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CHECKPOINT_MANIFEST.yaml

@@ -0,0 +1,75 @@
+schema_version: 2
+release_name: BlindMap OPV2V Checkpoints
+release_type: huggingface_model_repository
+code:
+  repository: https://github.com/AlexZhu2000/BlindMap
+  branch: TMC
+  commit: 075b2c7120277a619f41bc49a58e3905ffad7aa7
+license:
+  hf_metadata: other
+  file: LICENSE
+  note: Current BlindMap academic license contains redistribution restrictions.
+dataset:
+  name: OPV2V
+  included: false
+  expected_paths:
+    root_dir: /path/to/OPV2V/train
+    validate_dir: /path/to/OPV2V/validate
+    test_dir: /path/to/OPV2V/test
+artifacts:
+  - id: opv2v_lidar_heterogeneous_m1m2
+    description: Shared m1m2 checkpoint used for LiDAR-only and heterogeneous runtime profiles.
+    directory: models/opv2v/lidar_heterogeneous
+    checkpoint: models/opv2v/lidar_heterogeneous/net_epoch_bestval_at37.pth
+    config: models/opv2v/lidar_heterogeneous/config.yaml
+    result_log: models/opv2v/lidar_heterogeneous/source_result.txt
+    source_directory: opencood/logs/BlindMap_opv2v_m1m2_2025_12_23_19_23_52_thre_0.01_use_history*
+    source_checkpoint: net_epoch_bestval_at37.pth
+    epoch: 37
+    size_bytes: 82395477
+    sha256: 9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3
+    profiles:
+      - id: opv2v_lidar_runtime
+        modality: lidar
+        inference_modal_arg: 0
+        recommended_command_suffix: --fusion_method intermediate --modal 0 --comm_volume_MB 1 --range 102.4,102.4
+        local_log_reference:
+          range: 140.8,40
+          communication: 1 MB
+          ap_0_3: 95.85
+          ap_0_5: 95.63
+          ap_0_7: 93.09
+        note: Rerun at 102.4,102.4 if reporting paper-standard OPV2V LiDAR-only results.
+      - id: opv2v_heterogeneous
+        modality: heterogeneous
+        inference_modal_arg: 4
+        recommended_command_suffix: --fusion_method intermediate --modal 4 --comm_volume_MB 1 --range 102.4,102.4
+        local_log_reference:
+          range: 102.4,102.4
+          communication: 1 MB
+          ap_0_3: 88.82
+          ap_0_5: 87.33
+          ap_0_7: 79.43
+
+  - id: opv2v_camera
+    description: Camera-only OPV2V checkpoint.
+    directory: models/opv2v/camera
+    checkpoint: models/opv2v/camera/net_epoch_bestval_at17.pth
+    config: models/opv2v/camera/config.yaml
+    result_log: models/opv2v/camera/source_result.txt
+    source_directory: opencood/logs/BlindMap_opv2v_camera_pyramid_2025_12_13_14_05_49_thre_0.01_add_noise_use_history*
+    source_checkpoint: net_epoch_bestval_at17.pth
+    epoch: 17
+    size_bytes: 81465973
+    sha256: 33ddf54fe56d82d2719876a362b4838a4f21a14a068e9acd9c16066c41800fb3
+    profiles:
+      - id: opv2v_camera
+        modality: camera
+        inference_modal_arg: 1
+        recommended_command_suffix: --fusion_method intermediate --modal 1 --comm_volume_MB 1 --range 102.4,102.4
+        local_log_reference:
+          range: 102.4,102.4
+          communication: 1 MB
+          ap_0_3: 69.58
+          ap_0_5: 60.93
+          ap_0_7: 42.04

LICENSE

@@ -0,0 +1,10 @@
+
+Academic Software License: © 2021 UCLA Mobility Lab (“Institution”).  Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-3:
+
+Institution hereby grants to you free of charge, so long as you are an academic or nonprofit researcher, a nonexclusive license under Institution’s copyright ownership interest in this software and any derivative works made by you thereof (collectively, the “Software”) to use, copy, and make derivative works of the Software solely for educational or academic research purposes, in all cases subject to the terms of this Academic Software License. Except as granted herein, all rights are reserved by Institution, including the right to pursue patent protection of the Software.
+
+Please note you are prohibited from further transferring the Software -- including any derivatives you make thereof -- to any person or entity. Failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.
+
+IN NO EVENT SHALL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.
+
+Commercial entities: please contact the UCLA Mobility Lab at jiaqima@ucla.edu for licensing opportunities.

README.md

@@ -1,3 +1,221 @@
 ---
-license: apache-2.0
+license: other
+library_name: pytorch
+tags:
+  - blindmap
+  - collaborative-perception
+  - cooperative-perception
+  - autonomous-driving
+  - v2x
+  - opv2v
+  - lidar
+  - camera
+  - 3d-object-detection
+  - pytorch
+  - opencood
+model-index:
+  - name: BlindMap OPV2V Checkpoints
+    results:
+      - task:
+          type: object-detection
+          name: Cooperative 3D Object Detection
+        dataset:
+          type: OPV2V
+          name: OPV2V
+        metrics:
+          - type: AP@0.7
+            name: OPV2V Heterogeneous AP@0.7
+            value: 79.43
+          - type: AP@0.3
+            name: OPV2V Camera AP@0.3
+            value: 69.58
 ---
+
+# BlindMap OPV2V Checkpoints
+
+This repository contains OPV2V checkpoints for **BlindMap**, a
+communication-efficient collaborative perception method for deadline-constrained
+V2X perception.
+
+Paper:
+
+> **Deadline-Constrained Collaborative Perception via Third-Person Spatial Value
+> Modeling**  
+> Zhenhan Zhu, Yanchao Zhao, Yihang Jiang, Hao Han, and Jie Wu.
+
+Code release:
+
+- Repository: `https://github.com/AlexZhu2000/BlindMap`
+- Branch: `TMC`
+- Commit used for this release: `075b2c7120277a619f41bc49a58e3905ffad7aa7`
+
+## Files
+
+```text
+.
+├── README.md
+├── LICENSE
+├── CHECKPOINT_MANIFEST.yaml
+├── SHA256SUMS
+└── models
+    └── opv2v
+        ├── lidar_heterogeneous
+        │   ├── config.yaml
+        │   ├── net_epoch_bestval_at37.pth
+        │   └── source_result.txt
+        └── camera
+            ├── config.yaml
+            ├── net_epoch_bestval_at17.pth
+            └── source_result.txt
+```
+
+`models/opv2v/lidar_heterogeneous` is a single m1m2 checkpoint. It supports two
+runtime profiles through BlindMap's `--modal` option:
+
+- `--modal 0`: LiDAR-only inference profile.
+- `--modal 4`: heterogeneous LiDAR-camera inference profile.
+
+`models/opv2v/camera` is the camera-only checkpoint and should be used with
+`--modal 1`.
+
+## Checkpoints
+
+| Profile | Model directory | Checkpoint | SHA-256 |
+|---|---|---|---|
+| OPV2V LiDAR-only runtime profile | `models/opv2v/lidar_heterogeneous` | `net_epoch_bestval_at37.pth` | `9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3` |
+| OPV2V heterogeneous profile | `models/opv2v/lidar_heterogeneous` | `net_epoch_bestval_at37.pth` | `9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3` |
+| OPV2V camera-only profile | `models/opv2v/camera` | `net_epoch_bestval_at17.pth` | `33ddf54fe56d82d2719876a362b4838a4f21a14a068e9acd9c16066c41800fb3` |
+
+Verify downloaded checkpoint files with:
+
+```bash
+sha256sum --check SHA256SUMS
+```
+
+## Reported Local Results
+
+These values are copied from the included `source_result.txt` logs. They are
+provided for provenance; please rerun inference in your own environment before
+reporting new comparisons.
+
+| Profile | Command setting | Range | Communication | Metric |
+|---|---|---:|---:|---:|
+| OPV2V heterogeneous | `--modal 4` | `102.4,102.4` | `1 MB` | AP@0.7 = `79.43` |
+| OPV2V camera-only | `--modal 1` | `102.4,102.4` | `1 MB` | AP@0.3 = `69.58` |
+| OPV2V LiDAR-only runtime profile | `--modal 0` | `140.8,40` | `1 MB` | AP@0.7 = `93.09` |
+
+The OPV2V LiDAR-only entry above is a runtime profile from the m1m2 checkpoint
+included in this repository. If you need a paper-standard LiDAR-only number at
+`--range 102.4,102.4`, rerun the command below with `--modal 0` and record the
+new result from your environment.
+
+## Installation
+
+Install the BlindMap codebase first. The checkpoints are designed for the
+BlindMap/OpenCOOD-style `--model_dir` loader, where each checkpoint directory
+contains both `config.yaml` and `net_epoch_bestval_at*.pth`.
+
+```bash
+git clone --branch TMC https://github.com/AlexZhu2000/BlindMap.git
+cd BlindMap
+conda create -n blindmap python=3.8
+conda activate blindmap
+pip install -r requirements.txt
+python setup.py develop
+python opencood/utils/setup.py build_ext --inplace
+```
+
+Use the same CUDA, PyTorch, and `spconv` versions described in the BlindMap
+repository. The checkpoints were produced in the original BlindMap/OpenCOOD
+environment and may not be compatible with arbitrary `spconv` versions.
+
+## Data
+
+The OPV2V dataset is not included. Download OPV2V from its official provider
+and update these paths in the selected `config.yaml`:
+
+```yaml
+root_dir: /path/to/OPV2V/train
+validate_dir: /path/to/OPV2V/validate
+test_dir: /path/to/OPV2V/test
+```
+
+## Inference
+
+Download this model repository and point `--model_dir` to one of the model
+directories.
+
+### Heterogeneous OPV2V
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \
+  --model_dir /path/to/blindmap-opv2v/models/opv2v/lidar_heterogeneous \
+  --fusion_method intermediate \
+  --modal 4 \
+  --comm_volume_MB 1 \
+  --range 102.4,102.4
+```
+
+### LiDAR-Only Runtime Profile
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \
+  --model_dir /path/to/blindmap-opv2v/models/opv2v/lidar_heterogeneous \
+  --fusion_method intermediate \
+  --modal 0 \
+  --comm_volume_MB 1 \
+  --range 102.4,102.4
+```
+
+### Camera-Only OPV2V
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \
+  --model_dir /path/to/blindmap-opv2v/models/opv2v/camera \
+  --fusion_method intermediate \
+  --modal 1 \
+  --comm_volume_MB 1 \
+  --range 102.4,102.4
+```
+
+## Intended Use
+
+These checkpoints are intended for academic research on collaborative
+perception, communication-efficient feature sharing, and OPV2V-based
+reproducibility studies.
+
+They are not intended for deployment in autonomous vehicles or safety-critical
+systems.
+
+## Limitations
+
+- Results depend on the exact BlindMap code revision, OPV2V split, sensing
+  range, communication-budget accounting, and environment.
+- The LiDAR-only profile in this release reuses the m1m2 checkpoint through
+  `--modal 0`; it is not a separate LiDAR-only training artifact.
+- OPV2V is a simulated dataset and does not cover all real-world sensor,
+  traffic, weather, and network conditions.
+- The model card reports local log entries for provenance. Users should rerun
+  inference and report their own reproduced metrics.
+
+## License
+
+The included `LICENSE` file follows the current BlindMap source license. It is
+an academic research license with redistribution restrictions. If you intend to
+redistribute, modify, or use these checkpoints outside academic research, obtain
+the required permission from the rights holders first.
+
+Dataset licenses apply separately.
+
+## Citation
+
+```bibtex
+@article{zhu2026deadline,
+  title   = {Deadline-Constrained Collaborative Perception via Third-Person Spatial Value Modeling},
+  author  = {Zhu, Zhenhan and Zhao, Yanchao and Jiang, Yihang and Han, Hao and Wu, Jie},
+  journal = {IEEE Transactions on Mobile Computing},
+  year    = {2026},
+  note    = {Manuscript under review}
+}
+```
+

