Merge branch 'fix-cuda-file' into 'master'

Fix cuda file

See merge request open-mmlab/mmdet.3d!24

configs/kitti/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -93,7 +93,8 @@ class_names = ['Car']
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 input_modality = dict(
-    use_lidar=True,
+    use_lidar=False,
+    use_lidar_reduced=True,
     use_depth=False,
     use_lidar_intensity=True,
     use_camera=False,

configs/kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py

@@ -113,7 +113,8 @@ class_names = ['Pedestrian', 'Cyclist', 'Car']
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 input_modality = dict(
-    use_lidar=True,
+    use_lidar=False,
+    use_lidar_reduced=True,
     use_depth=False,
     use_lidar_intensity=True,
     use_camera=True,

configs/kitti/dv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -91,7 +91,8 @@ class_names = ['Car']
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 input_modality = dict(
-    use_lidar=True,
+    use_lidar=False,
+    use_lidar_reduced=True,
     use_depth=False,
     use_lidar_intensity=True,
     use_camera=True,

configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -90,7 +90,8 @@ class_names = ['Car']
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 input_modality = dict(
-    use_lidar=True,
+    use_lidar=False,
+    use_lidar_reduced=True,
     use_depth=False,
     use_lidar_intensity=True,
     use_camera=False,

configs/kitti/hv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -89,7 +89,8 @@ class_names = ['Car']
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 input_modality = dict(
-    use_lidar=True,
+    use_lidar=False,
+    use_lidar_reduced=True,
     use_depth=False,
     use_lidar_intensity=True,
     use_camera=False,

mmdet3d/datasets/kitti_dataset.py

@@ -184,6 +184,8 @@ class KittiDataset(torch_data.Dataset):
         if self.modality['use_depth'] and self.modality['use_lidar']:
             points = self.get_lidar_depth_reduced(sample_idx)
         elif self.modality['use_lidar']:
+            points = self.get_lidar(sample_idx)
+        elif self.modality['use_lidar_reduced']:
             points = self.get_lidar_reduced(sample_idx)
         elif self.modality['use_depth']:
             points = self.get_pure_depth_reduced(sample_idx)
@@ -238,8 +240,6 @@ class KittiDataset(torch_data.Dataset):
                                       axis=1).astype(np.float32)
         difficulty = annos['difficulty']
         # this change gt_bboxes_3d to velodyne coordinates
-        import pdb
-        pdb.set_trace()
         gt_bboxes_3d = box_np_ops.box_camera_to_lidar(gt_bboxes_3d, rect,
                                                       Trv2c)
         # only center format is allowed. so we need to convert

mmdet3d/datasets/pipelines/data_augment_utils.py

+import warnings
+
 import numba
 import numpy as np
+from numba.errors import NumbaPerformanceWarning
 
 from mmdet3d.core.bbox import box_np_ops
 
+warnings.filterwarnings("ignore", category=NumbaPerformanceWarning)
+
 
 @numba.njit
 def _rotation_box2d_jit_(corners, angle, rot_mat_T):

mmdet3d/ops/roiaware_pool3d/src/points_in_boxes_cuda.cu

@@ -7,8 +7,8 @@
 #include <assert.h>
 #include <math.h>
 #include <stdio.h>
-#include <torch/extension.h>
 #include <torch/serialize/tensor.h>
+#include <torch/types.h>
 
 #define THREADS_PER_BLOCK 256
 #define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))

mmdet3d/ops/sparse_block.py

 from mmcv.cnn import build_norm_layer
 from torch import nn
 
-import mmdet3d.ops.spconv as spconv
 from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
+from . import spconv
 
 
 def conv3x3(in_planes, out_planes, stride=1, indice_key=None):

tools/dist_test.sh

+#!/usr/bin/env bash
+
+CONFIG=$1
+CHECKPOINT=$2
+GPUS=$3
+PORT=${PORT:-29500}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
+    $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}

tools/dist_train.sh

 #!/usr/bin/env bash
 
-PYTHON=${PYTHON:-"python"}
-
 CONFIG=$1
 GPUS=$2
+PORT=${PORT:-29500}
 
-$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS \
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
     $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}

tools/slurm_test.sh

 #!/usr/bin/env bash
 
 set -x
-export PYTHONPATH=`pwd`:$PYTHONPATH
 
 PARTITION=$1
 JOB_NAME=$2
@@ -9,14 +8,17 @@ CONFIG=$3
 CHECKPOINT=$4
 GPUS=${GPUS:-8}
 GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
 PY_ARGS=${@:5}
 SRUN_ARGS=${SRUN_ARGS:-""}
 
