RuntimeError: Mismatch in Input Channels during Training with Custom Dataset

[Serzhanov]

I am encountering a runtime error when attempting to train a model with a custom dataset using OpenPCDet. The error seems to be related to a mismatch in the number of input channels during the forward pass.

I followed the custom dataset tutorial provided here and modified the pv_rcnn.yaml file accordingly. Below are the relevant configuration files:

Data set config:

DATA_PATH: '../data/custom'
BASE_CONFIG: tools/cfgs/dataset_configs/custom_dataset.yaml

POINT_CLOUD_RANGE: [-70055.2, -46996.7, -60359.5, 98233.5, 72900.7, 94498.5]

DATA_SPLIT: {
'train': train,
'test': val
}

INFO_PATH: {
'train': [custom_infos_train.pkl],
'test': [custom_infos_val.pkl],
}

POINT_FEATURE_ENCODING: {
encoding_type: absolute_coordinates_encoding,
used_feature_list: ['x', 'y', 'z'],
src_feature_list: ['x', 'y', 'z'],
}

DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: False
DB_INFO_PATH:
- custom_dbinfos_train.pkl
PREPARE: {
filter_by_min_points: ['Antenne4G:5', 'Antenne5G:5'],
}
SAMPLE_GROUPS: ['Antenne4G:20', 'Antenne5G:20']
NUM_POINT_FEATURES: 3
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.95, 1.05]

DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': False
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [244.6, 241.7, 3871.45]
MAX_POINTS_PER_VOXEL: 5
MAX_NUMBER_OF_VOXELS: {
'train': 150000,
'test': 150000
}

CLASS_NAMES: ['Antenne4G', 'Antenne5G']

OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1

MAP_CLASS_TO_KITTI: {
'Antenne4G': 'Car',
'Antenne5G': 'Car'
}
Model configuration:{
'class_name': 'Antenne4G',
'anchor_sizes': [[552.74, 325.86, 3030.90]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.55,
'unmatched_threshold': 0.4
},
{
'class_name': 'Antenne5G',
'anchor_sizes': [[545.50, 305.97, 837.39]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
Error Message: Upon launching the training script, I receive the following error:

File "/home/ubuntu/v-detr/OpenPCDet/tools/train.py", line 233, in
main()
File "/home/ubuntu/v-detr/OpenPCDet/tools/train.py", line 178, in main
train_model(
File "/home/ubuntu/v-detr/OpenPCDet/tools/train_utils/train_utils.py", line 180, in train_model
accumulated_iter = train_one_epoch(
File "/home/ubuntu/v-detr/OpenPCDet/tools/train_utils/train_utils.py", line 56, in train_one_epoch
loss, tb_dict, disp_dict = model_func(model, batch)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/init.py", line 44, in model_func
ret_dict, tb_dict, disp_dict = model(batch_dict)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/detectors/pv_rcnn.py", line 11, in forward
batch_dict = cur_module(batch_dict)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py", line 393, in forward
pooled_features = self.aggregate_keypoint_features_from_one_source(
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py", line 325, in aggregate_keypoint_features_from_one_source
pooled_points, pooled_features = aggregate_func(
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py", line 95, in forward
new_features = self.mlpsk # (1, C, M1 + M2 ..., nsample)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [64, 67, 1, 1], expected input[1, 3, 8192, 16] to have 67 channels, but got 3 channels instead
The error indicates that the input tensor is expected to have 67 channels, but it only has 3. I suspect this is due to a configuration mismatch, particularly in the NUM_OUTPUT_FEATURES or NUM_KEYPOINTS sections of the configuration. However, I have been unable to resolve the issue.

Any guidance on how to properly configure the model to accept the correct number of input channels would be greatly appreciated!I am encountering a runtime error when attempting to train a model with a custom dataset using OpenPCDet. The error seems to be related to a mismatch in the number of input channels during the forward pass.

I followed the custom dataset tutorial provided here and modified the pv_rcnn.yaml file accordingly. Below are the relevant configuration files:

Data set config:

DATA_PATH: '../data/custom'
BASE_CONFIG: tools/cfgs/dataset_configs/custom_dataset.yaml

POINT_CLOUD_RANGE: [-70055.2, -46996.7, -60359.5, 98233.5, 72900.7, 94498.5]

DATA_SPLIT: {
'train': train,
'test': val
}

INFO_PATH: {
'train': [custom_infos_train.pkl],
'test': [custom_infos_val.pkl],
}

POINT_FEATURE_ENCODING: {
encoding_type: absolute_coordinates_encoding,
used_feature_list: ['x', 'y', 'z'],
src_feature_list: ['x', 'y', 'z'],
}

DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: False
DB_INFO_PATH:
- custom_dbinfos_train.pkl
PREPARE: {
filter_by_min_points: ['Antenne4G:5', 'Antenne5G:5'],
}
SAMPLE_GROUPS: ['Antenne4G:20', 'Antenne5G:20']
NUM_POINT_FEATURES: 3
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.95, 1.05]

DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': False
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [244.6, 241.7, 3871.45]
MAX_POINTS_PER_VOXEL: 5
MAX_NUMBER_OF_VOXELS: {
'train': 150000,
'test': 150000
}

CLASS_NAMES: ['Antenne4G', 'Antenne5G']

OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1

MAP_CLASS_TO_KITTI: {
'Antenne4G': 'Car',
'Antenne5G': 'Car'
}
Model configuration:{
'class_name': 'Antenne4G',
'anchor_sizes': [[552.74, 325.86, 3030.90]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.55,
'unmatched_threshold': 0.4
},
{
'class_name': 'Antenne5G',
'anchor_sizes': [[545.50, 305.97, 837.39]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
Error Message: Upon launching the training script, I receive the following error:

File "/home/ubuntu/v-detr/OpenPCDet/tools/train.py", line 233, in
main()
File "/home/ubuntu/v-detr/OpenPCDet/tools/train.py", line 178, in main
train_model(
File "/home/ubuntu/v-detr/OpenPCDet/tools/train_utils/train_utils.py", line 180, in train_model
accumulated_iter = train_one_epoch(
File "/home/ubuntu/v-detr/OpenPCDet/tools/train_utils/train_utils.py", line 56, in train_one_epoch
loss, tb_dict, disp_dict = model_func(model, batch)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/init.py", line 44, in model_func
ret_dict, tb_dict, disp_dict = model(batch_dict)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/detectors/pv_rcnn.py", line 11, in forward
batch_dict = cur_module(batch_dict)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py", line 393, in forward
pooled_features = self.aggregate_keypoint_features_from_one_source(
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py", line 325, in aggregate_keypoint_features_from_one_source
pooled_points, pooled_features = aggregate_func(
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/v-detr/OpenPCDet/tools/../pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py", line 95, in forward
new_features = self.mlpsk # (1, C, M1 + M2 ..., nsample)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/ubuntu/miniconda3/envs/pcd/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [64, 67, 1, 1], expected input[1, 3, 8192, 16] to have 67 channels, but got 3 channels instead
The error indicates that the input tensor is expected to have 67 channels, but it only has 3. I suspect this is due to a configuration mismatch, particularly in the NUM_OUTPUT_FEATURES or NUM_KEYPOINTS sections of the configuration. However, I have been unable to resolve the issue.

Any guidance on how to properly configure the model to accept the correct number of input channels would be greatly appreciated!