[Feature] Support QueryInst (#6050)

* impl queryinst * bug free queryinst with crop and negative samples * use detr hyperparameters * pre-commit hooks * modified dynamic_mask_head docstrings * remove unused dropout in dynamic_mask_head * add docstring for dice_loss * add dice_loss unit test * impl unit test for dynamic_mask_head * update queryinst docstring and implementation * stability update for dice_loss and dynamic_mask_head * update for clarify * bug free in case of num_proposals equal to zero * detail docstrings * fixed CI issues * issues resolved * add queryinst docs
open-mmlab · Oct 26, 2021 · f003656 · f003656
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diff --git a/README.md b/README.md
@@ -147,6 +147,7 @@ Results and models are available in the [model zoo](docs/model_zoo.md).
 - [x] [CenterNet (CVPR'2019)](configs/centernet/README.md)
 - [x] [YOLOX (ArXiv'2021)](configs/yolox/README.md)
 - [x] [SOLO (ECCV'2020)](configs/solo/README.md)
+- [x] [QueryInst (ICCV'2021)](configs/queryinst/README.md)
 </details>
 
 Some other methods are also supported in [projects using MMDetection](./docs/projects.md).

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -145,6 +145,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 - [x] [CenterNet (CVPR'2019)](configs/centernet/README.md)
 - [x] [YOLOX (ArXiv'2021)](configs/yolox/README.md)
 - [x] [SOLO (ECCV'2020)](configs/solo/README.md)
+- [x] [QueryInst (ICCV'2021)](configs/queryinst/README.md)
 </details>
 
 我们在[基于 MMDetection 的项目](./docs/projects.md)中列举了一些其他的支持的算法。

diff --git a/configs/queryinst/README.md b/configs/queryinst/README.md
@@ -0,0 +1,26 @@
+# Instances as Queries
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```
+@InProceedings{Fang_2021_ICCV,
+    author    = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
+    title     = {Instances As Queries},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2021},
+    pages     = {6910-6919}
+}
+```
+
+## Results and Models
+
+| Model        | Backbone  | Style   | Lr schd | Number of Proposals |Multi-Scale| RandomCrop  | box AP  | mask AP  | Config | Download |
+|:------------:|:---------:|:-------:|:-------:|:-------:            |:-------: |:---------:|:------:|:------:|:------:|:--------:|
+| QueryInst | R-50-FPN  | pytorch | 1x      |   100               | False     |  False     |  42.0  |  37.5  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_1x_coco.py) | [model]() &#124; [log]() |
+| QueryInst | R-50-FPN  | pytorch | 3x      |   100               | True     |   False     |  44.8  |  39.8  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
+| QueryInst | R-50-FPN  | pytorch | 3x      |   300               | True      |  True      |  47.5  |  41.7  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
+| QueryInst | R-101-FPN | pytorch | 3x      |   100               | True      |  False     |  46.4  |  41.0  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
+| QueryInst | R-101-FPN | pytorch | 3x      |   300               | True      |  True      |  49.0  |  42.9  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
diff --git a/configs/queryinst/metafile.yml b/configs/queryinst/metafile.yml
@@ -0,0 +1,100 @@
+Collections:
+  - Name: QueryInst
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - QueryInst
+    Paper:
+      URL: https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf
+      Title: 'Instances as Queries'
+    README: configs/queryinst/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/queryinst.py
+      Version: v2.18.0
+
+Models:
+  - Name: queryinst_r50_fpn_1x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights:
+
+  - Name: queryinst_r50_fpn_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.8
+    Weights:
+
+  - Name: queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.7
+    Weights:
+
+  - Name: queryinst_r101_fpn_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.0
+    Weights:
+
+  - Name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 42.9
+    Weights:
diff --git a/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/queryinst/queryinst_r50_fpn_1x_coco.py b/configs/queryinst/queryinst_r50_fpn_1x_coco.py
@@ -0,0 +1,138 @@
+_base_ = [
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+num_stages = 6
+num_proposals = 100
+model = dict(
+    type='QueryInst',
+    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',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=0,
+        add_extra_convs='on_input',
+        num_outs=4),
+    rpn_head=dict(
+        type='EmbeddingRPNHead',
+        num_proposals=num_proposals,
+        proposal_feature_channel=256),
+    roi_head=dict(
+        type='SparseRoIHead',
+        num_stages=num_stages,
+        stage_loss_weights=[1] * num_stages,
+        proposal_feature_channel=256,
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='DIIHead',
+                num_classes=80,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_cls_fcs=1,
+                num_reg_fcs=3,
+                feedforward_channels=2048,
+                in_channels=256,
+                dropout=0.0,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                dynamic_conv_cfg=dict(
+                    type='DynamicConv',
+                    in_channels=256,
+                    feat_channels=64,
+                    out_channels=256,
+                    input_feat_shape=7,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN')),
+                loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+                loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                loss_cls=dict(
+                    type='FocalLoss',
+                    use_sigmoid=True,
+                    gamma=2.0,
+                    alpha=0.25,
+                    loss_weight=2.0),
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    clip_border=False,
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
+        ],
+        mask_head=[
+            dict(
+                type='DynamicMaskHead',
+                dynamic_conv_cfg=dict(
+                    type='DynamicConv',
+                    in_channels=256,
+                    feat_channels=64,
+                    out_channels=256,
+                    input_feat_shape=14,
+                    with_proj=False,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN')),
+                num_convs=4,
+                num_classes=80,
+                roi_feat_size=14,
+                in_channels=256,
+                conv_kernel_size=3,
+                conv_out_channels=256,
+                class_agnostic=False,
+                norm_cfg=dict(type='BN'),
+                upsample_cfg=dict(type='deconv', scale_factor=2),
+                loss_mask=dict(
+                    type='DiceLoss',
+                    loss_weight=8.0,
+                    use_sigmoid=True,
+                    activate=False,
+                    eps=1e-5)) for _ in range(num_stages)
+        ]),
+    # training and testing settings
+    train_cfg=dict(
+        rpn=None,
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='HungarianAssigner',
+                    cls_cost=dict(type='FocalLossCost', weight=2.0),
+                    reg_cost=dict(type='BBoxL1Cost', weight=5.0),
+                    iou_cost=dict(type='IoUCost', iou_mode='giou',
+                                  weight=2.0)),
+                sampler=dict(type='PseudoSampler'),
+                pos_weight=1,
+                mask_size=28,
+            ) for _ in range(num_stages)
+        ]),
+    test_cfg=dict(
+        rpn=None, rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
+
+# optimizer
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[8, 11], warmup_iters=1000)
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,54 @@
+_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
+num_proposals = 300
+model = dict(
+    rpn_head=dict(num_proposals=num_proposals),
+    test_cfg=dict(
+        _delete_=True,
+        rpn=None,
+        rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    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'])
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,23 @@
+_base_ = './queryinst_r50_fpn_1x_coco.py'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, value) for value in min_values],
+        multiscale_mode='value',
+        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'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
+lr_config = dict(policy='step', step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
@@ -238,6 +238,10 @@ Please refer to [PVT](https://github.com/open-mmlab/mmdetection/blob/master/conf
 
 Please refer to [SOLO](https://github.com/open-mmlab/mmdetection/blob/master/configs/solo) for details.
 
+### QueryInst
+
+Please refer to [QueryInst](https://github.com/open-mmlab/mmdetection/blob/master/configs/queryinst) 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/docs/projects.md b/docs/projects.md
@@ -55,3 +55,4 @@ Methods already supported and maintained by MMDetection are not listed.
 - Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS2021[[paper]](https://arxiv.org/abs/2107.00641)[[github]](https://github.com/microsoft/Focal-Transformer)
 - End-to-End Semi-Supervised Object Detection with Soft Teacher, ICCV2021[[paper]](https://arxiv.org/abs/2106.09018)[[github]](https://github.com/microsoft/SoftTeacher)
 - CBNetV2: A Novel Composite Backbone Network Architecture for Object Detection [[paper]](http://arxiv.org/abs/2107.00420)[[github]](https://github.com/VDIGPKU/CBNetV2)
+- Instances as Queries, ICCV2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf)[[github]](https://github.com/hustvl/QueryInst)