Support HTC with nuImages and update class order (#155)

* make nuimage cat order consistent

* support semantic seg

* update model links

* update readme

configs/nuimages/README.md

@@ -7,6 +7,18 @@ We follow the class mapping in nuScenes dataset, which maps the original categor
 The baseline results include instance segmentation models, e.g., Mask R-CNN and Cascade Mask R-CNN.
 We will support panoptic segmentation models in the future.
 
+The dataset converted by the script of v0.6.0 only supports instance segmentation. Since v0.7.0, we also support to produce semantic segmentation mask of each image; thus, we can train HTC or semantic segmentation models using the dataset. To convert the nuImages dataset into COCO format, please use the command below:
+
+```shell
+python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERIONS} \
+                                                    --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
+```
+
+- `--data-root`: the root of the dataset, defaults to `./data/nuimages`.
+- `--version`: the version of the dataset, defaults to `v1.0-mini`. To get the full dataset, please use `--version v1.0-train v1.0-val v1.0-mini`
+- `--out-dir`: the output directory of annotations and semantic masks, defaults to `./data/nuimages/annotations/`.
+- `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
+- `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.
 
 ## Results
 
@@ -16,17 +28,26 @@ We report Mask R-CNN and Cascade Mask R-CNN results on nuimages.
 
 |Method | Backbone|Pretraining | Lr schd | Mem (GB) | Box AP  | Mask AP  |Download |
 | :---------: |:---------: | :---------: | :-----: |:-----: | :------: | :------------: | :----: |
-| Mask R-CNN| [R-50](./mask_rcnn_r50_fpn_1x_nuim.py) |IN|1x|7.4|47.8 |38.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_fpn_1x_nuim/mask_rcnn_r50_fpn_1x_nuim_20200906_114546-902bb808.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_fpn_1x_nuim/mask_rcnn_r50_fpn_1x_nuim_20200906_114546.log.json)|
-| Mask R-CNN| [R-50](./mask_rcnn_r50_fpn_coco-2x_1x_nuim.py) |IN+COCO-2x|1x|7.4|49.6|40.0|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_fpn_coco-2x_1x_nuim/mask_rcnn_r50_fpn_coco-2x_1x_nuim_20200905_234546-01b6b9ba.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_fpn_coco-2x_1x_nuim/mask_rcnn_r50_fpn_coco-2x_1x_nuim_20200905_234546.log.json)|
-| Mask R-CNN| [R-50-CAFFE](./mask_rcnn_r50_caffe_fpn_1x_nuim.py) |IN|1x|7.0|47.7|38.2|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_caffe_fpn_1x_nuim/mask_rcnn_r50_caffe_fpn_1x_nuim_20200906_120052-733905fa.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_caffe_fpn_1x_nuim/mask_rcnn_r50_caffe_fpn_1x_nuim_20200906_120052.log.json)|
-| Mask R-CNN| [R-50-CAFFE](./mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py) |IN+COCO-3x|1x|7.0|49.7|40.3|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim_20200906_134613-e6dc1931.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim_20200906_134613.log.json)|
-| Mask R-CNN| [R-101](./mask_rcnn_r101_fpn_1x_nuim.py) |IN|1x|10.9|48.9|38.9|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r101_fpn_1x_nuim/mask_rcnn_r101_fpn_1x_nuim_20200906_182752-823be521.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_r101_fpn_1x_nuim/mask_rcnn_r101_fpn_1x_nuim_20200906_182752.log.json)|
-| Mask R-CNN| [X-101_32x4d](./mask_rcnn_x101_32x4d_fpn_1x_nuim.py) |IN|1x|13.3|50.3|40.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_x101_32x4d_fpn_1x_nuim/mask_rcnn_x101_32x4d_fpn_1x_nuim_20200906_134611-bd241530.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/mask_rcnn_x101_32x4d_fpn_1x_nuim/mask_rcnn_x101_32x4d_fpn_1x_nuim_20200906_134611.log.json)|
-| Cascade Mask R-CNN| [R-50](./cascade_mask_rcnn_r50_fpn_1x_nuim.py) |IN|1x|8.9|50.8|40.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/cascade_mask_rcnn_r50_fpn_1x_nuim/cascade_mask_rcnn_r50_fpn_1x_nuim_20200906_114546-22bf3085.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/cascade_mask_rcnn_r50_fpn_1x_nuim/cascade_mask_rcnn_r50_fpn_1x_nuim_20200906_114546.log.json)|
-| Cascade Mask R-CNN| [R-101](./cascade_mask_rcnn_r101_fpn_1x_nuim.py) |IN|1x|12.5|51.8|40.6|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/cascade_mask_rcnn_r101_fpn_1x_nuim/cascade_mask_rcnn_r101_fpn_1x_nuim_20200906_134611-ee279b07.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/cascade_mask_rcnn_r101_fpn_1x_nuim/cascade_mask_rcnn_r101_fpn_1x_nuim_20200906_134611.log.json)|
-| Cascade Mask R-CNN| [X-101_32x4d](./cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim.py) |IN|1x|14.9|52.9|41.6|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim_20200906_134611-47db31b0.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim_20200906_134611.log.json)|
