rock-100 / monodet Goto Github PK

How can we obtained the BEV?

I have installed the dependencies and downloaded dateset as explained in README.md
While executing the main.py as
./main.py --config-file config/MonoRCNN_KITTI.yaml --num-gpus 1 --resume --eval-only
and by setting the VISUALIZE to True.
I got output results in /output/evaluation/test/visualization. However, the bounding boxes in output images are completely random and incorrect, program doesn't seem to work correctly.

I don't know how I should approach troubleshooting this issue.
My Environment.
CUDA 11.0
Python 3.8.5
Torch 1.7.1
I installed Detectrone2 from its official page.

Edit:
This part of command line output seems to be the problem.

[05/09 02:15:03 d2.checkpoint.c2_model_loading]: Following weights matched with submodule backbone.bottom_up:

WARNING [05/09 02:15:04 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint:

backbone.bottom_up.res3.0.conv2_offset.{bias, weight}
backbone.bottom_up.res3.1.conv2_offset.{bias, weight}
backbone.bottom_up.res3.2.conv2_offset.{bias, weight}
backbone.bottom_up.res3.3.conv2_offset.{bias, weight}
backbone.bottom_up.res4.0.conv2_offset.{bias, weight}
backbone.bottom_up.res4.1.conv2_offset.{bias, weight}
backbone.bottom_up.res4.2.conv2_offset.{bias, weight}
backbone.bottom_up.res4.3.conv2_offset.{bias, weight}
backbone.bottom_up.res4.4.conv2_offset.{bias, weight}
backbone.bottom_up.res4.5.conv2_offset.{bias, weight}
backbone.bottom_up.res5.0.conv2_offset.{bias, weight}
backbone.bottom_up.res5.1.conv2_offset.{bias, weight}
backbone.bottom_up.res5.2.conv2_offset.{bias, weight}
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
backbone.fpn_lateral4.{bias, weight}
backbone.fpn_lateral5.{bias, weight}
backbone.fpn_output2.{bias, weight}
backbone.fpn_output3.{bias, weight}
backbone.fpn_output4.{bias, weight}
backbone.fpn_output5.{bias, weight}
proposal_generator.rpn_head.anchor_deltas.{bias, weight}
proposal_generator.rpn_head.conv.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
roi_heads.att_head.dim_layer.{bias, weight}
roi_heads.att_head.fc1.{bias, weight}
roi_heads.att_head.fc2.{bias, weight}
roi_heads.att_head.kpt_layer.{bias, weight}
roi_heads.att_head.yaw_layer.{bias, weight}
roi_heads.box_head.fc1.{bias, weight}
roi_heads.box_head.fc2.{bias, weight}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.dis_head.H_layer.{bias, weight}
roi_heads.dis_head.fc1.{bias, weight}
roi_heads.dis_head.fc2.{bias, weight}
roi_heads.dis_head.hrec_layer.{bias, weight}

WARNING [05/09 02:15:04 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model:

fc1000.{bias, weight}
stem.conv1.bias

[05/09 02:15:04 d2.MonoDet]: Loaded 3769 images in COCO format from ../KITTI/val1/KITTI_val1_val.json

While starting training. The code throws following error.
I guess this is because of some mismatch between model parameter and training labels. Reference
Has this problem been encountered before? Any insight would be helpful.

