Some detailed modifications for Argo2 and VoxelNeXt (#1327)

* Add files via upload

* Delete cbgs_voxel01_voxelnext_headkernel3.yaml

* Delete voxelnext_ioubranch.yaml

README.md

@@ -176,18 +176,19 @@ By default, all models are trained with **a single frame** of **20% data (~32k f
 
 Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)):
 
-|    Performance@(train with 100\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
-|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
-| [SECOND](tools/cfgs/waymo_models/second.yaml) | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05 | 
-| [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)| 73.37/72.86 | 65.09/64.62 | 75.35/65.11 | 67.61/58.25 | 67.76/66.22 | 65.25/63.77 | 
-| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
-| [VoxelNeXt-2D](tools/cfgs/waymo_models/voxelnext2d_ioubranch.yaml) | 77.94/77.47	|69.68/69.25	|80.24/73.47	|72.23/65.88	|73.33/72.20	|70.66/69.56 | 
-| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79 |
-| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
-| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) | 79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19 |
+| Performance@(train with 100\% Data)                                                       | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|-------------------------------------------------------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml)                                             | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05 | 
+| [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)                  | 73.37/72.86 | 65.09/64.62 | 75.35/65.11 | 67.61/58.25 | 67.76/66.22 | 65.25/63.77 | 
+| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml)                                     | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
+| [VoxelNeXt-2D](tools/cfgs/waymo_models/voxelnext2d_ioubranch.yaml)                        | 77.94/77.47	|69.68/69.25	|80.24/73.47	|72.23/65.88	|73.33/72.20	|70.66/69.56 | 
+| [VoxelNeXt](tools/cfgs/waymo_models/voxelnext_ioubranch_large.yaml)                       | 78.16/77.70	|69.86/69.42	|81.47/76.30	|73.48/68.63	|76.06/74.90	|73.29/72.18 |
+| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml)          | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79 |
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml)                                | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml)                | 79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19 |
 | [PV-RCNN++ (ResNet, 2 frames)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet_2frames.yaml) | 80.17/79.70 | 72.14/71.70 | 83.48/80.42 | 75.54/72.61 | 74.63/73.75 | 72.35/71.50 |
-| [MPPNet (4 frames)](docs/guidelines_of_approaches/mppnet.md) | 81.54/81.06 | 74.07/73.61 | 84.56/81.94 | 77.20/74.67 | 77.15/76.50 | 75.01/74.38 |
-| [MPPNet (16 frames)](docs/guidelines_of_approaches/mppnet.md) | 82.74/82.28 | 75.41/74.96 | 84.69/82.25 | 77.43/75.06 | 77.28/76.66 | 75.13/74.52 |
+| [MPPNet (4 frames)](docs/guidelines_of_approaches/mppnet.md)                              | 81.54/81.06 | 74.07/73.61 | 84.56/81.94 | 77.20/74.67 | 77.15/76.50 | 75.01/74.38 |
+| [MPPNet (16 frames)](docs/guidelines_of_approaches/mppnet.md)                             | 82.74/82.28 | 75.41/74.96 | 84.69/82.25 | 77.43/75.06 | 77.28/76.66 | 75.13/74.52 |
 
 
 
@@ -226,8 +227,7 @@ All models are trained with 4 GPUs.
 
 |                                                         | mAP  |                                              download                                              | 
 |---------------------------------------------------------|:----:|:--------------------------------------------------------------------------------------------------:|
-| [VoxelNeXt](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext.yaml)        | 30.0 | [model-30M](https://drive.google.com/file/d/1zr-it1ERJzLQ3a3hP060z_EQqS_RkNaC/view?usp=share_link) | 
-| [VoxelNeXt-K3](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext_headkernel3.yaml) | 30.7 | [model-45M](https://drive.google.com/file/d/1NrYRsiKbuWyL8jE4SY27IHpFMY9K0o__/view?usp=share_link) | 
+| [VoxelNeXt](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext.yaml)        | 30.5 | [model-32M](https://drive.google.com/file/d/1YP2UOz-yO-cWfYQkIqILEu6bodvCBVrR/view?usp=share_link) |
 
 ### Other datasets
 Welcome to support other datasets by submitting pull request. 

pcdet/datasets/argo2/argo2_dataset.py

@@ -488,39 +488,23 @@ def parse_config():
     parser = argparse.ArgumentParser(description='arg parser')
     parser.add_argument('--root_path', type=str, default="/data/argo2/sensor")
     parser.add_argument('--output_dir', type=str, default="/data/argo2/processed")
-    parser.add_argument('--num_process', type=int, default=16)
     args = parser.parse_args()
     return args
 
