Categorical Depth Distribution Network for Monocular 3D Object Detection

Last update: Jan 05, 2023

Related tags

Overview

CaDDN

CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet].

Categorical Depth Distribution Network for Monocular 3D Object Detection
Cody Reading, Ali Harakeh, Julia Chae, and Steven L. Waslander
[Paper]

Overview

Changelog

[2021-03-16] CaDDN v0.3.0 is released.

Introduction

What does `CaDDN` do?

CaDDN is a general PyTorch-based method for 3D object detection from monocular images. At the time of submission, CaDDN achieved first 1st place among published monocular methods on the Kitti 3D object detection benchmark. We welcome contributions to this project.

`CaDDN` design pattern

We inherit the design pattern from [OpenPCDet].

Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:

Unified 3D box definition: (x, y, z, dx, dy, dz, heading).

Model Zoo

KITTI 3D Object Detection Baselines

Selected supported methods are shown in the below table. The results are the 3D detection performance of Car class on the val set of KITTI dataset.

All models are trained with 2 Tesla T4 GPUs and are available for download.
The training time is measured with 2 Tesla T4 GPUs and PyTorch 1.4.

	training time	[email protected]	[email protected]	[email protected]	download
CaDDN	~76 hours	23.77	16.07	13.61	model-774M

Installation

Please refer to INSTALL.md for the installation of CaDDN.

Getting Started

Please refer to GETTING_STARTED.md to learn more usage about this project.

License

CaDDN is released under the Apache 2.0 license.

Acknowledgement

CaDDN is an open source project for monocular-based 3D scene perception. We would like to thank the authors of OpenPCDet for their open-source release of their 3D object detection codebase.

Citation

If you find this project useful in your research, please consider citing:

@article{CaDDN,
    title={Categorical Depth DistributionNetwork for Monocular 3D Object Detection},
    author={Cody Reading and
            Ali Harakeh and
            Julia Chae and
            Steven L. Waslander},
    journal = {CVPR},
    year={2021}
}

Contribution

Welcome to be a member of the CaDDN development team by contributing to this repo, and feel free to contact us for any potential contributions.

Comments

Out of memory, can not train on 11G machine
Hi,I set batch_size = 2, and I can not train resnet50 on 2080ti due to out of memory.Do you have any way to reduce the memory usage of the code? I tried to reduce the number of blocks in resnet50,but loss=nan, and error:

WARNING:root:NaN or Inf found in input tensor.

Thank you.
opened by DuZzzs 14
Can I train the Nuscene model on OpenPCDet?

Hello, I'm trying to use the CaDDN to train the Nuscene model. But it seems that it can not be directly applied. Do you implement the Nuscene model using CaDDN? If not, could you please give me some advice on what to do if I'd like to train the Nuscene model with CaDDN? As I know, the Nuscene dataset has six cameras, but the CaDDN only ran on the front camera. Thank you.

opened by rockywind 7
Cuda Out of Memory with Cam150 image (above 2M image size)

I train CaDDN on A100 with Kitti Dataset it works fluently. But with our private dataset (captured with lidar and camera 150 degree) (after conversion to Kitti), it produces errors: File "/home/ubuntu/CaDDN/CaDDN/pcdet/models/backbones_3d/ffe/depth_ffe.py", line 93, in create_frustum_features frustum_features = depth_probs * image_features RuntimeError: CUDA out of memory. Tried to allocate 5.53 GiB (GPU 0; 39.59 GiB total capacity; 32.48 GiB already allocated; 4.92 GiB free; 32.99 GiB reserved in total by PyTorch). depth probs: torch.Size([2, 1, 80, 302, 480]) image feature shape: torch.Size([2, 64, 1, 302, 480])

Pytorch 1.9.0 cudatoolkit 11.1 torchaudio 0.9.0
torchvision 0.5.0
It is the same error with cudatoolkit 11.0, pytorch 1.7.1 . Thank you very much !!!

opened by namnv78 7
Can I train the Waymo model on OpenPCDet?

HELLO, I'm trying to use the OpenPCDet to train the Waymo model. But it seems that it can not be directly applied, do you implement the Waymo model using OpenPCDet? If not, could you please give me some advice on what to do if I'd like to train the Waymo model with OpenPCDet. Thank you.

opened by Cc-Hy 6
How many gpus were used ?

hi, Thank you for sharing your work.

when training on Kitti benchmark, do you mean by batch size of 4 per GPU? which equals to batch size of 8 when using two GPUs? Am I getting this right..?

opened by ksh11023 6

Run test on pretrained model

i am getting the following error by running this command:

python ./tools/test.py --cfg_file ./tools/cfgs/kitti_models/CaDDN.yaml --launcher pytorch --eval_all

error:


Traceback (most recent call last):
  File "./tools/test.py", line 199, in <module>
    main()
  File "./tools/test.py", line 190, in main
    model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=test_set)
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/__init__.py", line 14, in build_network
    model = build_detector(
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/detectors/__init__.py", line 23, in build_detector
    model = __all__[model_cfg.NAME](
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/detectors/caddn.py", line 7, in __init__
    self.module_list = self.build_networks()
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/detectors/detector3d_template.py", line 50, in build_networks
    module, model_info_dict = getattr(self, 'build_%s' % module_name)(
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/detectors/detector3d_template.py", line 74, in build_ffe
    ffe_module = ffe.__all__[self.model_cfg.FFE.NAME](
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/backbones_3d/ffe/depth_ffe.py", line 25, in __init__
    self.ddn = ddn.__all__[ddn_cfg.NAME](
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/backbones_3d/ffe/ddn/ddn_deeplabv3.py", line 21, in __init__
    super().__init__(constructor=constructor, **kwargs)
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/backbones_3d/ffe/ddn/ddn_template.py", line 34, in __init__
    self.model = self.get_model(constructor=constructor)
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/backbones_3d/ffe/ddn/ddn_template.py", line 63, in get_model
    pretrained_dict = self.filter_pretrained_dict(model_dict=model_dict, pretrained_dict=pretrained_dict)
  File "/home/mm/MasterThesis/CaDDN/pcdet/models/backbones_3d/ffe/ddn/ddn_template.py", line 88, in filter_pretrained_dict
    pretrained_num_classes = pretrained_dict["classifier.4.weight"].shape[0]
KeyError: 'classifier.4.weight'

to be more precise: i downloaded the cadnn.pth from the link in README.md and set the path in the config file. also pretrained_dict only contains: pretrained_dict : dict_keys(['epoch', 'it', 'model_state', 'optimizer_state', 'version'])

installation is based on what you mentioned in INSTALL.md

opened by EmbeddedAndMore 6

How can I transform depth map to real depth ?

I notice that you turn the depth map to real depth by dividing 256. But when I change the depth to point cloud, I found this value not correct. Could you tell me why you divide 256 to get the depth?

opened by kaixinbear 5
Rendering 3D bounding boxes onto test images

Hello,

Thanks for your help on the previous issue. I'm running the tools/test.py script with the intent to generate 3D bounding boxes on the test images as a result image set. I'm using the KITTI dataset. Is there a flag I can set for test.py, or use another software package, or follow some other additional steps to be able to render those 3D bounding boxes?

Also, my next step is to try using a custom generated imageset. I'll be using the pre-trained model even though it's from the perspective of a street light. They would be frames from a video capture. I was going to try to follow the structure of the KITTI dataset and see if I can repurpose the script that generated the datainfos. Do you have any suggestions for the best way to go about using a custom set of images like this?

Ultimately, it's my goal to see 3D bounding box rendering working on those images.

Thanks!

opened by dpwolfe 5
Why does it show the loading model's EPOCH is 3?

Thank you for your help in advance! Why does it show the loading model's EPOCH is 3? 2021-10-22 20:57:44,189 INFO ==> Checkpoint trained from version: pcdet+0.3.0+0000000 2021-10-22 20:57:47,894 INFO ==> Done (loaded 903/903) 2021-10-22 20:57:48,489 INFO *************** EPOCH 3 EVALUATION *****************

opened by rockywind 4
Question about the Voxel Features
Congratulations on your great work!

I have read your paper and have several questions that bother me:

In your work,

Firstly, the voxel grid is first generated.

Secondly, use the gird_to_lidar, lidar_to_cam, cam_to_img transformation to find the correspondence between the grid coordinates and the image coordinates.

Thirdly, use grid_sample to sample features from Frustum to Voxel.

Finally, Voxelcollapse to BEV features

Since, in my opinion, the BEV features represent the world coordinates. My question is, why not just use BEV features to generate 'BEV grid', which represents the real world (lidar) coordinates? So, the gird_to_lidar step can be omitted. Am I right?

I am still confused about the 'Voxel Features'. I don'y know what is it used for?

Thank you very much, looking forward to your replay!
opened by taylover-pei 4
Can I change the lidar coordinate to camera coordinate?

Hi, I have eight camera data. The 3D lable is similar to KITTI style, it is based on camera coordinate. I think the network would be confused if converted to lidar coordinate. But, the generated voxel and anchors setting are based on lidar coordinate. What can I do to change the code based on camera coordinate?

opened by rockywind 4

`run.sh` caddn -> codyreading/caddn

When following our tutorial on using Docker, the pulled Docker Image name did not match the command used to run run.sh, resulting in an error with the docker run command. https://github.com/TRAILab/CaDDN/blob/master/docker/DOCKER.md#get-a-docker-image

docker pull codyreading/caddn

CMD="docker run -it \
    --runtime=nvidia \
    --net=host \
    --privileged=true \
    --ipc=host \
    --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
    --volume="$XAUTHORITY:/root/.Xauthority:rw" \
    --env="DISPLAY" \
    --env="QT_X11_NO_MITSHM=1" \
    --hostname="inside-DOCKER" \
    --name="CaDDN" \
    --volume $PROJ_DIR/checkpoints:/CaDDN/checkpoints \
    --volume $PROJ_DIR/data:/CaDDN/data \
    --volume $PROJ_DIR/output:/CaDDN/output \
    --volume $PROJ_DIR/tools:/CaDDN/tools \
    --volume $PROJ_DIR/.git:/CaDDN/.git \
    $DATA_VOLUMES \
    $PCDET_VOLUMES \
    --rm \
    caddn bash <--- codyreading/caddn

opened by PINTO0309 0

Error while training the model "ValueError: num_samples should be a positive integer value, but got num_samples=0"

