diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e6d7272b0f5615cf6ed577eedc54a600000f8453
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,12 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.pth
+*.pt
+*.log
+*.tmp
+.env
+.vscode/
+.idea/
+.DS_Store
diff --git a/build/lib/segformer_plusplus/__init__.py b/build/lib/segformer_plusplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd8a768648cca72d7cc393e7eb85b79b434d9fc7
--- /dev/null
+++ b/build/lib/segformer_plusplus/__init__.py
@@ -0,0 +1,4 @@
+from .build_model import create_model, create_custom_model
+from .random_benchmark import random_benchmark
+
+__all__ = ['create_model', 'create_custom_model', 'random_benchmark']
diff --git a/build/lib/segformer_plusplus/build_model.py b/build/lib/segformer_plusplus/build_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ad4eeca728fce54caf78fe49f3f372d21047d9
--- /dev/null
+++ b/build/lib/segformer_plusplus/build_model.py
@@ -0,0 +1,108 @@
+import os
+
+from mmengine import registry
+from mmengine.config import Config
+from mmengine.model import BaseModule
+
+from .utils import MODELS, imagenet_weights
+from .utils import tome_presets
+
+
+class SegFormer(BaseModule):
+    """
+    This class represents a SegFormer model that allows for the application of token merging.
+
+    Attributes:
+         backbone (BaseModule): MixVisionTransformer backbone
+         decode_head (BaseModule): SegFormer head
+
+    """
+    def __init__(self, cfg):
+        """
+        Initialize the SegFormer model.
+
+        Args:
+            cfg (Config): an mmengine Config object, which defines the backbone, head and token merging strategy used.
+
+        """
+        super().__init__()
+        self.backbone = registry.build_model_from_cfg(cfg.backbone, registry=MODELS)
+        self.decode_head = registry.build_model_from_cfg(cfg.decode_head, registry=MODELS)
+
+    def forward(self, x):
+        """
+        Forward pass of the model.
+
+        Args:
+            x (torch.Tensor): input tensor of shape [B, C, H, W]
+
+        Returns:
+            torch.Tensor: output tensor
+
+        """
+        x = self.backbone(x)
+        x = self.decode_head(x)
+        return x
+
+
+def create_model(
+        backbone: str = 'b0',
+        tome_strategy: str = None,
+        out_channels: int = 19,
+        pretrained: bool = False,
+):
+    """
+    Create a SegFormer model using the predefined SegFormer backbones from the MiT series (b0-b5).
+
+    Args:
+        backbone (str): backbone name (e.g. 'b0')
+        tome_strategy (str | list(dict)): select strategy from presets ('bsm_hq', 'bsm_fast', 'n2d_2x2') or define a
+            custom strategy using a list, that contains of dictionaries, in which the strategies for the stage are
+            defined
+        out_channels (int): number of output channels (e.g. 19 for the cityscapes semantic segmentation task)
+        pretrained: use pretrained (imagenet) weights
+
+    Returns:
+        BaseModule: SegFormer model
+
+    """
+    backbone = backbone.lower()
+    assert backbone in [f'b{i}' for i in range(6)]
+
+    wd = os.path.dirname(os.path.abspath(__file__))
+
+    cfg = Config.fromfile(os.path.join(wd, 'configs', f'segformer_mit_{backbone}.py'))
+
+    cfg.decode_head.out_channels = out_channels
+
+    if tome_strategy is not None:
+        if tome_strategy not in list(tome_presets.keys()):
+            print("Using custom merging strategy.")
+        cfg.backbone.tome_cfg = tome_presets[tome_strategy]
+
+    # load imagenet weights
+    if pretrained:
+        cfg.backbone.init_cfg = dict(type='Pretrained', checkpoint=imagenet_weights[backbone])
+
+    return SegFormer(cfg)
+
+
+def create_custom_model(
+        model_cfg: Config,
+        tome_strategy: list[dict] = None,
+):
+    """
+    Create a SegFormer model with customizable backbone and head.
+
+    Args:
+        model_cfg (Config): backbone name (e.g. 'b0')
+        tome_strategy (list(dict)): custom token merging strategy
+
+    Returns:
+        BaseModule: SegFormer model
+
+    """
+    if tome_strategy is not None:
+        model_cfg.backbone.tome_cfg = tome_strategy
+
+    return SegFormer(model_cfg)
diff --git a/build/lib/segformer_plusplus/configs/__init__.py b/build/lib/segformer_plusplus/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b680692d5ff945d85311a8c90c70766275444498
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/__init__.py
@@ -0,0 +1 @@
+__all__ = []
\ No newline at end of file
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b0.py b/build/lib/segformer_plusplus/configs/segformer_mit_b0.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4eb059010ad97d0360e779cbcca82e3a2b50717
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b0.py
@@ -0,0 +1,28 @@
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+backbone = dict(
+    type='MixVisionTransformer',
+    in_channels=3,
+    embed_dims=32,
+    num_stages=4,
+    num_layers=[2, 2, 2, 2],
+    num_heads=[1, 2, 5, 8],
+    patch_sizes=[7, 3, 3, 3],
+    sr_ratios=[8, 4, 2, 1],
+    out_indices=(0, 1, 2, 3),
+    mlp_ratio=4,
+    qkv_bias=True,
+    drop_rate=0.0,
+    attn_drop_rate=0.0,
+    drop_path_rate=0.1
+)
+decode_head = dict(
+    type='SegformerHead',
+    in_channels=[32, 64, 160, 256],
+    in_index=[0, 1, 2, 3],
+    channels=256,
+    dropout_ratio=0.1,
+    out_channels=19,
+    norm_cfg=norm_cfg,
+    align_corners=False,
+    interpolate_mode='bilinear'
+)
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b1.py b/build/lib/segformer_plusplus/configs/segformer_mit_b1.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ec3214aff7f298cf429b836e51e84cea5aeb771
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b1.py
@@ -0,0 +1,8 @@
+_base_ = ['./segformer_mit_b0.py']
+
+backbone = dict(
+        embed_dims=64,
+)
+decode_head = dict(
+    in_channels=[64, 128, 320, 512]
+)
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b2.py b/build/lib/segformer_plusplus/configs/segformer_mit_b2.py
new file mode 100644
index 0000000000000000000000000000000000000000..230e1e50daba53993126d58efc9bed642cb9f4ca
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b2.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 4, 6, 3]
+)
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b3.py b/build/lib/segformer_plusplus/configs/segformer_mit_b3.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c877f9d12459c9508a7627a3131144e94768856
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b3.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 4, 18, 3]
+)
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b4.py b/build/lib/segformer_plusplus/configs/segformer_mit_b4.py
new file mode 100644
index 0000000000000000000000000000000000000000..897e59765578c96a5a9a17ffb8cf6aceecc81e3b
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b4.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 8, 27, 3]
+)
diff --git a/build/lib/segformer_plusplus/configs/segformer_mit_b5.py b/build/lib/segformer_plusplus/configs/segformer_mit_b5.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f0762237dd35e601e3153ca2e52a89eac365e1d
--- /dev/null
+++ b/build/lib/segformer_plusplus/configs/segformer_mit_b5.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 6, 40, 3]
+)
diff --git a/build/lib/segformer_plusplus/model/__init__.py b/build/lib/segformer_plusplus/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b680692d5ff945d85311a8c90c70766275444498
--- /dev/null
+++ b/build/lib/segformer_plusplus/model/__init__.py
@@ -0,0 +1 @@
+__all__ = []
\ No newline at end of file
diff --git a/build/lib/segformer_plusplus/model/backbone/__init__.py b/build/lib/segformer_plusplus/model/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fc6fa95ea87f1419517325a0a49915b94996d74
--- /dev/null
+++ b/build/lib/segformer_plusplus/model/backbone/__init__.py
@@ -0,0 +1,3 @@
+from .mit import MixVisionTransformer
+
+__all__ = ['MixVisionTransformer']
\ No newline at end of file
diff --git a/build/lib/segformer_plusplus/model/backbone/mit.py b/build/lib/segformer_plusplus/model/backbone/mit.py
new file mode 100644
index 0000000000000000000000000000000000000000..239284919f6c5d14a7873305d7fbdf0ac8c4bd7f
--- /dev/null
+++ b/build/lib/segformer_plusplus/model/backbone/mit.py
@@ -0,0 +1,479 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
+from mmcv.cnn.bricks.drop import build_dropout
+from mmcv.cnn.bricks.transformer import MultiheadAttention
+from mmengine.model import BaseModule, ModuleList, Sequential
+from mmengine.model.weight_init import (constant_init, normal_init,
+                                        trunc_normal_init)
+from tomesd.merge import bipartite_soft_matching_random2d
+
+from ...utils import PatchEmbed
+from ...utils import nchw_to_nlc, nlc_to_nchw
+from ...utils import MODELS
+
+class MixFFN(BaseModule):
+    """An implementation of MixFFN of Segformer.
+
+    The differences between MixFFN & FFN:
+        1. Use 1X1 Conv to replace Linear layer.
+        2. Introduce 3X3 Conv to encode positional information.
+    Args:
+        embed_dims (int): The feature dimension. Same as
+            `MultiheadAttention`. Defaults: 256.
+        feedforward_channels (int): The hidden dimension of FFNs.
+            Defaults: 1024.
+        act_cfg (dict, optional): The activation config for FFNs.
+            Default: dict(type='ReLU')
+        ffn_drop (float, optional): Probability of an element to be
+            zeroed in FFN. Default 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 feedforward_channels,
+                 act_cfg=dict(type='GELU'),
+                 ffn_drop=0.,
+                 dropout_layer=None,
+                 init_cfg=None):
+        super().__init__(init_cfg)
+
+        self.embed_dims = embed_dims
+        self.feedforward_channels = feedforward_channels
+        self.act_cfg = act_cfg
+        self.activate = build_activation_layer(act_cfg)
+
+        in_channels = embed_dims
+        fc1 = Conv2d(
+            in_channels=in_channels,
+            out_channels=feedforward_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        # 3x3 depth wise conv to provide positional encode information
+        pe_conv = Conv2d(
+            in_channels=feedforward_channels,
+            out_channels=feedforward_channels,
+            kernel_size=3,
+            stride=1,
+            padding=(3 - 1) // 2,
+            bias=True,
+            groups=feedforward_channels)
+        fc2 = Conv2d(
+            in_channels=feedforward_channels,
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        drop = nn.Dropout(ffn_drop)
+        layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
+        self.layers = Sequential(*layers)
+        self.dropout_layer = build_dropout(
+            dropout_layer) if dropout_layer else torch.nn.Identity()
+
+    def forward(self, x, hw_shape, identity=None):
+        out = nlc_to_nchw(x, hw_shape)
+        out = self.layers(out)
+        out = nchw_to_nlc(out)
+        if identity is None:
+            identity = x
+        return identity + self.dropout_layer(out)
+
+
+class EfficientMultiheadAttention(MultiheadAttention):
+    """An implementation of Efficient Multi-head Attention of Segformer.
+
+    This module is modified from MultiheadAttention which is a module from
+    mmcv.cnn.bricks.transformer.
+    Args:
+        embed_dims (int): The embedding dimension.
+        num_heads (int): Parallel attention heads.
+        attn_drop (float): A Dropout layer on attn_output_weights.
+            Default: 0.0.
+        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
+            Default: 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut. Default: None.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+        batch_first (bool): Key, Query and Value are shape of
+            (batch, n, embed_dim)
+            or (n, batch, embed_dim). Default: False.
+        qkv_bias (bool): enable bias for qkv if True. Default True.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
+            Attention of Segformer. Default: 1.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 attn_drop=0.,
+                 proj_drop=0.,
+                 dropout_layer=None,
+                 init_cfg=None,
+                 batch_first=True,
+                 qkv_bias=False,
+                 tome_cfg=dict(),
+                 norm_cfg=dict(type='LN'),
+                 sr_ratio=1):
+        super().__init__(
+            embed_dims,
+            num_heads,
+            attn_drop,
+            proj_drop,
+            dropout_layer=dropout_layer,
+            init_cfg=init_cfg,
+            batch_first=batch_first,
+            bias=qkv_bias)
+
+        self.q_mode = tome_cfg.get('q_mode')
+        self.kv_mode = tome_cfg.get('kv_mode')
+        self.tome_cfg = tome_cfg
+
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1:
+            self.sr = Conv2d(
+                in_channels=embed_dims,
+                out_channels=embed_dims,
+                kernel_size=sr_ratio,
+                stride=sr_ratio)
+            # The ret[0] of build_norm_layer is norm name.
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+
+    def forward(self, x, hw_shape, identity=None):
+        x_q = x
+
+        if self.sr_ratio > 1:
+            x_kv = nlc_to_nchw(x, hw_shape)
+            x_kv = self.sr(x_kv)
+            x_kv = nchw_to_nlc(x_kv)
+            x_kv = self.norm(x_kv)
+        else:
+            x_kv = x
+
+        # 2D Neighbour Merging KV
+        if self.kv_mode == 'n2d':
+            kv_hw_shape = (int(hw_shape[0] / self.sr_ratio), int(hw_shape[1] / self.sr_ratio))
+            x_kv = nlc_to_nchw(x_kv, kv_hw_shape)
+            x_kv = torch.nn.functional.avg_pool2d(x_kv, kernel_size=self.tome_cfg['kv_s'],
+                                                  stride=self.tome_cfg['kv_s'],
+                                                  ceil_mode=True)
+            x_kv = nchw_to_nlc(x_kv)
+
+        # Bipartite Soft Matching (tomesd) KV
+        if self.kv_mode == 'bsm':
+            w_kv = int(hw_shape[1] / self.sr_ratio)
+            h_kv = int(hw_shape[0] / self.sr_ratio)
+            merge, unmerge = bipartite_soft_matching_random2d(metric=x_kv, w=w_kv, h=h_kv,
+                                                              r=int(x_kv.size()[1] * self.tome_cfg['kv_r']),
+                                                              sx=self.tome_cfg['kv_sx'], sy=self.tome_cfg['kv_sy'],
+                                                              no_rand=True)
+            x_kv = merge(x_kv)
+
+        if identity is None:
+            identity = x_q
+
+        # 1D Neighbor Merging Q
+        if self.q_mode == 'n1d':
+            x_q = x_q.transpose(-2, -1)
+            x_q = torch.nn.functional.avg_pool1d(x_q, kernel_size=self.tome_cfg['q_s'],
+                                                 stride=self.tome_cfg['q_s'],
+                                                 ceil_mode=True)
+            x_q = x_q.transpose(-2, -1)
+
+        # 2D Neighbor Merging Q
+        if self.q_mode == 'n2d':
+            reduced_hw = (int(torch.ceil(torch.tensor(hw_shape[0] / self.tome_cfg['q_s'][0]))),
+                          int(torch.ceil(torch.tensor(hw_shape[1] / self.tome_cfg['q_s'][1]))))
+            x_q = nlc_to_nchw(x_q, hw_shape)
+            x_q = torch.nn.functional.avg_pool2d(x_q, kernel_size=self.tome_cfg['q_s'],
+                                                 stride=self.tome_cfg['q_s'],
+                                                 ceil_mode=True)
+            x_q = nchw_to_nlc(x_q)
+
+        # Bipartite Soft Matching (tomesd) Q
+        if self.q_mode == 'bsm':
+            merge, unmerge = bipartite_soft_matching_random2d(metric=x_q, w=hw_shape[1], h=hw_shape[0],
+                                                              r=int(x_q.size()[1] * self.tome_cfg['q_r']),
+                                                              sx=self.tome_cfg['q_sx'], sy=self.tome_cfg['q_sy'],
+                                                              no_rand=True)
+            x_q = merge(x_q)
+
+        # Because the dataflow('key', 'query', 'value') of
+        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
+        # embed_dims), We should adjust the shape of dataflow from
+        # batch_first (batch, num_query, embed_dims) to num_query_first
+        # (num_query ,batch, embed_dims), and recover ``attn_output``
+        # from num_query_first to batch_first.
+
+        if self.batch_first:
+            x_q = x_q.transpose(0, 1)
+            x_kv = x_kv.transpose(0, 1)
+        out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]
+        if self.batch_first:
+            out = out.transpose(0, 1)
+
+        # Unmerging BSM (tome+tomesd)
+        if self.q_mode == 'bsm':
+            out = unmerge(out)
+
+        # Unmerging 1D Neighbour Merging
+        if self.q_mode == 'n1d':
+            out = out.transpose(-2, -1)
+            out = torch.nn.functional.interpolate(out, size=identity.size()[-2])
+            out = out.transpose(-2, -1)
+
+        # Unmerging 2D Neighbor Merging
+        if self.q_mode == 'n2d':
+            out = nlc_to_nchw(out, reduced_hw)
+            out = torch.nn.functional.interpolate(out, size=hw_shape)
+            out = nchw_to_nlc(out)
+
+        return identity + self.dropout_layer(self.proj_drop(out))
+
+
+class TransformerEncoderLayer(BaseModule):
+    """Implements one encoder layer in Segformer.
+
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        drop_rate (float): Probability of an element to be zeroed.
+            after the feed forward layer. Default 0.0.
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default 0.0.
+        drop_path_rate (float): stochastic depth rate. Default 0.0.
+        qkv_bias (bool): enable bias for qkv if True.
+            Default: True.
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        batch_first (bool): Key, Query and Value are shape of
+            (batch, n, embed_dim)
+            or (n, batch, embed_dim). Default: False.
+        init_cfg (dict, optional): Initialization config dict.
+            Default:None.