SHA256SUMS

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+9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3  models/opv2v/lidar_heterogeneous/net_epoch_bestval_at37.pth
+33ddf54fe56d82d2719876a362b4838a4f21a14a068e9acd9c16066c41800fb3  models/opv2v/camera/net_epoch_bestval_at17.pth

models/opv2v/camera/config.yaml

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+add_data_extension:
+- bev_visibility.png
+blindmap_loss:
+  args:
+    blind_map_loss_weight: 1
+  core_method: blindmap_loss
+cav_lidar_range: &id002
+- -51.2
+- -51.2
+- -3
+- 51.2
+- 51.2
+- 1
+comm_range: 70
+fusion:
+  args:
+    data_aug_conf: None
+    grid_conf: None
+    proj_first: false
+  core_method: blindmapintermediatev2xset
+  dataset: opv2v
+heter:
+  assignment_path: opencood/logs/heter_modality_assign/opv2v_4modality.json
+  ego_modality: m2
+  mapping_dict:
+    m1: m2
+    m2: m2
+    m3: m2
+    m4: m2
+  modality_setting:
+    m2:
+      core_method: lift_splat_shoot
+      data_aug_conf: &id004
+        H: 600
+        Ncams: 4
+        W: 800
+        bot_pct_lim:
+        - 0.0
+        - 0.05
+        cams:
+        - camera0
+        - camera1
+        - camera2
+        - camera3
+        final_dim:
+        - 384
+        - 512
+        rand_flip: false
+        resize_lim:
+        - 0.65
+        - 0.7
+        rot_lim:
+        - -3.6
+        - 3.6
+      grid_conf: &id003
+        ddiscr:
+        - 2
+        - 50
+        - 48
+        mode: LID
+        xbound:
+        - -51.2
+        - 51.2
+        - 0.4
+        ybound:
+        - -51.2
+        - 51.2
+        - 0.4
+        zbound:
+        - -10
+        - 10
+        - 20.0
+      sensor_type: camera
+history_num: 10
+input_source:
+- camera
+- depth
+label_type: camera
+loss:
+  args:
+    cls:
+      alpha: 0.25
+      gamma: 2.0
+      type: SigmoidFocalLoss
+      weight: 1.0
+    depth:
+      weight: 1.0
+    dir:
+      args: &id001
+        anchor_yaw: &id005
+        - 0
+        - 90
+        dir_offset: 0.7853
+        num_bins: 2
+      type: WeightedSoftmaxClassificationLoss
+      weight: 0.2
+    pos_cls_weight: 2.0
+    pyramid:
+      relative_downsample:
+      - 1
+      - 2
+      - 4
+      weight:
+      - 0.4
+      - 0.2
+      - 0.1
+    reg:
+      codewise: true
+      sigma: 3.0
+      type: WeightedSmoothL1Loss
+      weight: 2.0
+  core_method: point_pillar_pyramid_blindmap_loss
+lr_scheduler:
+  core_method: multistep
+  gamma: 0.1
+  step_size:
+  - 15
+  - 25
+model:
+  args:
+    anchor_number: 2
+    dir_args: *id001
+    fusion_backbone:
+      anchor_number: 2
+      blindmap:
+        hidden_dim: 64
+        history_dim: 64
+        history_fusion_strategy: weighted_average
+        history_num: 10
+        ripe_dim: 2
+        use_history: true
+        use_ripe: true
+      communication:
+        comm_volume_MB: 1
+        fusion_mode: MAX
+        gaussian_smooth:
+          c_sigma: 1.0
+          k_size: 5
+        thre: 0.01
+        use_threshold: true
+      layer_nums:
+      - 3
+      - 5
+      - 8
+      layer_strides:
+      - 1
+      - 2
+      - 2
+      num_filters:
+      - 64
+      - 128
+      - 256
+      num_upsample_filter:
+      - 128
+      - 128
+      - 128
+      resnext: true
+      upsample_strides:
+      - 1
+      - 2
+      - 4
+    in_head: 256
+    lidar_range: *id002
+    m2:
+      aligner_args:
+        core_method: identity
+      backbone_args:
+        inplanes: 128
+        layer_nums:
+        - 3
+        layer_strides:
+        - 2
+        num_filters:
+        - 64
+      camera_mask_args:
+        cav_lidar_range: *id002
+        grid_conf: *id003
+      core_method: lift_splat_shoot
+      encoder_args:
+        anchor_number: 2
+        camera_encoder: EfficientNet
+        data_aug_conf: *id004
+        depth_supervision: true
+        grid_conf: *id003
+        img_downsample: 8
+        img_features: 128
+        use_depth_gt: false
+      sensor_type: camera
+    shrink_header:
+      dim:
+      - 256
+      input_dim: 384
+      kernal_size:
+      - 3
+      padding:
+      - 1
+      stride:
+      - 1
+    supervise_single: true
+  core_method: blindmap_pyramid_collab_v2xset
+name: BlindMap_opv2v_lidar_pyramid
+noise_setting:
+  add_noise: true
+  args:
+    pos_mean: 0
+    pos_std: 0.2
+    rot_mean: 0
+    rot_std: 0.2
+optimizer:
+  args:
+    eps: 1.0e-10
+    weight_decay: 0.0001
+  core_method: Adam
+  lr: 0.002
+postprocess:
+  anchor_args:
+    D: 1
+    H: 256
+    W: 256
+    cav_lidar_range: *id002
+    feature_stride: 2
+    h: 1.56
+    l: 3.9
+    num: 2
+    r: *id005
+    vd: 4
+    vh: 0.4
+    vw: 0.4
+    w: 1.6
+  core_method: VoxelPostprocessor
+  dir_args: *id001
+  gt_range: *id002
+  max_num: 150
+  nms_thresh: 0.15
+  order: hwl
+  target_args:
+    neg_threshold: 0.45
+    pos_threshold: 0.6
+    score_threshold: 0.2
+preprocess:
+  args:
+    max_points_per_voxel: 1
+    max_voxel_test: 1
+    max_voxel_train: 1
+    voxel_size:
+    - 0.4
+    - 0.4
+    - 4
+  cav_lidar_range: *id002
+  core_method: SpVoxelPreprocessor
+root_dir: /path/to/OPV2V/train
+test_dir: /path/to/OPV2V/test
+train_params:
+  batch_size: 2
+  epoches: 30
+  eval_freq: 2
+  max_cav: 5
+  save_freq: 2
+use_history: true
+validate_dir: /path/to/OPV2V/validate
+yaml_parser: load_general_params

