update codes

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.coco_instance import *
+    from .._base_.default_runtime import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+    from .._base_.schedules.schedule_1x import *

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.coco_instance import *
+    from .._base_.default_runtime import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+    from .._base_.schedules.schedule_1x import *
+
+from mmengine.visualization import LocalVisBackend, WandbVisBackend
+
+vis_backends.update(dict(type=WandbVisBackend))
+vis_backends.update(dict(type=LocalVisBackend))
+visualizer.update(dict(vis_backends=vis_backends))
+
+# MMEngine support the following two ways, users can choose
+# according to convenience
+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+default_hooks.update(dict(checkpoint=dict(interval=4)))
+
+train_cfg.update(dict(val_interval=2))

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.coco_instance import *
+    from .._base_.default_runtime import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+    from .._base_.schedules.schedule_2x import *

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_8xb8_amp_lsj_200e_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_amp_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_r50_fpn_1x_coco import *
+
+from mmengine.optim.optimizer.amp_optimizer_wrapper import AmpOptimWrapper
+
+optim_wrapper.update(dict(type=AmpOptimWrapper))

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_ms_poly_-3x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.models.mask_rcnn_r50_fpn import *
+    from ..common.ms_poly_3x_coco_instance import *

mmdet/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.coco_instance import *
+    from .._base_.default_runtime import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+    from .._base_.schedules.schedule_1x import *
+
+train_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(
+        type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
+    dict(type=Resize, scale=(1333, 800), keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=PackDetInputs),
+]
+train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_r101_fpn_1x_coco import *
+
+from mmengine.model.weight_init import PretrainedInit
+
+from mmdet.models.backbones.resnext import ResNeXt
+
+model = dict(
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_r50_fpn_2x_coco import *
+
+from mmengine.model.weight_init import PretrainedInit
+
+from mmdet.models import ResNeXt
+
+model = dict(
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_ms_poly_3x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from ..common.ms_poly_3x_coco_instance import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+
+from mmengine.model.weight_init import PretrainedInit
+
+from mmdet.models.backbones import ResNeXt
+
+model = dict(
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_x101_32x4d_fpn_1x_coco import *
+
+model = dict(
+    # ResNeXt-101-32x8d model trained with Caffe2 at FB,
+    # so the mean and std need to be changed.
+    data_preprocessor=dict(
+        mean=[103.530, 116.280, 123.675],
+        std=[57.375, 57.120, 58.395],
+        bgr_to_rgb=False),
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit,
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_ms_poly_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_r101_fpn_1x_coco import *
+
+from mmcv.transforms import RandomChoiceResize, RandomFlip
+from mmcv.transforms.loading import LoadImageFromFile
+
+from mmdet.datasets.transforms.formatting import PackDetInputs
+from mmdet.datasets.transforms.loading import LoadAnnotations
+from mmdet.models.backbones import ResNeXt
+
+model = dict(
+    # ResNeXt-101-32x8d model trained with Caffe2 at FB,
+    # so the mean and std need to be changed.
+    data_preprocessor=dict(
+        mean=[103.530, 116.280, 123.675],
+        std=[57.375, 57.120, 58.395],
+        bgr_to_rgb=False),
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit,
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+backend_args = None
+train_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(
+        type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
+    dict(
+        type=RandomChoiceResize,
+        scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                (1333, 768), (1333, 800)],
+        keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=PackDetInputs),
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

mmdet/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_ms_poly_3x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from ..common.ms_poly_3x_coco_instance import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+
+from mmdet.models.backbones import ResNeXt
+
+model = dict(
+    # ResNeXt-101-32x8d model trained with Caffe2 at FB,
+    # so the mean and std need to be changed.
+    data_preprocessor=dict(
+        mean=[103.530, 116.280, 123.675],
+        std=[57.375, 57.120, 58.395],
+        bgr_to_rgb=False),
+    backbone=dict(
+        type=ResNeXt,
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit,
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_64_4d_fpn_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_x101_32x4d_fpn_1x_coco import *
+
+model = dict(
+    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=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_64x4d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from .mask_rcnn_x101_32x4d_fpn_2x_coco import *
+
+model = dict(
+    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=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_64x4d')))

mmdet/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_ms_poly_3x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+
+with read_base():
+    from ..common.ms_poly_3x_coco_instance import *
+    from .._base_.models.mask_rcnn_r50_fpn import *
+
+from mmdet.models.backbones import ResNeXt
+
+model = dict(
+    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=BatchNorm2d, requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='open-mmlab://resnext101_64x4d')))

mmdet/configs/maskformer/maskformer_r50_ms_16xb1_75e_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms import RandomChoice, RandomChoiceResize
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+from mmengine.optim.optimizer import OptimWrapper
+from mmengine.optim.scheduler import MultiStepLR
+from mmengine.runner import EpochBasedTrainLoop, TestLoop, ValLoop
+from torch.nn.modules.activation import ReLU
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.modules.normalization import GroupNorm
+from torch.optim.adamw import AdamW
+
+from mmdet.datasets.transforms.transforms import RandomCrop
+from mmdet.models import MaskFormer
+from mmdet.models.backbones import ResNet
+from mmdet.models.data_preprocessors.data_preprocessor import \
+    DetDataPreprocessor
+from mmdet.models.dense_heads.maskformer_head import MaskFormerHead
+from mmdet.models.layers.pixel_decoder import TransformerEncoderPixelDecoder
+from mmdet.models.losses import CrossEntropyLoss, DiceLoss, FocalLoss
+from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
+from mmdet.models.task_modules.assigners.hungarian_assigner import \
+    HungarianAssigner
+from mmdet.models.task_modules.assigners.match_cost import (ClassificationCost,
+                                                            DiceCost,
+                                                            FocalLossCost)
+from mmdet.models.task_modules.samplers import MaskPseudoSampler
+
+with read_base():
+    from .._base_.datasets.coco_panoptic import *
+    from .._base_.default_runtime import *
+
+data_preprocessor = dict(
+    type=DetDataPreprocessor,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    bgr_to_rgb=True,
+    pad_size_divisor=1,
+    pad_mask=True,
+    mask_pad_value=0,
+    pad_seg=True,
+    seg_pad_value=255)
+
+num_things_classes = 80
+num_stuff_classes = 53
+num_classes = num_things_classes + num_stuff_classes
+model = dict(
+    type=MaskFormer,
+    data_preprocessor=data_preprocessor,
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='torchvision://resnet50')),
+    panoptic_head=dict(
+        type=MaskFormerHead,
+        in_channels=[256, 512, 1024, 2048],  # pass to pixel_decoder inside
+        feat_channels=256,
+        out_channels=256,
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        num_queries=100,
+        pixel_decoder=dict(
+            type=TransformerEncoderPixelDecoder,
+            norm_cfg=dict(type=GroupNorm, num_groups=32),
+            act_cfg=dict(type=ReLU),
+            encoder=dict(  # DetrTransformerEncoder
+                num_layers=6,
+                layer_cfg=dict(  # DetrTransformerEncoderLayer
+                    self_attn_cfg=dict(  # MultiheadAttention
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1,
+                        batch_first=True),
+                    ffn_cfg=dict(
+                        embed_dims=256,
+                        feedforward_channels=2048,
+                        num_fcs=2,
+                        ffn_drop=0.1,
+                        act_cfg=dict(type=ReLU, inplace=True)))),
+            positional_encoding=dict(num_feats=128, normalize=True)),
+        enforce_decoder_input_project=False,
+        positional_encoding=dict(num_feats=128, normalize=True),
+        transformer_decoder=dict(  # DetrTransformerDecoder
+            num_layers=6,
+            layer_cfg=dict(  # DetrTransformerDecoderLayer
+                self_attn_cfg=dict(  # MultiheadAttention
+                    embed_dims=256,
+                    num_heads=8,
+                    dropout=0.1,
+                    batch_first=True),
+                cross_attn_cfg=dict(  # MultiheadAttention
+                    embed_dims=256,
+                    num_heads=8,
+                    dropout=0.1,
+                    batch_first=True),
+                ffn_cfg=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    ffn_drop=0.1,
+                    act_cfg=dict(type=ReLU, inplace=True))),
+            return_intermediate=True),
+        loss_cls=dict(
+            type=CrossEntropyLoss,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            reduction='mean',
+            class_weight=[1.0] * num_classes + [0.1]),
+        loss_mask=dict(
+            type=FocalLoss,
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            reduction='mean',
+            loss_weight=20.0),
+        loss_dice=dict(
+            type=DiceLoss,
+            use_sigmoid=True,
+            activate=True,
+            reduction='mean',
+            naive_dice=True,
+            eps=1.0,
+            loss_weight=1.0)),
+    panoptic_fusion_head=dict(
+        type=MaskFormerFusionHead,
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_panoptic=None,
+        init_cfg=None),
+    train_cfg=dict(
+        assigner=dict(
+            type=HungarianAssigner,
+            match_costs=[
+                dict(type=ClassificationCost, weight=1.0),
+                dict(type=FocalLossCost, weight=20.0, binary_input=True),
+                dict(type=DiceCost, weight=1.0, pred_act=True, eps=1.0)
+            ]),
+        sampler=dict(type=MaskPseudoSampler)),
+    test_cfg=dict(
+        panoptic_on=True,
+        # For now, the dataset does not support
+        # evaluating semantic segmentation metric.
+        semantic_on=False,
+        instance_on=False,
+        # max_per_image is for instance segmentation.
+        max_per_image=100,
+        object_mask_thr=0.8,
+        iou_thr=0.8,
+        # In MaskFormer's panoptic postprocessing,
+        # it will not filter masks whose score is smaller than 0.5 .
+        filter_low_score=False),
+    init_cfg=None)
+
+# dataset settings
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(
+        type=LoadPanopticAnnotations,
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type=RandomFlip, prob=0.5),
+    # dict(type=Resize, scale=(1333, 800), keep_ratio=True),
+    dict(
+        type=RandomChoice,
+        transforms=[[
+            dict(
+                type=RandomChoiceResize,
+                scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                        (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                        (736, 1333), (768, 1333), (800, 1333)],
+                resize_type=Resize,
+                keep_ratio=True)
+        ],
+                    [
+                        dict(
+                            type=RandomChoiceResize,
+                            scales=[(400, 1333), (500, 1333), (600, 1333)],
+                            resize_type=Resize,
+                            keep_ratio=True),
+                        dict(
+                            type=RandomCrop,
+                            crop_type='absolute_range',
+                            crop_size=(384, 600),
+                            allow_negative_crop=True),
+                        dict(
+                            type=RandomChoiceResize,
+                            scales=[(480, 1333), (512, 1333), (544, 1333),
+                                    (576, 1333), (608, 1333), (640, 1333),
+                                    (672, 1333), (704, 1333), (736, 1333),
+                                    (768, 1333), (800, 1333)],
+                            resize_type=Resize,
+                            keep_ratio=True)
+                    ]]),
+    dict(type=PackDetInputs)
+]
+
+train_dataloader.update(
+    dict(batch_size=1, num_workers=1, dataset=dict(pipeline=train_pipeline)))
+
+val_dataloader.update(dict(batch_size=1, num_workers=1))
+
+test_dataloader = val_dataloader
+
+# optimizer
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(
+        type=AdamW,
+        lr=0.0001,
+        weight_decay=0.0001,
+        eps=1e-8,
+        betas=(0.9, 0.999)),
+    paramwise_cfg=dict(
+        custom_keys={
+            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+            'query_embed': dict(lr_mult=1.0, decay_mult=0.0)
+        },
+        norm_decay_mult=0.0),
+    clip_grad=dict(max_norm=0.01, norm_type=2))
+
+max_epochs = 75
+
+# learning rate
+param_scheduler = dict(
+    type=MultiStepLR,
+    begin=0,
+    end=max_epochs,
+    by_epoch=True,
+    milestones=[50],
+    gamma=0.1)
+
+train_cfg = dict(
+    type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1)
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (1 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

mmdet/configs/maskformer/maskformer_swin_l_p4_w12_64xb1_ms_300e_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.optim.scheduler import LinearLR
+
+from mmdet.models.backbones import SwinTransformer
+from mmdet.models.layers import PixelDecoder
+
+with read_base():
+    from .maskformer_r50_ms_16xb1_75e_coco import *
+
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+depths = [2, 2, 18, 2]
+model.update(
+    dict(
+        backbone=dict(
+            _delete_=True,
+            type=SwinTransformer,
+            pretrain_img_size=384,
+            embed_dims=192,
+            patch_size=4,
+            window_size=12,
+            mlp_ratio=4,
+            depths=depths,
+            num_heads=[6, 12, 24, 48],
+            qkv_bias=True,
+            qk_scale=None,
+            drop_rate=0.,
+            attn_drop_rate=0.,
+            drop_path_rate=0.3,
+            patch_norm=True,
+            out_indices=(0, 1, 2, 3),
+            with_cp=False,
+            convert_weights=True,
+            init_cfg=dict(type=PretrainedInit, checkpoint=pretrained)),
+        panoptic_head=dict(
+            in_channels=[192, 384, 768, 1536],  # pass to pixel_decoder inside
+            pixel_decoder=dict(
+                _delete_=True,
+                type=PixelDecoder,
+                norm_cfg=dict(type=GroupNorm, num_groups=32),
+                act_cfg=dict(type=ReLU)),
+            enforce_decoder_input_project=True)))
+
+# optimizer
+
+# weight_decay = 0.01
+# norm_weight_decay = 0.0
+# embed_weight_decay = 0.0
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'norm': norm_multi,
+    'absolute_pos_embed': embed_multi,
+    'relative_position_bias_table': embed_multi,
+    'query_embed': embed_multi
+}
+
+optim_wrapper.update(
+    dict(
+        optimizer=dict(lr=6e-5, weight_decay=0.01),
+        paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)))
+
+max_epochs = 300
+
+# learning rate
+param_scheduler = [
+    dict(type=LinearLR, start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=max_epochs,
+        by_epoch=True,
+        milestones=[250],
+        gamma=0.1)
+]
+
+train_cfg.update(dict(max_epochs=max_epochs))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (64 GPUs) x (1 samples per GPU)
+auto_scale_lr.update(dict(base_batch_size=64))

mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+
+with read_base():
+    from .panoptic_fpn_r50_fpn_1x_coco import *
+
+model.update(
+    dict(
+        backbone=dict(
+            depth=101,
+            init_cfg=dict(
+                type=PretrainedInit, checkpoint='torchvision://resnet101'))))

mmdet/configs/panoptic_fpn/panoptic_fpn_r101_fpn_ms_3x_coco.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+
+with read_base():
+    from .panoptic_fpn_r50_fpn_ms_3x_coco import *
+
+model.update(
+    dict(
+        backbone=dict(
+            depth=101,
+            init_cfg=dict(
+                type=PretrainedInit, checkpoint='torchvision://resnet101'))))