+        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
+            Attention of Segformer. Default: 1.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save
+            some memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 qkv_bias=True,
+                 tome_cfg=dict(),
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 batch_first=True,
+                 sr_ratio=1,
+                 with_cp=False):
+        super().__init__()
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.attn = EfficientMultiheadAttention(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            attn_drop=attn_drop_rate,
+            proj_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            batch_first=batch_first,
+            qkv_bias=qkv_bias,
+            tome_cfg=tome_cfg,
+            norm_cfg=norm_cfg,
+            sr_ratio=sr_ratio)
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.ffn = MixFFN(
+            embed_dims=embed_dims,
+            feedforward_channels=feedforward_channels,
+            ffn_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            act_cfg=act_cfg)
+
+        self.with_cp = with_cp
+
+    def forward(self, x, hw_shape):
+
+        def _inner_forward(x):
+            x = self.attn(self.norm1(x), hw_shape, identity=x)
+            x = self.ffn(self.norm2(x), hw_shape, identity=x)
+            return x
+
+        if self.with_cp and x.requires_grad:
+            x = cp.checkpoint(_inner_forward, x)
+        else:
+            x = _inner_forward(x)
+        return x
+
+
+@MODELS.register_module()
+class MixVisionTransformer(BaseModule):
+    """The backbone of Segformer.
+
+    This backbone is the implementation of `SegFormer: Simple and
+    Efficient Design for Semantic Segmentation with
+    Transformers <https://arxiv.org/abs/2105.15203>`_.
+    Args:
+        in_channels (int): Number of input channels. Default: 3.
+        embed_dims (int): Embedding dimension. Default: 768.
+        num_stags (int): The num of stages. Default: 4.
+        num_layers (Sequence[int]): The layer number of each transformer encode
+            layer. Default: [3, 4, 6, 3].
+        num_heads (Sequence[int]): The attention heads of each transformer
+            encode layer. Default: [1, 2, 4, 8].
+        patch_sizes (Sequence[int]): The patch_size of each overlapped patch
+            embedding. Default: [7, 3, 3, 3].
+        strides (Sequence[int]): The stride of each overlapped patch embedding.
+            Default: [4, 2, 2, 2].
+        sr_ratios (Sequence[int]): The spatial reduction rate of each
+            transformer encode layer. Default: [8, 4, 2, 1].
+        out_indices (Sequence[int] | int): Output from which stages.
+            Default: (0, 1, 2, 3).
+        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
+            Default: 4.
+        qkv_bias (bool): Enable bias for qkv if True. Default: True.
+        drop_rate (float): Probability of an element to be zeroed.
+            Default 0.0
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default 0.0
+        drop_path_rate (float): stochastic depth rate. Default 0.0
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN')
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        pretrained (str, optional): model pretrained path. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save
+            some memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 embed_dims=64,
+                 num_stages=4,
+                 num_layers=[3, 4, 6, 3],
+                 num_heads=[1, 2, 4, 8],
+                 patch_sizes=[7, 3, 3, 3],
+                 strides=[4, 2, 2, 2],
+                 sr_ratios=[8, 4, 2, 1],
+                 out_indices=(0, 1, 2, 3),
+                 mlp_ratio=4,
+                 qkv_bias=True,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 tome_cfg=[dict(), dict(), dict(), dict()],
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN', eps=1e-6),
+                 init_cfg=None,
+                 with_cp=False,
+                 down_sample=False):
+        super().__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        self.num_stages = num_stages
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.patch_sizes = patch_sizes
+        self.strides = strides
+        self.sr_ratios = sr_ratios
+        self.with_cp = with_cp
+        self.down_sample = down_sample
+        assert num_stages == len(num_layers) == len(num_heads) \
+               == len(patch_sizes) == len(strides) == len(sr_ratios)
+
+        self.out_indices = out_indices
+        assert max(out_indices) < self.num_stages
+
+        # transformer encoder
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, drop_path_rate, sum(num_layers))
+        ]  # stochastic num_layer decay rule
+
+        cur = 0
+        self.layers = ModuleList()
+        for i, num_layer in enumerate(num_layers):
+            embed_dims_i = embed_dims * num_heads[i]
+            patch_embed = PatchEmbed(
+                in_channels=in_channels,
+                embed_dims=embed_dims_i,
+                kernel_size=patch_sizes[i],
+                stride=strides[i],
+                padding=patch_sizes[i] // 2,
+                norm_cfg=norm_cfg)
+            layer = ModuleList([
+                TransformerEncoderLayer(
+                    embed_dims=embed_dims_i,
+                    num_heads=num_heads[i],
+                    feedforward_channels=mlp_ratio * embed_dims_i,
+                    drop_rate=drop_rate,
+                    attn_drop_rate=attn_drop_rate,
+                    drop_path_rate=dpr[cur + idx],
+                    qkv_bias=qkv_bias,
+                    tome_cfg=tome_cfg[i],
+                    act_cfg=act_cfg,
+                    norm_cfg=norm_cfg,
+                    with_cp=with_cp,
+                    sr_ratio=sr_ratios[i]) for idx in range(num_layer)
+            ])
+            in_channels = embed_dims_i
+            # The ret[0] of build_norm_layer is norm name.
+            norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
+            self.layers.append(ModuleList([patch_embed, layer, norm]))
+            cur += num_layer
+
+    def init_weights(self):
+        if self.init_cfg is None:
+            for m in self.modules():
+                if isinstance(m, nn.Linear):
+                    trunc_normal_init(m, std=.02, bias=0.)
+                elif isinstance(m, nn.LayerNorm):
+                    constant_init(m, val=1.0, bias=0.)
+                elif isinstance(m, nn.Conv2d):
+                    fan_out = m.kernel_size[0] * m.kernel_size[
+                        1] * m.out_channels
+                    fan_out //= m.groups
+                    normal_init(
+                        m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
+        else:
+            super().init_weights()
+
+    def forward(self, x):
+        if self.down_sample:
+            x = torch.nn.functional.interpolate(x, scale_factor=(0.5, 0.5))
+        outs = []
+
+        for i, layer in enumerate(self.layers):
+            x, hw_shape = layer[0](x)
+            for block in layer[1]:
+                x = block(x, hw_shape)
+            x = layer[2](x)
+            x = nlc_to_nchw(x, hw_shape)
+            if i in self.out_indices:
+                outs.append(x)
+
+        return outs
diff --git a/build/lib/segformer_plusplus/model/head/__init__.py b/build/lib/segformer_plusplus/model/head/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..939f5552815b4e712af13103e34e72469f240ec9
--- /dev/null
+++ b/build/lib/segformer_plusplus/model/head/__init__.py
@@ -0,0 +1,3 @@
+from .segformer_head import SegformerHead
+
+__all__ = ['SegformerHead']
diff --git a/build/lib/segformer_plusplus/model/head/segformer_head.py b/build/lib/segformer_plusplus/model/head/segformer_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f34fcd6e540d4adbfdbfbbdae8704591e239fa0
--- /dev/null
+++ b/build/lib/segformer_plusplus/model/head/segformer_head.py
@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmengine.model import BaseModule
+
+from ...utils import MODELS
+from ...utils import resize
+
+
+@MODELS.register_module()
+class SegformerHead(BaseModule):
+    """The all mlp Head of segformer.
+
+    This head is the implementation of
+    `Segformer <https://arxiv.org/abs/2105.15203>` _.
+
+    Args:
+        interpolate_mode: The interpolate mode of MLP head upsample operation.
+            Default: 'bilinear'.
+    """
+
+    def __init__(self,
+                 in_channels=[32, 64, 160, 256],
+                 in_index=[0, 1, 2, 3],
+                 channels=256,
+                 dropout_ratio=0.1,
+                 out_channels=19,
+                 norm_cfg=None,
+                 align_corners=False,
+                 interpolate_mode='bilinear'):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.in_index = in_index
+        self.channels = channels
+        self.dropout_ratio = dropout_ratio
+        self.out_channels = out_channels
+        self.norm_cfg = norm_cfg
+        self.align_corners = align_corners
+        self.interpolate_mode = interpolate_mode
+
+        self.act_cfg = dict(type='ReLU')
+        self.conv_seg = nn.Conv2d(channels, self.out_channels, kernel_size=1)
+        if dropout_ratio > 0:
+            self.dropout = nn.Dropout2d(dropout_ratio)
+        else:
+            self.dropout = None
+
+        num_inputs = len(self.in_channels)
+
+        assert num_inputs == len(self.in_index)
+
+        self.convs = nn.ModuleList()
+        for i in range(num_inputs):
+            self.convs.append(
+                ConvModule(
+                    in_channels=self.in_channels[i],
+                    out_channels=self.channels,
+                    kernel_size=1,
+                    stride=1,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg))
+
+        self.fusion_conv = ConvModule(
+            in_channels=self.channels * num_inputs,
+            out_channels=self.channels,
+            kernel_size=1,
+            norm_cfg=self.norm_cfg)
+
+    def cls_seg(self, feat):
+        """Classify each pixel."""
+        if self.dropout is not None:
+            feat = self.dropout(feat)
+        output = self.conv_seg(feat)
+        return output
+
+    def forward(self, inputs):
+        # Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
+        outs = []
+        for idx in range(len(inputs)):
+            x = inputs[idx]
+            conv = self.convs[idx]
+            outs.append(
+                resize(
+                    input=conv(x),
+                    size=inputs[0].shape[2:],
+                    mode=self.interpolate_mode,
+                    align_corners=self.align_corners))
+
+        out = self.fusion_conv(torch.cat(outs, dim=1))
+
+        out = self.cls_seg(out)
+
+        return out
diff --git a/build/lib/segformer_plusplus/random_benchmark.py b/build/lib/segformer_plusplus/random_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cb1628279a081dd2c64635faea0d154d458ddd9
--- /dev/null
+++ b/build/lib/segformer_plusplus/random_benchmark.py
@@ -0,0 +1,61 @@
+from typing import Union, List, Tuple
+
+import numpy as np
+import torch
+
+from .utils import benchmark
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+
+def random_benchmark(
+        model: torch.nn.Module,
+        batch_size: Union[int, List[int]] = 1,
+        image_size: Union[Tuple[int], List[Tuple[int]]] = (3, 1024, 1024),
+):
+    """
+    Calculate the FPS of a given model using randomly generated tensors.
+
+    Args:
+        model: instance of a model (e.g. SegFormer)
+        batch_size: the batch size(s) at which to calculate the FPS (e.g. 1 or [1, 2, 4])
+        image_size: the size of the images to use (e.g. (3, 1024, 1024))
+
+    Returns: the FPS values calculated for all image sizes and batch sizes in the form of a dictionary
+
+    """
+    if isinstance(batch_size, int):
+        batch_size = [batch_size]
+    if isinstance(image_size, tuple):
+        image_size = [image_size]
+
+    values = {}
+    throughput_values = []
+
+    for i in image_size:
+        # fill with fps for each batch size
+        fps = []
+        for b in batch_size:
+            for _ in range(4):
+                # Baseline benchmark
+                if i[1] >= 1024:
+                    r = 16
+                else:
+                    r = 32
+                baseline_throughput = benchmark(
+                    model.to(device),
+                    device=device,
+                    verbose=True,
+                    runs=r,
+                    batch_size=b,
+                    input_size=i
+                )
+                throughput_values.append(baseline_throughput)
+            throughput_values = np.asarray(throughput_values)
+            throughput = np.around(np.mean(throughput_values), decimals=2)
+            print('Im_size:', i, 'Batch_size:', b, 'Mean:', throughput, 'Std:',
+                  np.around(np.std(throughput_values), decimals=2))
+            throughput_values = []
+            fps.append({b: throughput})
+        values[i] = fps
+    return values
diff --git a/build/lib/segformer_plusplus/utils/__init__.py b/build/lib/segformer_plusplus/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f7f00f36aa5ae06fd442c4f70f50540c18ebac3
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .embed import PatchEmbed
+from .shape_convert import nchw_to_nlc, nlc_to_nchw
+from .wrappers import resize
+from .tome_presets import tome_presets
+from .registry import MODELS
+from .imagenet_weights import imagenet_weights
+from .benchmark import benchmark
+
+__all__ = [
+    'PatchEmbed', 'nchw_to_nlc', 'nlc_to_nchw', 'resize', 'tome_presets', 'MODELS', 'imagenet_weights', 'benchmark'
+]
diff --git a/build/lib/segformer_plusplus/utils/benchmark.py b/build/lib/segformer_plusplus/utils/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc6fe92fddc7e5deeebc649bd5baf6f63e219912
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/benchmark.py
@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# Source: https://github.com/facebookresearch/ToMe/blob/main/tome/utils.py
+# --------------------------------------------------------
+
+import time
+from typing import Tuple
+
+import torch
+from tqdm import tqdm
+
+
+def benchmark(
+        model: torch.nn.Module,
+        device: torch.device = 0,
+        input_size: Tuple[int] = (3, 224, 224),
+        batch_size: int = 64,
+        runs: int = 40,
+        throw_out: float = 0.25,
+        use_fp16: bool = False,
+        verbose: bool = False,
+) -> float:
+    """
+    Benchmark the given model with random inputs at the given batch size.
+
+    Args:
+     - model: the module to benchmark
+     - device: the device to use for benchmarking
+     - input_size: the input size to pass to the model (channels, h, w)
+     - batch_size: the batch size to use for evaluation
+     - runs: the number of total runs to do
+     - throw_out: the percentage of runs to throw out at the start of testing
+     - use_fp16: whether or not to benchmark with float16 and autocast
+     - verbose: whether or not to use tqdm to print progress / print throughput at end
+
+    Returns:
+     - the throughput measured in images / second
+    """
+    if not isinstance(device, torch.device):
+        device = torch.device(device)
+    is_cuda = torch.device(device).type == "cuda"
+
+    model = model.eval().to(device)
+    input = torch.rand(batch_size, *input_size, device=device)
+    if use_fp16:
+        input = input.half()
+
+    warm_up = int(runs * throw_out)
+    total = 0
+    start = time.time()
+
+    with torch.autocast(device.type, enabled=use_fp16):
+        with torch.no_grad():
+            for i in tqdm(range(runs), disable=not verbose, desc="Benchmarking"):
+                if i == warm_up:
+                    if is_cuda:
+                        torch.cuda.synchronize()
+                    total = 0
+                    start = time.time()
+
+                model(input)
+                total += batch_size
+
+    if is_cuda:
+        torch.cuda.synchronize()
+
+    end = time.time()
+    elapsed = end - start
+
+    throughput = total / elapsed
+
+    if verbose:
+        print(f"Throughput: {throughput:.2f} im/s")
+
+    return throughput
diff --git a/build/lib/segformer_plusplus/utils/embed.py b/build/lib/segformer_plusplus/utils/embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..880fa062912fc62b7ed1fa2804f18b1fdc614f6d
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/embed.py
@@ -0,0 +1,330 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+from typing import Sequence
+
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmengine.model import BaseModule
+from mmengine.utils import to_2tuple
+
+
+class AdaptivePadding(nn.Module):
+    """Applies padding to input (if needed) so that input can get fully covered
+    by filter you specified. It supports two modes "same" and "corner". The
+    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
+    input. The "corner"  mode would pad zero to bottom right.
+
+    Args:
+        kernel_size (int | tuple): Size of the kernel:
+        stride (int | tuple): Stride of the filter. Default: 1:
+        dilation (int | tuple): Spacing between kernel elements.
+            Default: 1.
+        padding (str): Support "same" and "corner", "corner" mode
+            would pad zero to bottom right, and "same" mode would
+            pad zero around input. Default: "corner".
+    Example:
+        >>> kernel_size = 16
+        >>> stride = 16
+        >>> dilation = 1
+        >>> input = torch.rand(1, 1, 15, 17)
+        >>> adap_pad = AdaptivePadding(
+        >>>     kernel_size=kernel_size,
+        >>>     stride=stride,
+        >>>     dilation=dilation,
+        >>>     padding="corner")
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+        >>> input = torch.rand(1, 1, 16, 17)
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+    """
+
+    def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
+
+        super().__init__()
+
+        assert padding in ('same', 'corner')
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+
+    def get_pad_shape(self, input_shape):
+        input_h, input_w = input_shape
+        kernel_h, kernel_w = self.kernel_size
+        stride_h, stride_w = self.stride
+        output_h = math.ceil(input_h / stride_h)
+        output_w = math.ceil(input_w / stride_w)
+        pad_h = max((output_h - 1) * stride_h +
+                    (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
+        pad_w = max((output_w - 1) * stride_w +
+                    (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+        return pad_h, pad_w
+
+    def forward(self, x):
+        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
+        if pad_h > 0 or pad_w > 0:
+            if self.padding == 'corner':
+                x = F.pad(x, [0, pad_w, 0, pad_h])
+            elif self.padding == 'same':
+                x = F.pad(x, [
+                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
+                    pad_h - pad_h // 2
+                ])
+        return x
+
+
+class PatchEmbed(BaseModule):
+    """Image to Patch Embedding.
+
+    We use a conv layer to implement PatchEmbed.
+
+    Args:
+        in_channels (int): The num of input channels. Default: 3
+        embed_dims (int): The dimensions of embedding. Default: 768
+        conv_type (str): The config dict for embedding
+            conv layer type selection. Default: "Conv2d".
+        kernel_size (int): The kernel_size of embedding conv. Default: 16.
+        stride (int, optional): The slide stride of embedding conv.
+            Default: None (Would be set as `kernel_size`).
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int): The dilation rate of embedding conv. Default: 1.
+        bias (bool): Bias of embed conv. Default: True.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        input_size (int | tuple | None): The size of input, which will be
+            used to calculate the out size. Only work when `dynamic_size`
+            is False. Default: None.