Code we used for training: !python /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/train.py --cfg_file /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models/CaDDN.yaml

Log we got: 2022-11-19 21:23:27,419 INFO Start logging 2022-11-19 21:23:27,420 INFO CUDA_VISIBLE_DEVICES=ALL 2022-11-19 21:23:27,420 INFO cfg_file /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models/CaDDN.yaml 2022-11-19 21:23:27,420 INFO batch_size 4 2022-11-19 21:23:27,420 INFO epochs 80 2022-11-19 21:23:27,420 INFO workers 8 2022-11-19 21:23:27,420 INFO extra_tag default 2022-11-19 21:23:27,421 INFO ckpt None 2022-11-19 21:23:27,421 INFO pretrained_model None 2022-11-19 21:23:27,421 INFO launcher none 2022-11-19 21:23:27,421 INFO tcp_port 18888 2022-11-19 21:23:27,421 INFO sync_bn False 2022-11-19 21:23:27,421 INFO fix_random_seed False 2022-11-19 21:23:27,421 INFO ckpt_save_interval 1 2022-11-19 21:23:27,421 INFO local_rank 0 2022-11-19 21:23:27,421 INFO max_ckpt_save_num 30 2022-11-19 21:23:27,421 INFO merge_all_iters_to_one_epoch False 2022-11-19 21:23:27,421 INFO set_cfgs None 2022-11-19 21:23:27,421 INFO max_waiting_mins 0 2022-11-19 21:23:27,422 INFO start_epoch 0 2022-11-19 21:23:27,422 INFO save_to_file False 2022-11-19 21:23:27,422 INFO cfg.ROOT_DIR: /content/gdrive/MyDrive/IITH_VCA/CaDDN 2022-11-19 21:23:27,422 INFO cfg.LOCAL_RANK: 0 2022-11-19 21:23:27,422 INFO cfg.CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] 2022-11-19 21:23:27,422 INFO
cfg.DATA_CONFIG = edict() 2022-11-19 21:23:27,422 INFO cfg.DATA_CONFIG.DATASET: KittiDataset 2022-11-19 21:23:27,422 INFO cfg.DATA_CONFIG.DATA_PATH: /gdrive/MyDrive/3D_Obj/CaDDN/data/kitti 2022-11-19 21:23:27,422 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [2, -30.08, -3.0, 46.8, 30.08, 1.0] 2022-11-19 21:23:27,422 INFO
cfg.DATA_CONFIG.DATA_SPLIT = edict() 2022-11-19 21:23:27,422 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train 2022-11-19 21:23:27,423 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: test 2022-11-19 21:23:27,423 INFO
cfg.DATA_CONFIG.INFO_PATH = edict() 2022-11-19 21:23:27,423 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['kitti_infos_trainval.pkl'] 2022-11-19 21:23:27,423 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['kitti_infos_test.pkl'] 2022-11-19 21:23:27,423 INFO cfg.DATA_CONFIG.FOV_POINTS_ONLY: True 2022-11-19 21:23:27,423 INFO
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict() 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder'] 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'random_image_flip', 'ALONG_AXIS_LIST': ['horizontal']}] 2022-11-19 21:23:27,424 INFO
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict() 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity'] 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity'] 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}, {'NAME': 'calculate_grid_size', 'VOXEL_SIZE': [0.16, 0.16, 0.16]}] 2022-11-19 21:23:27,424 INFO cfg.DATA_CONFIG.BASE_CONFIG: cfgs/dataset_configs/kitti_dataset.yaml 2022-11-19 21:23:27,425 INFO
cfg.DATA_CONFIG.IMAGE = edict() 2022-11-19 21:23:27,425 INFO cfg.DATA_CONFIG.IMAGE.ENABLED: True 2022-11-19 21:23:27,425 INFO
cfg.DATA_CONFIG.IMAGE.COLLATE = edict() 2022-11-19 21:23:27,425 INFO cfg.DATA_CONFIG.IMAGE.COLLATE.MODE: Pad 2022-11-19 21:23:27,425 INFO cfg.DATA_CONFIG.IMAGE.COLLATE.CONSTANT_VALUES: [0.485, 0.456, 0.406] 2022-11-19 21:23:27,425 INFO
cfg.DATA_CONFIG.DEPTH_MAP = edict() 2022-11-19 21:23:27,425 INFO cfg.DATA_CONFIG.DEPTH_MAP.ENABLED: True 2022-11-19 21:23:27,425 INFO cfg.DATA_CONFIG.DEPTH_MAP.DOWNSAMPLE_FACTOR: 4 2022-11-19 21:23:27,426 INFO
cfg.DATA_CONFIG.CALIB = edict() 2022-11-19 21:23:27,426 INFO cfg.DATA_CONFIG.CALIB.ENABLED: True 2022-11-19 21:23:27,426 INFO
cfg.MODEL = edict() 2022-11-19 21:23:27,426 INFO cfg.MODEL.NAME: CaDDN 2022-11-19 21:23:27,426 INFO
