add v2vnet yaml file

v2xvit/hypes_yaml/point_pillar_v2vnet.yaml

@@ -0,0 +1,137 @@
+name: point_pillar_v2vney
+#root_dir: '/home/runshengxu/project/Cooperative_perception/opencood/tmp'
+#root_dir: '/home/ucla_cav/data/train'
+#validate_dir: '/home/ucla_cav/data/validate'
+root_dir: '/home/hao/code/mobility/data/train'
+validate_dir: '/home/hao/code/mobility/data/validate'
+wild_setting:
+  async: true
+  async_overhead: 100
+  seed: 20
+  loc_err: true
+  xyz_std: 0.2
+  ryp_std: 0.2
+  data_size: 1.06 # Mb!!
+  transmission_speed: 27 # Mbps!!
+  backbone_delay: 10 # ms
+
+yaml_parser: "load_point_pillar_params"
+train_params:
+  batch_size: &batch_size 4
+  epoches: 60
+  eval_freq: 1
+  save_freq: 1
+  max_cav: &max_cav 5
+
+fusion:
+  core_method: 'FeatureFusionDataset' # LateFusionDataset, EarlyFusionDataset, IntermediateFusionDataset, FeatureFusionDataset supported
+  args: []
+
+# preprocess-related
+preprocess:
+  # options: BasePreprocessor, VoxelPreprocessor, BevPreprocessor
+  core_method: 'SpVoxelPreprocessor'
+  args:
+    voxel_size: &voxel_size [0.4, 0.4, 4]
+    max_points_per_voxel: 32
+    max_voxel_train: 32000
+    max_voxel_test: 70000
+  # lidar range for each individual cav.
+  cav_lidar_range: &cav_lidar [-140.8, -38.4, -3, 140.8, 38.4, 1]
+
+data_augment:
+  - NAME: random_world_flip
+    ALONG_AXIS_LIST: [ 'x' ]
+
+  - NAME: random_world_rotation
+    WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
+
+  - NAME: random_world_scaling
+    WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
+
+# anchor box related
+postprocess:
+  core_method: 'VoxelPostprocessor' # VoxelPostprocessor, BevPostprocessor supported
+  anchor_args:
+    cav_lidar_range: *cav_lidar
+    l: 3.9
+    w: 1.6
+    h: 1.56
+    r: [0, 90]
+    feature_stride: 4
+    num: &achor_num 2
+  target_args:
+    pos_threshold: 0.6
+    neg_threshold: 0.45
+    score_threshold: 0.20
+  order: 'hwl' # hwl or lwh
+  max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames has the same dimension in the same batch
+  nms_thresh: 0.15
+
+# model related
+model:
+  core_method: point_pillar_v2vnet
+  args:
+    voxel_size: *voxel_size
+    lidar_range: *cav_lidar
+    anchor_number: *achor_num
+    max_cav: *max_cav
+    compression: 0 # compression rate
+    backbone_fix: false
+
+    pillar_vfe:
+      use_norm: true
+      with_distance: false
+      use_absolute_xyz: true
+      num_filters: [64]
+    point_pillar_scatter:
+      num_features: 64
+
+    base_bev_backbone:
+      layer_nums: [3, 5, 8]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filter: [128, 128, 128]
+    shrink_header:
+      kernal_size: [3]
+      stride: [2]
+      padding: [1]
+      dim: [256]
+      input_dim: 384 # 128 * 3
+
+    v2vfusion:
+      use_temporal_encoding: true
+      voxel_size: *voxel_size
+      downsample_rate: 4
+      num_iteration: 3
+      in_channels: 256
+      gru_flag: false
+      agg_operator: "avg" # max or avg
+      conv_gru:
+        H: 48
+        W: 176
+        num_layers: 1
+        kernel_size: [[3,3]]
+
+
+      # add decoder later
+
+loss:
+  core_method: point_pillar_loss
+  args:
+    cls_weight: 1.0
+    reg: 2.0
+
+optimizer:
+  core_method: Adam
+  lr: 0.001
+  args:
+    eps: 1e-10
+    weight_decay: 1e-4
+
+lr_scheduler:
+  core_method: multistep #step, multistep and Exponential support
+  gamma: 0.1
+  step_size: [15, 50]
+