be (800,320) only].
3. Got the predictions
4. Supplied the predictions to the appropriate post-processor with downscale = 8.
5. Supplied the output of the post-processor to adjust_results function with the following parameters :- crop_shape = (320,800) img_shape = (320,800) crop_offset = (0,0) ori_shape = (320,800)
6. The output I am getting is this - https://drive.google.com/file/d/1BwTaZ1I5VVWAEoQKjx2q6-Wg8UFzv6fT/view?usp=sharing

It would be great if you can tell me where I am going wrong.

Thank You

Exception has occurred: KeyError 'CurvelanesDataset is not in the dataset registry' File "/home/rx/ADAS/Lane/conditional-lane-detection/mmdetection/mmdet/datasets/builder.py", line 59, in build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/home/rx/ADAS/Lane/conditional-lane-detection/tools/condlanenet/curvelanes/test_curvelanes.py", line 218, in main dataset = build_dataset(cfg.data.test) File "/home/rx/ADAS/Lane/conditional-lane-detection/tools/condlanenet/curvelanes/test_curvelanes.py", line 248, in main() 报这个错，请帮忙看下什么原因？

Hi all,

when training the model, I get the following error when Pytorch's anomaly detection is on

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 64, 10, 25]], which is output 0 of ReluBackward0, is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!

The traceback is the following:

[W python_anomaly_mode.cpp:104] Warning: Error detected in ReluBackward0. Traceback of forward call that caused the error:
  File "tools/train.py", line 159, in <module>
    main()
  File "tools/train.py", line 155, in main
    meta=meta)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/apis/train.py", line 167, in train_detector
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/mmcv/runner/runner.py", line 383, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/mmcv/runner/runner.py", line 282, in train
    self.model, data_batch, train_mode=True, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/apis/train.py", line 74, in batch_processor
    losses = model(**data)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/models/detectors/condlanenet.py", line 327, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/models/detectors/condlanenet.py", line 344, in forward_train
    output, memory = self.neck(output)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/core/fp16/decorators.py", line 49, in new_func
    return old_func(*args, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/models/necks/trans_fpn.py", line 257, in forward
    trans_feat = self.trans_head(src[self.trans_idx])
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/models/necks/trans_fpn.py", line 153, in forward
    src = layer(src, pos.to(src.device))
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/Code/conditional-lane-detection/mmdet/models/necks/trans_fpn.py", line 105, in forward
    x = self.pre_conv(x)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/mmcv/cnn/bricks/conv_module.py", line 181, in forward
    x = self.activate(x)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/modules/activation.py", line 98, in forward
    return F.relu(input, inplace=self.inplace)
  File "/home/<user>/.conda/envs/conditional-lane-detection/lib/python3.7/site-packages/torch/nn/functional.py", line 1299, in relu
    result = torch.relu(input)
 (function _print_stack)

Anybody knows how to fix this?

请教大佬 ，用推荐的环境配置

MMCV: 0.5.6
MMDetection: 2.0.0+73c2043

train的时候没问题，但是test的时候报如下error，检查了数据集是没问题的，还可能是哪里的问题呢

(openmm) [wangjinsheng@HOST-10-198-32-69 tools]$ sh test_slurm.sh 
[                                                  ] 0/2782, elapsed: 0s, ETA:[                                                  ] 0/2782, elapsed: 0s, ETA:Traceback (most recent call last):
Traceback (most recent call last):
  File "test.py", line 149, in <module>
  File "test.py", line 149, in <module>
    main()
  File "test.py", line 127, in main
    args.show_score_thr)
  File "/mnt/lustre/wangjinsheng/project/lane-detection/conditional-lane-detection/mmdet/apis/test.py", line 28, in single_gpu_test
    main()
  File "test.py", line 127, in main
    args.show_score_thr)
  File "/mnt/lustre/wangjinsheng/project/lane-detection/conditional-lane-detection/mmdet/apis/test.py", line 28, in single_gpu_test
    img_tensor = data['img'][0]
TypeError: 'DataContainer' object is not subscriptable
    img_tensor = data['img'][0]
TypeError: 'DataContainer' object is not subscriptable

Hey! I tried to test on the tusimple large. I ran this code

python tools/condlanenet/tusimple/test_tusimple.py configs/condlanenet/tusimple/tusimple_large_test.py D:/conditional-lane-detection-master/tusimple_large.pth

and it gives error like this

File "tools/condlanenet/tusimple/test_tusimple.py", line 16, in from mmdet.datasets import build_dataloader, build_dataset File "d:\conditional-lane-detection-master\mmdetection\mmdet_init_.py", line 25, in f'MMCV=={mmcv.version} is used but incompatible. '
AssertionError: MMCV==0.5.6 is used but incompatible. Please install mmcv>=1.3.2, <=1.4.0.

What should I do?

Hi, I tried to use the given pretrained model for testing, however, I got the following results:

python tools/condlanenet/culane/test_culane.py configs/condlanenet/culane/culane_small_test.py history/official/culane_small.pth The model and loaded state dict do not match exactly

unexpected key in source state_dict: bbox_head.reg_branch.0.conv.weight, bbox_head.reg_branch.0.bn.weight, bbox_head.reg_branch.0.bn.bias, bbox_head.reg_branch.0.bn.running_mean, bbox_head.reg_branch.0.bn.running_var, bbox_head.reg_branch.0.bn.num_batches_tracked, bbox_head.reg_branch.1.conv.weight, bbox_head.reg_branch.1.bn.weight, bbox_head.reg_branch.1.bn.bias, bbox_head.reg_branch.1.bn.running_mean, bbox_head.reg_branch.1.bn.running_var, bbox_head.reg_branch.1.bn.num_batches_tracked, bbox_head.reg_branch.2.conv.weight, bbox_head.reg_branch.2.bn.weight, bbox_head.reg_branch.2.bn.bias, bbox_head.reg_branch.2.bn.running_mean, bbox_head.reg_branch.2.bn.running_var, bbox_head.reg_branch.2.bn.num_batches_tracked

completed: 0, elapsed: 0s% Is there something wrong with the pretrained models or the codes?

I am training culane_small with only two images to overfit. It's a crop-row image. But the row_loss is not going down. It's around 22. Is this normal? How can I tune to reduce the loss? Also after training, if I run culane_test, test output has no line drawn on them. Here is a ground truth image:

when l try to train my dataset（Curvelanenet), l use the instructions as follows： python tools/train.py configs/condlanenet/curvelanes/curvelanes_large_train.py then the problems occurs： RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 256, 10, 25]], which is output 0 of ReluBackward0, is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). How can l solve this problem？

Hi, the mean and std in img_norm_cfg is "mean=[75.3, 76.6, 77.6], std=[50.5, 53.8, 54.3], to_rgb=False". Why didn't you use the origin value mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True? Thank you !

Getting Error:

/content/conditional-lane-detection No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda' Traceback (most recent call last): File "tools/condlanenet/tusimple/test_tusimple.py", line 19, in from tools.condlanenet.common import tusimple_convert_formal, COLORS ModuleNotFoundError: No module named 'tools'

Any solution??

train_pipeline = [ dict(type='albumentation', pipelines=train_al_pipeline),

I can't seem to find this albumentation in MMDET,Could you please provide this one?