models/opv2v/camera/net_epoch_bestval_at17.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33ddf54fe56d82d2719876a362b4838a4f21a14a068e9acd9c16066c41800fb3
+size 81465973

models/opv2v/camera/source_result.txt

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+Epoch: 17 | AP @0.3: 0.7003 | AP @0.5: 0.6133 | AP @0.7: 0.4220 | comm_rate: 0.017213 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4378
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.6958 | AP @0.5: 0.6093 | AP @0.7: 0.4204 | comm_rate: 0.062500 | comm_volume_MB: 1.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 1.6195
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.6972 | AP @0.5: 0.6128 | AP @0.7: 0.4224 | comm_rate: 0.017857 | comm_volume_MB: 0.5000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8414
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.6746 | AP @0.5: 0.6011 | AP @0.7: 0.4178 | comm_rate: 0.008929 | comm_volume_MB: 0.2500 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8417
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.6297 | AP @0.5: 0.5590 | AP @0.7: 0.3775 | comm_rate: 0.004464 | comm_volume_MB: 0.1250 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8253
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5996 | AP @0.5: 0.5255 | AP @0.7: 0.3479 | comm_rate: 0.002232 | comm_volume_MB: 0.0625 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.7941
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5759 | AP @0.5: 0.4958 | AP @0.7: 0.3206 | comm_rate: 0.001116 | comm_volume_MB: 0.0312 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6836
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5513 | AP @0.5: 0.4678 | AP @0.7: 0.2958 | comm_rate: 0.000558 | comm_volume_MB: 0.0156 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6877
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5306 | AP @0.5: 0.4436 | AP @0.7: 0.2765 | comm_rate: 0.000279 | comm_volume_MB: 0.0078 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6792
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5168 | AP @0.5: 0.4291 | AP @0.7: 0.2648 | comm_rate: 0.000139 | comm_volume_MB: 0.0039 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6813
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5059 | AP @0.5: 0.4170 | AP @0.7: 0.2549 | comm_rate: 0.000070 | comm_volume_MB: 0.0020 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6840
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.5003 | AP @0.5: 0.4105 | AP @0.7: 0.2512 | comm_rate: 0.000035 | comm_volume_MB: 0.0010 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6836
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.4970 | AP @0.5: 0.4069 | AP @0.7: 0.2490 | comm_rate: 0.000000 | comm_volume_MB: 0.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 1.6239
+