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
 srun -p ${PARTITION} \
     --job-name=${JOB_NAME} \
     --gres=gpu:${GPUS_PER_NODE} \
     --ntasks=${GPUS} \
     --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
     --kill-on-bad-exit=1 \
     ${SRUN_ARGS} \
     python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}

tools/slurm_train.sh

@@ -8,15 +8,17 @@ CONFIG=$3
 WORK_DIR=$4
 GPUS=${GPUS:-8}
 GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
 SRUN_ARGS=${SRUN_ARGS:-""}
-PY_ARGS=${PY_ARGS:-"--validate"}
-
+PY_ARGS=${@:5}
 
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
 srun -p ${PARTITION} \
     --job-name=${JOB_NAME} \
     --gres=gpu:${GPUS_PER_NODE} \
     --ntasks=${GPUS} \
     --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
     --kill-on-bad-exit=1 \
     ${SRUN_ARGS} \
     python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS}

tools/train.py

@@ -8,7 +8,7 @@ import time
 
 import mmcv
 import torch
-from mmcv import Config
+from mmcv import Config, DictAction
 from mmcv.runner import init_dist
 
 from mmdet3d import __version__
@@ -26,9 +26,9 @@ def parse_args():
     parser.add_argument(
         '--resume-from', help='the checkpoint file to resume from')
     parser.add_argument(
-        '--validate',
+        '--no-validate',
         action='store_true',
-        help='whether to evaluate the checkpoint during training')
+        help='whether not to evaluate the checkpoint during training')
     group_gpus = parser.add_mutually_exclusive_group()
     group_gpus.add_argument(
         '--gpus',
@@ -46,6 +46,8 @@ def parse_args():
         '--deterministic',
         action='store_true',
         help='whether to set deterministic options for CUDNN backend.')
+    parser.add_argument(
+        '--options', nargs='+', action=DictAction, help='arguments in dict')
     parser.add_argument(
         '--launcher',
         choices=['none', 'pytorch', 'slurm', 'mpi'],
@@ -67,6 +69,9 @@ def main():
     args = parse_args()
 
     cfg = Config.fromfile(args.config)
+    if args.options is not None:
+        cfg.merge_from_dict(args.options)
+
     # set cudnn_benchmark
     if cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True
@@ -101,7 +106,7 @@ def main():
     mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
     # init the logger before other steps
     timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
-    log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
+    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
     logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
 
     # add a logging filter
@@ -113,28 +118,27 @@ def main():
     meta = dict()
     # log env info
     env_info_dict = collect_env()
-    env_info = '\n'.join([('{}: {}'.format(k, v))
-                          for k, v in env_info_dict.items()])
+    env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
     dash_line = '-' * 60 + '\n'
     logger.info('Environment info:\n' + dash_line + env_info + '\n' +
                 dash_line)
     meta['env_info'] = env_info
 
     # log some basic info
-    logger.info('Distributed training: {}'.format(distributed))
-    logger.info('Config:\n{}'.format(cfg.text))
+    logger.info(f'Distributed training: {distributed}')
+    logger.info(f'Config:\n{cfg.pretty_text}')
 
     # set random seeds
     if args.seed is not None:
-        logger.info('Set random seed to {}, deterministic: {}'.format(
-            args.seed, args.deterministic))
+        logger.info(f'Set random seed to {args.seed}, '
+                    f'deterministic: {args.deterministic}')
         set_random_seed(args.seed, deterministic=args.deterministic)
     cfg.seed = args.seed
     meta['seed'] = args.seed
 
     model = build_detector(
         cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
-    logger.info('Model:\n{}'.format(model))
+    logger.info(f'Model:\n{model}')
     datasets = [build_dataset(cfg.data.train)]
     if len(cfg.workflow) == 2:
         val_dataset = copy.deepcopy(cfg.data.val)
@@ -145,7 +149,7 @@ def main():
         # checkpoints as meta data
         cfg.checkpoint_config.meta = dict(
             mmdet_version=__version__,
-            config=cfg.text,
+            config=cfg.pretty_text,
             CLASSES=datasets[0].CLASSES)
     # add an attribute for visualization convenience
     model.CLASSES = datasets[0].CLASSES
@@ -154,7 +158,7 @@ def main():
         datasets,
         cfg,
         distributed=distributed,
-        validate=args.validate,
+        validate=(not args.no_validate),
         timestamp=timestamp,
         meta=meta)