Merge pull request #2867 from open-mmlab/dev-1.x

Bump version to 1.4.0

projects/NeRF-Det/nerfdet/nerf_det3d_data_sample.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+from mmengine.structures import InstanceData
+
+from mmdet3d.structures import Det3DDataSample
+
+
+class NeRFDet3DDataSample(Det3DDataSample):
+    """A data structure interface inheirted from Det3DDataSample. Some new
+    attributes are added to match the NeRF-Det project.
+
+    The attributes added in ``NeRFDet3DDataSample`` are divided into two parts:
+
+        - ``gt_nerf_images`` (InstanceData): Ground truth of the images which
+          will be used in the NeRF branch.
+        - ``gt_nerf_depths`` (InstanceData): Ground truth of the depth images
+          which will be used in the NeRF branch if needed.
+
+    For more details and examples, please refer to the 'Det3DDataSample' file.
+    """
+
+    @property
+    def gt_nerf_images(self) -> InstanceData:
+        return self._gt_nerf_images
+
+    @gt_nerf_images.setter
+    def gt_nerf_images(self, value: InstanceData) -> None:
+        self.set_field(value, '_gt_nerf_images', dtype=InstanceData)
+
+    @gt_nerf_images.deleter
+    def gt_nerf_images(self) -> None:
+        del self._gt_nerf_images
+
+    @property
+    def gt_nerf_depths(self) -> InstanceData:
+        return self._gt_nerf_depths
+
+    @gt_nerf_depths.setter
+    def gt_nerf_depths(self, value: InstanceData) -> None:
+        self.set_field(value, '_gt_nerf_depths', dtype=InstanceData)
+
+    @gt_nerf_depths.deleter
+    def gt_nerf_depths(self) -> None:
+        del self._gt_nerf_depths
+
+
+SampleList = List[NeRFDet3DDataSample]
+OptSampleList = Optional[SampleList]
+ForwardResults = Union[Dict[str, torch.Tensor], List[NeRFDet3DDataSample],
+                       Tuple[torch.Tensor], torch.Tensor]

projects/PETR/README.md

@@ -16,7 +16,7 @@ This is an implementation of *PETR*.
 In MMDet3D's root directory, run the following command to train the model:
 
 ```bash
-python tools/train.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.py
+python tools/train.py projects/PETR/configs/petr_vovnet_gridmask_p4_800x320.py
 ```
 
 ### Testing commands
@@ -24,7 +24,7 @@ python tools/train.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.
 In MMDet3D's root directory, run the following command to test the model:
 
 ```bash
-python tools/test.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.py ${CHECKPOINT_PATH}
+python tools/test.py projects/PETR/configs/petr_vovnet_gridmask_p4_800x320.py ${CHECKPOINT_PATH}
 ```
 
 ## Results

projects/PETR/petr/petr_head.py

@@ -446,7 +446,7 @@ class PETRHead(AnchorFreeHead):
         masks = x.new_ones((batch_size, num_cams, input_img_h, input_img_w))
         for img_id in range(batch_size):
             for cam_id in range(num_cams):
-                img_h, img_w, _ = img_metas[img_id]['img_shape'][cam_id]
+                img_h, img_w = img_metas[img_id]['img_shape'][cam_id]
                 masks[img_id, cam_id, :img_h, :img_w] = 0
         x = self.input_proj(x.flatten(0, 1))
         x = x.view(batch_size, num_cams, *x.shape[-3:])