models/opv2v/lidar_heterogeneous/config.yaml

@@ -0,0 +1,332 @@
+add_data_extension:
+- bev_visibility.png
+blindmap_loss:
+  args:
+    blind_map_loss_weight: 1
+  core_method: blindmap_loss
+cav_lidar_range: &id001
+- -102.4
+- -102.4
+- -3
+- 102.4
+- 102.4
+- 1
+comm_range: 70
+fusion:
+  args:
+    data_aug_conf: None
+    grid_conf: None
+    proj_first: false
+  core_method: blindmapintermediatev2xset
+  dataset: opv2v
+heter:
+  assignment_path: opencood/logs/heter_modality_assign/opv2v_4modality.json
+  # cav_preference:
+  #   m1: 0.0
+  #   m2: 1.0
+  # ego_modality: m1
+  # mapping_dict:
+  #   m1: m1
+  #   m2: m2
+  #   m3: m2
+  #   m4: m2
+  ego_modality: m1&m2
+  mapping_dict:
+    m1: m1
+    m2: m1
+    m3: m2
+    m4: m2
+  modality_setting:
+    m1:
+      core_method: point_pillar
+      preprocess:
+        args:
+          max_points_per_voxel: 32
+          max_voxel_test: 70000
+          max_voxel_train: 32000
+          voxel_size: &id003
+          - 0.4
+          - 0.4
+          - 4
+        cav_lidar_range: *id001
+        core_method: SpVoxelPreprocessor
+      sensor_type: lidar
+    m2:
+      core_method: lift_splat_shoot
+      data_aug_conf: &id005
+        H: 600
+        Ncams: 4
+        W: 800
+        bot_pct_lim:
+        - 0.0
+        - 0.05
+        cams:
+        - camera0
+        - camera1
+        - camera2
+        - camera3
+        final_dim:
+        - 384
+        - 512
+        rand_flip: false
+        resize_lim:
+        - 0.65
+        - 0.7
+        rot_lim:
+        - -3.6
+        - 3.6
+      grid_conf: &id004
+        ddiscr:
+        - 2
+        - 50
+        - 48
+        mode: LID
+        xbound:
+        - -51.2
+        - 51.2
+        - 0.4
+        ybound:
+        - -51.2
+        - 51.2
+        - 0.4
+        zbound:
+        - -10
+        - 10
+        - 20.0
+      sensor_type: camera
+history_num: 10
+input_source:
+- lidar
+- camera
+- depth
+label_type: lidar
+loss:
+  args:
+    cls:
+      alpha: 0.25
+      gamma: 2.0
+      type: SigmoidFocalLoss
+      weight: 1.0
+    depth:
+      weight: 1.0
+    dir:
+      args: &id002
+        anchor_yaw: &id006
+        - 0
+        - 90
+        dir_offset: 0.7853
+        num_bins: 2
+      type: WeightedSoftmaxClassificationLoss
+      weight: 0.2
+    pos_cls_weight: 2.0
+    pyramid:
+      relative_downsample:
+      - 1
+      - 2
+      - 4
+      weight:
+      - 0.4
+      - 0.2
+      - 0.1
+    reg:
+      codewise: true
+      sigma: 3.0
+      type: WeightedSmoothL1Loss
+      weight: 2.0
+  core_method: point_pillar_pyramid_blindmap_loss
+lr_scheduler:
+  core_method: multistep
+  gamma: 0.1
+  step_size:
+  - 15
+  - 30
+model:
+  args:
+    anchor_number: 2
+    dir_args: *id002
+    fusion_backbone:
+      anchor_number: 2
+      blindmap:
+        hidden_dim: 64
+        history_dim: 64
+        history_fusion_strategy: weighted_average
+        history_num: 10
+        ripe_dim: 2
+        use_history: true
+        use_ripe: true
+      communication:
+        comm_volume_MB: 1
+        fusion_mode: MAX
+        gaussian_smooth:
+          c_sigma: 1.0
+          k_size: 5
+        thre: 0.01
+        use_threshold: true
+      layer_nums:
+      - 3
+      - 5
+      - 8
+      layer_strides:
+      - 1
+      - 2
+      - 2
+      num_filters:
+      - 64
+      - 128
+      - 256
+      num_upsample_filter:
+      - 128
+      - 128
+      - 128
+      resnext: true
+      upsample_strides:
+      - 1
+      - 2
+      - 4
+    in_head: 256
+    lidar_range: *id001
+    m1:
+      aligner_args:
+        core_method: identity
+      backbone_args:
+        layer_nums:
+        - 3
+        layer_strides:
+        - 2
+        num_filters:
+        - 64
+      core_method: point_pillar
+      encoder_args:
+        lidar_range: *id001
+        pillar_vfe:
+          num_filters:
+          - 64
+          use_absolute_xyz: true
+          use_norm: true
+          with_distance: false
+        point_pillar_scatter:
+          grid_size: !!python/object/apply:numpy.core.multiarray._reconstruct
+            args:
+            - !!python/name:numpy.ndarray ''
+            - !!python/tuple
+              - 0
+            - !!binary |
+              Yg==
+            state: !!python/tuple
+            - 1
+            - !!python/tuple
+              - 3
+            - !!python/object/apply:numpy.dtype
+              args:
+              - i8
+              - false
+              - true
+              state: !!python/tuple
+              - 3
+              - <
+              - null
+              - null
+              - null
+              - -1
+              - -1
+              - 0
+            - false
+            - !!binary |
+              AAIAAAAAAAAAAgAAAAAAAAEAAAAAAAAA
+          num_features: 64
+        voxel_size: *id003
+      sensor_type: lidar
+    m2:
+      aligner_args:
+        core_method: identity
+      backbone_args:
+        inplanes: 128
+        layer_nums:
+        - 3
+        layer_strides:
+        - 2
+        num_filters:
+        - 64
+      camera_mask_args:
+        cav_lidar_range: *id001
+        grid_conf: *id004
+      core_method: lift_splat_shoot
+      encoder_args:
+        anchor_number: 2
+        camera_encoder: EfficientNet
+        data_aug_conf: *id005
+        depth_supervision: true
+        grid_conf: *id004
+        img_downsample: 8
+        img_features: 128
+        use_depth_gt: false
+      sensor_type: camera
+    shrink_header:
+      dim:
+      - 256
+      input_dim: 384
+      kernal_size:
+      - 3
+      padding:
+      - 1
+      stride:
+      - 1
+    supervise_single: true
+  core_method: blindmap_pyramid_collab_v2xset
+name: BlindMap_opv2v_m1m2
+noise_setting: !!python/object/apply:collections.OrderedDict
+- - - add_noise
+    - false
+optimizer:
+  args:
+    eps: 1.0e-10
+    weight_decay: 0.0001
+  core_method: Adam
+  lr: 0.002
+postprocess:
+  anchor_args:
+    D: 1
+    H: 512
+    W: 512
+    cav_lidar_range: *id001
+    feature_stride: 2
+    h: 1.56
+    l: 3.9
+    num: 2
+    r: *id006
+    vd: 4
+    vh: 0.4
+    vw: 0.4
+    w: 1.6
+  core_method: VoxelPostprocessor
+  dir_args: *id002
+  gt_range: *id001
+  max_num: 150
+  nms_thresh: 0.15
+  order: hwl
+  target_args:
+    neg_threshold: 0.45
+    pos_threshold: 0.6
+    score_threshold: 0.2
+preprocess:
+  args:
+    max_points_per_voxel: 1
+    max_voxel_test: 1
+    max_voxel_train: 1
+    voxel_size:
+    - 0.4
+    - 0.4
+    - 4
+  cav_lidar_range: *id001
+  core_method: SpVoxelPreprocessor
+root_dir: /path/to/OPV2V/train
+test_dir: /path/to/OPV2V/test
+train_params:
+  batch_size: 1
+  epoches: 40
+  eval_freq: 2
+  max_cav: 5
+  save_freq: 2
+use_history: true
+validate_dir: /path/to/OPV2V/validate
+yaml_parser: load_general_params

