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models/dair/camera/config.yaml

@@ -0,0 +1,247 @@
+blindmap_loss:
+  args:
+    blind_map_loss_weight: 1
+  core_method: blindmap_loss
+cav_lidar_range: &id002
+- -102.4
+- -51.2
+- -3.5
+- 102.4
+- 51.2
+- 1.5
+comm_range: 100
+data_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure
+fusion:
+  args:
+    data_aug_conf: None
+    grid_conf: None
+    proj_first: false
+  core_method: blindmapintermediate
+  dataset: dairv2x
+heter:
+  assignment_path: null
+  ego_modality: m2
+  mapping_dict:
+    m1: m2
+    m2: m2
+  modality_setting:
+    m2:
+      core_method: lift_splat_shoot
+      data_aug_conf: &id004
+        H: 1080
+        Ncams: 4
+        W: 1920
+        bot_pct_lim:
+        - 0.0
+        - 0.05
+        cams:
+        - camera0
+        - camera1
+        - camera2
+        - camera3
+        final_dim:
+        - 288
+        - 512
+        rand_flip: false
+        resize_lim:
+        - 0.27
+        - 0.28
+        rot_lim:
+        - 0
+        - 0
+      grid_conf: &id003
+        ddiscr:
+        - 2
+        - 100
+        - 98
+        mode: LID
+        xbound:
+        - -102.4
+        - 102.4
+        - 0.4
+        ybound:
+        - -51.2
+        - 51.2
+        - 0.4
+        zbound:
+        - -10
+        - 10
+        - 20.0
+      sensor_type: camera
+input_source:
+- camera
+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
+  - 30
+model:
+  args:
+    anchor_number: 2
+    dir_args: *id001
+    fusion_backbone:
+      anchor_number: 2
+      blindmap:
+        ripe_dim: 2
+        use_ripe: true
+      communication:
+        comm_volume_MB: 100
+        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: false
+        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
+name: BlindMap_DAIR_camera_pyramid
+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: 256
+    W: 512
+    cav_lidar_range: *id002
+    feature_stride: 2
+    h: 1.56
+    l: 3.9
+    num: 2
+    r: *id005
+    vd: 5
+    vh: 0.4
+    vw: 0.4
+    w: 1.6
+  core_method: VoxelPostprocessor
+  dir_args: *id001
+  gt_range: *id002
+  max_num: 150
+  nms_thresh: 0.05
+  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
+    - 5
+  cav_lidar_range: *id002
+  core_method: SpVoxelPreprocessor
+root_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/train.json
+test_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/val.json
+train_params:
+  batch_size: 4
+  epoches: 40
+  eval_freq: 2
+  max_cav: 2
+  save_freq: 2
+use_history: false
+validate_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/val.json
+yaml_parser: load_general_params

models/dair/camera/net_epoch_bestval_at17.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b95722f3b58a13a979315a84133da971415cdc9b6a94bb555b013bd834576d81
+size 81567801

