fix some warnings, set default workers=4, depend on torchvision/kornia (CaDDN) softly

pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_grid_generator.py

@@ -6,7 +6,8 @@ try:
     from kornia.geometry.linalg import transform_points
 except Exception as e:
     # Note: Kornia team will fix this import issue to try to allow the usage of lower torch versions.
-    print('Warning: kornia is not installed correctly, please ignore this warning if you do not use CaDDN. Otherwise, it is recommended to use torch version greater than 1.2 to use kornia properly.')
+    # print('Warning: kornia is not installed correctly, please ignore this warning if you do not use CaDDN. Otherwise, it is recommended to use torch version greater than 1.2 to use kornia properly.')
+    pass
 
 from pcdet.utils import transform_utils
 
@@ -22,6 +23,14 @@ class FrustumGridGenerator(nn.Module):
             disc_cfg: EasyDict, Depth discretiziation configuration
         """
         super().__init__()
+        try:
+            import kornia
+        except Exception as e:
+            # Note: Kornia team will fix this import issue to try to allow the usage of lower torch versions.
+            print('Error: kornia is not installed correctly, please ignore this warning if you do not use CaDDN. '
+                  'Otherwise, it is recommended to use torch version greater than 1.2 to use kornia properly.')
+            exit(-1)
+
         self.dtype = torch.float32
         self.grid_size = torch.as_tensor(grid_size, dtype=self.dtype)
         self.pc_range = pc_range

pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_deeplabv3.py

-import torchvision
-
 from .ddn_template import DDNTemplate
 
+try:
+    import torchvision
+except:
+    pass
+
 
 class DDNDeepLabV3(DDNTemplate):
 

pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_template.py

@@ -2,11 +2,9 @@ from collections import OrderedDict
 from pathlib import Path
 from torch import hub
 
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision
 
 try:
     from kornia.enhance.normalize import normalize

tools/train.py

@@ -24,7 +24,7 @@ def parse_config():
 
     parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
     parser.add_argument('--epochs', type=int, default=None, required=False, help='number of epochs to train for')
-    parser.add_argument('--workers', type=int, default=8, help='number of workers for dataloader')
+    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('--pretrained_model', type=str, default=None, help='pretrained_model')
@@ -170,8 +170,6 @@ def main():
         merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch
     )
 
-    import pdb
-    pdb.set_trace()
     if hasattr(train_set, 'use_shared_memory') and train_set.use_shared_memory:
         train_set.clean_shared_memory()
 

tools/train_utils/optimization/fastai_optim.py

 # This file is modified from https://github.com/traveller59/second.pytorch
 
-from collections import Iterable
+try:
+    from collections.abc import Iterable
+except:
+    from collections import Iterable
 
 import torch
 from torch import nn