+| Mask R-CNN| [R-50](./mask_rcnn_r50_fpn_1x_nuim.py) |IN|1x|7.4|47.8 |38.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_1x_nuim/mask_rcnn_r50_fpn_1x_nuim_20201008_195238-e99f5182.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_1x_nuim/mask_rcnn_r50_fpn_1x_nuim_20201008_195238.log.json)|
+| Mask R-CNN| [R-50](./mask_rcnn_r50_fpn_coco-2x_1x_nuim.py) |IN+COCO-2x|1x|7.4|49.7|40.5|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_coco-2x_1x_nuim/mask_rcnn_r50_fpn_coco-2x_1x_nuim_20201008_195238-b1742a60.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_coco-2x_1x_nuim/mask_rcnn_r50_fpn_coco-2x_1x_nuim_20201008_195238.log.json)|
+| Mask R-CNN| [R-50-CAFFE](./mask_rcnn_r50_caffe_fpn_1x_nuim.py) |IN|1x|7.0|47.7|38.2|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_1x_nuim/) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_1x_nuim/)|
+| Mask R-CNN| [R-50-CAFFE](./mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py) |IN+COCO-3x|1x|7.0|49.9|40.8|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim_20201008_195305-661a992e.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim_20201008_195305.log.json)|
+| Mask R-CNN| [R-50-CAFFE](./mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py) |IN+COCO-3x|20e|7.0|50.6|41.3|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim_20201009_125002-5529442c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim_20201009_125002.log.json)|
+| Mask R-CNN| [R-101](./mask_rcnn_r101_fpn_1x_nuim.py) |IN|1x|10.9|48.9|39.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r101_fpn_1x_nuim/mask_rcnn_r101_fpn_1x_nuim_20201024_134803-65c7623a.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r101_fpn_1x_nuim/mask_rcnn_r101_fpn_1x_nuim_20201024_134803.log.json)|
+| Mask R-CNN| [X-101_32x4d](./mask_rcnn_x101_32x4d_fpn_1x_nuim.py) |IN|1x|13.3|50.4|40.5|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_x101_32x4d_fpn_1x_nuim/mask_rcnn_x101_32x4d_fpn_1x_nuim_20201024_135741-b699ab37.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_x101_32x4d_fpn_1x_nuim/mask_rcnn_x101_32x4d_fpn_1x_nuim_20201024_135741.log.json)|
+| Cascade Mask R-CNN| [R-50](./cascade_mask_rcnn_r50_fpn_1x_nuim.py) |IN|1x|8.9|50.8|40.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_1x_nuim/cascade_mask_rcnn_r50_fpn_1x_nuim_20201008_195342-1147c036.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_1x_nuim/cascade_mask_rcnn_r50_fpn_1x_nuim_20201008_195342.log.json)|
+| Cascade Mask R-CNN| [R-50](./cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim.py) |IN+COCO-20e|1x|8.9|52.8|42.2|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim_20201009_124158-ad0540e3.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim_20201009_124158.log.json)|
+| Cascade Mask R-CNN| [R-50](./cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim.py) |IN+COCO-20e|20e|8.9|52.8|42.2|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951.log.json)|
+| Cascade Mask R-CNN| [R-101](./cascade_mask_rcnn_r101_fpn_1x_nuim.py) |IN|1x|12.5|51.5|40.7|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r101_fpn_1x_nuim/cascade_mask_rcnn_r101_fpn_1x_nuim_20201024_134804-45215b1e.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r101_fpn_1x_nuim/cascade_mask_rcnn_r101_fpn_1x_nuim_20201024_134804.log.json)|
+| Cascade Mask R-CNN| [X-101_32x4d](./cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim.py) |IN|1x|14.9|52.8|41.6|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim_20201024_135753-e0e49778.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim_20201024_135753.log.json)|
+| HTC w/o semantic|[R-50](./htc_without_semantic_r50_fpn_1x_nuim.py) |IN|1x||[model]() | [log]()|
+| HTC|[R-50](./htc_r50_fpn_1x_nuim.py) |IN|1x||[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/)|
+| HTC|[R-50](./htc_r50_fpn_coco-20e_1x_nuim.py) |IN+COCO-20e|1x|11.6|53.8|43.8|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_r50_fpn_coco-20e_1x_nuim/htc_r50_fpn_coco-20e_1x_nuim_20201010_070203-0b53a65e.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_r50_fpn_coco-20e_1x_nuim/htc_r50_fpn_coco-20e_1x_nuim_20201010_070203.log.json)|
+| HTC|[R-50](./htc_r50_fpn_coco-20e_20e_nuim.py) |IN+COCO-20e|20e|11.6|54.8|44.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_r50_fpn_coco-20e_20e_nuim/htc_r50_fpn_coco-20e_20e_nuim_20201008_211415-d6c60a2c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_r50_fpn_coco-20e_20e_nuim/htc_r50_fpn_coco-20e_20e_nuim_20201008_211415.log.json)|
+| HTC|[X-101_64x4d + DCN_c3-c5](./htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim.py) |IN+COCO-20e|20e|13.3|57.3|46.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim_20201008_211222-0b16ac4b.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim_20201008_211222.log.json)|
 