-def main(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, token, num_process):
-    for seg_i, seg_path in enumerate(seg_path_list):
-        if seg_i % num_process != token:
-            continue
-        print(f'processing segment: {seg_i}/{len(seg_path_list)}')
-        split = seg_split_list[seg_i]
-        process_single_segment(seg_path, split, info_list, ts2idx, output_dir, save_bin)
 
 if __name__ == '__main__':
     args = parse_config()
     root = args.root_path
     output_dir = args.output_dir
-    num_process = args.num_process
     save_bin = True
     ts2idx, seg_path_list, seg_split_list = prepare(root)
 
-    if num_process > 1:
-        with mp.Manager() as manager:
-            info_list = manager.list()
-            pool = mp.Pool(num_process)
-            for token in range(num_process):
-                result = pool.apply_async(main, args=(
-                seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, token, num_process))
-            pool.close()
-            pool.join()
-            info_list = list(info_list)
-    else:
-        info_list = []
-        main(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, 0, 1)
+    velodyne_dir = Path(output_dir) / 'training' / 'velodyne'
+    if not velodyne_dir.exists():
+        velodyne_dir.mkdir(parents=True, exist_ok=True)
+
+    info_list = []
+    create_argo2_infos(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, 0, 1)
 
     assert len(info_list) > 0
 
@@ -551,4 +535,3 @@ if __name__ == '__main__':
 
     gts = pd.concat(seg_anno_list).reset_index()
     gts.to_feather(save_feather_path)
-

pcdet/models/backbones_3d/__init__.py

@@ -3,6 +3,7 @@ from .spconv_backbone import VoxelBackBone8x, VoxelResBackBone8x
 from .spconv_backbone_2d import PillarBackBone8x, PillarRes18BackBone8x
 from .spconv_backbone_focal import VoxelBackBone8xFocal
 from .spconv_backbone_voxelnext import VoxelResBackBone8xVoxelNeXt
+from .spconv_backbone_voxelnext2d import VoxelResBackBone8xVoxelNeXt2D
 from .spconv_unet import UNetV2
 
 __all__ = {
@@ -13,6 +14,7 @@ __all__ = {
     'VoxelResBackBone8x': VoxelResBackBone8x,
     'VoxelBackBone8xFocal': VoxelBackBone8xFocal,
     'VoxelResBackBone8xVoxelNeXt': VoxelResBackBone8xVoxelNeXt,
+    'VoxelResBackBone8xVoxelNeXt2D': VoxelResBackBone8xVoxelNeXt2D,
     'PillarBackBone8x': PillarBackBone8x,
     'PillarRes18BackBone8x': PillarRes18BackBone8x
 }

pcdet/models/backbones_3d/spconv_backbone_voxelnext2d.py

+from functools import partial
+import torch
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv2d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv2d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class VoxelResBackBone8xVoxelNeXt2D(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+
+        spconv_kernel_sizes = model_cfg.get('SPCONV_KERNEL_SIZES', [3, 3, 3, 3])
+
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+        )
+
+        self.conv5 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+        )
+
+        self.conv6 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+        )
+
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv2d(256, 256, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2'),
+            norm_fn(256),
+            nn.ReLU(),
+        )
+
+        self.shared_conv = spconv.SparseSequential(
+            spconv.SubMConv2d(256, 256, 3, stride=1, padding=1, bias=True),
+            nn.BatchNorm1d(256),
+            nn.ReLU(True),
+        )
+
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+        self.forward_ret_dict = {}
+
+    def bev_out(self, x_conv):
+        features_cat = x_conv.features
+        indices_cat = x_conv.indices
+
+        indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True)
+        features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1]))
+        features_unique.index_add_(0, _inv, features_cat)
+
+        x_out = spconv.SparseConvTensor(
+            features=features_unique,
+            indices=indices_unique,
+            spatial_shape=x_conv.spatial_shape,
+            batch_size=x_conv.batch_size
+        )
+        return x_out
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv5 = self.conv5(x_conv4)
+        x_conv6 = self.conv6(x_conv5)
+
+        x_conv5.indices[:, 1:] *= 2
+        x_conv6.indices[:, 1:] *= 4
+        x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features]))
+        x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices])
+
+        out = self.bev_out(x_conv4)
+
+        out = self.conv_out(out)
+        out = self.shared_conv(out)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+        
+        return batch_dict