+        init_cfg (`mmengine.ConfigDict`, optional): The Config for
+            initialization. Default: None.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 embed_dims=768,
+                 conv_type='Conv2d',
+                 kernel_size=16,
+                 stride=None,
+                 padding='corner',
+                 dilation=1,
+                 bias=True,
+                 norm_cfg=None,
+                 input_size=None,
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        if stride is None:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of conv
+            padding = 0
+        else:
+            self.adap_padding = None
+        padding = to_2tuple(padding)
+
+        self.projection = build_conv_layer(
+            dict(type=conv_type),
+            in_channels=in_channels,
+            out_channels=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+        else:
+            self.norm = None
+
+        if input_size:
+            input_size = to_2tuple(input_size)
+            # `init_out_size` would be used outside to
+            # calculate the num_patches
+            # when `use_abs_pos_embed` outside
+            self.init_input_size = input_size
+            if self.adap_padding:
+                pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
+                input_h, input_w = input_size
+                input_h = input_h + pad_h
+                input_w = input_w + pad_w
+                input_size = (input_h, input_w)
+
+            # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+            h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
+                     (kernel_size[0] - 1) - 1) // stride[0] + 1
+            w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
+                     (kernel_size[1] - 1) - 1) // stride[1] + 1
+            self.init_out_size = (h_out, w_out)
+        else:
+            self.init_input_size = None
+            self.init_out_size = None
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (out_h, out_w).
+        """
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+
+        x = self.projection(x)
+        out_size = (x.shape[2], x.shape[3])
+        x = x.flatten(2).transpose(1, 2)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x, out_size
+
+
+class PatchMerging(BaseModule):
+    """Merge patch feature map.
+
+    This layer groups feature map by kernel_size, and applies norm and linear
+    layers to the grouped feature map. Our implementation uses `nn.Unfold` to
+    merge patch, which is about 25% faster than original implementation.
+    Instead, we need to modify pretrained models for compatibility.
+
+    Args:
+        in_channels (int): The num of input channels.
+        out_channels (int): The num of output channels.
+        kernel_size (int | tuple, optional): the kernel size in the unfold
+            layer. Defaults to 2.
+        stride (int | tuple, optional): the stride of the sliding blocks in the
+            unfold layer. Default: None. (Would be set as `kernel_size`)
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int | tuple, optional): dilation parameter in the unfold
+            layer. Default: 1.
+        bias (bool, optional): Whether to add bias in linear layer or not.
+            Defaults: False.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: dict(type='LN').
+        init_cfg (dict, optional): The extra config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=2,
+                 stride=None,
+                 padding='corner',
+                 dilation=1,
+                 bias=False,
+                 norm_cfg=dict(type='LN'),
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        if stride:
+            stride = stride
+        else:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of unfold
+            padding = 0
+        else:
+            self.adap_padding = None
+
+        padding = to_2tuple(padding)
+        self.sampler = nn.Unfold(
+            kernel_size=kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride)
+
+        sample_dim = kernel_size[0] * kernel_size[1] * in_channels
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
+        else:
+            self.norm = None
+
+        self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
+
+    def forward(self, x, input_size):
+        """
+        Args:
+            x (Tensor): Has shape (B, H*W, C_in).
+            input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
+                Default: None.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (Merged_H, Merged_W).
+        """
+        B, L, C = x.shape
+        assert isinstance(input_size, Sequence), f'Expect ' \
+                                                 f'input_size is ' \
+                                                 f'`Sequence` ' \
+                                                 f'but get {input_size}'
+
+        H, W = input_size
+        assert L == H * W, 'input feature has wrong size'
+
+        x = x.view(B, H, W, C).permute([0, 3, 1, 2])  # B, C, H, W
+        # Use nn.Unfold to merge patch. About 25% faster than original method,
+        # but need to modify pretrained model for compatibility
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+            H, W = x.shape[-2:]
+
+        x = self.sampler(x)
+        # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
+
+        out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
+                 (self.sampler.kernel_size[0] - 1) -
+                 1) // self.sampler.stride[0] + 1
+        out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
+                 (self.sampler.kernel_size[1] - 1) -
+                 1) // self.sampler.stride[1] + 1
+
+        output_size = (out_h, out_w)
+        x = x.transpose(1, 2)  # B, H/2*W/2, 4*C
+        x = self.norm(x) if self.norm else x
+        x = self.reduction(x)
+        return x, output_size
diff --git a/build/lib/segformer_plusplus/utils/imagenet_weights.py b/build/lib/segformer_plusplus/utils/imagenet_weights.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9ef1d12b01027e22b2053c796f12a9a2e554ef5
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/imagenet_weights.py
@@ -0,0 +1,8 @@
+imagenet_weights = {
+    'b0': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth',
+    'b1': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth',
+    'b2': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth',
+    'b3': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth',
+    'b4': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth',
+    'b5': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
+}
\ No newline at end of file
diff --git a/build/lib/segformer_plusplus/utils/registry.py b/build/lib/segformer_plusplus/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..c991ea9d6909141d1d4dfe51e861d61b530f355a
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/registry.py
@@ -0,0 +1,6 @@
+from mmengine import Registry
+
+MODELS = Registry(
+    'models',
+    locations=['segformer_plusplus.model.backbone', 'segformer_plusplus.model.head']
+)
diff --git a/build/lib/segformer_plusplus/utils/shape_convert.py b/build/lib/segformer_plusplus/utils/shape_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..cce1e220b645d4b02df1ec2d9ed3137c8acba707
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/shape_convert.py
@@ -0,0 +1,107 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+def nlc_to_nchw(x, hw_shape):
+    """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, L, C] before conversion.
+        hw_shape (Sequence[int]): The height and width of output feature map.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W] after conversion.
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len doesn\'t match H, W'
+    return x.transpose(1, 2).reshape(B, C, H, W)
+
+
+def nchw_to_nlc(x):
+    """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, C, H, W] before conversion.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C] after conversion.
+    """
+    assert len(x.shape) == 4
+    return x.flatten(2).transpose(1, 2).contiguous()
+
+
+def nchw2nlc2nchw(module, x, contiguous=False, **kwargs):
+    """Flatten [N, C, H, W] shape tensor `x` to [N, L, C] shape tensor. Use the
+    reshaped tensor as the input of `module`, and the convert the output of
+    `module`, whose shape is.
+
+    [N, L, C], to [N, C, H, W].
+
+    Args:
+        module (Callable): A callable object the takes a tensor
+            with shape [N, L, C] as input.
+        x (Tensor): The input tensor of shape [N, C, H, W].
+                contiguous:
+        contiguous (Bool): Whether to make the tensor contiguous
+            after each shape transform.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W].
+
+    Example:
+        >>> import torch
+        >>> import torch.nn as nn
+        >>> norm = nn.LayerNorm(4)
+        >>> feature_map = torch.rand(4, 4, 5, 5)
+        >>> output = nchw2nlc2nchw(norm, feature_map)
+    """
+    B, C, H, W = x.shape
+    if not contiguous:
+        x = x.flatten(2).transpose(1, 2)
+        x = module(x, **kwargs)
+        x = x.transpose(1, 2).reshape(B, C, H, W)
+    else:
+        x = x.flatten(2).transpose(1, 2).contiguous()
+        x = module(x, **kwargs)
+        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+    return x
+
+
+def nlc2nchw2nlc(module, x, hw_shape, contiguous=False, **kwargs):
+    """Convert [N, L, C] shape tensor `x` to [N, C, H, W] shape tensor. Use the
+    reshaped tensor as the input of `module`, and convert the output of
+    `module`, whose shape is.
+
+    [N, C, H, W], to [N, L, C].
+
+    Args:
+        module (Callable): A callable object the takes a tensor
+            with shape [N, C, H, W] as input.
+        x (Tensor): The input tensor of shape [N, L, C].
+        hw_shape: (Sequence[int]): The height and width of the
+            feature map with shape [N, C, H, W].
+        contiguous (Bool): Whether to make the tensor contiguous
+            after each shape transform.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C].
+
+    Example:
+        >>> import torch
+        >>> import torch.nn as nn
+        >>> conv = nn.Conv2d(16, 16, 3, 1, 1)
+        >>> feature_map = torch.rand(4, 25, 16)
+        >>> output = nlc2nchw2nlc(conv, feature_map, (5, 5))
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len doesn\'t match H, W'
+    if not contiguous:
+        x = x.transpose(1, 2).reshape(B, C, H, W)
+        x = module(x, **kwargs)
+        x = x.flatten(2).transpose(1, 2)
+    else:
+        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+        x = module(x, **kwargs)
+        x = x.flatten(2).transpose(1, 2).contiguous()
+    return x
diff --git a/build/lib/segformer_plusplus/utils/tome_presets.py b/build/lib/segformer_plusplus/utils/tome_presets.py
new file mode 100644
index 0000000000000000000000000000000000000000..152c88689834f48338198657974de42db406957b
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/tome_presets.py
@@ -0,0 +1,20 @@
+tome_presets = {
+    'bsm_hq': [
+        dict(q_mode=None, kv_mode='bsm', kv_r=0.6, kv_sx=2, kv_sy=2),
+        dict(q_mode=None, kv_mode='bsm', kv_r=0.6, kv_sx=2, kv_sy=2),
+        dict(q_mode='bsm', kv_mode=None, q_r=0.8, q_sx=4, q_sy=4),
+        dict(q_mode='bsm', kv_mode=None, q_r=0.8, q_sx=4, q_sy=4)
+    ],
+    'bsm_fast': [
+        dict(q_mode=None, kv_mode='bsm_r2D', kv_r=0.9, kv_sx=4, kv_sy=4),
+        dict(q_mode=None, kv_mode='bsm_r2D', kv_r=0.9, kv_sx=4, kv_sy=4),
+        dict(q_mode='bsm_r2D', kv_mode=None, q_r=0.9, q_sx=4, q_sy=4),
+        dict(q_mode='bsm_r2D', kv_mode=None, q_r=0.9, q_sx=4, q_sy=4)
+    ],
+    'n2d_2x2': [
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2))
+    ]
+}
diff --git a/build/lib/segformer_plusplus/utils/wrappers.py b/build/lib/segformer_plusplus/utils/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..abbd0c029623b4f480a067e4b78adfec234ef8d0
--- /dev/null
+++ b/build/lib/segformer_plusplus/utils/wrappers.py
@@ -0,0 +1,51 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def resize(input,
+           size=None,
+           scale_factor=None,
+           mode='nearest',
+           align_corners=None,
+           warning=True):
+    if warning:
+        if size is not None and align_corners:
+            input_h, input_w = tuple(int(x) for x in input.shape[2:])
+            output_h, output_w = tuple(int(x) for x in size)
+            if output_h > input_h or output_w > output_h:
+                if ((output_h > 1 and output_w > 1 and input_h > 1
+                     and input_w > 1) and (output_h - 1) % (input_h - 1)
+                        and (output_w - 1) % (input_w - 1)):
+                    warnings.warn(
+                        f'When align_corners={align_corners}, '
+                        'the output would more aligned if '
+                        f'input size {(input_h, input_w)} is `x+1` and '
+                        f'out size {(output_h, output_w)} is `nx+1`')
+    return F.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+class Upsample(nn.Module):
+
+    def __init__(self,
+                 size=None,
+                 scale_factor=None,
+                 mode='nearest',
+                 align_corners=None):
+        super().__init__()
+        self.size = size
+        if isinstance(scale_factor, tuple):
+            self.scale_factor = tuple(float(factor) for factor in scale_factor)
+        else:
+            self.scale_factor = float(scale_factor) if scale_factor else None
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        if not self.size:
+            size = [int(t * self.scale_factor) for t in x.shape[-2:]]
+        else:
+            size = self.size
+        return resize(x, size, None, self.mode, self.align_corners)
diff --git a/cityscapes_prediction_output_reference.txt b/cityscapes_prediction_output_reference.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0154c94fb0ccbed140d9288f328003d9b2f754f
--- /dev/null
+++ b/cityscapes_prediction_output_reference.txt
@@ -0,0 +1,256 @@
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
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diff --git a/segformer_plusplus.egg-info/PKG-INFO b/segformer_plusplus.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..cba86d59473b3d1e43f68639b46746e183b448ba
--- /dev/null
+++ b/segformer_plusplus.egg-info/PKG-INFO
@@ -0,0 +1,11 @@
+Metadata-Version: 2.1
+Name: segformer-plusplus
+Version: 0.2
+Summary: Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
+Home-page: UNKNOWN
+Author: Marco Kantonis
+License: MIT
+Platform: UNKNOWN
+
+https://arxiv.org/abs/2405.14467
+
diff --git a/segformer_plusplus.egg-info/SOURCES.txt b/segformer_plusplus.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..009c4df383c107496f8931c322ee8010b7b5f591
--- /dev/null
+++ b/segformer_plusplus.egg-info/SOURCES.txt
@@ -0,0 +1,29 @@
+setup.py
+segformer_plusplus/__init__.py
+segformer_plusplus/build_model.py
+segformer_plusplus/random_benchmark.py
+segformer_plusplus.egg-info/PKG-INFO
+segformer_plusplus.egg-info/SOURCES.txt
+segformer_plusplus.egg-info/dependency_links.txt
+segformer_plusplus.egg-info/requires.txt
+segformer_plusplus.egg-info/top_level.txt
+segformer_plusplus/configs/__init__.py
+segformer_plusplus/configs/segformer_mit_b0.py
+segformer_plusplus/configs/segformer_mit_b1.py
+segformer_plusplus/configs/segformer_mit_b2.py
+segformer_plusplus/configs/segformer_mit_b3.py
+segformer_plusplus/configs/segformer_mit_b4.py
+segformer_plusplus/configs/segformer_mit_b5.py
+segformer_plusplus/model/__init__.py
+segformer_plusplus/model/backbone/__init__.py
+segformer_plusplus/model/backbone/mit.py
+segformer_plusplus/model/head/__init__.py
+segformer_plusplus/model/head/segformer_head.py
+segformer_plusplus/utils/__init__.py
+segformer_plusplus/utils/benchmark.py
+segformer_plusplus/utils/embed.py
+segformer_plusplus/utils/imagenet_weights.py
+segformer_plusplus/utils/registry.py
+segformer_plusplus/utils/shape_convert.py
+segformer_plusplus/utils/tome_presets.py
+segformer_plusplus/utils/wrappers.py
\ No newline at end of file
diff --git a/segformer_plusplus.egg-info/dependency_links.txt b/segformer_plusplus.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/segformer_plusplus.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/segformer_plusplus.egg-info/requires.txt b/segformer_plusplus.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc91ad39950ede4091577bfc881fae9d7c6fff24
--- /dev/null
+++ b/segformer_plusplus.egg-info/requires.txt
@@ -0,0 +1,2 @@
+tomesd
+torch>=2.0.1
diff --git a/segformer_plusplus.egg-info/top_level.txt b/segformer_plusplus.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7c7c3d0feeda21dced282857dfdb2e8aa168357a
--- /dev/null
+++ b/segformer_plusplus.egg-info/top_level.txt
@@ -0,0 +1 @@
+segformer_plusplus
diff --git a/segformer_plusplus/Registry/default_scope.py b/segformer_plusplus/Registry/default_scope.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad2253b6ebf651d54b8c6f26583e90ad8598297
--- /dev/null
+++ b/segformer_plusplus/Registry/default_scope.py
@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import time
+from contextlib import contextmanager
+from typing import Generator, Optional
+
+from ..utils.manager import ManagerMixin, _accquire_lock, _release_lock
+
+
+class DefaultScope(ManagerMixin):
+    """Scope of current task used to reset the current registry, which can be
+    accessed globally.
+
+    Consider the case of resetting the current ``Registry`` by
+    ``default_scope`` in the internal module which cannot access runner
+    directly, it is difficult to get the ``default_scope`` defined in
+    ``Runner``. However, if ``Runner`` created ``DefaultScope`` instance
+    by given ``default_scope``, the internal module can get
+    ``default_scope`` by ``DefaultScope.get_current_instance`` everywhere.
+
+    Args:
+        name (str): Name of default scope for global access.
+        scope_name (str): Scope of current task.
+
+    Examples:
+        >>> from mmengine.model import MODELS
+        >>> # Define default scope in runner.
+        >>> DefaultScope.get_instance('task', scope_name='mmdet')
+        >>> # Get default scope globally.
+        >>> scope_name = DefaultScope.get_instance('task').scope_name
+    """
+
+    def __init__(self, name: str, scope_name: str):
+        super().__init__(name)
+        assert isinstance(
+            scope_name,
+            str), (f'scope_name should be a string, but got {scope_name}')
+        self._scope_name = scope_name
+
+    @property
+    def scope_name(self) -> str:
+        """
+        Returns:
+            str: Get current scope.
+        """
+        return self._scope_name
+
+    @classmethod
+    def get_current_instance(cls) -> Optional['DefaultScope']:
+        """Get latest created default scope.
+
+        Since default_scope is an optional argument for ``Registry.build``.
+        ``get_current_instance`` should return ``None`` if there is no
+        ``DefaultScope`` created.
+
+        Examples:
+            >>> default_scope = DefaultScope.get_current_instance()
+            >>> # There is no `DefaultScope` created yet,
+            >>> # `get_current_instance` return `None`.
+            >>> default_scope = DefaultScope.get_instance(
+            >>>     'instance_name', scope_name='mmengine')
+            >>> default_scope.scope_name
+            mmengine
+            >>> default_scope = DefaultScope.get_current_instance()
+            >>> default_scope.scope_name
+            mmengine
+
+        Returns:
+            Optional[DefaultScope]: Return None If there has not been
+            ``DefaultScope`` instance created yet, otherwise return the
+            latest created DefaultScope instance.
