diff --git a/mmseg/configs/_base_/datasets/ade20k.py b/mmseg/configs/_base_/datasets/ade20k.py
new file mode 100644
index 0000000000..22e5929157
--- /dev/null
+++ b/mmseg/configs/_base_/datasets/ade20k.py
@@ -0,0 +1,80 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms.loading import LoadImageFromFile
+from mmcv.transforms.processing import (RandomFlip, RandomResize, Resize,
+ TestTimeAug)
+from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
+
+from mmseg.datasets.ade import ADE20KDataset
+from mmseg.datasets.transforms.formatting import PackSegInputs
+from mmseg.datasets.transforms.loading import LoadAnnotations
+from mmseg.datasets.transforms.transforms import (PhotoMetricDistortion,
+ RandomCrop)
+from mmseg.evaluation import IoUMetric
+
+# dataset settings
+dataset_type = ADE20KDataset
+data_root = 'data/ade/ADEChallengeData2016'
+crop_size = (512, 512)
+train_pipeline = [
+ dict(type=LoadImageFromFile),
+ dict(type=LoadAnnotations, reduce_zero_label=True),
+ dict(
+ type=RandomResize,
+ scale=(2048, 512),
+ ratio_range=(0.5, 2.0),
+ keep_ratio=True),
+ dict(type=RandomCrop, crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type=RandomFlip, prob=0.5),
+ dict(type=PhotoMetricDistortion),
+ dict(type=PackSegInputs)
+]
+test_pipeline = [
+ dict(type=LoadImageFromFile),
+ dict(type=Resize, scale=(2048, 512), keep_ratio=True),
+ # add loading annotation after ``Resize`` because ground truth
+ # does not need to do resize data transform
+ dict(type=LoadAnnotations, reduce_zero_label=True),
+ dict(type=PackSegInputs)
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+ dict(type=LoadImageFromFile, backend_args=None),
+ dict(
+ type=TestTimeAug,
+ transforms=[[
+ dict(type=Resize, scale_factor=r, keep_ratio=True)
+ for r in img_ratios
+ ],
+ [
+ dict(type=RandomFlip, prob=0., direction='horizontal'),
+ dict(type=RandomFlip, prob=1., direction='horizontal')
+ ], [dict(type=LoadAnnotations)],
+ [dict(type=PackSegInputs)]])
+]
+train_dataloader = dict(
+ batch_size=4,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type=InfiniteSampler, shuffle=True),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='images/training', seg_map_path='annotations/training'),
+ pipeline=train_pipeline))
+val_dataloader = dict(
+ batch_size=1,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type=DefaultSampler, shuffle=False),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='images/validation',
+ seg_map_path='annotations/validation'),
+ pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type=IoUMetric, iou_metrics=['mIoU'])
+test_evaluator = val_evaluator
diff --git a/mmseg/configs/maskformer/maskformer_r101_d32_8xb2_160k_ade20k_512x512.py b/mmseg/configs/maskformer/maskformer_r101_d32_8xb2_160k_ade20k_512x512.py
new file mode 100644
index 0000000000..f83e4e67bd
--- /dev/null
+++ b/mmseg/configs/maskformer/maskformer_r101_d32_8xb2_160k_ade20k_512x512.py
@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+ from .maskformer_r50_d32_8xb2_160k_ade20k_512x512 import *
+
+model.update(
+ dict(
+ backbone=dict(
+ depth=101,
+ init_cfg=dict(
+ type=PretrainedInit, checkpoint='torchvision://resnet101'))))
diff --git a/mmseg/configs/maskformer/maskformer_r50_d32_8xb2_160k_ade20k_512x512.py b/mmseg/configs/maskformer/maskformer_r50_d32_8xb2_160k_ade20k_512x512.py
new file mode 100644
index 0000000000..336a2adc87
--- /dev/null
+++ b/mmseg/configs/maskformer/maskformer_r50_d32_8xb2_160k_ade20k_512x512.py
@@ -0,0 +1,156 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.models.layers import PixelDecoder
+from mmdet.models.losses import CrossEntropyLoss
+from mmdet.models.losses.dice_loss import DiceLoss
+from mmdet.models.losses.focal_loss import FocalLoss
+from mmdet.models.task_modules.assigners import (ClassificationCost,
+ HungarianAssigner)
+from mmdet.models.task_modules.assigners.match_cost import (DiceCost,
+ FocalLossCost)
+from mmdet.models.task_modules.samplers.mask_pseudo_sampler import \
+ MaskPseudoSampler
+from mmengine.config import read_base
+from mmengine.model.weight_init import PretrainedInit
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from torch.nn.modules.activation import ReLU
+from torch.nn.modules.batchnorm import SyncBatchNorm as SyncBN
+from torch.nn.modules.normalization import GroupNorm as GN
+from torch.optim.adamw import AdamW
+
+from mmseg.models.backbones import ResNet
+from mmseg.models.data_preprocessor import SegDataPreProcessor
+from mmseg.models.decode_heads import MaskFormerHead
+from mmseg.models.segmentors import EncoderDecoder
+
+with read_base():
+ from .._base_.datasets.ade20k import *
+ from .._base_.default_runtime import *
+ from .._base_.schedules.schedule_160k import *
+
+norm_cfg = dict(type=SyncBN, requires_grad=True)
+crop_size = (512, 512)
+data_preprocessor = dict(
+ type=SegDataPreProcessor,
+ size=crop_size,
+ mean=[123.675, 116.28, 103.53],
+ std=[58.395, 57.12, 57.375],
+ bgr_to_rgb=True,
+ pad_val=0,
+ seg_pad_val=255)
+# model_cfg
+num_classes = 150
+model = dict(
+ type=EncoderDecoder,
+ data_preprocessor=data_preprocessor,
+ backbone=dict(
+ type=ResNet,
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ dilations=(1, 1, 1, 1),
+ strides=(1, 2, 2, 2),
+ norm_cfg=norm_cfg,
+ norm_eval=True,
+ style='pytorch',
+ contract_dilation=True,
+ init_cfg=dict(
+ type=PretrainedInit, checkpoint='torchvision://resnet50')),
+ decode_head=dict(
+ type=MaskFormerHead,
+ in_channels=[256, 512, 1024,
+ 2048], # input channels of pixel_decoder modules
+ feat_channels=256,
+ in_index=[0, 1, 2, 3],
+ num_classes=150,
+ out_channels=256,
+ num_queries=100,
+ pixel_decoder=dict(
+ type=PixelDecoder,
+ norm_cfg=dict(type=GN, num_groups=32),
+ act_cfg=dict(type=ReLU)),
+ enforce_decoder_input_project=False,
+ positional_encoding=dict( # SinePositionalEncoding
+ num_feats=128, normalize=True),
+ transformer_decoder=dict( # DetrTransformerDecoder
+ return_intermediate=True,
+ num_layers=6,
+ layer_cfg=dict( # DetrTransformerDecoderLayer
+ self_attn_cfg=dict( # MultiheadAttention
+ embed_dims=256,
+ num_heads=8,
+ attn_drop=0.1,
+ proj_drop=0.1,
+ dropout_layer=None,
+ batch_first=True),
+ cross_attn_cfg=dict( # MultiheadAttention
+ embed_dims=256,
+ num_heads=8,
+ attn_drop=0.1,
+ proj_drop=0.1,
+ dropout_layer=None,
+ batch_first=True),
+ ffn_cfg=dict(
+ embed_dims=256,
+ feedforward_channels=2048,
+ num_fcs=2,
+ act_cfg=dict(type=ReLU, inplace=True),
+ ffn_drop=0.1,
+ dropout_layer=None,
+ add_identity=True)),
+ init_cfg=None),
+ 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),
+ 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))),
+ # training and testing settings
+ train_cfg=dict(),
+ test_cfg=dict(mode='whole'),
+)
+# optimizer
+optimizer.update(
+ dict(type=AdamW, lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001))
+optim_wrapper.merge(
+ dict(
+ _delete_=True,
+ type=OptimWrapper,
+ optimizer=optimizer,
+ clip_grad=dict(max_norm=0.01, norm_type=2),
+ paramwise_cfg=dict(custom_keys={
+ 'backbone': dict(lr_mult=0.1),
+ })))
+# learning policy
+param_scheduler = [
+ dict(
+ type=PolyLR, eta_min=0, power=0.9, begin=0, end=160000, by_epoch=False)
+]
+
+# In MaskFormer implementation we use batch size 2 per GPU as default
+train_dataloader.update(dict(batch_size=2, num_workers=2))
+val_dataloader.update(dict(batch_size=1, num_workers=4))
+test_dataloader = val_dataloader
diff --git a/mmseg/configs/maskformer/maskformer_swin_s_upernet_8xb2_160k_ade20k_512x512.py b/mmseg/configs/maskformer/maskformer_swin_s_upernet_8xb2_160k_ade20k_512x512.py
new file mode 100644
index 0000000000..7719f160c7
--- /dev/null
+++ b/mmseg/configs/maskformer/maskformer_swin_s_upernet_8xb2_160k_ade20k_512x512.py
@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.optim.scheduler.lr_scheduler import LinearLR
+from torch.nn.modules.activation import GELU
+from torch.nn.modules.normalization import LayerNorm as LN
+
+from mmseg.models.backbones import SwinTransformer
+
+with read_base():
+ from .maskformer_r50_d32_8xb2_160k_ade20k_512x512 import *
+
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa
+
+backbone_norm_cfg = dict(type=LN, requires_grad=True)
+depths = [2, 2, 18, 2]
+model.merge(
+ dict(
+ backbone=dict(
+ _delete_=True,
+ type=SwinTransformer,
+ pretrain_img_size=224,
+ embed_dims=96,
+ patch_size=4,
+ window_size=7,
+ mlp_ratio=4,
+ depths=depths,
+ num_heads=[3, 6, 12, 24],
+ strides=(4, 2, 2, 2),
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=True,
+ qk_scale=None,
+ patch_norm=True,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.3,
+ use_abs_pos_embed=False,
+ act_cfg=dict(type=GELU),
+ norm_cfg=backbone_norm_cfg,
+ init_cfg=dict(type=PretrainedInit, checkpoint=checkpoint_file)),
+ decode_head=dict(
+ type=MaskFormerHead,
+ in_channels=[96, 192, 384,
+ 768], # input channels of pixel_decoder modules
+ )))
+
+# optimizer
+optimizer.update(
+ dict(type=AdamW, lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01))
+# set all layers in backbone to lr_mult=1.0
+# set all norm layers, position_embeding,
+# query_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+embed_multi = dict(decay_mult=0.0)
+custom_keys = {
+ 'backbone': dict(lr_mult=1.0),
+ 'backbone.patch_embed.norm': backbone_norm_multi,
+ 'backbone.norm': backbone_norm_multi,
+ 'relative_position_bias_table': backbone_embed_multi,
+ 'query_embed': embed_multi,
+}
+custom_keys.update({
+ f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+ for stage_id, num_blocks in enumerate(depths)
+ for block_id in range(num_blocks)
+})
+custom_keys.update({
+ f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+ for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optim_wrapper.merge(
+ dict(
+ _delete_=True,
+ type=OptimWrapper,
+ optimizer=optimizer,
+ clip_grad=dict(max_norm=0.01, norm_type=2),
+ paramwise_cfg=dict(custom_keys=custom_keys)))
+
+# learning policy
+param_scheduler = [
+ dict(type=LinearLR, start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+ dict(
+ type=PolyLR,
+ eta_min=0.0,
+ power=1.0,
+ begin=1500,
+ end=160000,
+ by_epoch=False,
+ )
+]
diff --git a/mmseg/configs/maskformer/maskformer_swin_t_upernet_8xb2_160k_ade20k_512x512.py b/mmseg/configs/maskformer/maskformer_swin_t_upernet_8xb2_160k_ade20k_512x512.py
new file mode 100644
index 0000000000..4370fb762c
--- /dev/null
+++ b/mmseg/configs/maskformer/maskformer_swin_t_upernet_8xb2_160k_ade20k_512x512.py
@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+from mmengine.optim.scheduler.lr_scheduler import LinearLR, PolyLR
+from torch.nn.modules.activation import GELU
+from torch.nn.modules.normalization import LayerNorm as LN
+
+from mmseg.models.backbones import SwinTransformer
+
+with read_base():
+ from .maskformer_r50_d32_8xb2_160k_ade20k_512x512 import *
+
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
+backbone_norm_cfg = dict(type=LN, requires_grad=True)
+depths = [2, 2, 6, 2]
+model.merge(
+ dict(
+ backbone=dict(
+ _delete_=True,
+ type=SwinTransformer,
+ pretrain_img_size=224,
+ embed_dims=96,
+ patch_size=4,
+ window_size=7,
+ mlp_ratio=4,
+ depths=depths,
+ num_heads=[3, 6, 12, 24],
+ strides=(4, 2, 2, 2),
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=True,
+ qk_scale=None,
+ patch_norm=True,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.3,
+ use_abs_pos_embed=False,
+ act_cfg=dict(type=GELU),
+ norm_cfg=backbone_norm_cfg,
+ init_cfg=dict(type=PretrainedInit, checkpoint=checkpoint_file)),
+ decode_head=dict(
+ type=MaskFormerHead,
+ in_channels=[96, 192, 384,
+ 768], # input channels of pixel_decoder modules
+ )))
+
+# optimizer
+optimizer.update(
+ dict(type=AdamW, lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01))
+
+# set all layers in backbone to lr_mult=1.0
+# set all norm layers, position_embeding,
+# query_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+embed_multi = dict(decay_mult=0.0)
+custom_keys = {
+ 'backbone': dict(lr_mult=1.0),
+ 'backbone.patch_embed.norm': backbone_norm_multi,
+ 'backbone.norm': backbone_norm_multi,
+ 'relative_position_bias_table': backbone_embed_multi,
+ 'query_embed': embed_multi,
+}
+custom_keys.update({
+ f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+ for stage_id, num_blocks in enumerate(depths)
+ for block_id in range(num_blocks)
+})
+custom_keys.update({
+ f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+ for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optim_wrapper.merge(
+ dict(
+ _delete_=True,
+ type=OptimWrapper,
+ optimizer=optimizer,
+ clip_grad=dict(max_norm=0.01, norm_type=2),
+ paramwise_cfg=dict(custom_keys=custom_keys)))
+
+# learning policy
+param_scheduler = [
+ dict(type=LinearLR, start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+ dict(
+ type=PolyLR,
+ eta_min=0.0,
+ power=1.0,
+ begin=1500,
+ end=160000,
+ by_epoch=False,
+ )
+]