tools/cfgs/argo2_models/cbgs_voxel01_voxelnext_headkernel3.yaml

-CLASS_NAMES: ['Regular_vehicle', 'Pedestrian', 'Bicyclist', 'Motorcyclist', 'Wheeled_rider',
-            'Bollard', 'Construction_cone', 'Sign', 'Construction_barrel', 'Stop_sign', 'Mobile_pedestrian_crossing_sign',
-            'Large_vehicle', 'Bus', 'Box_truck', 'Truck', 'Vehicular_trailer', 'Truck_cab', 'School_bus', 'Articulated_bus', 
-            'Message_board_trailer', 'Bicycle', 'Motorcycle', 'Wheeled_device', 'Wheelchair', 'Stroller', 'Dog']
-
-DATA_CONFIG:
-    _BASE_CONFIG_: cfgs/dataset_configs/argo2_dataset.yaml
-
-    DATA_PROCESSOR:
-        - NAME: mask_points_and_boxes_outside_range
-          REMOVE_OUTSIDE_BOXES: True
-
-        - NAME: shuffle_points
-          SHUFFLE_ENABLED: {
-            'train': True,
-            'test': True
-          }
-
-        - NAME: transform_points_to_voxels
-          VOXEL_SIZE: [0.1, 0.1, 0.2]
-          MAX_POINTS_PER_VOXEL: 20
-          MAX_NUMBER_OF_VOXELS: {
-            'train': 120000,
-            'test': 160000
-          }
-
-MODEL:
-    NAME: VoxelNeXt
-
-    VFE:
-        NAME: MeanVFE
-
-    BACKBONE_3D:
-        NAME: VoxelResBackBone8xVoxelNeXt
-
-    DENSE_HEAD:
-        NAME: VoxelNeXtHead
-        CLASS_AGNOSTIC: False
-        INPUT_FEATURES: 128
-
-        CLASS_NAMES_EACH_HEAD: [
-            ['Regular_vehicle',],
-            ['Pedestrian', 'Bicyclist', 'Motorcyclist', 'Wheeled_rider'],
-            ['Bollard', 'Construction_cone', 'Sign', 'Construction_barrel', 'Stop_sign', 'Mobile_pedestrian_crossing_sign'],
-            ['Large_vehicle', 'Bus', 'Box_truck', 'Truck', 'Vehicular_trailer', 'Truck_cab', 'School_bus', 'Articulated_bus', 'Message_board_trailer'],
-            ['Bicycle', 'Motorcycle', 'Wheeled_device', 'Wheelchair', 'Stroller'],
-            ['Dog'],
-        ]
-
-        KERNEL_SIZE_HEAD: 3
-        SHARED_CONV_CHANNEL: 128
-        USE_BIAS_BEFORE_NORM: True
-        NUM_HM_CONV: 2
-        SEPARATE_HEAD_CFG:
-            HEAD_ORDER: ['center', 'center_z', 'dim', 'rot']
-            HEAD_DICT: {
-                'center': {'out_channels': 2, 'num_conv': 2},
-                'center_z': {'out_channels': 1, 'num_conv': 2},
-                'dim': {'out_channels': 3, 'num_conv': 2},
-                'rot': {'out_channels': 2, 'num_conv': 2},
-            }
-
-        TARGET_ASSIGNER_CONFIG:
-            FEATURE_MAP_STRIDE: 8
-            NUM_MAX_OBJS: 500
-            GAUSSIAN_OVERLAP: 0.1
-            MIN_RADIUS: 2
-
-        LOSS_CONFIG:
-            LOSS_WEIGHTS: {
-                'cls_weight': 1.0,
-                'loc_weight': 0.25,
-                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
-            }
-
-        POST_PROCESSING:
-            SCORE_THRESH: 0.1
-            POST_CENTER_LIMIT_RANGE: [-200, -200, -20, 200, 200, 20]
-            MAX_OBJ_PER_SAMPLE: 500
-            NMS_CONFIG:
-                NMS_TYPE: nms_gpu
-                NMS_THRESH: 0.2
-                NMS_PRE_MAXSIZE: 1000
-                NMS_POST_MAXSIZE: 83
-
-    POST_PROCESSING:
-        RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
-
-        EVAL_METRIC: kitti
-
-
-
-OPTIMIZATION:
-    BATCH_SIZE_PER_GPU: 4
-    NUM_EPOCHS: 6
-
-    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
-
-    GRAD_NORM_CLIP: 10

tools/cfgs/waymo_models/voxelnext_ioubranch.yaml → tools/cfgs/waymo_models/voxelnext_ioubranch_large.yaml

@@ -11,18 +11,21 @@ MODEL:
 
     BACKBONE_3D:
         NAME: VoxelResBackBone8xVoxelNeXt
+        SPCONV_KERNEL_SIZES: [5, 5, 3, 3]
+        OUT_CHANNEL: 256
+        CHANNELS: [32, 64, 128, 256, 256]
 
     DENSE_HEAD:
         NAME: VoxelNeXtHead
         IOU_BRANCH: True
         CLASS_AGNOSTIC: False
-        INPUT_FEATURES: 128
+        INPUT_FEATURES: 256
 
         CLASS_NAMES_EACH_HEAD: [
             ['Vehicle', 'Pedestrian', 'Cyclist']
         ]
 
-        SHARED_CONV_CHANNEL: 128
+        SHARED_CONV_CHANNEL: 256
         USE_BIAS_BEFORE_NORM: True
         NUM_HM_CONV: 2
         SEPARATE_HEAD_CFG: