[Feature] Support Label Assignment Distillation (LAD)

configs/lad/README.md

@@ -0,0 +1,32 @@
+# Improving Object Detection by Label Assignment Distillation
+
+<!-- [ALGORITHM] -->
+
+```latex
+@inproceedings{nguyen2021improving,
+  title={Improving Object Detection by Label Assignment Distillation},
+  author={Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan},
+  booktitle = {WACV},
+  year={2022}
+}
+```
+
+## Results and Models
+
+We provide config files to reproduce the object detection results in the
+WACV 2022 paper for Improving Object Detection by Label Assignment
+Distillation.
+
+### PAA with LAD
+
+|  Teacher  | Student | Training schedule | AP (val) |                         Config                          |
+| :-------: | :-----: | :---------------: | :------: |  :----------------------------------------------------: |
+|    --     |  R-50   |        1x         |   40.4   |                                                         |
+|    --     |  R-101  |        1x         |   42.6   |                                                         |
+|   R-101   |  R-50   |        1x         |   41.6   |  [config](configs/lad/lad_r50_paa_r101_fpn_coco_1x.py)  |
+|   R-50    |  R-101  |        1x         |   43.2   |  [config](configs/lad/lad_r101_paa_r50_fpn_coco_1x.py)  |
+
+## Note
+
+- Meaning of Config name: lad_r50(student model)_paa(based on paa)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py
+- Results may fluctuate by about 0.2 mAP.

configs/lad/lad_r101_paa_r50_fpn_coco_1x.py

@@ -0,0 +1,121 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth'  # noqa
+model = dict(
+    type='LAD',
+    # student
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        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://resnet101')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # teacher
+    teacher_ckpt=teacher_ckpt,
+    teacher_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'),
+    teacher_neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    teacher_bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.1,
+            neg_iou_thr=0.1,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        score_voting=True,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+optimizer = dict(lr=0.01)
+fp16 = dict(loss_scale=512.)

configs/lad/lad_r50_paa_r101_fpn_coco_1x.py

@@ -0,0 +1,120 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth'  # noqa
+model = dict(
+    type='LAD',
+    # student
+    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=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # teacher
+    teacher_ckpt=teacher_ckpt,
+    teacher_backbone=dict(
+        type='ResNet',
+        depth=101,
+        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'),
+    teacher_neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    teacher_bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.1,
+            neg_iou_thr=0.1,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        score_voting=True,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+optimizer = dict(lr=0.01)
+fp16 = dict(loss_scale=512.)

mmdet/models/dense_heads/__init__.py

@@ -18,6 +18,7 @@
 from .ga_rpn_head import GARPNHead
 from .gfl_head import GFLHead
 from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
+from .lad_head import LADHead
 from .ld_head import LDHead
 from .nasfcos_head import NASFCOSHead
 from .paa_head import PAAHead
@@ -47,5 +48,5 @@
     'CascadeRPNHead', 'EmbeddingRPNHead', 'LDHead', 'CascadeRPNHead',
     'AutoAssignHead', 'DETRHead', 'YOLOFHead', 'DeformableDETRHead',
     'SOLOHead', 'DecoupledSOLOHead', 'CenterNetHead', 'YOLOXHead',
-    'DecoupledSOLOLightHead'
+    'DecoupledSOLOLightHead', 'LADHead'
 ]