+        """
+        _accquire_lock()
+        if cls._instance_dict:
+            instance = super().get_current_instance()
+        else:
+            instance = None
+        _release_lock()
+        return instance
+
+    @classmethod
+    @contextmanager
+    def overwrite_default_scope(cls, scope_name: Optional[str]) -> Generator:
+        """Overwrite the current default scope with `scope_name`"""
+        if scope_name is None:
+            yield
+        else:
+            tmp = copy.deepcopy(cls._instance_dict)
+            # To avoid create an instance with the same name.
+            time.sleep(1e-6)
+            cls.get_instance(f'overwrite-{time.time()}', scope_name=scope_name)
+            try:
+                yield
+            finally:
+                cls._instance_dict = tmp
diff --git a/segformer_plusplus/Registry/registry.py b/segformer_plusplus/Registry/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..5263f4c60c62e2caf9f35756274dd80c24245b18
--- /dev/null
+++ b/segformer_plusplus/Registry/registry.py
@@ -0,0 +1,735 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import inspect
+import sys
+import types
+from collections import abc
+from collections.abc import Callable
+from contextlib import contextmanager
+from importlib import import_module
+from typing import Any, Dict, Generator, List, Optional, Tuple, Type, Union
+from rich.console import Console
+from rich.table import Table
+
+from .default_scope import DefaultScope
+
+
+MODULE2PACKAGE = {
+    'mmcls': 'mmcls',
+    'mmdet': 'mmdet',
+    'mmdet3d': 'mmdet3d',
+    'mmseg': 'mmsegmentation',
+    'mmaction': 'mmaction2',
+    'mmtrack': 'mmtrack',
+    'mmpose': 'mmpose',
+    'mmedit': 'mmedit',
+    'mmocr': 'mmocr',
+    'mmgen': 'mmgen',
+    'mmfewshot': 'mmfewshot',
+    'mmrazor': 'mmrazor',
+    'mmflow': 'mmflow',
+    'mmhuman3d': 'mmhuman3d',
+    'mmrotate': 'mmrotate',
+    'mmselfsup': 'mmselfsup',
+    'mmyolo': 'mmyolo',
+    'mmpretrain': 'mmpretrain',
+    'mmagic': 'mmagic',
+}
+
+class Registry:
+    """A registry to map strings to classes or functions.
+
+    Registered object could be built from registry. Meanwhile, registered
+    functions could be called from registry.
+
+    Args:
+        name (str): Registry name.
+        build_func (callable, optional): A function to construct instance
+            from Registry. :func:`build_from_cfg` is used if neither ``parent``
+            or ``build_func`` is specified. If ``parent`` is specified and
+            ``build_func`` is not given,  ``build_func`` will be inherited
+            from ``parent``. Defaults to None.
+        parent (:obj:`Registry`, optional): Parent registry. The class
+            registered in children registry could be built from parent.
+            Defaults to None.
+        scope (str, optional): The scope of registry. It is the key to search
+            for children registry. If not specified, scope will be the name of
+            the package where class is defined, e.g. mmdet, mmcls, mmseg.
+            Defaults to None.
+        locations (list): The locations to import the modules registered
+            in this registry. Defaults to [].
+            New in version 0.4.0.
+
+    Examples:
+        >>> # define a registry
+        >>> MODELS = Registry('models')
+        >>> # registry the `ResNet` to `MODELS`
+        >>> @MODELS.register_module()
+        >>> class ResNet:
+        >>>     pass
+        >>> # build model from `MODELS`
+        >>> resnet = MODELS.build(dict(type='ResNet'))
+        >>> @MODELS.register_module()
+        >>> def resnet50():
+        >>>     pass
+        >>> resnet = MODELS.build(dict(type='resnet50'))
+
+        >>> # hierarchical registry
+        >>> DETECTORS = Registry('detectors', parent=MODELS, scope='det')
+        >>> @DETECTORS.register_module()
+        >>> class FasterRCNN:
+        >>>     pass
+        >>> fasterrcnn = DETECTORS.build(dict(type='FasterRCNN'))
+
+        >>> # add locations to enable auto import
+        >>> DETECTORS = Registry('detectors', parent=MODELS,
+        >>>     scope='det', locations=['det.models.detectors'])
+        >>> # define this class in 'det.models.detectors'
+        >>> @DETECTORS.register_module()
+        >>> class MaskRCNN:
+        >>>     pass
+        >>> # The registry will auto import det.models.detectors.MaskRCNN
+        >>> fasterrcnn = DETECTORS.build(dict(type='det.MaskRCNN'))
+
+    More advanced usages can be found at
+    https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.
+    """
+
+    def __init__(self,
+                 name: str,
+                 build_func: Optional[Callable] = None,
+                 parent: Optional['Registry'] = None,
+                 scope: Optional[str] = None,
+                 locations: List = []):
+        self._name = name
+        self._module_dict: Dict[str, Type] = dict()
+        self._children: Dict[str, 'Registry'] = dict()
+        self._locations = locations
+        self._imported = False
+
+        if scope is not None:
+            assert isinstance(scope, str)
+            self._scope = scope
+        else:
+            self._scope = self.infer_scope()
+
+        # See https://mypy.readthedocs.io/en/stable/common_issues.html#
+        # variables-vs-type-aliases for the use
+        self.parent: Optional['Registry']
+        if parent is not None:
+            assert isinstance(parent, Registry)
+            parent._add_child(self)
+            self.parent = parent
+        else:
+            self.parent = None
+
+        # self.build_func will be set with the following priority:
+        # 1. build_func
+        # 2. parent.build_func
+        # 3. build_from_cfg
+        self.build_func: Callable
+        if build_func is None:
+            if self.parent is not None:
+                self.build_func = self.parent.build_func
+            else:
+                from ..utils.build_functions import build_from_cfg
+                self.build_func = build_from_cfg
+        else:
+            self.build_func = build_func
+
+    def __len__(self):
+        return len(self._module_dict)
+
+    def __contains__(self, key):
+        return self.get(key) is not None
+
+    def __repr__(self):
+        table = Table(title=f'Registry of {self._name}')
+        table.add_column('Names', justify='left', style='cyan')
+        table.add_column('Objects', justify='left', style='green')
+
+        for name, obj in sorted(self._module_dict.items()):
+            table.add_row(name, str(obj))
+
+        console = Console()
+        with console.capture() as capture:
+            console.print(table, end='')
+
+        return capture.get()
+
+    @staticmethod
+    def infer_scope() -> str:
+        """Infer the scope of registry.
+
+        The name of the package where registry is defined will be returned.
+
+        Returns:
+            str: The inferred scope name.
+
+        Examples:
+            >>> # in mmdet/models/backbone/resnet.py
+            >>> MODELS = Registry('models')
+            >>> @MODELS.register_module()
+            >>> class ResNet:
+            >>>     pass
+            >>> # The scope of ``ResNet`` will be ``mmdet``.
+        """
+
+        # `sys._getframe` returns the frame object that many calls below the
+        # top of the stack. The call stack for `infer_scope` can be listed as
+        # follow:
+        # frame-0: `infer_scope` itself
+        # frame-1: `__init__` of `Registry` which calls the `infer_scope`
+        # frame-2: Where the `Registry(...)` is called
+        module = inspect.getmodule(sys._getframe(2))
+        if module is not None:
+            filename = module.__name__
+            split_filename = filename.split('.')
+            scope = split_filename[0]
+        else:
+            # use "mmengine" to handle some cases which can not infer the scope
+            # like initializing Registry in interactive mode
+            scope = 'mmengine'
+        return scope
+
+    @staticmethod
+    def split_scope_key(key: str) -> Tuple[Optional[str], str]:
+        """Split scope and key.
+
+        The first scope will be split from key.
+
+        Return:
+            tuple[str | None, str]: The former element is the first scope of
+            the key, which can be ``None``. The latter is the remaining key.
+
+        Examples:
+            >>> Registry.split_scope_key('mmdet.ResNet')
+            'mmdet', 'ResNet'
+            >>> Registry.split_scope_key('ResNet')
+            None, 'ResNet'
+        """
+        split_index = key.find('.')
+        if split_index != -1:
+            return key[:split_index], key[split_index + 1:]
+        else:
+            return None, key
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def scope(self):
+        return self._scope
+
+    @property
+    def module_dict(self):
+        return self._module_dict
+
+    @property
+    def children(self):
+        return self._children
+
+    @property
+    def root(self):
+        return self._get_root_registry()
+
+    @contextmanager
+    def switch_scope_and_registry(self, scope: Optional[str]) -> Generator:
+        """Temporarily switch default scope to the target scope, and get the
+        corresponding registry.
+
+        If the registry of the corresponding scope exists, yield the
+        registry, otherwise yield the current itself.
+
+        Args:
+            scope (str, optional): The target scope.
+
+        Examples:
+            >>> from mmengine.registry import Registry, DefaultScope, MODELS
+            >>> import time
+            >>> # External Registry
+            >>> MMDET_MODELS = Registry('mmdet_model', scope='mmdet',
+            >>>     parent=MODELS)
+            >>> MMCLS_MODELS = Registry('mmcls_model', scope='mmcls',
+            >>>     parent=MODELS)
+            >>> # Local Registry
+            >>> CUSTOM_MODELS = Registry('custom_model', scope='custom',
+            >>>     parent=MODELS)
+            >>>
+            >>> # Initiate DefaultScope
+            >>> DefaultScope.get_instance(f'scope_{time.time()}',
+            >>>     scope_name='custom')
+            >>> # Check default scope
+            >>> DefaultScope.get_current_instance().scope_name
+            custom
+            >>> # Switch to mmcls scope and get `MMCLS_MODELS` registry.
+            >>> with CUSTOM_MODELS.switch_scope_and_registry(scope='mmcls') as registry:
+            >>>     DefaultScope.get_current_instance().scope_name
+            mmcls
+            >>>     registry.scope
+            mmcls
+            >>> # Nested switch scope
+            >>> with CUSTOM_MODELS.switch_scope_and_registry(scope='mmdet') as mmdet_registry:
+            >>>     DefaultScope.get_current_instance().scope_name
+            mmdet
+            >>>     mmdet_registry.scope
+            mmdet
+            >>>     with CUSTOM_MODELS.switch_scope_and_registry(scope='mmcls') as mmcls_registry:
+            >>>         DefaultScope.get_current_instance().scope_name
+            mmcls
+            >>>         mmcls_registry.scope
+            mmcls
+            >>>
+            >>> # Check switch back to original scope.
+            >>> DefaultScope.get_current_instance().scope_name
+            custom
+        """  # noqa: E501
+
+        # Switch to the given scope temporarily. If the corresponding registry
+        # can be found in root registry, return the registry under the scope,
+        # otherwise return the registry itself.
+        with DefaultScope.overwrite_default_scope(scope):
+            # Get the global default scope
+            default_scope = DefaultScope.get_current_instance()
+            # Get registry by scope
+            if default_scope is not None:
+                scope_name = default_scope.scope_name
+                try:
+                    import_module(f'{scope_name}.registry')
+                except (ImportError, AttributeError, ModuleNotFoundError):
+                    if scope in MODULE2PACKAGE:
+                        print(
+                            f'{scope} is not installed and its '
+                            'modules will not be registered. If you '
+                            'want to use modules defined in '
+                            f'{scope}, Please install {scope} by '
+                            f'`pip install {MODULE2PACKAGE[scope]}.')
+                    else:
+                        print(
+                            f'Failed to import `{scope}.registry` '
+                            f'make sure the registry.py exists in `{scope}` '
+                            'package.',)
+                root = self._get_root_registry()
+                registry = root._search_child(scope_name)
+                if registry is None:
+                    # if `default_scope` can not be found, fallback to argument
+                    # `registry`
+                    print(
+                        f'Failed to search registry with scope "{scope_name}" '
+                        f'in the "{root.name}" registry tree. '
+                        f'As a workaround, the current "{self.name}" registry '
+                        f'in "{self.scope}" is used to build instance. This '
+                        'may cause unexpected failure when running the built '
+                        f'modules. Please check whether "{scope_name}" is a '
+                        'correct scope, or whether the registry is '
+                        'initialized.',)
+                    registry = self
+            # If there is no built default scope, just return current registry.
+            else:
+                registry = self
+            yield registry
+
+    def _get_root_registry(self) -> 'Registry':
+        """Return the root registry."""
+        root = self
+        while root.parent is not None:
+            root = root.parent
+        return root
+
+    def import_from_location(self) -> None:
+        """Import modules from the pre-defined locations in self._location."""
+        if not self._imported:
+            # avoid BC breaking
+            if len(self._locations) == 0 and self.scope in MODULE2PACKAGE:
+                print(
+                    f'The "{self.name}" registry in {self.scope} did not '
+                    'set import location. Fallback to call '
+                    f'`{self.scope}.utils.register_all_modules` '
+                    'instead.',)
+                try:
+                    module = import_module(f'{self.scope}.utils')
+                except (ImportError, AttributeError, ModuleNotFoundError):
+                    if self.scope in MODULE2PACKAGE:
+                        print(
+                            f'{self.scope} is not installed and its '
+                            'modules will not be registered. If you '
+                            'want to use modules defined in '
+                            f'{self.scope}, Please install {self.scope} by '
+                            f'`pip install {MODULE2PACKAGE[self.scope]}.',)
+                    else:
+                        print(
+                            f'Failed to import {self.scope} and register '
+                            'its modules, please make sure you '
+                            'have registered the module manually.',)
+                else:
+                    # The import errors triggered during the registration
+                    # may be more complex, here just throwing
+                    # the error to avoid causing more implicit registry errors
+                    # like `xxx`` not found in `yyy` registry.
+                    module.register_all_modules(False)  # type: ignore
+
+            for loc in self._locations:
+                import_module(loc)
+                print(
+                    f"Modules of {self.scope}'s {self.name} registry have "
+                    f'been automatically imported from {loc}',)
+            self._imported = True
+
+    def get(self, key: str) -> Optional[Type]:
+        """Get the registry record.
+
+        If `key`` represents the whole object name with its module
+        information, for example, `mmengine.model.BaseModel`, ``get``
+        will directly return the class object :class:`BaseModel`.
+
+        Otherwise, it will first parse ``key`` and check whether it
+        contains a scope name. The logic to search for ``key``:
+
+        - ``key`` does not contain a scope name, i.e., it is purely a module
+          name like "ResNet": :meth:`get` will search for ``ResNet`` from the
+          current registry to its parent or ancestors until finding it.
+
+        - ``key`` contains a scope name and it is equal to the scope of the
+          current registry (e.g., "mmcls"), e.g., "mmcls.ResNet": :meth:`get`
+          will only search for ``ResNet`` in the current registry.
+
+        - ``key`` contains a scope name and it is not equal to the scope of
+          the current registry (e.g., "mmdet"), e.g., "mmcls.FCNet": If the
+          scope exists in its children, :meth:`get` will get "FCNet" from
+          them. If not, :meth:`get` will first get the root registry and root
+          registry call its own :meth:`get` method.
+
+        Args:
+            key (str): Name of the registered item, e.g., the class name in
+                string format.
+
+        Returns:
+            Type or None: Return the corresponding class if ``key`` exists,
+            otherwise return None.
+
+        Examples:
+            >>> # define a registry
+            >>> MODELS = Registry('models')
+            >>> # register `ResNet` to `MODELS`
+            >>> @MODELS.register_module()
+            >>> class ResNet:
+            >>>     pass
+            >>> resnet_cls = MODELS.get('ResNet')
+
+            >>> # hierarchical registry
+            >>> DETECTORS = Registry('detector', parent=MODELS, scope='det')
+            >>> # `ResNet` does not exist in `DETECTORS` but `get` method
+            >>> # will try to search from its parents or ancestors
+            >>> resnet_cls = DETECTORS.get('ResNet')
+            >>> CLASSIFIER = Registry('classifier', parent=MODELS, scope='cls')
+            >>> @CLASSIFIER.register_module()
+            >>> class MobileNet:
+            >>>     pass
+            >>> # `get` from its sibling registries
+            >>> mobilenet_cls = DETECTORS.get('cls.MobileNet')
+        """
+
+        if not isinstance(key, str):
+            raise TypeError(
+                'The key argument of `Registry.get` must be a str, '
+                f'got {type(key)}')
+
+        scope, real_key = self.split_scope_key(key)
+        obj_cls = None
+        registry_name = self.name
+        scope_name = self.scope
+
+        # lazy import the modules to register them into the registry
+        self.import_from_location()
+
+        if scope is None or scope == self._scope:
+            # get from self
+            if real_key in self._module_dict:
+                obj_cls = self._module_dict[real_key]
+            elif scope is None:
+                # try to get the target from its parent or ancestors
+                parent = self.parent
+                while parent is not None:
+                    if real_key in parent._module_dict:
+                        obj_cls = parent._module_dict[real_key]
+                        registry_name = parent.name
+                        scope_name = parent.scope
+                        break
+                    parent = parent.parent
+        else:
+            # import the registry to add the nodes into the registry tree
+            try:
+                import_module(f'{scope}.registry')
+                print(
+                    f'Registry node of {scope} has been automatically '
+                    'imported.',)
+            except (ImportError, AttributeError, ModuleNotFoundError):
+                print(
+                    f'Cannot auto import {scope}.registry, please check '
+                    f'whether the package "{scope}" is installed correctly '
+                    'or import the registry manually.',)
+            # get from self._children
+            if scope in self._children:
+                obj_cls = self._children[scope].get(real_key)
+                registry_name = self._children[scope].name
+                scope_name = scope
+            else:
+                root = self._get_root_registry()
+
+                if scope != root._scope and scope not in root._children:
+                    # If not skip directly, `root.get(key)` will recursively
+                    # call itself until RecursionError is thrown.
