fix seed for sample_points data_processsor, update configs

README.md

@@ -85,14 +85,13 @@ All models are trained with 8 GTX 1080Ti GPUs and are available for download.
 
 |                                             | training time | Car | Pedestrian | Cyclist  | download | 
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
-| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~95 mins| 77.28 | - | - | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) | 
-| [SECOND](tools/cfgs/kitti_models/second.yaml)       |  ~2 hours  | 78.62 | - | - | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
-| [SECOND-MultiHead](tools/cfgs/kitti_models/second_multihead.yaml) | - | - | - | - | ongoing |
+| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~95 mins| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) | 
+| [SECOND](tools/cfgs/kitti_models/second.yaml)       |  ~2 hours  | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
 | [PointRCNN](tools/cfgs/kitti_models/pointrcnn.yaml) | ~3 hours | 78.70 | - | - | ongoing| 
-| [PointRCNN-IoU](tools/cfgs/kitti_models/pointrcnn_iou.yaml) | ~3 hours | 78.70 | - | - | ongoing|
-| [Part-A^2-Free](tools/cfgs/kitti_models/PartA2_free.yaml)   | ~4 hours| 78.72 | 65.99 | 74.29 | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
+| [PointRCNN-IoU](tools/cfgs/kitti_models/pointrcnn_iou.yaml) | ~3 hours | 78.90 | 54.62 | 71.52 | [model-]()|
+| [Part-A^2-Free](tools/cfgs/kitti_models/PartA2_free.yaml)   | ~4 hours| 78.72 | 65.99 | 74.29 | [model-](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
 | [Part-A^2-Anchor](tools/cfgs/kitti_models/PartA2.yaml)    | ~5 hours| 79.40 | - | - | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
-| [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~6 hours| 83.69 | - | - | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) |
+| [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~6 hours| 83.69 | 54.49 | 69.47 | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) |
 
 ### NuScenes 3D Object Detection Baselines
 All models are trained with 8 GTX 1080Ti GPUs and are available for download.

pcdet/datasets/processor/data_processor.py

@@ -70,6 +70,9 @@ class DataProcessor(object):
         if num_points == -1:
             return data_dict
 
+        if config.FIX_SEED[self.mode]:
+            np.random.seed(512)
+
         points = data_dict['points']
         if num_points < len(points):
             pts_depth = np.linalg.norm(points[:, 0:3], axis=1)

pcdet/models/backbones_2d/base_bev_backbone.py

@@ -46,7 +46,7 @@ class BaseBEVBackbone(nn.Module):
             self.blocks.append(nn.Sequential(*cur_layers))
             if len(upsample_strides) > 0:
                 stride = upsample_strides[idx]
-                if stride > 1:
+                if stride >= 1:
                     self.deblocks.append(nn.Sequential(
                         nn.ConvTranspose2d(
                             num_filters[idx], num_upsample_filters[idx],

tools/cfgs/kitti_models/pointrcnn.yaml

@@ -12,6 +12,10 @@ DATA_CONFIG:
                 'train': 16384,
                 'test': 16384
             }
+            FIX_SEED: {
+                'train': False,
+                'test': True
+            }
 
         -   NAME: shuffle_points
             SHUFFLE_ENABLED: {

tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml

@@ -217,11 +217,11 @@ MODEL:
         EVAL_METRIC: kitti
 
         NMS_CONFIG:
-            MULTI_CLASSES_NMS: False
+            MULTI_CLASSES_NMS: True
             NMS_TYPE: nms_gpu
             NMS_THRESH: 0.2
-            NMS_PRE_MAXSIZE: 4096
-            NMS_POST_MAXSIZE: 100
+            NMS_PRE_MAXSIZE: 1000
+            NMS_POST_MAXSIZE: 83
 
 
 OPTIMIZATION:

tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml

@@ -199,11 +199,11 @@ MODEL:
         EVAL_METRIC: kitti
 
         NMS_CONFIG:
-            MULTI_CLASSES_NMS: False
+            MULTI_CLASSES_NMS: True
             NMS_TYPE: nms_gpu
             NMS_THRESH: 0.2
             NMS_PRE_MAXSIZE: 1000
-            NMS_POST_MAXSIZE: 100
+            NMS_POST_MAXSIZE: 83
 
 
 OPTIMIZATION:

tools/test.py

@@ -7,7 +7,6 @@ import re
 import datetime
 import argparse
 from pathlib import Path
-import torch.distributed as dist
 from pcdet.datasets import build_dataloader
 from pcdet.models import build_network
 from pcdet.utils import common_utils
@@ -19,12 +18,10 @@ def parse_config():
     parser = argparse.ArgumentParser(description='arg parser')
     parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
 
-    parser.add_argument('--batch_size', type=int, default=16, required=False, help='batch size for training')
-    parser.add_argument('--epochs', type=int, default=80, required=False, help='Number of epochs to train for')
+    parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
     parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader')
     parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
     parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
-    parser.add_argument('--mgpus', action='store_true', default=False, help='whether to use multiple gpu')
     parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
     parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
     parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
@@ -133,11 +130,13 @@ def main():
     if args.launcher == 'none':
         dist_test = False
     else:
-        args.batch_size, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
-            args.batch_size, args.tcp_port, args.local_rank, backend='nccl'
+        total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
+            args.tcp_port, args.local_rank, backend='nccl'
         )
         dist_test = True
 
+    args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU if args.batch_size is None else args.batch_size
+
     output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
     output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -163,7 +162,6 @@ def main():
     logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
 
     if dist_test:
-        total_gpus = dist.get_world_size()
         logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
     for key, val in vars(args).items():
         logger.info('{:16} {}'.format(key, val))