 **Note**:
-1. `IN` means only using ImageNet pre-trained backbone. `IN+COCO-Nx` means the backbone is first pre-trained on ImageNet, and then the detector is pre-trained on COCO train2017 dataset by `Nx` schedules.
-2. All the training hyper-parameters follow the standard 1x schedules on COCO dataset except that the images are resized from
+1. `IN` means only using ImageNet pre-trained backbone. `IN+COCO-Nx` and `IN+COCO-Ne` means the backbone is first pre-trained on ImageNet, and then the detector is pre-trained on COCO train2017 dataset by `Nx` and `N` epochs schedules, respectively.
+2. All the training hyper-parameters follow the standard schedules on COCO dataset except that the images are resized from
 1280 x 720 to 1920 x 1080 (relative ratio 0.8 to 1.2) since the images are in size 1600 x 900.
+3. The class order in the detectors released in v0.6.0 is different from the order in the configs because the bug in the convertion script. This bug has been fixed since v0.7.0 and the models trained by the correct class order are also released. If you used nuImages since v0.6.0, please re-convert the data through the convertion script using the above-mentioned command.

configs/nuimages/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim.py

+_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth'  # noqa

configs/nuimages/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim.py

+_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+
+# learning policy
+lr_config = dict(step=[16, 19])
+total_epochs = 20
+
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth'  # noqa

configs/nuimages/htc_r50_fpn_1x_nuim.py

+_base_ = './htc_without_semantic_r50_fpn_1x_nuim.py'
+model = dict(
+    roi_head=dict(
+        semantic_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[8]),
+        semantic_head=dict(
+            type='FusedSemanticHead',
+            num_ins=5,
+            fusion_level=1,
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=32,
+            ignore_label=0,
+            loss_weight=0.2)))
+
+data_root = 'data/nuimages/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(
+        type='Resize',
+        img_scale=[(1280, 720), (1920, 1080)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 8),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
+]
+data = dict(
+    train=dict(
+        seg_prefix=data_root + 'annotations/semantic_masks/',
+        pipeline=train_pipeline))

configs/nuimages/htc_r50_fpn_coco-20e_1x_nuim.py

+_base_ = './htc_r50_fpn_1x_nuim.py'
+
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth'  # noqa

configs/nuimages/htc_r50_fpn_coco-20e_20e_nuim.py

+_base_ = './htc_r50_fpn_coco-20e_1x_nuim.py'
+# learning policy
+lr_config = dict(step=[16, 19])
+total_epochs = 20

configs/nuimages/htc_without_semantic_r50_fpn_1x_nuim.py

+_base_ = [
+    '../_base_/datasets/nuim_instance.py',
+    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='HybridTaskCascade',
+    pretrained='torchvision://resnet50',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='HybridTaskCascadeRoIHead',
+        interleaved=True,
+        mask_info_flow=True,
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=10,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=10,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=10,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=[
+            dict(
+                type='HTCMaskHead',
+                with_conv_res=False,
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=10,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=10,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=10,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
+        ]))
+# model training and testing settings
+train_cfg = dict(
+    rpn=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.7,
+            neg_iou_thr=0.3,
+            min_pos_iou=0.3,
+            ignore_iof_thr=-1),
+        sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False),
+        allowed_border=0,
+        pos_weight=-1,
+        debug=False),
+    rpn_proposal=dict(
+        nms_across_levels=False,
+        nms_pre=2000,
+        nms_post=2000,
+        max_num=2000,
+        nms_thr=0.7,
+        min_bbox_size=0),
+    rcnn=[
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.6,
+                neg_iou_thr=0.6,
+                min_pos_iou=0.6,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.7,
+                min_pos_iou=0.7,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)
+    ])
+test_cfg = dict(
+    rpn=dict(
+        nms_across_levels=False,
+        nms_pre=1000,
+        nms_post=1000,
+        max_num=1000,
+        nms_thr=0.7,
+        min_bbox_size=0),
+    rcnn=dict(
+        score_thr=0.001,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100,
+        mask_thr_binary=0.5))

configs/nuimages/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim.py

+_base_ = './htc_r50_fpn_1x_nuim.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_64x4d',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+# learning policy
+lr_config = dict(step=[16, 19])
+total_epochs = 20
+
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth'  # noqa

configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim.py

+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/nuim_instance.py',
+    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet50_caffe',
+    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
+    roi_head=dict(
+        bbox_head=dict(num_classes=10), mask_head=dict(num_classes=10)))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1280, 720), (1920, 1080)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1600, 900),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# learning policy
+lr_config = dict(step=[16, 19])
+total_epochs = 20
+
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'  # noqa

configs/nuimages/mask_rcnn_r50_fpn_coco-2x_1x_nus-2d.py

+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/nuim_instance.py',
+    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=10), mask_head=dict(num_classes=10)))
+
+file_client_args = dict(
+    backend='petrel',
+    path_mapping=dict({
+        './data/nuscenes/': 's3://nuscenes/nuscenes/',
+        'data/nuscenes/': 's3://nuscenes/nuscenes/'
+    }))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1600, 900),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data_root = 'data/nuimages/'
+# data = dict(
+#     val=dict(
+#         ann_file=data_root + 'annotations/nuimages_v1.0-mini.json'),
+#     test=dict(
+#         ann_file=data_root + 'annotations/nuimages_v1.0-mini.json'))

docs/model_zoo.md

@@ -36,7 +36,7 @@ Please refer to [MVXNet](https://github.com/open-mmlab/mmdetection3d/blob/master
 Please refer to [RegNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/regnet) for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.
 
 ### nuImages
-We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages) for details. We report Mask R-CNN and Cascade Mask R-CNN results currently.
+We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages) for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.
 
 ### H3DNet
 

tools/data_converter/nuimage_converter.py

@@ -3,8 +3,29 @@ import base64
 import mmcv
 import numpy as np
 from nuimages import NuImages
+from nuimages.utils.utils import mask_decode, name_to_index_mapping
 from os import path as osp
-from pycocotools import mask as mask_util
+
+nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
+                  'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
+                  'barrier')
+
+NAME_MAPPING = {
+    'movable_object.barrier': 'barrier',
+    'vehicle.bicycle': 'bicycle',
+    'vehicle.bus.bendy': 'bus',
+    'vehicle.bus.rigid': 'bus',
+    'vehicle.car': 'car',
+    'vehicle.construction': 'construction_vehicle',
+    'vehicle.motorcycle': 'motorcycle',
+    'human.pedestrian.adult': 'pedestrian',
+    'human.pedestrian.child': 'pedestrian',
+    'human.pedestrian.construction_worker': 'pedestrian',
+    'human.pedestrian.police_officer': 'pedestrian',
+    'movable_object.trafficcone': 'traffic_cone',
+    'vehicle.trailer': 'trailer',
+    'vehicle.truck': 'truck',
+}
 
 
 def parse_args():
@@ -18,7 +39,7 @@ def parse_args():
         '--version',
         type=str,
         nargs='+',
-        default='v1.0-mini',
+        default=['v1.0-mini'],
         required=False,
         help='specify the dataset version')
     parser.add_argument(
@@ -27,42 +48,114 @@ def parse_args():
         default='./data/nuimages/annotations/',
         required=False,
         help='path to save the exported json')
+    parser.add_argument(
+        '--nproc',
+        type=int,
+        default=4,
+        required=False,
+        help='workers to process semantic masks')
     parser.add_argument('--extra-tag', type=str, default='nuimages')
     args = parser.parse_args()
     return args
 
 
-def export_nuim_to_coco(nuim, out_dir, extra_tag, version):
-    NameMapping = {
-        'movable_object.barrier': 'barrier',
-        'vehicle.bicycle': 'bicycle',
-        'vehicle.bus.bendy': 'bus',
-        'vehicle.bus.rigid': 'bus',
-        'vehicle.car': 'car',
-        'vehicle.construction': 'construction_vehicle',
-        'vehicle.motorcycle': 'motorcycle',
-        'human.pedestrian.adult': 'pedestrian',
-        'human.pedestrian.child': 'pedestrian',
-        'human.pedestrian.construction_worker': 'pedestrian',
-        'human.pedestrian.police_officer': 'pedestrian',
-        'movable_object.trafficcone': 'traffic_cone',
-        'vehicle.trailer': 'trailer',
-        'vehicle.truck': 'truck',
-    }
+def get_img_annos(nuim, img_info, cat2id, out_dir, data_root, seg_root):
+    """Get semantic segmentation map for an image.
+
+    Args:
+        nuim (obj:`NuImages`): NuImages dataset object
+        img_info (dict): Meta information of img
+
+    Returns:
+        np.ndarray: Semantic segmentation map of the image
+    """
+    sd_token = img_info['token']
+    image_id = img_info['id']
+    name_to_index = name_to_index_mapping(nuim.category)
+
+    # Get image data.
+    width, height = img_info['width'], img_info['height']
+    semseg_mask = np.zeros((height, width)).astype('uint8')
+
+    # Load stuff / surface regions.
+    surface_anns = [
+        o for o in nuim.surface_ann if o['sample_data_token'] == sd_token
+    ]
+
+    # Draw stuff / surface regions.
+    for ann in surface_anns:
+        # Get color and mask.
+        category_token = ann['category_token']
+        category_name = nuim.get('category', category_token)['name']
+        if ann['mask'] is None:
+            continue
+        mask = mask_decode(ann['mask'])
+
+        # Draw mask for semantic segmentation.
+        semseg_mask[mask == 1] = name_to_index[category_name]
+
+    # Load object instances.
+    object_anns = [
+        o for o in nuim.object_ann if o['sample_data_token'] == sd_token
+    ]
 
+    # Sort by token to ensure that objects always appear in the
+    # instance mask in the same order.
+    object_anns = sorted(object_anns, key=lambda k: k['token'])
+
+    # Draw object instances.
+    # The 0 index is reserved for background; thus, the instances
+    # should start from index 1.
+    annotations = []
+    for i, ann in enumerate(object_anns, start=1):
+        # Get color, box, mask and name.
+        category_token = ann['category_token']
+        category_name = nuim.get('category', category_token)['name']
+        if ann['mask'] is None:
+            continue
+        mask = mask_decode(ann['mask'])
+
+        # Draw masks for semantic segmentation and instance segmentation.
+        semseg_mask[mask == 1] = name_to_index[category_name]
+
+        if category_name in NAME_MAPPING:
+            cat_name = NAME_MAPPING[category_name]
+            cat_id = cat2id[cat_name]
+
+            x_min, y_min, x_max, y_max = ann['bbox']
+            # encode calibrated instance mask
+            mask_anno = dict()
+            mask_anno['counts'] = base64.b64decode(
+                ann['mask']['counts']).decode()
+            mask_anno['size'] = ann['mask']['size']
+
+            data_anno = dict(
+                image_id=image_id,
+                category_id=cat_id,
+                bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
+                area=(x_max - x_min) * (y_max - y_min),
+                segmentation=mask_anno,
+                iscrowd=0)
+            annotations.append(data_anno)
+
+    # after process, save semantic masks
+    img_filename = img_info['file_name']
+    seg_filename = img_filename.replace('jpg', 'png')
+    seg_filename = osp.join(seg_root, seg_filename)
+    mmcv.imwrite(semseg_mask, seg_filename)
+    return annotations, np.max(semseg_mask)
+
+
+def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
     print('Process category information')
     categories = []
-    cat2id = dict()
-    for cate_info in mmcv.track_iter_progress(nuim.category):
-        if cate_info['name'] in NameMapping:
-            name = NameMapping[cate_info['name']]
-            if name not in cat2id:
-                idx = len(categories)
-                categories.append(dict(id=idx, name=name))
-                cat2id.update({name: idx})
+    categories = [
+        dict(id=nus_categories.index(cat_name), name=cat_name)
+        for cat_name in nus_categories
+    ]
+    cat2id = {k_v['name']: k_v['id'] for k_v in categories}
 