+                    pass
+                else:
+                    obj_cls = root.get(key)
+
+        if obj_cls is None:
+            # Actually, it's strange to implement this `try ... except` to
+            # get the object by its name in `Registry.get`. However, If we
+            # want to build the model using a configuration like
+            # `dict(type='mmengine.model.BaseModel')`, which can
+            # be dumped by lazy import config, we need this code snippet
+            # for `Registry.get` to work.
+            try:
+                obj_cls = get_object_from_string(key)
+            except Exception:
+                raise RuntimeError(f'Failed to get {key}')
+
+        if obj_cls is not None:
+            # For some rare cases (e.g. obj_cls is a partial function), obj_cls
+            # doesn't have `__name__`. Use default value to prevent error
+            cls_name = getattr(obj_cls, '__name__', str(obj_cls))
+        return obj_cls
+
+    def _search_child(self, scope: str) -> Optional['Registry']:
+        """Depth-first search for the corresponding registry in its children.
+
+        Note that the method only search for the corresponding registry from
+        the current registry. Therefore, if we want to search from the root
+        registry, :meth:`_get_root_registry` should be called to get the
+        root registry first.
+
+        Args:
+            scope (str): The scope name used for searching for its
+                corresponding registry.
+
+        Returns:
+            Registry or None: Return the corresponding registry if ``scope``
+            exists, otherwise return None.
+        """
+        if self._scope == scope:
+            return self
+
+        for child in self._children.values():
+            registry = child._search_child(scope)
+            if registry is not None:
+                return registry
+
+        return None
+
+    def build(self, cfg: dict, *args, **kwargs) -> Any:
+        """Build an instance.
+
+        Build an instance by calling :attr:`build_func`.
+
+        Args:
+            cfg (dict): Config dict needs to be built.
+
+        Returns:
+            Any: The constructed object.
+
+        Examples:
+            >>> from mmengine import Registry
+            >>> MODELS = Registry('models')
+            >>> @MODELS.register_module()
+            >>> class ResNet:
+            >>>     def __init__(self, depth, stages=4):
+            >>>         self.depth = depth
+            >>>         self.stages = stages
+            >>> cfg = dict(type='ResNet', depth=50)
+            >>> model = MODELS.build(cfg)
+        """
+        return self.build_func(cfg, *args, **kwargs, registry=self)
+
+    def _add_child(self, registry: 'Registry') -> None:
+        """Add a child for a registry.
+
+        Args:
+            registry (:obj:`Registry`): The ``registry`` will be added as a
+                child of the ``self``.
+        """
+
+        assert isinstance(registry, Registry)
+        assert registry.scope is not None
+        assert registry.scope not in self.children, \
+            f'scope {registry.scope} exists in {self.name} registry'
+        self.children[registry.scope] = registry
+
+    def _register_module(self,
+                         module: Type,
+                         module_name: Optional[Union[str, List[str]]] = None,
+                         force: bool = False) -> None:
+        """Register a module.
+
+        Args:
+            module (type): Module to be registered. Typically a class or a
+                function, but generally all ``Callable`` are acceptable.
+            module_name (str or list of str, optional): The module name to be
+                registered. If not specified, the class name will be used.
+                Defaults to None.
+            force (bool): Whether to override an existing class with the same
+                name. Defaults to False.
+        """
+        if not callable(module):
+            raise TypeError(f'module must be Callable, but got {type(module)}')
+
+        if module_name is None:
+            module_name = module.__name__
+        if isinstance(module_name, str):
+            module_name = [module_name]
+        for name in module_name:
+            if not force and name in self._module_dict:
+                existed_module = self.module_dict[name]
+                raise KeyError(f'{name} is already registered in {self.name} '
+                               f'at {existed_module.__module__}')
+            self._module_dict[name] = module
+
+    def register_module(
+            self,
+            name: Optional[Union[str, List[str]]] = None,
+            force: bool = False,
+            module: Optional[Type] = None) -> Union[type, Callable]:
+        """Register a module.
+
+        A record will be added to ``self._module_dict``, whose key is the class
+        name or the specified name, and value is the class itself.
+        It can be used as a decorator or a normal function.
+
+        Args:
+            name (str or list of str, optional): The module name to be
+                registered. If not specified, the class name will be used.
+            force (bool): Whether to override an existing class with the same
+                name. Defaults to False.
+            module (type, optional): Module class or function to be registered.
+                Defaults to None.
+
+        Examples:
+            >>> backbones = Registry('backbone')
+            >>> # as a decorator
+            >>> @backbones.register_module()
+            >>> class ResNet:
+            >>>     pass
+            >>> backbones = Registry('backbone')
+            >>> @backbones.register_module(name='mnet')
+            >>> class MobileNet:
+            >>>     pass
+
+            >>> # as a normal function
+            >>> class ResNet:
+            >>>     pass
+            >>> backbones.register_module(module=ResNet)
+        """
+        if not isinstance(force, bool):
+            raise TypeError(f'force must be a boolean, but got {type(force)}')
+
+        # raise the error ahead of time
+        if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
+            raise TypeError(
+                'name must be None, an instance of str, or a sequence of str, '
+                f'but got {type(name)}')
+
+        # use it as a normal method: x.register_module(module=SomeClass)
+        if module is not None:
+            self._register_module(module=module, module_name=name, force=force)
+            return module
+
+        # use it as a decorator: @x.register_module()
+        def _register(module):
+            self._register_module(module=module, module_name=name, force=force)
+            return module
+
+        return _register
+
+
+def is_seq_of(seq: Any,
+              expected_type: Union[Type, tuple],
+              seq_type: Optional[Type] = None) -> bool:
+    """Check whether it is a sequence of some type.
+
+    Args:
+        seq (Sequence): The sequence to be checked.
+        expected_type (type or tuple): Expected type of sequence items.
+        seq_type (type, optional): Expected sequence type. Defaults to None.
+
+    Returns:
+        bool: Return True if ``seq`` is valid else False.
+
+    Examples:
+        >>> from mmengine.utils import is_seq_of
+        >>> seq = ['a', 'b', 'c']
+        >>> is_seq_of(seq, str)
+        True
+        >>> is_seq_of(seq, int)
+        False
+    """
+    if seq_type is None:
+        exp_seq_type = abc.Sequence
+    else:
+        assert isinstance(seq_type, type)
+        exp_seq_type = seq_type
+    if not isinstance(seq, exp_seq_type):
+        return False
+    for item in seq:
+        if not isinstance(item, expected_type):
+            return False
+    return True
+
+
+def get_object_from_string(obj_name: str):
+    """Get object from name.
+
+    Args:
+        obj_name (str): The name of the object.
+
+    Examples:
+        >>> get_object_from_string('torch.optim.sgd.SGD')
+        >>> torch.optim.sgd.SGD
+    """
+    parts = iter(obj_name.split('.'))
+    module_name = next(parts)
+    # import module
+    while True:
+        try:
+            module = import_module(module_name)
+            part = next(parts)
+            # mmcv.ops has nms.py and nms function at the same time. So the
+            # function will have a higher priority
+            obj = getattr(module, part, None)
+            if obj is not None and not ismodule(obj):
+                break
+            module_name = f'{module_name}.{part}'
+        except StopIteration:
+            # if obj is a module
+            return module
+        except ImportError:
+            return None
+
+    # get class or attribute from module
+    obj = module
+    while True:
+        try:
+            obj = getattr(obj, part)
+            part = next(parts)
+        except StopIteration:
+            return obj
+        except AttributeError:
+            return None     
+
+def ismodule(object):
+    """Return true if the object is a module.
+
+    Module objects provide these attributes:
+        __cached__      pathname to byte compiled file
+        __doc__         documentation string
+        __file__        filename (missing for built-in modules)"""
+    return isinstance(object, types.ModuleType)
\ No newline at end of file
diff --git a/segformer_plusplus/__init__.py b/segformer_plusplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd8a768648cca72d7cc393e7eb85b79b434d9fc7
--- /dev/null
+++ b/segformer_plusplus/__init__.py
@@ -0,0 +1,4 @@
+from .build_model import create_model, create_custom_model
+from .random_benchmark import random_benchmark
+
+__all__ = ['create_model', 'create_custom_model', 'random_benchmark']
diff --git a/segformer_plusplus/build_model.py b/segformer_plusplus/build_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..250786c42e70943461d6cb6e2ce4c09c6984e652
--- /dev/null
+++ b/segformer_plusplus/build_model.py
@@ -0,0 +1,107 @@
+import os
+
+from .utils import MODELS, imagenet_weights
+from .utils import tome_presets
+from .model.base_module import BaseModule
+from .configs.config.config import Config
+from .utils.build_functions import build_model_from_cfg
+
+
+class SegFormer(BaseModule):
+    """
+    This class represents a SegFormer model that allows for the application of token merging.
+
+    Attributes:
+         backbone (BaseModule): MixVisionTransformer backbone
+         decode_head (BaseModule): SegFormer head
+
+    """
+    def __init__(self, cfg):
+        """
+        Initialize the SegFormer model.
+
+        Args:
+            cfg (Config): an mmengine Config object, which defines the backbone, head and token merging strategy used.
+
+        """
+        super().__init__()
+        self.backbone = build_model_from_cfg(cfg.backbone, registry=MODELS)
+        self.decode_head = build_model_from_cfg(cfg.decode_head, registry=MODELS)
+
+    def forward(self, x):
+        """
+        Forward pass of the model.
+
+        Args:
+            x (torch.Tensor): input tensor of shape [B, C, H, W]
+
+        Returns:
+            torch.Tensor: output tensor
+
+        """
+        x = self.backbone(x)
+        x = self.decode_head(x)
+        return x
+
+
+def create_model(
+        backbone: str = 'b0',
+        tome_strategy: str = None,
+        out_channels: int = 19,
+        pretrained: bool = False,
+):
+    """
+    Create a SegFormer model using the predefined SegFormer backbones from the MiT series (b0-b5).
+
+    Args:
+        backbone (str): backbone name (e.g. 'b0')
+        tome_strategy (str | list(dict)): select strategy from presets ('bsm_hq', 'bsm_fast', 'n2d_2x2') or define a
+            custom strategy using a list, that contains of dictionaries, in which the strategies for the stage are
+            defined
+        out_channels (int): number of output channels (e.g. 19 for the cityscapes semantic segmentation task)
+        pretrained: use pretrained (imagenet) weights
+
+    Returns:
+        BaseModule: SegFormer model
+
+    """
+    backbone = backbone.lower()
+    assert backbone in [f'b{i}' for i in range(6)]
+
+    wd = os.path.dirname(os.path.abspath(__file__))
+
+    cfg = Config.fromfile(os.path.join(wd, 'configs', f'segformer_mit_{backbone}.py'))
+
+    cfg.decode_head.out_channels = out_channels
+
+    if tome_strategy is not None:
+        if tome_strategy not in list(tome_presets.keys()):
+            print("Using custom merging strategy.")
+        cfg.backbone.tome_cfg = tome_presets[tome_strategy]
+
+    # load imagenet weights
+    if pretrained:
+        cfg.backbone.init_cfg = dict(type='Pretrained', checkpoint=imagenet_weights[backbone])
+
+    return SegFormer(cfg)
+
+
+def create_custom_model(
+        model_cfg: Config,
+        tome_strategy: list[dict] = None,
+):
+    """
+    Create a SegFormer model with customizable backbone and head.
+
+    Args:
+        model_cfg (Config): backbone name (e.g. 'b0')
+        tome_strategy (list(dict)): custom token merging strategy
+
+    Returns:
+        BaseModule: SegFormer model
+
+    """
+    if tome_strategy is not None:
+        model_cfg.backbone.tome_cfg = tome_strategy
+
+    return SegFormer(model_cfg)
diff --git a/segformer_plusplus/cityscape_benchmark.py b/segformer_plusplus/cityscape_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cbd882156476e05b3314a9fa513f7939b3589e6
--- /dev/null
+++ b/segformer_plusplus/cityscape_benchmark.py
@@ -0,0 +1,117 @@
+import torch
+from PIL import Image
+import torchvision.transforms as T
+import os
+from typing import Union, List, Tuple
+import numpy as np
+
+from .utils.benchmark import benchmark
+
+
+# Gerät auswählen
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Using device: {device}")
+if device.type == 'cuda':
+    print(f"CUDA Device Name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
+
+
+def cityscape_benchmark(
+        model: torch.nn.Module,
+        image_path: str,
+        batch_size: Union[int, List[int]] = 1,
+        image_size: Union[Tuple[int], List[Tuple[int]]] = (3, 1024, 1024),
+        save_output: bool = True,
+
+):
+    """
+    Calculate the FPS of a given model using an actual Cityscapes image.
+
+    Args:
+        model: instance of a model (e.g. SegFormer)
+        image_path: the path to the Cityscapes image
+        batch_size: the batch size(s) at which to calculate the FPS (e.g. 1 or [1, 2, 4])
+        image_size: the size of the images to use (e.g. (3, 1024, 1024))
+        save_output: whether to save the output prediction (default True)
+
+    Returns:
+        the FPS values calculated for all image sizes and batch sizes in the form of a dictionary
+    """
+
+
+    if isinstance(batch_size, int):
+        batch_size = [batch_size]
+    if isinstance(image_size, tuple):
+        image_size = [image_size]
+
+    values = {}
+    throughput_values = []
+
+    model = model.to(device)
+    model.eval()
+
+    assert os.path.exists(image_path), f"Image not found: {image_path}"
+    image = Image.open(image_path).convert("RGB")
+
+    img_tensor = T.ToTensor()(image)
+    mean = img_tensor.mean(dim=(1, 2))
+    std = img_tensor.std(dim=(1, 2))
+    print(f"Calculated Mean: {mean}")
+    print(f"Calculated Std: {std}")
+
+    transform = T.Compose([
+        T.Resize((image_size[0][1], image_size[0][2])),
+        T.ToTensor(),
+        T.Normalize(mean=mean.tolist(),
+                    std=std.tolist())
+    ])
+
+    img_tensor = transform(image).unsqueeze(0).to(device)
+
+    for i in image_size:
+            # fill with fps for each batch size
+            fps = []
+            for b in batch_size:
+                for _ in range(4):
+                    # Baseline benchmark
+                    if i[1] >= 1024:
+                        r = 16
+                    else:
+                        r = 32
+                    baseline_throughput = benchmark(
+                        model.to(device),
+                        device=device,
+                        verbose=True,
+                        runs=r,
+                        batch_size=b,
+                        input_size=i
+                    )
+                    throughput_values.append(baseline_throughput)
+                throughput_values = np.asarray(throughput_values)
+                throughput = np.around(np.mean(throughput_values), decimals=2)
+                print('Im_size:', i, 'Batch_size:', b, 'Mean:', throughput, 'Std:',
+                    np.around(np.std(throughput_values), decimals=2))
+                throughput_values = []
+                fps.append({b: throughput})
+            values[i] = fps
+
+    if save_output:
+        with torch.no_grad():
+            with open("model_output_log.txt", "w") as f:
+                f.write("=== Model Input Info ===\n")
+                f.write(f"Input tensor:\n{img_tensor}\n")
+                f.write(f"Input shape: {img_tensor.shape}\n")
+                f.write(f"Input stats: mean = {img_tensor.mean().item()}, std = {img_tensor.std().item()}\n\n")
+
+                output = model(img_tensor)
+
+                f.write("=== Raw Model Output ===\n")
+                f.write(f"{output}\n\n")
+
+            pred = torch.argmax(output, dim=1).squeeze(0).cpu().numpy()
+
+            # Speichere Prediction als Text ab
+            np.savetxt("cityscapes_prediction_output.txt", pred, fmt="%d")
+
+            print("Prediction saved as cityscapes_prediction_output.txt")
+
+    return values
diff --git a/segformer_plusplus/configs/__init__.py b/segformer_plusplus/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b680692d5ff945d85311a8c90c70766275444498
--- /dev/null
+++ b/segformer_plusplus/configs/__init__.py
@@ -0,0 +1 @@
+__all__ = []
\ No newline at end of file
diff --git a/segformer_plusplus/configs/config/config.py b/segformer_plusplus/configs/config/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b68b848b0d95a861f5c0b351768d6b9abea76b2
--- /dev/null
+++ b/segformer_plusplus/configs/config/config.py
@@ -0,0 +1,1545 @@
+import ast
+import copy
+import os
+import os.path as osp
+import platform
+import shutil
+import sys
+import tempfile
+import types
+import uuid
+import re
+import warnings
+from argparse import ArgumentParser
+from collections import OrderedDict, abc
+from pathlib import Path
+from typing import Any, Optional, Tuple, Union
+from omegaconf import OmegaConf
+import yapf
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+from .lazy import LazyAttr, LazyObject
+from .utils import (check_file_exist, get_installed_path, import_modules_from_strings, is_installed, RemoveAssignFromAST, 
+                    ImportTransformer, _gather_abs_import_lazyobj, _get_external_cfg_base_path,
+                    _get_external_cfg_path, _get_package_and_cfg_path, _is_builtin_module, dump)
+
+
+BASE_KEY = '_base_'
+DELETE_KEY = '_delete_'
+DEPRECATION_KEY = '_deprecation_'
+RESERVED_KEYS = ['filename', 'text', 'pretty_text', 'env_variables']
+
+
+def _lazy2string(cfg_dict, dict_type=None):
+    if isinstance(cfg_dict, dict):
+        dict_type = dict_type or type(cfg_dict)
+        return dict_type(
+            {k: _lazy2string(v, dict_type)
+             for k, v in dict.items(cfg_dict)})
+    elif isinstance(cfg_dict, (tuple, list)):
+        return type(cfg_dict)(_lazy2string(v, dict_type) for v in cfg_dict)
+    elif isinstance(cfg_dict, (LazyAttr, LazyObject)):
+        return f'{cfg_dict.module}.{str(cfg_dict)}'
+    else:
+        return cfg_dict
+
+
+class ConfigDict(Dict):
+    """A dictionary for config which has the same interface as python's built-
+    in dictionary and can be used as a normal dictionary.