cfg.MODEL.FFE = edict() 2022-11-19 21:23:27,426 INFO cfg.MODEL.FFE.NAME: DepthFFE 2022-11-19 21:23:27,426 INFO
cfg.MODEL.FFE.DDN = edict() 2022-11-19 21:23:27,426 INFO cfg.MODEL.FFE.DDN.NAME: DDNDeepLabV3 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.DDN.BACKBONE_NAME: ResNet101 2022-11-19 21:23:27,427 INFO
cfg.MODEL.FFE.DDN.ARGS = edict() 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.DDN.ARGS.feat_extract_layer: layer1 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.DDN.ARGS.pretrained_path: ../checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth 2022-11-19 21:23:27,427 INFO
cfg.MODEL.FFE.CHANNEL_REDUCE = edict() 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.in_channels: 256 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.out_channels: 64 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.kernel_size: 1 2022-11-19 21:23:27,427 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.stride: 1 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.bias: False 2022-11-19 21:23:27,428 INFO
cfg.MODEL.FFE.DISCRETIZE = edict() 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DISCRETIZE.mode: LID 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DISCRETIZE.num_bins: 80 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DISCRETIZE.depth_min: 2.0 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DISCRETIZE.depth_max: 46.8 2022-11-19 21:23:27,428 INFO
cfg.MODEL.FFE.DDN_LOSS = edict() 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DDN_LOSS.weight: 3.0 2022-11-19 21:23:27,428 INFO cfg.MODEL.FFE.DDN_LOSS.alpha: 0.25 2022-11-19 21:23:27,429 INFO cfg.MODEL.FFE.DDN_LOSS.gamma: 2.0 2022-11-19 21:23:27,429 INFO cfg.MODEL.FFE.DDN_LOSS.fg_weight: 13 2022-11-19 21:23:27,429 INFO cfg.MODEL.FFE.DDN_LOSS.bg_weight: 1 2022-11-19 21:23:27,429 INFO
cfg.MODEL.F2V = edict() 2022-11-19 21:23:27,429 INFO cfg.MODEL.F2V.NAME: FrustumToVoxel 2022-11-19 21:23:27,429 INFO
cfg.MODEL.F2V.SAMPLER = edict() 2022-11-19 21:23:27,429 INFO cfg.MODEL.F2V.SAMPLER.mode: bilinear 2022-11-19 21:23:27,429 INFO cfg.MODEL.F2V.SAMPLER.padding_mode: zeros 2022-11-19 21:23:27,429 INFO
cfg.MODEL.MAP_TO_BEV = edict() 2022-11-19 21:23:27,430 INFO cfg.MODEL.MAP_TO_BEV.NAME: Conv2DCollapse 2022-11-19 21:23:27,430 INFO cfg.MODEL.MAP_TO_BEV.NUM_BEV_FEATURES: 64 2022-11-19 21:23:27,430 INFO
cfg.MODEL.MAP_TO_BEV.ARGS = edict() 2022-11-19 21:23:27,430 INFO cfg.MODEL.MAP_TO_BEV.ARGS.kernel_size: 1 2022-11-19 21:23:27,430 INFO cfg.MODEL.MAP_TO_BEV.ARGS.stride: 1 2022-11-19 21:23:27,430 INFO cfg.MODEL.MAP_TO_BEV.ARGS.bias: False 2022-11-19 21:23:27,430 INFO
cfg.MODEL.BACKBONE_2D = edict() 2022-11-19 21:23:27,430 INFO cfg.MODEL.BACKBONE_2D.NAME: BaseBEVBackbone 2022-11-19 21:23:27,431 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [10, 10, 10] 2022-11-19 21:23:27,431 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [2, 2, 2] 2022-11-19 21:23:27,431 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [64, 128, 256] 2022-11-19 21:23:27,431 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2, 4] 2022-11-19 21:23:27,431 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [128, 128, 128] 2022-11-19 21:23:27,431 INFO
cfg.MODEL.DENSE_HEAD = edict() 2022-11-19 21:23:27,431 INFO cfg.MODEL.DENSE_HEAD.NAME: AnchorHeadSingle 2022-11-19 21:23:27,431 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False 2022-11-19 21:23:27,431 INFO cfg.MODEL.DENSE_HEAD.USE_DIRECTION_CLASSIFIER: True 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.DIR_OFFSET: 0.78539 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.DIR_LIMIT_OFFSET: 0.0 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.NUM_DIR_BINS: 2 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.ANCHOR_GENERATOR_CONFIG: [{'class_name': 'Car', 'anchor_sizes': [[3.9, 1.6, 1.56]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-1.78], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.6, 'unmatched_threshold': 0.45}, {'class_name': 'Pedestrian', 'anchor_sizes': [[0.8, 0.6, 1.73]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35}, {'class_name': 'Cyclist', 'anchor_sizes': [[1.76, 0.6, 1.73]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35}] 2022-11-19 21:23:27,432 INFO
cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG = edict() 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NAME: AxisAlignedTargetAssigner 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.POS_FRACTION: -1.0 2022-11-19 21:23:27,432 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.SAMPLE_SIZE: 512 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NORM_BY_NUM_EXAMPLES: False 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MATCH_HEIGHT: False 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.BOX_CODER: ResidualCoder 2022-11-19 21:23:27,433 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG = edict() 2022-11-19 21:23:27,433 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict() 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.loc_weight: 2.0 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.dir_weight: 0.2 2022-11-19 21:23:27,433 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] 2022-11-19 21:23:27,434 INFO
cfg.MODEL.POST_PROCESSING = edict() 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7] 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.SCORE_THRESH: 0.1 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.OUTPUT_RAW_SCORE: False 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: kitti 2022-11-19 21:23:27,434 INFO
cfg.MODEL.POST_PROCESSING.NMS_CONFIG = edict() 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.MULTI_CLASSES_NMS: False 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_TYPE: nms_gpu 2022-11-19 21:23:27,434 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_THRESH: 0.01 2022-11-19 21:23:27,435 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_PRE_MAXSIZE: 4096 2022-11-19 21:23:27,435 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_POST_MAXSIZE: 500 2022-11-19 21:23:27,435 INFO
cfg.OPTIMIZATION = edict() 2022-11-19 21:23:27,435 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 4 2022-11-19 21:23:27,435 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 80 2022-11-19 21:23:27,435 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle 2022-11-19 21:23:27,435 INFO cfg.OPTIMIZATION.LR: 0.001 2022-11-19 21:23:27,435 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85] 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.PCT_START: 0.4 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45] 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.LR_WARMUP: False 2022-11-19 21:23:27,436 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1 2022-11-19 21:23:27,437 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10 2022-11-19 21:23:27,437 INFO cfg.TAG: CaDDN 2022-11-19 21:23:27,437 INFO cfg.EXP_GROUP_PATH: content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models 2022-11-19 21:23:28,525 INFO Loading KITTI dataset 2022-11-19 21:23:28,526 INFO Total samples for KITTI dataset: 0 Traceback (most recent call last): File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/train.py", line 198, in main() File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/train.py", line 112, in main total_epochs=args.epochs File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/pcdet/datasets/init.py", line 69, in build_dataloader drop_last=False, sampler=sampler, timeout=0 File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 213, in init sampler = RandomSampler(dataset) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/sampler.py", line 94, in init "value, but got num_samples={}".format(self.num_samples)) ValueError: num_samples should be a positive integer value, but got num_samples=0