     images = []
-    img2id = dict()
     print('Process image meta information...')
     for sample_info in mmcv.track_iter_progress(nuim.sample_data):
         if sample_info['is_key_frame']:
@@ -70,43 +163,44 @@ def export_nuim_to_coco(nuim, out_dir, extra_tag, version):
             images.append(
                 dict(
                     id=img_idx,
+                    token=sample_info['token'],
                     file_name=sample_info['filename'],
                     width=sample_info['width'],
                     height=sample_info['height']))
-            img2id.update({sample_info['token']: img_idx})
 
-    print('Process annotation information...')
-    annotations = []
-    for single_obj in mmcv.track_iter_progress(nuim.object_ann):
-        category_info = nuim.get('category', single_obj['category_token'])
-        if category_info['name'] in NameMapping:
-            cat_name = NameMapping[category_info['name']]
-            cat_id = cat2id[cat_name]
-        else:
-            continue
+    seg_root = f'{out_dir}semantic_masks'
+    mmcv.mkdir_or_exist(seg_root)
+    mmcv.mkdir_or_exist(osp.join(data_root, 'calibrated'))
 
-        image_id = img2id[single_obj['sample_data_token']]
-        x_min, y_min, x_max, y_max = single_obj['bbox']
+    global process_img_anno
 
-        mask_anno = dict()
-        if single_obj['mask'] is None:
-            empty_mask = np.zeros((900, 1600, 1), order='F', dtype='uint8')
-            mask_anno = mask_util.encode(empty_mask)[0]
-            mask_anno['counts'] = mask_anno['counts'].decode()
+    def process_img_anno(img_info):
+        single_img_annos, max_cls_id = get_img_annos(nuim, img_info, cat2id,
+                                                     out_dir, data_root,
+                                                     seg_root)
+        return single_img_annos, max_cls_id
+
+    print('Process img annotations...')
+    if nproc > 1:
+        outputs = mmcv.track_parallel_progress(
+            process_img_anno, images, nproc=nproc)
     else:
-            mask_anno['counts'] = base64.b64decode(
-                single_obj['mask']['counts']).decode()
-            mask_anno['size'] = single_obj['mask']['size']
+        outputs = []
+        for img_info in mmcv.track_iter_progress(images):
+            outputs.append(process_img_anno(img_info))
 
-        data_anno = dict(
-            image_id=image_id,
-            id=len(annotations),
-            category_id=cat_id,
-            bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
-            area=(x_max - x_min) * (y_max - y_min),
-            segmentation=mask_anno,
-            iscrowd=0)
-        annotations.append(data_anno)
+    # Determine the index of object annotation
+    print('Process annotation information...')
+    annotations = []
+    max_cls_ids = []
+    for single_img_annos, max_cls_id in outputs:
+        max_cls_ids.append(max_cls_id)
+        for img_anno in single_img_annos:
+            img_anno.update(id=len(annotations))
+            annotations.append(img_anno)
+
+    max_cls_id = max(max_cls_ids)
+    print(f'Max ID of class in the semantic map: {max_cls_id}')
 
     coco_format_json = dict(
         images=images, annotations=annotations, categories=categories)
@@ -122,7 +216,8 @@ def main():
     for version in args.version:
         nuim = NuImages(
             dataroot=args.data_root, version=version, verbose=True, lazy=True)
-        export_nuim_to_coco(nuim, args.out_dir, args.extra_tag, version)
+        export_nuim_to_coco(nuim, args.data_root, args.out_dir, args.extra_tag,
+                            version, args.nproc)
 
 
 if __name__ == '__main__':