+
+    The Config class would transform the nested fields (dictionary-like fields)
+    in config file into ``ConfigDict``.
+
+    If the class attribute ``lazy``  is ``False``, users will get the
+    object built by ``LazyObject`` or ``LazyAttr``, otherwise users will get
+    the ``LazyObject`` or ``LazyAttr`` itself.
+
+    The ``lazy`` should be set to ``True`` to avoid building the imported
+    object during configuration parsing, and it should be set to False outside
+    the Config to ensure that users do not experience the ``LazyObject``.
+    """
+    lazy = False
+
+    def __init__(__self, *args, **kwargs):
+        object.__setattr__(__self, '__parent', kwargs.pop('__parent', None))
+        object.__setattr__(__self, '__key', kwargs.pop('__key', None))
+        object.__setattr__(__self, '__frozen', False)
+        for arg in args:
+            if not arg:
+                continue
+            # Since ConfigDict.items will convert LazyObject to real object
+            # automatically, we need to call super().items() to make sure
+            # the LazyObject will not be converted.
+            if isinstance(arg, ConfigDict):
+                for key, val in dict.items(arg):
+                    __self[key] = __self._hook(val)
+            elif isinstance(arg, dict):
+                for key, val in arg.items():
+                    __self[key] = __self._hook(val)
+            elif isinstance(arg, tuple) and (not isinstance(arg[0], tuple)):
+                __self[arg[0]] = __self._hook(arg[1])
+            else:
+                for key, val in iter(arg):
+                    __self[key] = __self._hook(val)
+
+        for key, val in dict.items(kwargs):
+            __self[key] = __self._hook(val)
+
+    def __missing__(self, name):
+        raise KeyError(name)
+
+    def __getattr__(self, name):
+        try:
+            value = super().__getattr__(name)
+            if isinstance(value, (LazyAttr, LazyObject)) and not self.lazy:
+                value = value.build()
+        except KeyError:
+            raise AttributeError(f"'{self.__class__.__name__}' object has no "
+                                 f"attribute '{name}'")
+        except Exception as e:
+            raise e
+        else:
+            return value
+
+    @classmethod
+    def _hook(cls, item):
+        # avoid to convert user defined dict to ConfigDict.
+        if type(item) in (dict, OrderedDict):
+            return cls(item)
+        elif isinstance(item, (list, tuple)):
+            return type(item)(cls._hook(elem) for elem in item)
+        return item
+
+    def __setattr__(self, name, value):
+        value = self._hook(value)
+        return super().__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        value = self._hook(value)
+        return super().__setitem__(name, value)
+
+    def __getitem__(self, key):
+        return self.build_lazy(super().__getitem__(key))
+
+    def __deepcopy__(self, memo):
+        other = self.__class__()
+        memo[id(self)] = other
+        for key, value in super().items():
+            other[copy.deepcopy(key, memo)] = copy.deepcopy(value, memo)
+        return other
+
+    def __copy__(self):
+        other = self.__class__()
+        for key, value in super().items():
+            other[key] = value
+        return other
+
+    copy = __copy__
+
+    def __iter__(self):
+        # Implement `__iter__` to overwrite the unpacking operator `**cfg_dict`
+        # to get the built lazy object
+        return iter(self.keys())
+
+    def get(self, key: str, default: Optional[Any] = None) -> Any:
+        """Get the value of the key. If class attribute ``lazy`` is True, the
+        LazyObject will be built and returned.
+
+        Args:
+            key (str): The key.
+            default (any, optional): The default value. Defaults to None.
+
+        Returns:
+            Any: The value of the key.
+        """
+        return self.build_lazy(super().get(key, default))
+
+    def pop(self, key, default=None):
+        """Pop the value of the key. If class attribute ``lazy`` is True, the
+        LazyObject will be built and returned.
+
+        Args:
+            key (str): The key.
+            default (any, optional): The default value. Defaults to None.
+
+        Returns:
+            Any: The value of the key.
+        """
+        return self.build_lazy(super().pop(key, default))
+
+    def update(self, *args, **kwargs) -> None:
+        """Override this method to make sure the LazyObject will not be built
+        during updating."""
+        other = {}
+        if args:
+            if len(args) > 1:
+                raise TypeError('update only accept one positional argument')
+            # Avoid to used self.items to build LazyObject
+            for key, value in dict.items(args[0]):
+                other[key] = value
+
+        for key, value in dict(kwargs).items():
+            other[key] = value
+        for k, v in other.items():
+            if ((k not in self) or (not isinstance(self[k], dict))
+                    or (not isinstance(v, dict))):
+                self[k] = self._hook(v)
+            else:
+                self[k].update(v)
+
+    def build_lazy(self, value: Any) -> Any:
+        """If class attribute ``lazy`` is False, the LazyObject will be built
+        and returned.
+
+        Args:
+            value (Any): The value to be built.
+
+        Returns:
+            Any: The built value.
+        """
+        if isinstance(value, (LazyAttr, LazyObject)) and not self.lazy:
+            value = value.build()
+        return value
+
+    def values(self):
+        """Yield the values of the dictionary.
+
+        If class attribute ``lazy`` is False, the value of ``LazyObject`` or
+        ``LazyAttr`` will be built and returned.
+        """
+        values = []
+        for value in super().values():
+            values.append(self.build_lazy(value))
+        return values
+
+    def items(self):
+        """Yield the keys and values of the dictionary.
+
+        If class attribute ``lazy`` is False, the value of ``LazyObject`` or
+        ``LazyAttr`` will be built and returned.
+        """
+        items = []
+        for key, value in super().items():
+            items.append((key, self.build_lazy(value)))
+        return items
+
+    def merge(self, other: dict):
+        """Merge another dictionary into current dictionary.
+
+        Args:
+            other (dict): Another dictionary.
+        """
+        default = object()
+
+        def _merge_a_into_b(a, b):
+            if isinstance(a, dict):
+                if not isinstance(b, dict):
+                    a.pop(DELETE_KEY, None)
+                    return a
+                if a.pop(DELETE_KEY, False):
+                    b.clear()
+                all_keys = list(b.keys()) + list(a.keys())
+                return {
+                    key:
+                    _merge_a_into_b(a.get(key, default), b.get(key, default))
+                    for key in all_keys if key != DELETE_KEY
+                }
+            else:
+                return a if a is not default else b
+
+        merged = _merge_a_into_b(copy.deepcopy(other), copy.deepcopy(self))
+        self.clear()
+        for key, value in merged.items():
+            self[key] = value
+
+    def __reduce_ex__(self):
+        # Override __reduce_ex__ to avoid `self.items` will be
+        # called by CPython interpreter during pickling. See more details in
+        # https://github.com/python/cpython/blob/8d61a71f9c81619e34d4a30b625922ebc83c561b/Objects/typeobject.c#L6196  # noqa: E501
+        from ...utils import digit_version
+        if digit_version(platform.python_version()) < digit_version('3.8'):
+            return (self.__class__, ({k: v
+                                      for k, v in super().items()}, ), None,
+                    None, None)
+        else:
+            return (self.__class__, ({k: v
+                                      for k, v in super().items()}, ), None,
+                    None, None, None)
+
+    def __eq__(self, other):
+        if isinstance(other, ConfigDict):
+            return other.to_dict() == self.to_dict()
+        elif isinstance(other, dict):
+            return {k: v for k, v in self.items()} == other
+        else:
+            return False
+
+    def _to_lazy_dict(self):
+        """Convert the ConfigDict to a normal dictionary recursively, and keep
+        the ``LazyObject`` or ``LazyAttr`` object not built."""
+
+        def _to_dict(data):
+            if isinstance(data, ConfigDict):
+                return {
+                    key: _to_dict(value)
+                    for key, value in Dict.items(data)
+                }
+            elif isinstance(data, dict):
+                return {key: _to_dict(value) for key, value in data.items()}
+            elif isinstance(data, (list, tuple)):
+                return type(data)(_to_dict(item) for item in data)
+            else:
+                return data
+
+        return _to_dict(self)
+
+    def to_dict(self):
+        """Convert the ConfigDict to a normal dictionary recursively, and
+        convert the ``LazyObject`` or ``LazyAttr`` to string."""
+        return _lazy2string(self, dict_type=dict)
+
+
+def add_args(parser: ArgumentParser,
+             cfg: dict,
+             prefix: str = '') -> ArgumentParser:
+    """Add config fields into argument parser.
+
+    Args:
+        parser (ArgumentParser): Argument parser.
+        cfg (dict): Config dictionary.
+        prefix (str, optional): Prefix of parser argument.
+            Defaults to ''.
+
+    Returns:
+        ArgumentParser: Argument parser containing config fields.
+    """
+    for k, v in cfg.items():
+        if isinstance(v, str):
+            parser.add_argument('--' + prefix + k)
+        elif isinstance(v, bool):
+            parser.add_argument('--' + prefix + k, action='store_true')
+        elif isinstance(v, int):
+            parser.add_argument('--' + prefix + k, type=int)
+        elif isinstance(v, float):
+            parser.add_argument('--' + prefix + k, type=float)
+        elif isinstance(v, dict):
+            add_args(parser, v, prefix + k + '.')
+        elif isinstance(v, abc.Iterable):
+            parser.add_argument(
+                '--' + prefix + k, type=type(next(iter(v))), nargs='+')
+    return parser
+
+
+class Config:
+    """A facility for config and config files.
+
+    It supports common file formats as configs: python/json/yaml.
+    ``Config.fromfile`` can parse a dictionary from a config file, then
+    build a ``Config`` instance with the dictionary.
+    The interface is the same as a dict object and also allows access config
+    values as attributes.
+
+    Args:
+        cfg_dict (dict, optional): A config dictionary. Defaults to None.
+        cfg_text (str, optional): Text of config. Defaults to None.
+        filename (str or Path, optional): Name of config file.
+            Defaults to None.
+        format_python_code (bool): Whether to format Python code by yapf.
+            Defaults to True.
+    """  # noqa: E501
+
+    def __init__(
+        self,
+        cfg_dict: Optional[dict] = None,
+        cfg_text: Optional[str] = None,
+        filename: Optional[Union[str, Path]] = None,
+        env_variables: Optional[dict] = None,
+        format_python_code: bool = True,
+    ):
+        filename = str(filename) if isinstance(filename, Path) else filename
+        if cfg_dict is None:
+            cfg_dict = dict()
+        elif not isinstance(cfg_dict, dict):
+            raise TypeError('cfg_dict must be a dict, but '
+                            f'got {type(cfg_dict)}')
+        for key in cfg_dict:
+            if key in RESERVED_KEYS:
+                raise KeyError(f'{key} is reserved for config file')
+
+        if not isinstance(cfg_dict, ConfigDict):
+            cfg_dict = ConfigDict(cfg_dict)
+        super().__setattr__('_cfg_dict', cfg_dict)
+        super().__setattr__('_filename', filename)
+        super().__setattr__('_format_python_code', format_python_code)
+        if not hasattr(self, '_imported_names'):
+            super().__setattr__('_imported_names', set())
+
+        if cfg_text:
+            text = cfg_text
+        elif filename:
+            with open(filename, encoding='utf-8') as f:
+                text = f.read()
+        else:
+            text = ''
+        super().__setattr__('_text', text)
+        if env_variables is None:
+            env_variables = dict()
+        super().__setattr__('_env_variables', env_variables)
+
+    @staticmethod
+    def fromfile(filename: Union[str, Path],
+                 use_predefined_variables: bool = True,
+                 import_custom_modules: bool = True,
+                 use_environment_variables: bool = True,
+                 lazy_import: Optional[bool] = None,
+                 format_python_code: bool = True) -> 'Config':
+        """Build a Config instance from config file.
+
+        Args:
+            filename (str or Path): Name of config file.
+            use_predefined_variables (bool, optional): Whether to use
+                predefined variables. Defaults to True.
+            import_custom_modules (bool, optional): Whether to support
+                importing custom modules in config. Defaults to None.
+            use_environment_variables (bool, optional): Whether to use
+                environment variables. Defaults to True.
+            lazy_import (bool): Whether to load config in `lazy_import` mode.
+                If it is `None`, it will be deduced by the content of the
+                config file. Defaults to None.
+            format_python_code (bool): Whether to format Python code by yapf.
+                Defaults to True.
+
+        Returns:
+            Config: Config instance built from config file.
+        """
+        filename = str(filename) if isinstance(filename, Path) else filename
+        if lazy_import is False or \
+           lazy_import is None and not Config._is_lazy_import(filename):
+            cfg_dict, cfg_text, env_variables = Config._file2dict(
+                filename, use_predefined_variables, use_environment_variables,
+                lazy_import)
+            if import_custom_modules and cfg_dict.get('custom_imports', None):
+                try:
+                    import_modules_from_strings(**cfg_dict['custom_imports'])
+                except ImportError as e:
+                    err_msg = (
+                        'Failed to import custom modules from '
+                        f"{cfg_dict['custom_imports']}, the current sys.path "
+                        'is: ')
+                    for p in sys.path:
+                        err_msg += f'\n    {p}'
+                    err_msg += (
+                        '\nYou should set `PYTHONPATH` to make `sys.path` '
+                        'include the directory which contains your custom '
+                        'module')
+                    raise ImportError(err_msg) from e
+            return Config(
+                cfg_dict,
+                cfg_text=cfg_text,
+                filename=filename,
+                env_variables=env_variables,
+            )
+        else:
+            # Enable lazy import when parsing the config.
+            # Using try-except to make sure ``ConfigDict.lazy`` will be reset
+            # to False. See more details about lazy in the docstring of
+            # ConfigDict
+            ConfigDict.lazy = True
+            try:
+                cfg_dict, imported_names = Config._parse_lazy_import(filename)
+            except Exception as e:
+                raise e
+            finally:
+                # disable lazy import to get the real type. See more details
+                # about lazy in the docstring of ConfigDict
+                ConfigDict.lazy = False
+
+            cfg = Config(
+                cfg_dict,
+                filename=filename,
+                format_python_code=format_python_code)
+            object.__setattr__(cfg, '_imported_names', imported_names)
+            return cfg
+
+    @staticmethod
+    def _get_base_modules(nodes: list) -> list:
+        """Get base module name from parsed code.
+
+        Args:
+            nodes (list): Parsed code of the config file.
+
+        Returns:
+            list: Name of base modules.
+        """
+
+        def _get_base_module_from_with(with_nodes: list) -> list:
+            """Get base module name from if statement in python file.
+
+            Args:
+                with_nodes (list): List of if statement.
+
+            Returns:
+                list: Name of base modules.
+            """
+            base_modules = []
+            for node in with_nodes:
+                assert isinstance(node, ast.ImportFrom), (
+                    'Illegal syntax in config file! Only '
+                    '`from ... import ...` could be implemented` in '
+                    'with read_base()`')
+                assert node.module is not None, (
+                    'Illegal syntax in config file! Syntax like '
+                    '`from . import xxx` is not allowed in `with read_base()`')
+                base_modules.append(node.level * '.' + node.module)
+            return base_modules
+
+        for idx, node in enumerate(nodes):
+            if (isinstance(node, ast.Assign)
+                    and isinstance(node.targets[0], ast.Name)
+                    and node.targets[0].id == BASE_KEY):
+                raise SyntaxError(
+                    'The configuration file type in the inheritance chain '
+                    'must match the current configuration file type, either '
+                    '"lazy_import" or non-"lazy_import". You got this error '
+                    f'since you use the syntax like `_base_ = "{node.targets[0].id}"` '  # noqa: E501
+                    'in your config. You should use `with read_base(): ... to` '  # noqa: E501
+                    'mark the inherited config file. See more information '# noqa: E501
+                )
+
+            if not isinstance(node, ast.With):
+                continue
+
+            expr = node.items[0].context_expr
+            if (not isinstance(expr, ast.Call)
+                    or not expr.func.id == 'read_base' or  # type: ignore
+                    len(node.items) > 1):
+                raise SyntaxError(
+                    'Only `read_base` context manager can be used in the '
+                    'config')
+            for nested_idx, nested_node in enumerate(node.body):
+                nodes.insert(idx + nested_idx + 1, nested_node)
+            nodes.pop(idx)
+            return _get_base_module_from_with(node.body)
+        return []
+
+    @staticmethod
+    def _validate_py_syntax(filename: str):
+        """Validate syntax of python config.
+
+        Args:
+            filename (str): Filename of python config file.
+        """
+        with open(filename, encoding='utf-8') as f:
+            content = f.read()
+        try:
+            ast.parse(content)
+        except SyntaxError as e:
+            raise SyntaxError('There are syntax errors in config '
+                              f'file {filename}: {e}')
+
+    @staticmethod
+    def _substitute_predefined_vars(filename: str, temp_config_name: str):
+        """Substitute predefined variables in config with actual values.