models/dair/camera/source_result.txt

@@ -0,0 +1,45 @@
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2780 | AP @0.5: 0.1211 | AP @0.7: 0.0253 | comm_rate: 0.022743 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1319
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2751 | AP @0.5: 0.1197 | AP @0.7: 0.0248 | comm_rate: 1.000000 | comm_volume_MB: 32.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1325
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2751 | AP @0.5: 0.1197 | AP @0.7: 0.0248 | comm_rate: 1.000000 | comm_volume_MB: 16.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1847
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2751 | AP @0.5: 0.1197 | AP @0.7: 0.0248 | comm_rate: 1.000000 | comm_volume_MB: 8.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1330
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2766 | AP @0.5: 0.1207 | AP @0.7: 0.0251 | comm_rate: 0.500000 | comm_volume_MB: 4.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1344
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2770 | AP @0.5: 0.1208 | AP @0.7: 0.0250 | comm_rate: 0.250000 | comm_volume_MB: 2.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1334
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2781 | AP @0.5: 0.1213 | AP @0.7: 0.0251 | comm_rate: 0.125000 | comm_volume_MB: 1.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1340
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2783 | AP @0.5: 0.1212 | AP @0.7: 0.0252 | comm_rate: 0.062500 | comm_volume_MB: 0.5000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1350
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2782 | AP @0.5: 0.1207 | AP @0.7: 0.0251 | comm_rate: 0.031250 | comm_volume_MB: 0.2500 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1606
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2721 | AP @0.5: 0.1174 | AP @0.7: 0.0254 | comm_rate: 0.015625 | comm_volume_MB: 0.1250 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1339
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2565 | AP @0.5: 0.1132 | AP @0.7: 0.0230 | comm_rate: 0.007812 | comm_volume_MB: 0.0625 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1312
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2129 | AP @0.5: 0.0954 | AP @0.7: 0.0162 | comm_rate: 0.003906 | comm_volume_MB: 0.0312 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1273
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.1539 | AP @0.5: 0.0636 | AP @0.7: 0.0101 | comm_rate: 0.001953 | comm_volume_MB: 0.0156 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1241
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.1056 | AP @0.5: 0.0439 | AP @0.7: 0.0068 | comm_rate: 0.000976 | comm_volume_MB: 0.0078 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1225
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.0764 | AP @0.5: 0.0336 | AP @0.7: 0.0053 | comm_rate: 0.000488 | comm_volume_MB: 0.0039 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1217
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.0634 | AP @0.5: 0.0287 | AP @0.7: 0.0045 | comm_rate: 0.000244 | comm_volume_MB: 0.0020 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1212
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.0575 | AP @0.5: 0.0261 | AP @0.7: 0.0041 | comm_rate: 0.000000 | comm_volume_MB: 0.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1639
+
+
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2721 | AP @0.5: 0.1174 | AP @0.7: 0.0254 | comm_rate: 0.015625 | comm_volume_MB: 0.1250 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3858
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2565 | AP @0.5: 0.1132 | AP @0.7: 0.0230 | comm_rate: 0.007812 | comm_volume_MB: 0.0625 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3783
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2129 | AP @0.5: 0.0954 | AP @0.7: 0.0162 | comm_rate: 0.003906 | comm_volume_MB: 0.0312 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3620
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.1539 | AP @0.5: 0.0636 | AP @0.7: 0.0101 | comm_rate: 0.001953 | comm_volume_MB: 0.0156 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3580
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.1056 | AP @0.5: 0.0439 | AP @0.7: 0.0068 | comm_rate: 0.000976 | comm_volume_MB: 0.0078 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3658
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.0764 | AP @0.5: 0.0336 | AP @0.7: 0.0053 | comm_rate: 0.000488 | comm_volume_MB: 0.0039 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3644
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.0634 | AP @0.5: 0.0287 | AP @0.7: 0.0045 | comm_rate: 0.000244 | comm_volume_MB: 0.0020 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3631
+#########################以上是在推理过程中固定veh为ego，由于Infra有时距离较远所以导致协同效果低#################################
+
+#########################以下是在推理过程中随机选择veh和Infra作为ego代理，会显著提高验证效果#################################
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3321 | AP @0.5: 0.1736 | AP @0.7: 0.0402 | comm_rate: 0.500000 | comm_volume_MB: 4.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1348
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3281 | AP @0.5: 0.1719 | AP @0.7: 0.0396 | comm_rate: 0.250000 | comm_volume_MB: 2.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1343
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3269 | AP @0.5: 0.1721 | AP @0.7: 0.0383 | comm_rate: 0.125000 | comm_volume_MB: 1.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1344
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3241 | AP @0.5: 0.1694 | AP @0.7: 0.0388 | comm_rate: 0.062500 | comm_volume_MB: 0.5000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1330
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3260 | AP @0.5: 0.1659 | AP @0.7: 0.0380 | comm_rate: 0.031250 | comm_volume_MB: 0.2500 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1317
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3269 | AP @0.5: 0.1716 | AP @0.7: 0.0404 | comm_rate: 0.015625 | comm_volume_MB: 0.1250 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1299
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.3140 | AP @0.5: 0.1679 | AP @0.7: 0.0395 | comm_rate: 0.007812 | comm_volume_MB: 0.0625 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1288
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2936 | AP @0.5: 0.1617 | AP @0.7: 0.0386 | comm_rate: 0.003906 | comm_volume_MB: 0.0312 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1267
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2600 | AP @0.5: 0.1419 | AP @0.7: 0.0332 | comm_rate: 0.001953 | comm_volume_MB: 0.0156 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1247
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2322 | AP @0.5: 0.1288 | AP @0.7: 0.0327 | comm_rate: 0.000976 | comm_volume_MB: 0.0078 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1235
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2158 | AP @0.5: 0.1218 | AP @0.7: 0.0296 | comm_rate: 0.000488 | comm_volume_MB: 0.0039 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1230
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2135 | AP @0.5: 0.1211 | AP @0.7: 0.0318 | comm_rate: 0.000244 | comm_volume_MB: 0.0020 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1237
+
+modal 1 _camonly | Epoch: 17 | AP @0.3: 0.2013 | AP @0.5: 0.1163 | AP @0.7: 0.0297 | comm_rate: 0.000000 | comm_volume_MB: 0.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1240
+
+

