diff --git a/.gitignore b/.gitignore
index e83ec70ebe57efac39ff559dee5c5743913bf675..9918ddfd120d72ac6c8bf2a6f4615e7551c87471 100644
--- a/.gitignore
+++ b/.gitignore
@@ -104,7 +104,7 @@ venv.bak/
.mypy_cache/
mmdet/version.py
-./data/
+data/
.vscode
.idea
.DS_Store
diff --git a/README.md b/README.md
index a737a2df4e2163c820ddfd4f5eb46fd45bd37eae..c9dc9ac851d7fe3e0f8307226efbbe0dd057cba1 100644
--- a/README.md
+++ b/README.md
@@ -88,6 +88,7 @@ Other features
- [x] [GCNet](configs/gcnet/README.md)
- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
- [x] [InstaBoost](configs/instaboost/README.md)
+- [x] [GRoIE](configs/groie/README.md)
Some other methods are also supported in [projects using MMDetection](./docs/projects.md).
diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc3e3ef594243be1335aa3b3d2f78f50f4477082
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = ['../grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py']
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[8, 11])
+checkpoint_config = dict(interval=1)
+# runtime settings
+total_epochs = 12
diff --git a/configs/groie/README.md b/configs/groie/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..50a2f1373927a8eb4b0300f1d1e20844930e954d
--- /dev/null
+++ b/configs/groie/README.md
@@ -0,0 +1,65 @@
+# GRoIE
+
+## A novel Region of Interest Extraction Layer for Instance Segmentation
+
+By Leonardo Rossi, Akbar Karimi and Andrea Prati from
+[IMPLab](http://implab.ce.unipr.it/).
+
+We provide config files to reproduce the results in the paper for
+"*A novel Region of Interest Extraction Layer for Instance Segmentation*"
+on COCO object detection.
+
+## Introduction
+
+This paper is motivated by the need to overcome to the limitations of existing
+RoI extractors which select only one (the best) layer from FPN.
+
+Our intuition is that all the layers of FPN retain useful information.
+
+Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**)
+introduces non-local building blocks and attention mechanisms to boost the
+performance.
+
+## Results and models
+
+The results on COCO 2017 minival (5k images) are shown in the below table.
+You can find
+[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT)
+the trained models.
+
+### Application of GRoIE to different architectures
+
+| Backbone | Model | Lr schd | box AP | mask AP | Config file |
+| :-------: | :--------------: | :-----: | :----: | :-----: | :-----------------------------------------------------------------: |
+| R-50-FPN | Faster Original | 1x | 37.4 | | [config file](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) |
+| R-50-FPN | + GRoIE | 1x | 38.3 | | [config file](./faster_rcnn_r50_fpn_groie_1x_coco.py) |
+| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config file](../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py) |
+| R-50-FPN | + GRoIE | 1x | | | [config file](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py) |
+| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config file](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) |
+| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config file](./mask_rcnn_r50_fpn_groie_1x_coco.py) |
+| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config file](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) |
+| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config file](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |
+| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config file](../configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) |
+| R-101-FPN | + GRoIE | 1x | | | [config file](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |
+
+
+
+## Citation
+
+If you use this work or benchmark in your research, please cite this project.
+
+```
+@misc{rossi2020novel,
+ title={A novel Region of Interest Extraction Layer for Instance Segmentation},
+ author={Leonardo Rossi and Akbar Karimi and Andrea Prati},
+ year={2020},
+ eprint={2004.13665},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
+
+## Contact
+
+The implementation of GROI is currently maintained by
+[Leonardo Rossi](https://github.com/hachreak/).
diff --git a/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..f916653890c145d1ac4680a4dd1141eb54aae311
--- /dev/null
+++ b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
@@ -0,0 +1,24 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+ roi_head=dict(
+ bbox_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2))))
diff --git a/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..668ce3a04dae0216da55d6c65d143d4c92295513
--- /dev/null
+++ b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
@@ -0,0 +1,44 @@
+_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
+# model settings
+model = dict(
+ roi_head=dict(
+ bbox_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2)),
+ grid_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cf22a09aed3fceb9f9c20f189b3f0f31ad470ff
--- /dev/null
+++ b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
@@ -0,0 +1,44 @@
+_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+ roi_head=dict(
+ bbox_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2)),
+ mask_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..3afa52ef81324835134e5c037dd6ae109bc4c9c5
--- /dev/null
+++ b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
@@ -0,0 +1,44 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+ roi_head=dict(
+ bbox_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2)),
+ mask_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dc9ed4574ae6278806e7ce989a76b3e3e214aa2
--- /dev/null
+++ b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
@@ -0,0 +1,44 @@
+_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+ roi_head=dict(
+ bbox_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2)),
+ mask_roi_extractor=dict(
+ type='SumGenericRoiExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='GeneralizedAttention',
+ in_channels=256,
+ spatial_range=-1,
+ num_heads=6,
+ attention_type='0100',
+ kv_stride=2))))
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index d8e9c6d3e9f59627540c8299ed1ba1ae0fdcb0ef..f216f054536576b118e60e7ea2d0b1dfaf7c87c6 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -129,6 +129,9 @@ Please refer to [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/c
### Res2Net
Please refer to [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net) for details.
+### GRoIE
+Please refer to [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie) for details.
+
### Other datasets
We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face).
diff --git a/mmdet/models/roi_heads/roi_extractors/__init__.py b/mmdet/models/roi_heads/roi_extractors/__init__.py
index 9161708ce13fa4f0a6bb188e82a19a163b9b7e4f..2fed89ba58c117bfa64ce2cb7d7059e81cdf91a9 100644
--- a/mmdet/models/roi_heads/roi_extractors/__init__.py
+++ b/mmdet/models/roi_heads/roi_extractors/__init__.py
@@ -1,3 +1,7 @@
+from .groie import SumGenericRoiExtractor
from .single_level import SingleRoIExtractor
-__all__ = ['SingleRoIExtractor']
+__all__ = [
+ 'SingleRoIExtractor',
+ 'SumGenericRoiExtractor',
+]
diff --git a/mmdet/models/roi_heads/roi_extractors/groie.py b/mmdet/models/roi_heads/roi_extractors/groie.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c7105538ce808bb6a76106308aae38412d374e8
--- /dev/null
+++ b/mmdet/models/roi_heads/roi_extractors/groie.py
@@ -0,0 +1,62 @@
+"""Generic RoI Extractor.
+
+A novel Region of Interest Extraction Layer for Instance Segmentation.
+"""
+
+from torch import nn
+
+from mmdet.core import force_fp32
+from mmdet.models.builder import ROI_EXTRACTORS
+from mmdet.ops.plugin import build_plugin_layer
+from .single_level import SingleRoIExtractor
+
+
+@ROI_EXTRACTORS.register_module
+class SumGenericRoiExtractor(SingleRoIExtractor):
+ """Extract RoI features from all summed feature maps levels.
+
+ https://arxiv.org/abs/2004.13665
+
+ Args:
+ pre_cfg (dict): Specify pre-processing modules.
+ post_cfg (dict): Specify post-processing modules.
+ kwargs (keyword arguments): Arguments that are the same
+ as :class:`SingleRoIExtractor`.
+ """
+
+ def __init__(self, pre_cfg, post_cfg, **kwargs):
+ super(SumGenericRoiExtractor, self).__init__(**kwargs)
+
+ # build pre/post processing modules
+ self.post_module = build_plugin_layer(post_cfg, '_post_module')[1]
+ self.pre_module = build_plugin_layer(pre_cfg, '_pre_module')[1]
+ self.relu = nn.ReLU(inplace=False)
+
+ @force_fp32(apply_to=('feats', ), out_fp16=True)
+ def forward(self, feats, rois, roi_scale_factor=None):
+ if len(feats) == 1:
+ return self.roi_layers[0](feats[0], rois)
+
+ out_size = self.roi_layers[0].out_size
+ num_levels = len(feats)
+ roi_feats = feats[0].new_zeros(
+ rois.size(0), self.out_channels, *out_size)
+
+ # some times rois is an empty tensor
+ if roi_feats.shape[0] == 0:
+ return roi_feats
+
+ if roi_scale_factor is not None:
+ rois = self.roi_rescale(rois, roi_scale_factor)
+
+ for i in range(num_levels):
+ # apply pre-processing to a RoI extracted from each layer
+ roi_feats_t = self.roi_layers[i](feats[i], rois)
+ roi_feats_t = self.pre_module(roi_feats_t)
+ roi_feats_t = self.relu(roi_feats_t)
+ # and sum them all
+ roi_feats += roi_feats_t
+
+ # apply post-processing before return the result
+ x = self.post_module(roi_feats)
+ return x
diff --git a/mmdet/ops/plugin.py b/mmdet/ops/plugin.py
index a104c1f56b10de23d95a0239f752cb9552dcfb19..4270f471a49ce8064c5ee4bd604b24ec75984c0a 100644
--- a/mmdet/ops/plugin.py
+++ b/mmdet/ops/plugin.py
@@ -1,3 +1,5 @@
+from mmcv.cnn import ConvModule
+
from .context_block import ContextBlock
from .generalized_attention import GeneralizedAttention
from .non_local import NonLocal2D
@@ -6,7 +8,8 @@ plugin_cfg = {
# format: layer_type: (abbreviation, module)
'ContextBlock': ('context_block', ContextBlock),
'GeneralizedAttention': ('gen_attention_block', GeneralizedAttention),
- 'NonLocal2D': ('nonlocal_block', NonLocal2D)
+ 'NonLocal2D': ('nonlocal_block', NonLocal2D),
+ 'ConvModule': ('conv_block', ConvModule),
}
diff --git a/tests/test_roi_extractor.py b/tests/test_roi_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..332f8b07edf70932938dedb2f9f984746216dc95
--- /dev/null
+++ b/tests/test_roi_extractor.py
@@ -0,0 +1,41 @@
+import mmcv
+import torch
+
+from mmdet.models.roi_heads.roi_extractors import SumGenericRoiExtractor
+
+
+def test_groie():
+ cfg = mmcv.Config(
+ dict(
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32],
+ pre_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False,
+ ),
+ post_cfg=dict(
+ type='ConvModule',
+ in_channels=256,
+ out_channels=256,
+ kernel_size=5,
+ padding=2,
+ inplace=False)))
+
+ groie = SumGenericRoiExtractor(**cfg)
+
+ feats = (
+ torch.rand((1, 256, 200, 336)),
+ torch.rand((1, 256, 100, 168)),
+ torch.rand((1, 256, 50, 84)),
+ torch.rand((1, 256, 25, 42)),
+ )
+
+ rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
+
+ res = groie(feats, rois)
+ assert res.shape == torch.Size([1, 256, 7, 7])