+
+        Sometimes we want some variables in the config to be related to the
+        current path or file name, etc.
+
+        Here is an example of a typical usage scenario. When training a model,
+        we define a working directory in the config that save the models and
+        logs. For different configs, we expect to define different working
+        directories. A common way for users is to use the config file name
+        directly as part of the working directory name, e.g. for the config
+        ``config_setting1.py``, the working directory is
+        ``. /work_dir/config_setting1``.
+
+        This can be easily achieved using predefined variables, which can be
+        written in the config `config_setting1.py` as follows
+
+        .. code-block:: python
+
+           work_dir = '. /work_dir/{{ fileBasenameNoExtension }}'
+
+
+        Here `{{ fileBasenameNoExtension }}` indicates the file name of the
+        config (without the extension), and when the config class reads the
+        config file, it will automatically parse this double-bracketed string
+        to the corresponding actual value.
+
+        .. code-block:: python
+
+           cfg = Config.fromfile('. /config_setting1.py')
+           cfg.work_dir # ". /work_dir/config_setting1"
+
+
+        For details, Please refer to docs/zh_cn/advanced_tutorials/config.md .
+
+        Args:
+            filename (str): Filename of config.
+            temp_config_name (str): Temporary filename to save substituted
+                config.
+        """
+        file_dirname = osp.dirname(filename)
+        file_basename = osp.basename(filename)
+        file_basename_no_extension = osp.splitext(file_basename)[0]
+        file_extname = osp.splitext(filename)[1]
+        support_templates = dict(
+            fileDirname=file_dirname,
+            fileBasename=file_basename,
+            fileBasenameNoExtension=file_basename_no_extension,
+            fileExtname=file_extname)
+        with open(filename, encoding='utf-8') as f:
+            config_file = f.read()
+        for key, value in support_templates.items():
+            regexp = r'\{\{\s*' + str(key) + r'\s*\}\}'
+            value = value.replace('\\', '/')
+            config_file = re.sub(regexp, value, config_file)
+        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
+            tmp_config_file.write(config_file)
+
+    @staticmethod
+    def _substitute_env_variables(filename: str, temp_config_name: str):
+        """Substitute environment variables in config with actual values.
+
+        Sometimes, we want to change some items in the config with environment
+        variables. For examples, we expect to change dataset root by setting
+        ``DATASET_ROOT=/dataset/root/path`` in the command line. This can be
+        easily achieved by writing lines in the config as follows
+
+        .. code-block:: python
+
+           data_root = '{{$DATASET_ROOT:/default/dataset}}/images'
+
+
+        Here, ``{{$DATASET_ROOT:/default/dataset}}`` indicates using the
+        environment variable ``DATASET_ROOT`` to replace the part between
+        ``{{}}``. If the ``DATASET_ROOT`` is not set, the default value
+        ``/default/dataset`` will be used.
+
+        Environment variables not only can replace items in the string, they
+        can also substitute other types of data in config. In this situation,
+        we can write the config as below
+
+        .. code-block:: python
+
+           model = dict(
+               bbox_head = dict(num_classes={{'$NUM_CLASSES:80'}}))
+
+
+        For details, Please refer to docs/zh_cn/tutorials/config.md .
+
+        Args:
+            filename (str): Filename of config.
+            temp_config_name (str): Temporary filename to save substituted
+                config.
+        """
+        with open(filename, encoding='utf-8') as f:
+            config_file = f.read()
+        regexp = r'\{\{[\'\"]?\s*\$(\w+)\s*\:\s*(\S*?)\s*[\'\"]?\}\}'
+        keys = re.findall(regexp, config_file)
+        env_variables = dict()
+        for var_name, value in keys:
+            regexp = r'\{\{[\'\"]?\s*\$' + var_name + r'\s*\:\s*' \
+                + value + r'\s*[\'\"]?\}\}'
+            if var_name in os.environ:
+                value = os.environ[var_name]
+                env_variables[var_name] = value
+            if not value:
+                raise KeyError(f'`{var_name}` cannot be found in `os.environ`.'
+                               f' Please set `{var_name}` in environment or '
+                               'give a default value.')
+            config_file = re.sub(regexp, value, config_file)
+
+        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
+            tmp_config_file.write(config_file)
+        return env_variables
+
+    @staticmethod
+    def _pre_substitute_base_vars(filename: str,
+                                  temp_config_name: str) -> dict:
+        """Preceding step for substituting variables in base config with actual
+        value.
+
+        Args:
+            filename (str): Filename of config.
+            temp_config_name (str): Temporary filename to save substituted
+                config.
+
+        Returns:
+            dict: A dictionary contains variables in base config.
+        """
+        with open(filename, encoding='utf-8') as f:
+            config_file = f.read()
+        base_var_dict = {}
+        regexp = r'\{\{\s*' + BASE_KEY + r'\.([\w\.]+)\s*\}\}'
+        base_vars = set(re.findall(regexp, config_file))
+        for base_var in base_vars:
+            randstr = f'_{base_var}_{uuid.uuid4().hex.lower()[:6]}'
+            base_var_dict[randstr] = base_var
+            regexp = r'\{\{\s*' + BASE_KEY + r'\.' + base_var + r'\s*\}\}'
+            config_file = re.sub(regexp, f'"{randstr}"', config_file)
+        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
+            tmp_config_file.write(config_file)
+        return base_var_dict
+
+    @staticmethod
+    def _substitute_base_vars(cfg: Any, base_var_dict: dict,
+                              base_cfg: dict) -> Any:
+        """Substitute base variables from strings to their actual values.
+
+        Args:
+            Any : Config dictionary.
+            base_var_dict (dict): A dictionary contains variables in base
+                config.
+            base_cfg (dict): Base config dictionary.
+
+        Returns:
+            Any : A dictionary with origin base variables
+                substituted with actual values.
+        """
+        cfg = copy.deepcopy(cfg)
+
+        if isinstance(cfg, dict):
+            for k, v in cfg.items():
+                if isinstance(v, str) and v in base_var_dict:
+                    new_v = base_cfg
+                    for new_k in base_var_dict[v].split('.'):
+                        new_v = new_v[new_k]
+                    cfg[k] = new_v
+                elif isinstance(v, (list, tuple, dict)):
+                    cfg[k] = Config._substitute_base_vars(
+                        v, base_var_dict, base_cfg)
+        elif isinstance(cfg, tuple):
+            cfg = tuple(
+                Config._substitute_base_vars(c, base_var_dict, base_cfg)
+                for c in cfg)
+        elif isinstance(cfg, list):
+            cfg = [
+                Config._substitute_base_vars(c, base_var_dict, base_cfg)
+                for c in cfg
+            ]
+        elif isinstance(cfg, str) and cfg in base_var_dict:
+            new_v = base_cfg
+            for new_k in base_var_dict[cfg].split('.'):
+                new_v = new_v[new_k]
+            cfg = new_v
+
+        return cfg
+
+    @staticmethod
+    def _file2dict(
+            filename: str,
+            use_predefined_variables: bool = True,
+            use_environment_variables: bool = True,
+            lazy_import: Optional[bool] = None) -> Tuple[dict, str, dict]:
+        """Transform file to variables dictionary.
+
+        Args:
+            filename (str): Name of config file.
+            use_predefined_variables (bool, optional): Whether to use
+                predefined variables. Defaults to True.
+            use_environment_variables (bool, optional): Whether to use
+                environment variables. Defaults to True.
+            lazy_import (bool): Whether to load config in `lazy_import` mode.
+                If it is `None`, it will be deduced by the content of the
+                config file. Defaults to None.
+
+        Returns:
+            Tuple[dict, str]: Variables dictionary and text of Config.
+        """
+        if lazy_import is None and Config._is_lazy_import(filename):
+            raise RuntimeError(
+                'The configuration file type in the inheritance chain '
+                'must match the current configuration file type, either '
+                '"lazy_import" or non-"lazy_import". You got this error '
+                'since you use the syntax like `with read_base(): ...` '
+                f'or import non-builtin module in {filename}.'  # noqa: E501
+            )
+
+        filename = osp.abspath(osp.expanduser(filename))
+        check_file_exist(filename)
+        fileExtname = osp.splitext(filename)[1]
+        if fileExtname not in ['.py', '.json', '.yaml', '.yml']:
+            raise OSError('Only py/yml/yaml/json type are supported now!')
+        try:
+            with tempfile.TemporaryDirectory() as temp_config_dir:
+                temp_config_file = tempfile.NamedTemporaryFile(
+                    dir=temp_config_dir, suffix=fileExtname, delete=False)
+                if platform.system() == 'Windows':
+                    temp_config_file.close()
+
+                # Substitute predefined variables
+                if use_predefined_variables:
+                    Config._substitute_predefined_vars(filename,
+                                                       temp_config_file.name)
+                else:
+                    shutil.copyfile(filename, temp_config_file.name)
+                # Substitute environment variables
+                env_variables = dict()
+                if use_environment_variables:
+                    env_variables = Config._substitute_env_variables(
+                        temp_config_file.name, temp_config_file.name)
+                # Substitute base variables from placeholders to strings
+                base_var_dict = Config._pre_substitute_base_vars(
+                    temp_config_file.name, temp_config_file.name)
+
+                # Handle base files
+                base_cfg_dict = ConfigDict()
+                cfg_text_list = list()
+                for base_cfg_path in Config._get_base_files(
+                        temp_config_file.name):
+                    base_cfg_path, scope = Config._get_cfg_path(
+                        base_cfg_path, filename)
+                    _cfg_dict, _cfg_text, _env_variables = Config._file2dict(
+                        filename=base_cfg_path,
+                        use_predefined_variables=use_predefined_variables,
+                        use_environment_variables=use_environment_variables,
+                        lazy_import=lazy_import,
+                    )
+                    cfg_text_list.append(_cfg_text)
+                    env_variables.update(_env_variables)
+                    duplicate_keys = base_cfg_dict.keys() & _cfg_dict.keys()
+                    if len(duplicate_keys) > 0:
+                        raise KeyError(
+                            'Duplicate key is not allowed among bases. '
+                            f'Duplicate keys: {duplicate_keys}')
+
+                    # _dict_to_config_dict will do the following things:
+                    # 1. Recursively converts ``dict`` to :obj:`ConfigDict`.
+                    # 2. Set `_scope_` for the outer dict variable for the base
+                    # config.
+                    # 3. Set `scope` attribute for each base variable.
+                    # Different from `_scope_`, `scope` is not a key of base
+                    # dict, `scope` attribute will be parsed to key `_scope_`
+                    # by function `_parse_scope` only if the base variable is
+                    # accessed by the current config.
+                    _cfg_dict = Config._dict_to_config_dict(_cfg_dict, scope)
+                    base_cfg_dict.update(_cfg_dict)
+
+                if filename.endswith('.py'):
+                    with open(temp_config_file.name, encoding='utf-8') as f:
+                        parsed_codes = ast.parse(f.read())
+                        parsed_codes = RemoveAssignFromAST(BASE_KEY).visit(
+                            parsed_codes)
+                    codeobj = compile(parsed_codes, filename, mode='exec')
+                    # Support load global variable in nested function of the
+                    # config.
+                    global_locals_var = {BASE_KEY: base_cfg_dict}
+                    ori_keys = set(global_locals_var.keys())
+                    eval(codeobj, global_locals_var, global_locals_var)
+                    cfg_dict = {
+                        key: value
+                        for key, value in global_locals_var.items()
+                        if (key not in ori_keys and not key.startswith('__'))
+                    }
+                elif filename.endswith(('.yml', '.yaml', '.json')):
+                    cfg = OmegaConf.load(temp_config_file.name)
+                    cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+                # close temp file
+                for key, value in list(cfg_dict.items()):
+                    if isinstance(value,
+                                  (types.FunctionType, types.ModuleType)):
+                        cfg_dict.pop(key)
+                temp_config_file.close()
+
+                # If the current config accesses a base variable of base
+                # configs, The ``scope`` attribute of corresponding variable
+                # will be converted to the `_scope_`.
+                Config._parse_scope(cfg_dict)
+        except Exception as e:
+            if osp.exists(temp_config_dir):
+                shutil.rmtree(temp_config_dir)
+            raise e
+
+        # check deprecation information
+        if DEPRECATION_KEY in cfg_dict:
+            deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
+            warning_msg = f'The config file {filename} will be deprecated ' \
+                'in the future.'
+            if 'expected' in deprecation_info:
+                warning_msg += f' Please use {deprecation_info["expected"]} ' \
+                    'instead.'
+            if 'reference' in deprecation_info:
+                warning_msg += ' More information can be found at ' \
+                    f'{deprecation_info["reference"]}'
+            warnings.warn(warning_msg, DeprecationWarning)
+
+        cfg_text = filename + '\n'
+        with open(filename, encoding='utf-8') as f:
+            # Setting encoding explicitly to resolve coding issue on windows
+            cfg_text += f.read()
+
+        # Substitute base variables from strings to their actual values
+        cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict,
+                                                base_cfg_dict)
+        cfg_dict.pop(BASE_KEY, None)
+
+        cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
+        cfg_dict = {
+            k: v
+            for k, v in cfg_dict.items() if not k.startswith('__')
+        }
+
+        # merge cfg_text
+        cfg_text_list.append(cfg_text)
+        cfg_text = '\n'.join(cfg_text_list)
+
+        return cfg_dict, cfg_text, env_variables
+
+    @staticmethod
+    def _parse_lazy_import(filename: str) -> Tuple[ConfigDict, set]:
+        """Transform file to variables dictionary.
+
+        Args:
+            filename (str): Name of config file.
+
+        Returns:
+            Tuple[dict, dict]: ``cfg_dict`` and ``imported_names``.
+
+              - cfg_dict (dict): Variables dictionary of parsed config.
+              - imported_names (set): Used to mark the names of
+                imported object.
+        """
+        # In lazy import mode, users can use the Python syntax `import` to
+        # implement inheritance between configuration files, which is easier
+        # for users to understand the hierarchical relationships between
+        # different configuration files.
+
+        # Besides, users can also using `import` syntax to import corresponding
+        # module which will be filled in the `type` field. It means users
+        # can directly navigate to the source of the module in the
+        # configuration file by clicking the `type` field.
+
+        # To avoid really importing the third party package like `torch`
+        # during import `type` object, we use `_parse_lazy_import` to parse the
+        # configuration file, which will not actually trigger the import
+        # process, but simply parse the imported `type`s as LazyObject objects.
+
+        # The overall pipeline of _parse_lazy_import is:
+        # 1. Parse the base module from the config file.
+        #                       ||
+        #                       \/
+        #       base_module = ['mmdet.configs.default_runtime']
+        #                       ||
+        #                       \/
+        # 2. recursively parse the base module and gather imported objects to
+        #    a dict.
+        #                       ||
+        #                       \/
+        #       The base_dict will be:
+        #       {
+        #           'mmdet.configs.default_runtime': {...}
+        #           'mmdet.configs.retinanet_r50_fpn_1x_coco': {...}
+        #           ...
+        #       }, each item in base_dict is a dict of `LazyObject`
+        # 3. parse the current config file filling the imported variable
+        #    with the base_dict.
+        #
+        # 4. During the parsing process, all imported variable will be
+        #    recorded in the `imported_names` set. These variables can be
+        #    accessed, but will not be dumped by default.
+
+        with open(filename, encoding='utf-8') as f:
+            global_dict = {'LazyObject': LazyObject, '__file__': filename}
+            base_dict = {}
+
+            parsed_codes = ast.parse(f.read())
+            # get the names of base modules, and remove the
+            # `with read_base():'` statement
+            base_modules = Config._get_base_modules(parsed_codes.body)
+            base_imported_names = set()
+            for base_module in base_modules:
+                # If base_module means a relative import, assuming the level is
+                # 2, which means the module is imported like
+                # "from ..a.b import c". we must ensure that c is an
+                # object `defined` in module b, and module b should not be a
+                # package including `__init__` file but a single python file.
+                level = len(re.match(r'\.*', base_module).group())
+                if level > 0:
+                    # Relative import
+                    base_dir = osp.dirname(filename)
+                    module_path = osp.join(
+                        base_dir, *(['..'] * (level - 1)),
+                        f'{base_module[level:].replace(".", "/")}.py')
+                else:
+                    # Absolute import
+                    module_list = base_module.split('.')
+                    if len(module_list) == 1:
+                        raise SyntaxError(
+                            'The imported configuration file should not be '
+                            f'an independent package {module_list[0]}. Here '
+                            'is an example: '
+                            '`with read_base(): from mmdet.configs.retinanet_r50_fpn_1x_coco import *`'  # noqa: E501
+                        )
+                    else:
+                        package = module_list[0]
+                        root_path = get_installed_path(package)
+                        module_path = f'{osp.join(root_path, *module_list[1:])}.py'  # noqa: E501
+                if not osp.isfile(module_path):
+                    raise SyntaxError(
+                        f'{module_path} not found! It means that incorrect '
+                        'module is defined in '
+                        f'`with read_base(): = from {base_module} import ...`, please '  # noqa: E501
+                        'make sure the base config module is valid '
+                        'and is consistent with the prior import '
+                        'logic')
+                _base_cfg_dict, _base_imported_names = Config._parse_lazy_import(  # noqa: E501
+                    module_path)
+                base_imported_names |= _base_imported_names
+                # The base_dict will be:
+                # {
+                #     'mmdet.configs.default_runtime': {...}
+                #     'mmdet.configs.retinanet_r50_fpn_1x_coco': {...}
+                #     ...
