Fix benchmark

configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py

+# model settings
+voxel_size = [0.16, 0.16, 4]
+point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
+model = dict(
+    type='VoxelNet',
+    voxel_layer=dict(
+        max_num_points=64,
+        point_cloud_range=point_cloud_range,
+        voxel_size=voxel_size,
+        max_voxels=(12000, 20000)),
+    voxel_encoder=dict(
+        type='PillarFeatureNet',
+        in_channels=4,
+        feat_channels=[64],
+        with_distance=False,
+        voxel_size=voxel_size,
+        point_cloud_range=point_cloud_range),
+    middle_encoder=dict(
+        type='PointPillarsScatter', in_channels=64, output_shape=[496, 432]),
+    backbone=dict(
+        type='SECOND',
+        in_channels=64,
+        layer_nums=[3, 5, 5],
+        layer_strides=[2, 2, 2],
+        out_channels=[64, 128, 256]),
+    neck=dict(
+        type='SECONDFPN',
+        in_channels=[64, 128, 256],
+        upsample_strides=[1, 2, 4],
+        out_channels=[128, 128, 128]),
+    bbox_head=dict(
+        type='Anchor3DHead',
+        num_classes=1,
+        in_channels=384,
+        feat_channels=384,
+        use_direction_classifier=True,
+        anchor_generator=dict(
+            type='Anchor3DRangeGenerator',
+            ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
+            sizes=[[1.6, 3.9, 1.56]],
+            rotations=[0, 1.57],
+            reshape_out=True),
+        diff_rad_by_sin=True,
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
+        loss_dir=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
+# model training and testing settings
+train_cfg = dict(
+    assigner=dict(
+        type='MaxIoUAssigner',
+        iou_calculator=dict(type='BboxOverlapsNearest3D'),
+        pos_iou_thr=0.6,
+        neg_iou_thr=0.45,
+        min_pos_iou=0.45,
+        ignore_iof_thr=-1),
+    allowed_border=0,
+    pos_weight=-1,
+    debug=False)
+test_cfg = dict(
+    use_rotate_nms=True,
+    nms_across_levels=False,
+    nms_thr=0.01,
+    score_thr=0.1,
+    min_bbox_size=0,
+    nms_pre=100,
+    max_num=50)
+
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Car']
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    object_rot_range=[0.0, 0.0],
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    sample_groups=dict(Car=15),
+    classes=class_names)
+
+train_pipeline = [
+    dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        loc_noise_std=[0.25, 0.25, 0.25],
+        global_rot_range=[0.0, 0.0],
+        rot_uniform_noise=[-0.15707963267, 0.15707963267]),
+    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(
+        type='GlobalRotScale',
+        rot_uniform_noise=[-0.78539816, 0.78539816],
+        scaling_uniform_noise=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=3,
+    workers_per_gpu=3,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'kitti_infos_train.pkl',
+            split='training',
+            pts_prefix='velodyne_reduced',
+            pipeline=train_pipeline,
+            modality=input_modality,
+            classes=class_names,
+            test_mode=False)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True))
+# optimizer
+lr = 0.001  # max learning rate
+optimizer = dict(
+    type='AdamW',
+    lr=lr,
+    betas=(0.95, 0.99),  # the momentum is change during training
+    weight_decay=0.01)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='cyclic',
+    target_ratio=(10, 1e-4),
+    cyclic_times=1,
+    step_ratio_up=0.4)
+momentum_config = dict(
+    policy='cyclic',
+    target_ratio=(0.85 / 0.95, 1),
+    cyclic_times=1,
+    step_ratio_up=0.4)
+checkpoint_config = dict(interval=1)
+evaluation = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 50
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/pp_secfpn_100e'
+load_from = None
+resume_from = None
+workflow = [('train', 50)]

tools/benchmark.py

@@ -7,15 +7,16 @@ from mmcv.parallel import MMDataParallel
 from mmcv.runner import load_checkpoint
 from tools.fuse_conv_bn import fuse_module
 
+from mmdet3d.datasets import build_dataloader, build_dataset
+from mmdet3d.models import build_detector
 from mmdet.core import wrap_fp16_model
-from mmdet.datasets import build_dataloader, build_dataset
-from mmdet.models import build_detector
 
 
 def parse_args():
     parser = argparse.ArgumentParser(description='MMDet benchmark a model')
     parser.add_argument('config', help='test config file path')
     parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--samples', default=2000, help='samples to benchmark')
     parser.add_argument(
         '--log-interval', default=50, help='interval of logging')
     parser.add_argument(
@@ -64,7 +65,7 @@ def main():
     num_warmup = 5
     pure_inf_time = 0
 
-    # benchmark with 2000 image and take the average
+    # benchmark with several samples and take the average
     for i, data in enumerate(data_loader):
 
         torch.cuda.synchronize()
@@ -80,9 +81,10 @@ def main():
             pure_inf_time += elapsed
             if (i + 1) % args.log_interval == 0:
                 fps = (i + 1 - num_warmup) / pure_inf_time
-                print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s')
+                print(f'Done image [{i + 1:<3}/ {args.samples}], '
+                      f'fps: {fps:.1f} img / s')
 
-        if (i + 1) == 2000:
+        if (i + 1) == args.samples:
             pure_inf_time += elapsed
             fps = (i + 1 - num_warmup) / pure_inf_time
             print(f'Overall fps: {fps:.1f} img / s')