models/dair/heterogeneous/config.yaml

@@ -0,0 +1,313 @@
+blindmap_loss:
+  args:
+    blind_map_loss_weight: 1
+  core_method: blindmap_loss
+cav_lidar_range: &id001
+- -102.4
+- -51.2
+- -3.5
+- 102.4
+- 51.2
+- 1.5
+comm_range: 100
+data_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure
+fusion:
+  args:
+    data_aug_conf: None
+    grid_conf: None
+    proj_first: false
+  core_method: blindmapintermediate
+  dataset: dairv2x
+heter:
+  assignment_path: null
+  ego_modality: m1&m2
+  mapping_dict:
+    m1: m1
+    m2: 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
+          - 5
+        cav_lidar_range: *id001
+        core_method: SpVoxelPreprocessor
+      sensor_type: lidar
+    m2:
+      core_method: lift_splat_shoot
+      data_aug_conf: &id005
+        H: 1080
+        Ncams: 4
+        W: 1920
+        bot_pct_lim:
+        - 0.0
+        - 0.05
+        cams:
+        - camera0
+        - camera1
+        - camera2
+        - camera3
+        final_dim:
+        - 288
+        - 512
+        rand_flip: false
+        resize_lim:
+        - 0.27
+        - 0.28
+        rot_lim:
+        - 0
+        - 0
+      grid_conf: &id004
+        ddiscr:
+        - 2
+        - 100
+        - 98
+        mode: LID
+        xbound:
+        - -102.4
+        - 102.4
+        - 0.4
+        ybound:
+        - -51.2
+        - 51.2
+        - 0.4
+        zbound:
+        - -10
+        - 10
+        - 20.0
+      sensor_type: camera
+input_source:
+- lidar
+- camera
+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:
+        ripe_dim: 2
+        use_ripe: true
+      communication:
+        comm_volume_MB: 100
+        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 |
+              AAIAAAAAAAAAAQAAAAAAAAEAAAAAAAAA
+          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: false
+        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
+name: Pyramid_DAIR_m1m2_base
+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: 256
+    W: 512
+    cav_lidar_range: *id001
+    feature_stride: 2
+    h: 1.56
+    l: 3.9
+    num: 2
+    r: *id006
+    vd: 5
+    vh: 0.4
+    vw: 0.4
+    w: 1.6
+  core_method: VoxelPostprocessor
+  dir_args: *id002
+  gt_range: *id001
+  max_num: 150
+  nms_thresh: 0.05
+  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
+    - 5
+  cav_lidar_range: *id001
+  core_method: SpVoxelPreprocessor
+root_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/train.json
+test_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/val.json
+train_params:
+  batch_size: 2
+  epoches: 40
+  eval_freq: 2
+  max_cav: 2
+  save_freq: 2
+use_history: false
+validate_dir: /home/zzh/projects/HEAL/dataset/my_dair_v2x/v2x_c/cooperative-vehicle-infrastructure/val.json
+yaml_parser: load_general_params

models/dair/heterogeneous/net_epoch_bestval_at35.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10511d4a9419f42051f01be2b9e6313b6718b2843f74795b5d9ceac7b200f7f5
+size 82497369

models/dair/heterogeneous/source_result.txt

@@ -0,0 +1,14 @@
+modal 0 _lidaronly | Epoch: 35 | AP @0.3: 0.7913 | AP @0.5: 0.7468 | AP @0.7: 0.5821 | comm_rate: 0.018736 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.1279
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.6599 | AP @0.5: 0.6138 | AP @0.7: 0.4907 | comm_rate: 0.035714 | comm_volume_MB: 1.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.4105
+modal 1 _camonly | Epoch: 35 | AP @0.3: 0.1212 | AP @0.5: 0.0564 | AP @0.7: 0.0100 | comm_rate: 0.035714 | comm_volume_MB: 1.0000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.7832
+modal 1 _camonly | Epoch: 35 | AP @0.3: 0.0889 | AP @0.5: 0.0421 | AP @0.7: 0.0072 | comm_rate: 0.017857 | comm_volume_MB: 0.5000 |# 102.4,102.4 | # no_noiseno_delay | time_av: 0.7686
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.7551 | AP @0.5: 0.6498 | AP @0.7: 0.4042 | comm_rate: 0.125000 | comm_volume_MB: 1.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.2471
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.7492 | AP @0.5: 0.6436 | AP @0.7: 0.4040 | comm_rate: 0.062500 | comm_volume_MB: 0.5000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3050
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.7252 | AP @0.5: 0.6221 | AP @0.7: 0.3818 | comm_rate: 0.031250 | comm_volume_MB: 0.2500 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3071
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.7116 | AP @0.5: 0.6052 | AP @0.7: 0.3716 | comm_rate: 0.015625 | comm_volume_MB: 0.1250 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3118
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.6898 | AP @0.5: 0.5815 | AP @0.7: 0.3524 | comm_rate: 0.007812 | comm_volume_MB: 0.0625 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3143
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.6563 | AP @0.5: 0.5472 | AP @0.7: 0.3344 | comm_rate: 0.003906 | comm_volume_MB: 0.0312 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.0980
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.6215 | AP @0.5: 0.5211 | AP @0.7: 0.3333 | comm_rate: 0.001953 | comm_volume_MB: 0.0156 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.1276
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.5820 | AP @0.5: 0.4857 | AP @0.7: 0.3148 | comm_rate: 0.000976 | comm_volume_MB: 0.0078 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.2410
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.5629 | AP @0.5: 0.4650 | AP @0.7: 0.3020 | comm_rate: 0.000488 | comm_volume_MB: 0.0039 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.3215
+modal 4 ego_random_ratio0.5 | Epoch: 35 | AP @0.3: 0.5593 | AP @0.5: 0.4638 | AP @0.7: 0.3117 | comm_rate: 0.000000 | comm_volume_MB: 0.0000 |# 102.4,51.2 | # no_noiseno_delay | time_av: 0.4803