Screen shots of the same:

Error screen shot:

opened by prabhata07 1
KeyError: 'model_state'

Facing KeyError: 'model_state' while trying to test with "Pretrained model" Command used:

!python /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/test.py --cfg_file /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models/CaDDN.yaml --batch_size 1 --ckpt /content/gdrive/MyDrive/IITH_VCA/CaDDN/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth

Environemnt: Collab

Model: as given in the github repo deeplabv3_resnet101_coco-586e9e4e.pth

Error log:

2022-11-19 21:16:39,034 INFO Start logging 2022-11-19 21:16:39,035 INFO CUDA_VISIBLE_DEVICES=ALL 2022-11-19 21:16:39,035 INFO cfg_file /content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models/CaDDN.yaml 2022-11-19 21:16:39,035 INFO batch_size 1 2022-11-19 21:16:39,035 INFO workers 4 2022-11-19 21:16:39,035 INFO extra_tag default 2022-11-19 21:16:39,035 INFO ckpt /content/gdrive/MyDrive/IITH_VCA/CaDDN/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth 2022-11-19 21:16:39,035 INFO launcher none 2022-11-19 21:16:39,036 INFO tcp_port 18888 2022-11-19 21:16:39,036 INFO local_rank 0 2022-11-19 21:16:39,036 INFO set_cfgs None 2022-11-19 21:16:39,036 INFO max_waiting_mins 30 2022-11-19 21:16:39,036 INFO start_epoch 0 2022-11-19 21:16:39,036 INFO eval_tag default 2022-11-19 21:16:39,036 INFO eval_all False 2022-11-19 21:16:39,036 INFO ckpt_dir None 2022-11-19 21:16:39,036 INFO save_to_file False 2022-11-19 21:16:39,037 INFO cfg.ROOT_DIR: /content/gdrive/MyDrive/IITH_VCA/CaDDN 2022-11-19 21:16:39,037 INFO cfg.LOCAL_RANK: 0 2022-11-19 21:16:39,037 INFO cfg.CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] 2022-11-19 21:16:39,037 INFO
cfg.DATA_CONFIG = edict() 2022-11-19 21:16:39,037 INFO cfg.DATA_CONFIG.DATASET: KittiDataset 2022-11-19 21:16:39,037 INFO cfg.DATA_CONFIG.DATA_PATH: /gdrive/MyDrive/3D_Obj/CaDDN/data/kitti 2022-11-19 21:16:39,037 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [2, -30.08, -3.0, 46.8, 30.08, 1.0] 2022-11-19 21:16:39,037 INFO
cfg.DATA_CONFIG.DATA_SPLIT = edict() 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: test 2022-11-19 21:16:39,038 INFO
cfg.DATA_CONFIG.INFO_PATH = edict() 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['kitti_infos_trainval.pkl'] 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['kitti_infos_test.pkl'] 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.FOV_POINTS_ONLY: True 2022-11-19 21:16:39,038 INFO
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict() 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder'] 2022-11-19 21:16:39,038 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'random_image_flip', 'ALONG_AXIS_LIST': ['horizontal']}] 2022-11-19 21:16:39,038 INFO
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict() 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity'] 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity'] 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}, {'NAME': 'calculate_grid_size', 'VOXEL_SIZE': [0.16, 0.16, 0.16]}] 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.BASE_CONFIG: cfgs/dataset_configs/kitti_dataset.yaml 2022-11-19 21:16:39,039 INFO
cfg.DATA_CONFIG.IMAGE = edict() 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.IMAGE.ENABLED: True 2022-11-19 21:16:39,039 INFO
cfg.DATA_CONFIG.IMAGE.COLLATE = edict() 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.IMAGE.COLLATE.MODE: Pad 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.IMAGE.COLLATE.CONSTANT_VALUES: [0.485, 0.456, 0.406] 2022-11-19 21:16:39,039 INFO
cfg.DATA_CONFIG.DEPTH_MAP = edict() 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.DEPTH_MAP.ENABLED: True 2022-11-19 21:16:39,039 INFO cfg.DATA_CONFIG.DEPTH_MAP.DOWNSAMPLE_FACTOR: 4 2022-11-19 21:16:39,040 INFO
cfg.DATA_CONFIG.CALIB = edict() 2022-11-19 21:16:39,040 INFO cfg.DATA_CONFIG.CALIB.ENABLED: True 2022-11-19 21:16:39,040 INFO
cfg.MODEL = edict() 2022-11-19 21:16:39,040 INFO cfg.MODEL.NAME: CaDDN 2022-11-19 21:16:39,040 INFO
cfg.MODEL.FFE = edict() 2022-11-19 21:16:39,040 INFO cfg.MODEL.FFE.NAME: DepthFFE 2022-11-19 21:16:39,040 INFO
cfg.MODEL.FFE.DDN = edict() 2022-11-19 21:16:39,040 INFO cfg.MODEL.FFE.DDN.NAME: DDNDeepLabV3 2022-11-19 21:16:39,040 INFO cfg.MODEL.FFE.DDN.BACKBONE_NAME: ResNet101 2022-11-19 21:16:39,040 INFO
cfg.MODEL.FFE.DDN.ARGS = edict() 2022-11-19 21:16:39,040 INFO cfg.MODEL.FFE.DDN.ARGS.feat_extract_layer: layer1 2022-11-19 21:16:39,040 INFO cfg.MODEL.FFE.DDN.ARGS.pretrained_path: ../checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth 2022-11-19 21:16:39,040 INFO
cfg.MODEL.FFE.CHANNEL_REDUCE = edict() 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.in_channels: 256 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.out_channels: 64 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.kernel_size: 1 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.stride: 1 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.CHANNEL_REDUCE.bias: False 2022-11-19 21:16:39,041 INFO
cfg.MODEL.FFE.DISCRETIZE = edict() 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DISCRETIZE.mode: LID 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DISCRETIZE.num_bins: 80 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DISCRETIZE.depth_min: 2.0 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DISCRETIZE.depth_max: 46.8 2022-11-19 21:16:39,041 INFO
cfg.MODEL.FFE.DDN_LOSS = edict() 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DDN_LOSS.weight: 3.0 2022-11-19 21:16:39,041 INFO cfg.MODEL.FFE.DDN_LOSS.alpha: 0.25 2022-11-19 21:16:39,042 INFO cfg.MODEL.FFE.DDN_LOSS.gamma: 2.0 2022-11-19 21:16:39,042 INFO cfg.MODEL.FFE.DDN_LOSS.fg_weight: 13 2022-11-19 21:16:39,042 INFO cfg.MODEL.FFE.DDN_LOSS.bg_weight: 1 2022-11-19 21:16:39,042 INFO
cfg.MODEL.F2V = edict() 2022-11-19 21:16:39,042 INFO cfg.MODEL.F2V.NAME: FrustumToVoxel 2022-11-19 21:16:39,042 INFO