[05/10 01:19:15 d2.MonoDet]: Loaded 3712 images in COCO format from ../KITTI/val1/KITTI_val1_train.json
[05/10 01:19:22 d2.MonoDet]: Positive labels: ['Car']
[05/10 01:19:22 d2.MonoDet]: Positive samples: 10081
[05/10 01:19:22 d2.data.build]: Removed 645 images with no usable annotations. 3067 images left.
[05/10 01:19:22 d2.data.common]: Serializing 3067 elements to byte tensors and concatenating them all ...
[05/10 01:19:22 d2.data.common]: Serialized dataset takes 7.84 MiB
[05/10 01:19:22 d2.data.build]: Using training sampler TrainingSampler
[05/10 01:19:22 detectron2]: Starting training from iteration 0
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [102,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [109,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [34,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [35,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [42,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [49,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [50,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [57,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [4,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [5,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [12,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [19,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [20,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [27,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [64,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [65,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [72,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [79,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [87,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:142: operator(): block: [0,0,0], thread: [94,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
Traceback (most recent call last):
  File "./main.py", line 179, in <module>
    launch(
  File "/workspace/detectron2/detectron2/engine/launch.py", line 57, in launch
    main_func(*args)
  File "./main.py", line 174, in main
    do_train(cfg, model, resume=args.resume)
  File "./main.py", line 108, in do_train
    loss_dict = model(data)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/workspace/detectron2/detectron2/modeling/meta_arch/rcnn.py", line 117, in forward
    proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/workspace/detectron2/detectron2/modeling/proposal_generator/rpn.py", line 425, in forward
    gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances)
  File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/detectron2/detectron2/modeling/proposal_generator/rpn.py", line 289, in label_and_sample_anchors
    matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix)
  File "/workspace/detectron2/detectron2/utils/memory.py", line 72, in wrapped
    return func(*args, **kwargs)
  File "/workspace/detectron2/detectron2/modeling/matcher.py", line 88, in __call__
    assert torch.all(match_quality_matrix >= 0)
RuntimeError: CUDA error: device-side assert triggered

box.height : 2d regression box ( height )
predicted height : projected visual height in the image plane

how to solve this problem? thanks a lot!

[05/15 20:16:43 d2.MonoDet]: Total inference pure compute time: 0:01:20 (0.021297 s / img per device, on 1 devices)
Traceback (most recent call last):
File "/home/cym/MonoDet/projects/MonoRCNN/./main.py", line 179, in
launch(
File "/home/cym/detectron2/detectron2/engine/launch.py", line 84, in launch
main_func(*args)
File "/home/cym/MonoDet/projects/MonoRCNN/./main.py", line 166, in main
do_test(cfg, model)
File "/home/cym/MonoDet/projects/MonoRCNN/./main.py", line 69, in do_test
evaluate_on_dataset(cfg, result_dir, iteration)
File "/home/cym/MonoDet/projects/MonoRCNN/lib/train_test.py", line 474, in evaluate_on_dataset
p = subprocess.Popen([script, result_dir.replace('/data', '')], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
File "/home/cym/.conda/envs/py/lib/python3.9/subprocess.py", line 951, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "/home/cym/.conda/envs/py/lib/python3.9/subprocess.py", line 1821, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
PermissionError: [Errno 13] Permission denied: '/media/bata/KITTI/val1/cpp/evaluate_object'

Hi,

it seems as if only the readme has been updated recently. How can I run MonoRCNN++ instead of the old MonoRCNN?

Thanks a lot!

Dear Monodet team,

Thank you for your work and I hope this message finds you well. I am writing to report an issue that I encountered while working with Monodet.
I have created a new environment with Python 3.6, PyTorch 1.5.0 and Detectron2 0.1.3 as per the instructions in the README file. However, I am still facing this issue.
from detectron2 import _C
ImportError: cannot import name '_C'
also i have used another method used your folder detectron2 but the same problem
Could you please help me with this problem? Any help or guidance would be greatly appreciated.

Thank you for your time and consideration.

Best regards,

I could get the expected results by testing the model on validation set of KITTI dataset.
How can I use the MonoRCNN for my own video?

As MonoRCNN is built upon a model from detectron, some of the model parameters don't seem to be recognized by Detectron API when I use demo.py with MonoRCNN Configuration.
@Rock-100

The code only provides the preparation method of the KITTI datasets.

Can the author introduce how to prepare the training set and validation set data for the Waymo datasets?

Thank you ！

这里有什么依据吗？ 感谢感谢

Hello, now I have successfully conducted experiments on the waymo dataset following data_setup

But the experimental results in the paper cannot be reproduced.

For some metrics, it is impossible to achieve the values stated in the paper.

This is the training environment I use:

In addition, I tested the detection results on waymo you provided in the code：
Waymo

It is also impossible to reproduce the results in the paper.

I would like to know if there is an error in the data you uploaded? Can you provide the evaluation script you used to reproduce the results in the paper?

Thank you！

While I have trained a custom model with the training command below, I want to get the AP of my testing dataset. How to run the code to get the AP with my own custom dataset as KITTI format ?

Thanks