models/opv2v/lidar_heterogeneous/net_epoch_bestval_at37.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3
+size 82395477

models/opv2v/lidar_heterogeneous/source_result.txt

@@ -0,0 +1,50 @@
+Epoch: 37 | AP @0.3: 0.8862 | AP @0.5: 0.8724 | AP @0.7: 0.7949 | comm_rate: 0.022534 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.2641
+Epoch: 37 | AP @0.3: 0.8863 | AP @0.5: 0.8724 | AP @0.7: 0.7950 | comm_rate: 0.022533 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.3963
+Epoch: 37 | AP @0.3: 0.8861 | AP @0.5: 0.8703 | AP @0.7: 0.7964 | comm_rate: 0.037296 |# 140.8,40 | # no_noiseno_delay | time_av: 0.3790 | ego&co: m1&m2
+Epoch: 37 | AP @0.3: 0.8315 | AP @0.5: 0.7982 | AP @0.7: 0.6816 | comm_rate: 0.038467 |# 140.8,40 | # no_noiseno_delay | time_av: 0.5007  | ego:m1 & co:m2
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.6342 | AP @0.5: 0.5489 | AP @0.7: 0.3652 | comm_rate: 0.022098 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.9238
+modal 2 ego_lidar_other_cam | Epoch: 37 | AP @0.3: 0.8114 | AP @0.5: 0.7786 | AP @0.7: 0.6593 | comm_rate: 0.022085 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.7251
+modal 3 _ego_cam_other_lidar | Epoch: 37 | AP @0.3: 0.9312 | AP @0.5: 0.9211 | AP @0.7: 0.8656 | comm_rate: 0.022885 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4092
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8863 | AP @0.5: 0.8724 | AP @0.7: 0.7949 | comm_rate: 0.022534 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5556
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.6344 | AP @0.5: 0.5485 | AP @0.7: 0.3641 | comm_rate: 0.035714 | comm_volume_MB: 1.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4882
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.6307 | AP @0.5: 0.5467 | AP @0.7: 0.3644 | comm_rate: 0.017857 | comm_volume_MB: 0.5000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4884
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.6179 | AP @0.5: 0.5360 | AP @0.7: 0.3543 | comm_rate: 0.008929 | comm_volume_MB: 0.2500 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5825
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.6022 | AP @0.5: 0.5207 | AP @0.7: 0.3333 | comm_rate: 0.004464 | comm_volume_MB: 0.1250 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8425
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.5838 | AP @0.5: 0.5030 | AP @0.7: 0.3124 | comm_rate: 0.002232 | comm_volume_MB: 0.0625 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8085
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.5599 | AP @0.5: 0.4743 | AP @0.7: 0.2842 | comm_rate: 0.001116 | comm_volume_MB: 0.0312 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.8191
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.5196 | AP @0.5: 0.4286 | AP @0.7: 0.2482 | comm_rate: 0.000558 | comm_volume_MB: 0.0156 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5248
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.4822 | AP @0.5: 0.3900 | AP @0.7: 0.2260 | comm_rate: 0.000279 | comm_volume_MB: 0.0078 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4069
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.4609 | AP @0.5: 0.3680 | AP @0.7: 0.2138 | comm_rate: 0.000139 | comm_volume_MB: 0.0039 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4067
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.4466 | AP @0.5: 0.3539 | AP @0.7: 0.2061 | comm_rate: 0.000070 | comm_volume_MB: 0.0020 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4065
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.4402 | AP @0.5: 0.3482 | AP @0.7: 0.2029 | comm_rate: 0.000035 | comm_volume_MB: 0.0010 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4061
+modal 1 _camonly | Epoch: 37 | AP @0.3: 0.4379 | AP @0.5: 0.3460 | AP @0.7: 0.2012 | comm_rate: 0.000017 | comm_volume_MB: 0.0005 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4061
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8882 | AP @0.5: 0.8733 | AP @0.7: 0.7943 | comm_rate: 0.062500 | comm_volume_MB: 1.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6072
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8864 | AP @0.5: 0.8717 | AP @0.7: 0.7947 | comm_rate: 0.031250 | comm_volume_MB: 0.5000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.3705
