Can you provide more details about how to run yours project.

issue

We ran python scripts/draw_odometry.py with the pre-trained model, kitti_raw_road.pth on the "KITTI Odometry" dataset.
But, the resulting trajectory is different from yours.
Both the evaluated values of RMSE, rotation error and the estimated values for the trajectory are different from the results in your paper(https://arxiv.org/pdf/2207.07895.pdf).

・The result in the paper
According to Table4
Average sequence translation RMSE (%): 4.57
Average sequence rotation error (deg/100m): 2.94
trajectry : Fig. 7

・Our result
Average sequence translation RMSE (%): 56.6012
Average sequence rotation error (deg/m): 0.4300

Runtime environment

azureml 1.44.0

Reproduction procedures

1. At draw_odometry.py:L28, KITTIOdomDataset argument "type" is now "static".
2. I created test_files_07.txt with 07/road_dense128/000000.png to 07/road_dense128/001100.png and placed it in JPerceiver/mono/datasets/splits/odometry/.
3. I set "~/<my_directory>/kitti_odometry_road.pth" in the path of model_path.
4. I set 7 in sequence_id.

our puopose

How to get the results of your paper ?

Hi,

I am confused that which config file I should choose to obtain the pretrained models. Could you please provide scripts for training and evaluating these models?

Besides, I have some questions about the setups in the configuration file:
(1) what is the gt depth file? Does it correspond to the validation set of kitti odometry?

（2）Although loss_sum is set to 3, loss_weightS is missing in the configuration file.

Hi.
The agroverse 1.0 is deprecated and is now replaced with agroverse 1.1.
Do you have any plans to make modification?

a tutorial of Nuscenes

Really thanks!

Hello, nice work and thanks for the releasing source code!

As there was same issue before.. i am confused that which config file I should choose to obtain the pretrained models. Could you please provide scripts for training and evaluating these models?

After the datasets needed are downloaded, I ran the commanding code
# Training CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port 25629 train.py --config config/cfg_kitti_baseline_odometry_boundary_ce_iou_1024_20.py --work_dir log/odometry/
However, KeyError: ('bothD', 0, 0) happened when computing the loss_dict of odometry dataset..
I noticed that when computing the loss of kitti_object, inputs['bothD', 0, 0] could be provided by getting groundtruth vehicle256, while inputs[''bothS, 0, 0] could be provided by getting groundtruth road dense128 .
Since the topview_lossB requires inputs['bothD', 0, 0] when I run odometry dataset, how can I get this variable?
How about running kitti object dataset, where can I find inputs['bothS', 0, 0] ?
Thanks.

Hi, thank you for your wonderful work！
I tried to download GT layout of KITTI using the links in monolayout, only to get "404 FILE NOT FOUND".
Can you provide accessible links or other suggestions?

After downloading dataset and pretrained model, I ran [eval_argo_both_video.py] this error:

T_ = outputs[("cam_T_cam", 0, -1)].cpu().numpy()[0]
KeyError: ('cam_T_cam', 0, -1)

According to the original config setting, the IMGS_PER_GPU is set to 3. However, the GPUs I use now are GTX 3080 10 GB so I changed the IMGS_PER_GPU to 1. Up to 120 epochs training (kitti odom), the eval results are
abs_rel: 0.2068, sq_rel: 1.5355, rmse: 4.9815, rmse_log: 0.2898, a1: 0.7079, a2: 0.9595, scale_mean: 1.7448, iou_road: 0.7133, mAP_road: 0.8781
These results have been achieved for around 50 epochs and haven't shown any descending trends.
Does the IMGS_PER_GPU set to 1 have a negative effect on the training/eval procedure?

Thank you very much for sharing your work results with us. Recently, I am also learning relevant knowledge. Can you tell me how I need to get the results as shown below?

I try to evaluate the result of layout on kitti-dataset. But I meet this question of Missing key(s) in state_dict:"CycledViewProjectionB.transform_module.fc_transform.0.weight", "CycledViewProjectionB.transform_module.fc_transform.0.bias", "CycledViewProjectionB.transform_module.fc_transform.2.weight", "CycledViewProjectionB.transform_module.fc_transform.2.bias", "CycledViewProjectionB.retransform_module.fc_transform.0.weight", "CycledViewProjectionB.retransform_module.fc_transform.0.bias", "CycledViewProjectionB.retransform_module.fc_transform.2.weight", "CycledViewProjectionB.retransform_module.fc_transform.2.bias", "CrossViewTransformerB.query_conv.weight", "CrossViewTransformerB.query_conv.bias", "CrossViewTransformerB.key_conv.weight", "CrossViewTransformerB.key_conv.bias", "CrossViewTransformerB.value_conv.weight", "CrossViewTransformerB.value_conv.bias", "CrossViewTransformerB.f_conv.weight", "CrossViewTransformerB.f_conv.bias", "CrossViewTransformerB.res_conv.weight", "CrossViewTransformerB.res_conv.bias", "CrossViewTransformerB.query_conv_depth.weight", "CrossViewTransformerB.query_conv_depth.bias", "CrossViewTransformerB.key_conv_depth.weight", "CrossViewTransformerB.key_conv_depth.bias", "CrossViewTransformerB.value_conv_depth.weight", "CrossViewTransformerB.value_conv_depth.bias", "CrossViewTransformerB.conv1.conv.weight", "CrossViewTransformerB.conv1.conv.bias", "CrossViewTransformerB.conv2.conv.weight", "CrossViewTransformerB.conv2.conv.bias", "LayoutDecoderB.decoder.0.weight", "LayoutDecoderB.decoder.0.bias", "LayoutDecoderB.decoder.1.weight", "LayoutDecoderB.decoder.1.bias", "LayoutDecoderB.decoder.1.running_mean", "LayoutDecoderB.decoder.1.running_var", "LayoutDecoderB.decoder.3.weight", "LayoutDecoderB.decoder.3.bias", "LayoutDecoderB.decoder.4.weight", "LayoutDecoderB.decoder.4.bias", "LayoutDecoderB.decoder.4.running_mean", "LayoutDecoderB.decoder.4.running_var", "LayoutDecoderB.decoder.5.weight", "LayoutDecoderB.decoder.5.bias", "LayoutDecoderB.decoder.6.weight", 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