cfg.MODEL.F2V.SAMPLER = edict() 2022-11-19 21:16:39,042 INFO cfg.MODEL.F2V.SAMPLER.mode: bilinear 2022-11-19 21:16:39,042 INFO cfg.MODEL.F2V.SAMPLER.padding_mode: zeros 2022-11-19 21:16:39,042 INFO
cfg.MODEL.MAP_TO_BEV = edict() 2022-11-19 21:16:39,042 INFO cfg.MODEL.MAP_TO_BEV.NAME: Conv2DCollapse 2022-11-19 21:16:39,042 INFO cfg.MODEL.MAP_TO_BEV.NUM_BEV_FEATURES: 64 2022-11-19 21:16:39,042 INFO
cfg.MODEL.MAP_TO_BEV.ARGS = edict() 2022-11-19 21:16:39,042 INFO cfg.MODEL.MAP_TO_BEV.ARGS.kernel_size: 1 2022-11-19 21:16:39,043 INFO cfg.MODEL.MAP_TO_BEV.ARGS.stride: 1 2022-11-19 21:16:39,043 INFO cfg.MODEL.MAP_TO_BEV.ARGS.bias: False 2022-11-19 21:16:39,043 INFO
cfg.MODEL.BACKBONE_2D = edict() 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.NAME: BaseBEVBackbone 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [10, 10, 10] 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [2, 2, 2] 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [64, 128, 256] 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2, 4] 2022-11-19 21:16:39,043 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [128, 128, 128] 2022-11-19 21:16:39,043 INFO
cfg.MODEL.DENSE_HEAD = edict() 2022-11-19 21:16:39,043 INFO cfg.MODEL.DENSE_HEAD.NAME: AnchorHeadSingle 2022-11-19 21:16:39,043 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False 2022-11-19 21:16:39,043 INFO cfg.MODEL.DENSE_HEAD.USE_DIRECTION_CLASSIFIER: True 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.DIR_OFFSET: 0.78539 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.DIR_LIMIT_OFFSET: 0.0 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.NUM_DIR_BINS: 2 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.ANCHOR_GENERATOR_CONFIG: [{'class_name': 'Car', 'anchor_sizes': [[3.9, 1.6, 1.56]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-1.78], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.6, 'unmatched_threshold': 0.45}, {'class_name': 'Pedestrian', 'anchor_sizes': [[0.8, 0.6, 1.73]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35}, {'class_name': 'Cyclist', 'anchor_sizes': [[1.76, 0.6, 1.73]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35}] 2022-11-19 21:16:39,044 INFO
cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG = edict() 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NAME: AxisAlignedTargetAssigner 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.POS_FRACTION: -1.0 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.SAMPLE_SIZE: 512 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NORM_BY_NUM_EXAMPLES: False 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MATCH_HEIGHT: False 2022-11-19 21:16:39,044 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.BOX_CODER: ResidualCoder 2022-11-19 21:16:39,044 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG = edict() 2022-11-19 21:16:39,045 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict() 2022-11-19 21:16:39,045 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0 2022-11-19 21:16:39,045 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.loc_weight: 2.0 2022-11-19 21:16:39,045 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.dir_weight: 0.2 2022-11-19 21:16:39,045 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] 2022-11-19 21:16:39,045 INFO
cfg.MODEL.POST_PROCESSING = edict() 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7] 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.SCORE_THRESH: 0.1 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.OUTPUT_RAW_SCORE: False 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: kitti 2022-11-19 21:16:39,045 INFO
cfg.MODEL.POST_PROCESSING.NMS_CONFIG = edict() 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.MULTI_CLASSES_NMS: False 2022-11-19 21:16:39,045 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_TYPE: nms_gpu 2022-11-19 21:16:39,046 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_THRESH: 0.01 2022-11-19 21:16:39,046 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_PRE_MAXSIZE: 4096 2022-11-19 21:16:39,046 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_POST_MAXSIZE: 500 2022-11-19 21:16:39,046 INFO
cfg.OPTIMIZATION = edict() 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 4 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 80 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.LR: 0.001 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85] 2022-11-19 21:16:39,046 INFO cfg.OPTIMIZATION.PCT_START: 0.4 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45] 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.LR_WARMUP: False 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1 2022-11-19 21:16:39,047 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10 2022-11-19 21:16:39,048 INFO cfg.TAG: CaDDN 2022-11-19 21:16:39,048 INFO cfg.EXP_GROUP_PATH: content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/cfgs/kitti_models 2022-11-19 21:16:39,048 INFO Loading KITTI dataset 2022-11-19 21:16:39,048 INFO Total samples for KITTI dataset: 0 /content/gdrive/MyDrive/IITH_VCA/CaDDN/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth 2022-11-19 21:16:42,793 INFO ==> Loading parameters from checkpoint /content/gdrive/MyDrive/IITH_VCA/CaDDN/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth to GPU Traceback (most recent call last): File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/test.py", line 200, in main() File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/test.py", line 196, in main eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id, dist_test=dist_test) File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/tools/test.py", line 58, in eval_single_ckpt model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=dist_test) File "/content/gdrive/MyDrive/IITH_VCA/CaDDN/pcdet/models/detectors/detector3d_template.py", line 359, in load_params_from_file model_state_disk = checkpoint['model_state'] KeyError: 'model_state'