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8793 | AP @0.5: 0.8659 | AP @0.7: 0.7902 | comm_rate: 0.015625 | comm_volume_MB: 0.2500 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5149
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8686 | AP @0.5: 0.8564 | AP @0.7: 0.7805 | comm_rate: 0.007812 | comm_volume_MB: 0.1250 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5144
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8561 | AP @0.5: 0.8429 | AP @0.7: 0.7607 | comm_rate: 0.003906 | comm_volume_MB: 0.0625 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.6032
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8462 | AP @0.5: 0.8319 | AP @0.7: 0.7414 | comm_rate: 0.001953 | comm_volume_MB: 0.0312 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5246
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8349 | AP @0.5: 0.8182 | AP @0.7: 0.7185 | comm_rate: 0.000977 | comm_volume_MB: 0.0156 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5288
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8194 | AP @0.5: 0.8011 | AP @0.7: 0.6967 | comm_rate: 0.000488 | comm_volume_MB: 0.0078 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5286
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.8030 | AP @0.5: 0.7828 | AP @0.7: 0.6761 | comm_rate: 0.000244 | comm_volume_MB: 0.0039 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.5271
+modal 4 ego_random_ratio0.5 | Epoch: 37 | AP @0.3: 0.7814 | AP @0.5: 0.7601 | AP @0.7: 0.6545 | comm_rate: 0.000000 | comm_volume_MB: 0.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.7967
+
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.7854 | AP @0.5: 0.7745 | AP @0.7: 0.6868 | comm_rate: -1.000000 | comm_volume_MB: 1.0000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0781
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9601 | AP @0.5: 0.9574 | AP @0.7: 0.9316 | comm_rate: 1.000000 | comm_volume_MB: 100.0000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0381
+
+
+#############subset300_totalbudget
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9869 | AP @0.5: 0.9860 | AP @0.7: 0.9564 | comm_rate: 0.052973 | comm_volume_MB: 1.0000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0525
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9844 | AP @0.5: 0.9829 | AP @0.7: 0.9509 | comm_rate: 0.026487 | comm_volume_MB: 0.5000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0522
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9694 | AP @0.5: 0.9672 | AP @0.7: 0.9237 | comm_rate: 0.013243 | comm_volume_MB: 0.2500 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0504
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9315 | AP @0.5: 0.9261 | AP @0.7: 0.8574 | comm_rate: 0.005297 | comm_volume_MB: 0.1000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0475
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.8805 | AP @0.5: 0.8715 | AP @0.7: 0.7924 | comm_rate: 0.002649 | comm_volume_MB: 0.0500 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0450
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.7933 | AP @0.5: 0.7784 | AP @0.7: 0.6858 | comm_rate: 0.000530 | comm_volume_MB: 0.0100 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0417
+
+##############full test #########
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9585 | AP @0.5: 0.9563 | AP @0.7: 0.9309 | comm_rate: 0.046507 | comm_volume_MB: 1.0000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0587
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9533 | AP @0.5: 0.9513 | AP @0.7: 0.9249 | comm_rate: 0.023254 | comm_volume_MB: 0.5000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0842
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9405 | AP @0.5: 0.9382 | AP @0.7: 0.9056 | comm_rate: 0.011627 | comm_volume_MB: 0.2500 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0401
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.9080 | AP @0.5: 0.9041 | AP @0.7: 0.8547 | comm_rate: 0.004651 | comm_volume_MB: 0.1000 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0384
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.8779 | AP @0.5: 0.8720 | AP @0.7: 0.8070 | comm_rate: 0.002325 | comm_volume_MB: 0.0500 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0377
+modal 0 _lidaronly | Epoch: 37 | AP @0.3: 0.8123 | AP @0.5: 0.8025 | AP @0.7: 0.7217 | comm_rate: 0.000465 | comm_volume_MB: 0.0100 |# 140.8,40 | # no_noiseno_delay | time_av: 0.0956