change image padding from nan to zero

pcdet/datasets/dataset.py

@@ -44,12 +44,6 @@ class DatasetTemplate(torch_data.Dataset):
         else:
             self.depth_downsample_factor = None
             
-        self.gt_sampler_with_img = False
-        for augmentor in self.data_augmentor.data_augmentor_queue:
-            if isinstance(augmentor, DataBaseSampler):
-                if augmentor.aug_with_img:
-                    self.gt_sampler_with_img = True
-            
     @property
     def mode(self):
         return 'train' if self.training else 'test'
@@ -212,8 +206,7 @@ class DatasetTemplate(torch_data.Dataset):
                         pad_h = common_utils.get_pad_params(desired_size=max_h, cur_size=image.shape[0])
                         pad_w = common_utils.get_pad_params(desired_size=max_w, cur_size=image.shape[1])
                         pad_width = (pad_h, pad_w)
-                        # Pad with nan, to be replaced later in the pipeline.
-                        pad_value = 0 if self.gt_sampler_with_img else np.nan
+                        pad_value = 0
 
                         if key == "images":
                             pad_width = (pad_h, pad_w, (0, 0))

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

@@ -151,7 +151,7 @@ class DDNTemplate(nn.Module):
         x = images
         if self.pretrained:
             # Create a mask for padded pixels
-            mask = torch.isnan(x)
+            mask = (x == 0)
 
             # Match ResNet pretrained preprocessing
             x = normalize(x, mean=self.norm_mean, std=self.norm_std)