opened by prabhata07 1
the result is not reproduced

I trained the model with 2 Tesla V100 GPUs in the default configuration and got [email protected], [email protected], [email protected] 21.203, 15.1487,12.7407 respectively， which is lower than the 23.77,16.07, 13.61 you declared. But when I use the pre-trained weight, I can get 23.77,16.07, 13.61. So, are there any training tricks that have not been published?

opened by leo038 0

Releases(v0.3.1)

v0.3.1(Apr 1, 2021)
CaDDN 0.3.1 Release

Updates

Removed the requirement for Depth Map config to be within kitii_dataset.yaml

Updated PIllow version from 8.1.0 to 8.1.2

Source code(tar.gz)
Source code(zip)
v0.3.0(Mar 23, 2021)

Initial release for CaDDN
Source code(tar.gz)
Source code(zip)

Owner

Toronto Robotics and AI Laboratory

GitHub Repository

It's A ML based Web Site build with python and Django to find the breed of the dog

ML-Based-Dog-Breed-Identifier This is a Django Based Web Site To Identify the Breed of which your DOG belogs All You Need To Do is to Follow These Ste

2 Oct 12, 2022

LinkNet - This repository contains our Torch7 implementation of the network developed by us at e-Lab.

LinkNet This repository contains our Torch7 implementation of the network developed by us at e-Lab. You can go to our blogpost or read the article Lin

158 Nov 11, 2022

Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022

Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

[AAAI22] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification We point out the overlooked unbiasedness in long-tailed clas

28 Oct 18, 2022

Chinese Advertisement Board Identification(Pytorch)

Chinese-Advertisement-Board-Identification. We use YoloV5 to extract the ROI of the location of the chinese word. Next, we sort the bounding box and recognize every chinese words which we extracted.

12 Jul 21, 2022

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon.

DD3D: "Is Pseudo-Lidar needed for Monocular 3D Object detection?" Install // Datasets // Experiments // Models // License // Reference Full video Offi

364 Dec 27, 2022

Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

SimReg: A Simple Regression Based Framework for Self-supervised Knowledge Distillation Source code for the paper "SimReg: Regression as a Simple Yet E

9 Oct 15, 2022

Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch

13 Jan 06, 2023

An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

알고리즘 스터디 🔥 부스트캠프 웹모바일 6기 iOS 10조의 알고리즘 스터디 입니다. 개인적인 사정 등으로 S034, S055만 참가하였습니다. 스터디 목적 상진: 코테 합격 + 부캠끝나고 아침에 일어나기 위해 필요한 사이클 기완: 꾸준하게 자리에 앉아 공부하기 +

2 Jan 11, 2022

Users can free try their models on SIDD dataset based on this code

SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p

2 May 20, 2022

A python script to lookup Passport Index Dataset

visa-cli A python script to lookup Passport Index Dataset Installation pip install visa-cli Usage usage: visa-cli [-h] [-d DESTINATION_COUNTRY] [-f]

16 Oct 18, 2022

Playing around with FastAPI and streamlit to create a YoloV5 object detector

FastAPI-Streamlit-based-YoloV5-detector Playing around with FastAPI and streamlit to create a YoloV5 object detector It turns out that a User Interfac

2 Jan 20, 2022

Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022

Python implementation of Bayesian optimization over permutation spaces.

Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o

9 Dec 23, 2022

Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

9 Jun 30, 2022

The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

Deep Residual Fourier Transformation for Single Image Deblurring Xintian Mao, Yiming Liu, Wei Shen, Qingli Li and Yan Wang code will be released soon

145 Dec 13, 2022

HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.

HTSeq DEVS: https://github.com/htseq/htseq DOCS: https://htseq.readthedocs.io A Python library to facilitate programmatic analysis of data from high-t

57 Dec 20, 2022

Categorical Depth Distribution Network for Monocular 3D Object Detection

Related tags

Overview

CaDDN

Overview

Changelog

Introduction

What does CaDDN do?

CaDDN design pattern

Model Zoo

KITTI 3D Object Detection Baselines

Installation

Getting Started

License

Acknowledgement

Citation

Contribution

Comments

Releases(v0.3.1)

v0.3.1(Apr 1, 2021)

CaDDN 0.3.1 Release

Updates

v0.3.0(Mar 23, 2021)

Owner

Toronto Robotics and AI Laboratory

It's A ML based Web Site build with python and Django to find the breed of the dog

LinkNet - This repository contains our Torch7 implementation of the network developed by us at e-Lab.

Code for "Typilus: Neural Type Hints" PLDI 2020

Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

Chinese Advertisement Board Identification(Pytorch)

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

Users can free try their models on SIDD dataset based on this code

A python script to lookup Passport Index Dataset

Playing around with FastAPI and streamlit to create a YoloV5 object detector

Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Python implementation of Bayesian optimization over permutation spaces.

Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.

Official code for the paper "Self-Supervised Prototypical Transfer Learning for Few-Shot Classification"

Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method

Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?"

What does `CaDDN` do?

`CaDDN` design pattern

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon.