[Refactor] Refactor configs of dgcnn for different areas (#1967)

* refactor configs of dgcnn

* update

* Update metafile.yml

* Refactor dgcnn

* Minor fixes in docstring
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

configs/dgcnn/README.md

@@ -22,15 +22,15 @@ We implement DGCNN and provide the results and checkpoints on S3DIS dataset.
 
 ### S3DIS
 
-|                     Method                      | Split  |   Lr schd   | Mem (GB) | Inf time (fps) | mIoU (Val set) |                                                                                                                                                                                                 Download                                                                                                                                                                                                 |
-| :---------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_1 | cosine 100e |   13.1   |                |     68.33      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_2 | cosine 100e |   13.1   |                |     40.68      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_3 | cosine 100e |   13.1   |                |     69.38      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_4 | cosine 100e |   13.1   |                |     50.07      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_5 | cosine 100e |   13.1   |                |     50.59      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_6 | cosine 100e |   13.1   |                |     77.94      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
-| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | 6-fold |             |          |                |     59.43      |                                                                                                                                                                                                                                                                                                                                                                                                          |
+|                           Method                           | Split  |   Lr schd   | Mem (GB) | Inf time (fps) | mIoU (Val set) |                                                                                                                                                                                                 Download                                                                                                                                                                                                 |
+| :--------------------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py) | Area_1 | cosine 100e |   13.1   |                |     68.33      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py) | Area_2 | cosine 100e |   13.1   |                |     40.68      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py) | Area_3 | cosine 100e |   13.1   |                |     69.38      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py) | Area_4 | cosine 100e |   13.1   |                |     50.07      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py) | Area_5 | cosine 100e |   13.1   |                |     50.59      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
+| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py) | Area_6 | cosine 100e |   13.1   |                |     77.94      | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
+|                           DGCNN                            | 6-fold |             |          |                |     59.43      |                                                                                                                                                                                                                                                                                                                                                                                                          |
 
 **Notes:**
 

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py

+_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
+
+# data settings
+train_area = [2, 3, 4, 5, 6]
+test_area = 1
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ]))
+test_dataloader = dict(
+    dataset=dict(
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
+val_dataloader = test_dataloader

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py

+_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
+
+# data settings
+train_area = [1, 3, 4, 5, 6]
+test_area = 2
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ]))
+test_dataloader = dict(
+    dataset=dict(
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
+val_dataloader = test_dataloader

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py

+_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
+
+# data settings
+train_area = [1, 2, 4, 5, 6]
+test_area = 3
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ]))
+test_dataloader = dict(
+    dataset=dict(
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
+val_dataloader = test_dataloader

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py

+_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
+
+# data settings
+train_area = [1, 2, 3, 5, 6]
+test_area = 4
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ]))
+test_dataloader = dict(
+    dataset=dict(
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
+val_dataloader = test_dataloader

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py

+_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
+
+# data settings
+train_area = [1, 2, 3, 4, 5]
+test_area = 6
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ]))
+test_dataloader = dict(
+    dataset=dict(
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
+val_dataloader = test_dataloader

configs/dgcnn/metafile.yml

@@ -10,15 +10,80 @@ Collections:
     README: configs/dgcnn/README.md
 
 Models:
-  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg.py
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py
     In Collection: DGCNN
-    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg.py
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py
     Metadata:
       Training Data: S3DIS
       Training Memory (GB): 13.3
     Results:
       - Task: 3D Semantic Segmentation
-        Dataset: S3DIS
+        Dataset: S3DIS Area1
+        Metrics:
+          mIoU: 68.33
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth
+
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py
+    In Collection: DGCNN
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 13.3
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS Area2
+        Metrics:
+          mIoU: 40.68
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth
+
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py
+    In Collection: DGCNN
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 13.3
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS Area3
+        Metrics:
+          mIoU: 69.38
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth
+
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py
+    In Collection: DGCNN
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 13.3
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS Area4
+        Metrics:
+          mIoU: 50.07
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth
+
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py
+    In Collection: DGCNN
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 13.3
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS Area5
         Metrics:
           mIoU: 50.59
     Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth
+
+  - Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py
+    In Collection: DGCNN
+    Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 13.3
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS Area6
+        Metrics:
+          mIoU: 77.94
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth

mmdet3d/models/decode_heads/decode_head.py

@@ -87,8 +87,6 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
         else:
             self.dropout = None
 
-        self.fp16_enabled = False
-
     def init_weights(self):
         """Initialize weights of classification layer."""
         super().init_weights()
@@ -112,9 +110,9 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
 
         Args:
             inputs (list[torch.Tensor]): List of multi-level point features.
-            img_metas (list[dict]): Meta information of each sample.
-            pts_semantic_mask (torch.Tensor): Semantic segmentation masks
-                used if the architecture supports semantic segmentation task.
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The seg
+                data samples. It usually includes information such
+                as `metainfo` and `gt_pts_seg`.
             train_cfg (dict): The training config.
 
         Returns:
@@ -130,7 +128,9 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
 
         Args:
             inputs (list[Tensor]): List of multi-level point features.
-            batch_img_metas (list[dict]): Meta information of each sample.
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The seg
+                data samples. It usually includes information such
+                as `metainfo` and `gt_pts_seg`.
             test_cfg (dict): The testing config.
 
         Returns: