diff --git a/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py b/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..29f63791e4d4ca947f5784dc84836e0a155a5a85
--- /dev/null
+++ b/configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py
@@ -0,0 +1,140 @@
+_base_ = [
+ '../_base_/models/cascade_mask_rcnn_swin_fpn.py',
+ '../_base_/datasets/coco_instance.py',
+ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+ backbone=dict(
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ ape=False,
+ drop_path_rate=0.2,
+ patch_norm=True,
+ use_checkpoint=False
+ ),
+ neck=dict(in_channels=[96, 192, 384, 768]),
+ roi_head=dict(
+ bbox_head=[
+ dict(
+ type='ConvFCBBoxHead',
+ num_shared_convs=4,
+ num_shared_fcs=1,
+ in_channels=256,
+ conv_out_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=80,
+ bbox_coder=dict(
+ type='DeltaXYWHBBoxCoder',
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.1, 0.1, 0.2, 0.2]),
+ reg_class_agnostic=False,
+ reg_decoded_bbox=True,
+ norm_cfg=dict(type='SyncBN', requires_grad=True),
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+ loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+ dict(
+ type='ConvFCBBoxHead',
+ num_shared_convs=4,
+ num_shared_fcs=1,
+ in_channels=256,
+ conv_out_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=80,
+ bbox_coder=dict(
+ type='DeltaXYWHBBoxCoder',
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.05, 0.05, 0.1, 0.1]),
+ reg_class_agnostic=False,
+ reg_decoded_bbox=True,
+ norm_cfg=dict(type='SyncBN', requires_grad=True),
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+ loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+ dict(
+ type='ConvFCBBoxHead',
+ num_shared_convs=4,
+ num_shared_fcs=1,
+ in_channels=256,
+ conv_out_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=80,
+ bbox_coder=dict(
+ type='DeltaXYWHBBoxCoder',
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.033, 0.033, 0.067, 0.067]),
+ reg_class_agnostic=False,
+ reg_decoded_bbox=True,
+ norm_cfg=dict(type='SyncBN', requires_grad=True),
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+ loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
+ ]))
+
+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 / Sparse RCNN
+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))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+ paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+ 'relative_position_bias_table': dict(decay_mult=0.),
+ 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[8, 11])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=12)
+
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+ type="DistOptimizerHook",
+ update_interval=1,
+ grad_clip=None,
+ coalesce=True,
+ bucket_size_mb=-1,
+ use_fp16=True,
+)
diff --git a/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py b/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..63cc39cd800bc95b4f5bd8f1b7c183e2e20cc318
--- /dev/null
+++ b/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
@@ -0,0 +1,80 @@
+_base_ = [
+ '../_base_/models/mask_rcnn_swin_fpn.py',
+ '../_base_/datasets/coco_instance.py',
+ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+ backbone=dict(
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ ape=False,
+ drop_path_rate=0.2,
+ patch_norm=True,
+ use_checkpoint=False
+ ),
+ neck=dict(in_channels=[96, 192, 384, 768]))
+
+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 / Sparse RCNN
+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))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+ paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+ 'relative_position_bias_table': dict(decay_mult=0.),
+ 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[8, 11])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=12)
+
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+ type="DistOptimizerHook",
+ update_interval=1,
+ grad_clip=None,
+ coalesce=True,
+ bucket_size_mb=-1,
+ use_fp16=True,
+)
diff --git a/mmcv_custom/checkpoint.py b/mmcv_custom/checkpoint.py
index 7f43885470af50ee5d1c4c5ff1f463f3d64c2029..51322c1c3802f357481065a70dc5152469d80eb8 100644
--- a/mmcv_custom/checkpoint.py
+++ b/mmcv_custom/checkpoint.py
@@ -319,6 +319,10 @@ def load_checkpoint(model,
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for k, v in state_dict.items()}
+ # for MoBY, load model of online branch
+ if sorted(list(state_dict.keys()))[0].startswith('encoder'):
+ state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
+
# reshape absolute position embedding
if state_dict.get('absolute_pos_embed') is not None:
absolute_pos_embed = state_dict['absolute_pos_embed']