+                # }
+                base_dict[base_module] = _base_cfg_dict
+
+            # `base_dict` contains all the imported modules from `base_cfg`.
+            # In order to collect the specific imported module from `base_cfg`
+            # before parse the current file, we using AST Transform to
+            # transverse the imported module from base_cfg and merge then into
+            # the global dict. After the ast transformation, most of import
+            # syntax will be removed (except for the builtin import) and
+            # replaced with the `LazyObject`
+            transform = ImportTransformer(
+                global_dict=global_dict,
+                base_dict=base_dict,
+                filename=filename)
+            modified_code = transform.visit(parsed_codes)
+            modified_code, abs_imported = _gather_abs_import_lazyobj(
+                modified_code, filename=filename)
+            imported_names = transform.imported_obj | abs_imported
+            imported_names |= base_imported_names
+            modified_code = ast.fix_missing_locations(modified_code)
+            exec(
+                compile(modified_code, filename, mode='exec'), global_dict,
+                global_dict)
+
+            ret: dict = {}
+            for key, value in global_dict.items():
+                if key.startswith('__') or key in ['LazyObject']:
+                    continue
+                ret[key] = value
+            # convert dict to ConfigDict
+            cfg_dict = Config._dict_to_config_dict_lazy(ret)
+
+            return cfg_dict, imported_names
+
+    @staticmethod
+    def _dict_to_config_dict_lazy(cfg: dict):
+        """Recursively converts ``dict`` to :obj:`ConfigDict`. The only
+        difference between ``_dict_to_config_dict_lazy`` and
+        ``_dict_to_config_dict_lazy`` is that the former one does not consider
+        the scope, and will not trigger the building of ``LazyObject``.
+
+        Args:
+            cfg (dict): Config dict.
+
+        Returns:
+            ConfigDict: Converted dict.
+        """
+        # Only the outer dict with key `type` should have the key `_scope_`.
+        if isinstance(cfg, dict):
+            cfg_dict = ConfigDict()
+            for key, value in cfg.items():
+                cfg_dict[key] = Config._dict_to_config_dict_lazy(value)
+            return cfg_dict
+        if isinstance(cfg, (tuple, list)):
+            return type(cfg)(
+                Config._dict_to_config_dict_lazy(_cfg) for _cfg in cfg)
+        return cfg
+
+    @staticmethod
+    def _dict_to_config_dict(cfg: dict,
+                             scope: Optional[str] = None,
+                             has_scope=True):
+        """Recursively converts ``dict`` to :obj:`ConfigDict`.
+
+        Args:
+            cfg (dict): Config dict.
+            scope (str, optional): Scope of instance.
+            has_scope (bool): Whether to add `_scope_` key to config dict.
+
+        Returns:
+            ConfigDict: Converted dict.
+        """
+        # Only the outer dict with key `type` should have the key `_scope_`.
+        if isinstance(cfg, dict):
+            if has_scope and 'type' in cfg:
+                has_scope = False
+                if scope is not None and cfg.get('_scope_', None) is None:
+                    cfg._scope_ = scope  # type: ignore
+            cfg = ConfigDict(cfg)
+            dict.__setattr__(cfg, 'scope', scope)
+            for key, value in cfg.items():
+                cfg[key] = Config._dict_to_config_dict(
+                    value, scope=scope, has_scope=has_scope)
+        elif isinstance(cfg, tuple):
+            cfg = tuple(
+                Config._dict_to_config_dict(_cfg, scope, has_scope=has_scope)
+                for _cfg in cfg)
+        elif isinstance(cfg, list):
+            cfg = [
+                Config._dict_to_config_dict(_cfg, scope, has_scope=has_scope)
+                for _cfg in cfg
+            ]
+        return cfg
+
+    @staticmethod
+    def _parse_scope(cfg: dict) -> None:
+        """Adds ``_scope_`` to :obj:`ConfigDict` instance, which means a base
+        variable.
+
+        If the config dict already has the scope, scope will not be
+        overwritten.
+
+        Args:
+            cfg (dict): Config needs to be parsed with scope.
+        """
+        if isinstance(cfg, ConfigDict):
+            cfg._scope_ = cfg.scope
+        elif isinstance(cfg, (tuple, list)):
+            [Config._parse_scope(value) for value in cfg]
+        else:
+            return
+
+    @staticmethod
+    def _get_base_files(filename: str) -> list:
+        """Get the base config file.
+
+        Args:
+            filename (str): The config file.
+
+        Raises:
+            TypeError: Name of config file.
+
+        Returns:
+            list: A list of base config.
+        """
+        file_format = osp.splitext(filename)[1]
+        if file_format == '.py':
+            Config._validate_py_syntax(filename)
+            with open(filename, encoding='utf-8') as f:
+                parsed_codes = ast.parse(f.read()).body
+
+                def is_base_line(c):
+                    return (isinstance(c, ast.Assign)
+                            and isinstance(c.targets[0], ast.Name)
+                            and c.targets[0].id == BASE_KEY)
+
+                base_code = next((c for c in parsed_codes if is_base_line(c)),
+                                 None)
+                if base_code is not None:
+                    base_code = ast.Expression(  # type: ignore
+                        body=base_code.value)  # type: ignore
+                    base_files = eval(compile(base_code, '',
+                                              mode='eval'))  # type: ignore
+                else:
+                    base_files = []
+        elif file_format in ('.yml', '.yaml', '.json'):
+            cfg = OmegaConf.load(filename)
+            cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+            base_files = cfg_dict.get(BASE_KEY, [])
+        else:
+            raise SyntaxError(
+                'The config type should be py, json, yaml or '
+                f'yml, but got {file_format}')
+        base_files = base_files if isinstance(base_files,
+                                              list) else [base_files]
+        return base_files
+
+    @staticmethod
+    def _get_cfg_path(cfg_path: str,
+                      filename: str) -> Tuple[str, Optional[str]]:
+        """Get the config path from the current or external package.
+
+        Args:
+            cfg_path (str): Relative path of config.
+            filename (str): The config file being parsed.
+
+        Returns:
+            Tuple[str, str or None]: Path and scope of config. If the config
+            is not an external config, the scope will be `None`.
+        """
+        if '::' in cfg_path:
+            # `cfg_path` startswith '::' means an external config path.
+            # Get package name and relative config path.
+            scope = cfg_path.partition('::')[0]
+            package, cfg_path = _get_package_and_cfg_path(cfg_path)
+
+            if not is_installed(package):
+                raise ModuleNotFoundError(
+                    f'{package} is not installed, please install {package} '
+                    f'manually')
+
+            # Get installed package path.
+            package_path = get_installed_path(package)
+            try:
+                # Get config path from meta file.
+                cfg_path = _get_external_cfg_path(package_path, cfg_path)
+            except ValueError:
+                # Since base config does not have a metafile, it should be
+                # concatenated with package path and relative config path.
+                cfg_path = _get_external_cfg_base_path(package_path, cfg_path)
+            except FileNotFoundError as e:
+                raise e
+            return cfg_path, scope
+        else:
+            # Get local config path.
+            cfg_dir = osp.dirname(filename)
+            cfg_path = osp.join(cfg_dir, cfg_path)
+            return cfg_path, None
+
+    @staticmethod
+    def _merge_a_into_b(a: dict,
+                        b: dict,
+                        allow_list_keys: bool = False) -> dict:
+        """Merge dict ``a`` into dict ``b`` (non-inplace).
+
+        Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
+        in-place modifications.
+
+        Args:
+            a (dict): The source dict to be merged into ``b``.
+            b (dict): The origin dict to be fetch keys from ``a``.
+            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
+              are allowed in source ``a`` and will replace the element of the
+              corresponding index in b if b is a list. Defaults to False.
+
+        Returns:
+            dict: The modified dict of ``b`` using ``a``.
+
+        Examples:
+            # Normally merge a into b.
+            >>> Config._merge_a_into_b(
+            ...     dict(obj=dict(a=2)), dict(obj=dict(a=1)))
+            {'obj': {'a': 2}}
+
+            # Delete b first and merge a into b.
+            >>> Config._merge_a_into_b(
+            ...     dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
+            {'obj': {'a': 2}}
+
+            # b is a list
+            >>> Config._merge_a_into_b(
+            ...     {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
+            [{'a': 2}, {'b': 2}]
+        """
+        b = b.copy()
+        for k, v in a.items():
+            if allow_list_keys and k.isdigit() and isinstance(b, list):
+                k = int(k)
+                if len(b) <= k:
+                    raise KeyError(f'Index {k} exceeds the length of list {b}')
+                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+            elif isinstance(v, dict):
+                if k in b and not v.pop(DELETE_KEY, False):
+                    allowed_types: Union[Tuple, type] = (
+                        dict, list) if allow_list_keys else dict
+                    if not isinstance(b[k], allowed_types):
+                        raise TypeError(
+                            f'{k}={v} in child config cannot inherit from '
+                            f'base because {k} is a dict in the child config '
+                            f'but is of type {type(b[k])} in base config. '
+                            f'You may set `{DELETE_KEY}=True` to ignore the '
+                            f'base config.')
+                    b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+                else:
+                    b[k] = ConfigDict(v)
+            else:
+                b[k] = v
+        return b
+
+    @property
+    def filename(self) -> str:
+        """Get file name of config."""
+        return self._filename
+
+    @property
+    def text(self) -> str:
+        """Get config text."""
+        return self._text
+
+    @property
+    def env_variables(self) -> dict:
+        """Get used environment variables."""
+        return self._env_variables
+
+    @property
+    def pretty_text(self) -> str:
+        """Get formatted python config text."""
+
+        indent = 4
+
+        def _indent(s_, num_spaces):
+            s = s_.split('\n')
+            if len(s) == 1:
+                return s_
+            first = s.pop(0)
+            s = [(num_spaces * ' ') + line for line in s]
+            s = '\n'.join(s)
+            s = first + '\n' + s
+            return s
+
+        def _format_basic_types(k, v, use_mapping=False):
+            if isinstance(v, str):
+                v_str = repr(v)
+            else:
+                v_str = str(v)
+
+            if use_mapping:
+                k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                attr_str = f'{k_str}: {v_str}'
+            else:
+                attr_str = f'{str(k)}={v_str}'
+            attr_str = _indent(attr_str, indent)
+
+            return attr_str
+
+        def _format_list_tuple(k, v, use_mapping=False):
+            if isinstance(v, list):
+                left = '['
+                right = ']'
+            else:
+                left = '('
+                right = ')'
+
+            v_str = f'{left}\n'
+            # check if all items in the list are dict
+            for item in v:
+                if isinstance(item, dict):
+                    v_str += f'dict({_indent(_format_dict(item), indent)}),\n'
+                elif isinstance(item, tuple):
+                    v_str += f'{_indent(_format_list_tuple(None, item), indent)},\n'  # noqa: 501
+                elif isinstance(item, list):
+                    v_str += f'{_indent(_format_list_tuple(None, item), indent)},\n'  # noqa: 501
+                elif isinstance(item, str):
+                    v_str += f'{_indent(repr(item), indent)},\n'
+                else:
+                    v_str += str(item) + ',\n'
+            if k is None:
+                return _indent(v_str, indent) + right
+            if use_mapping:
+                k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                attr_str = f'{k_str}: {v_str}'
+            else:
+                attr_str = f'{str(k)}={v_str}'
+            attr_str = _indent(attr_str, indent) + right
+            return attr_str
+
+        def _contain_invalid_identifier(dict_str):
+            contain_invalid_identifier = False
+            for key_name in dict_str:
+                contain_invalid_identifier |= \
+                    (not str(key_name).isidentifier())
+            return contain_invalid_identifier
+
+        def _format_dict(input_dict, outest_level=False):
+            r = ''
+            s = []
+
+            use_mapping = _contain_invalid_identifier(input_dict)
+            if use_mapping:
+                r += '{'
+            for idx, (k, v) in enumerate(
+                    sorted(input_dict.items(), key=lambda x: str(x[0]))):
+                is_last = idx >= len(input_dict) - 1
+                end = '' if outest_level or is_last else ','
+                if isinstance(v, dict):
+                    v_str = '\n' + _format_dict(v)
+                    if use_mapping:
+                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                        attr_str = f'{k_str}: dict({v_str}'
+                    else:
+                        attr_str = f'{str(k)}=dict({v_str}'
+                    attr_str = _indent(attr_str, indent) + ')' + end
+                elif isinstance(v, (list, tuple)):
+                    attr_str = _format_list_tuple(k, v, use_mapping) + end
+                else:
+                    attr_str = _format_basic_types(k, v, use_mapping) + end
+
+                s.append(attr_str)
+            r += '\n'.join(s)
+            if use_mapping:
+                r += '}'
+            return r
+
+        cfg_dict = self.to_dict()
+        text = _format_dict(cfg_dict, outest_level=True)
+        if self._format_python_code:
+            # copied from setup.cfg
+            yapf_style = dict(
+                based_on_style='pep8',
+                blank_line_before_nested_class_or_def=True,
+                split_before_expression_after_opening_paren=True)
+            try:
+                from ...utils import digit_version
+                if digit_version(yapf.__version__) >= digit_version('0.40.2'):
+                    text, _ = FormatCode(text, style_config=yapf_style)
+                else:
+                    text, _ = FormatCode(
+                        text, style_config=yapf_style, verify=True)
+            except:  # noqa: E722
+                raise SyntaxError('Failed to format the config file, please '
+                                  f'check the syntax of: \n{text}')
+        return text
+
+    def __repr__(self):
+        return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}'
+
+    def __len__(self):
+        return len(self._cfg_dict)
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._cfg_dict, name)
+
+    def __getitem__(self, name):
+        return self._cfg_dict.__getitem__(name)
+
+    def __setattr__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setitem__(name, value)
+
+    def __iter__(self):
+        return iter(self._cfg_dict)
+
+    def __getstate__(
+            self
+    ) -> Tuple[dict, Optional[str], Optional[str], dict, bool, set]:
+        state = (self._cfg_dict, self._filename, self._text,
+                 self._env_variables, self._format_python_code,
+                 self._imported_names)
+        return state
+
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        other = cls.__new__(cls)
+        memo[id(self)] = other
+
+        for key, value in self.__dict__.items():
+            super(Config, other).__setattr__(key, copy.deepcopy(value, memo))
+
+        return other
+
+    def __copy__(self):
+        cls = self.__class__
+        other = cls.__new__(cls)
+        other.__dict__.update(self.__dict__)
+        super(Config, other).__setattr__('_cfg_dict', self._cfg_dict.copy())
+
+        return other
+
+    copy = __copy__
+
+    def __setstate__(self, state: Tuple[dict, Optional[str], Optional[str],
+                                        dict, bool, set]):
+        super().__setattr__('_cfg_dict', state[0])
+        super().__setattr__('_filename', state[1])
+        super().__setattr__('_text', state[2])
+        super().__setattr__('_env_variables', state[3])
+        super().__setattr__('_format_python_code', state[4])
+        super().__setattr__('_imported_names', state[5])
+
+    def dump(self, file: Optional[Union[str, Path]] = None):
+        """Dump config to file or return config text.
+
+        Args:
+            file (str or Path, optional): If not specified, then the object
+            is dumped to a str, otherwise to a file specified by the filename.
+            Defaults to None.
+
+        Returns:
+            str or None: Config text.
+        """
+        file = str(file) if isinstance(file, Path) else file
+        cfg_dict = self.to_dict()
+        if file is None:
+            if self.filename is None or self.filename.endswith('.py'):
+                return self.pretty_text
+            else:
+                file_format = self.filename.split('.')[-1]
+                return dump(cfg_dict, file_format=file_format)
+        elif file.endswith('.py'):
+            with open(file, 'w', encoding='utf-8') as f:
+                f.write(self.pretty_text)
+        else:
+            file_format = file.split('.')[-1]
+            return dump(cfg_dict, file=file, file_format=file_format)
+
+    @staticmethod
+    def _is_lazy_import(filename: str) -> bool:
+        if not filename.endswith('.py'):
+            return False
+        with open(filename, encoding='utf-8') as f:
+            codes_str = f.read()
+            parsed_codes = ast.parse(codes_str)
+        for node in ast.walk(parsed_codes):
+            if (isinstance(node, ast.Assign)
+                    and isinstance(node.targets[0], ast.Name)
+                    and node.targets[0].id == BASE_KEY):
+                return False
+
+            if isinstance(node, ast.With):
+                expr = node.items[0].context_expr
+                if (not isinstance(expr, ast.Call)
+                        or not expr.func.id == 'read_base'):  # type: ignore
+                    raise SyntaxError(
+                        'Only `read_base` context manager can be used in the '
+                        'config')
+                return True
+            if isinstance(node, ast.ImportFrom):
+                # relative import -> lazy_import
+                if node.level != 0:
+                    return True
+                # Skip checking when using `mmengine.config` in cfg file
+                if (node.module == 'mmengine' and len(node.names) == 1
+                        and node.names[0].name == 'Config'):
+                    continue
+                if not isinstance(node.module, str):
+                    continue
+                # non-builtin module -> lazy_import
+                if not _is_builtin_module(node.module):
+                    return True
+            if isinstance(node, ast.Import):
+                for alias_node in node.names:
+                    if not _is_builtin_module(alias_node.name):
+                        return True
+        return False
+
+    def _to_lazy_dict(self, keep_imported: bool = False) -> dict:
+        """Convert config object to dictionary with lazy object, and filter the
+        imported object."""
+        res = self._cfg_dict._to_lazy_dict()
+        if hasattr(self, '_imported_names') and not keep_imported:
+            res = {
+                key: value
+                for key, value in res.items()
+                if key not in self._imported_names
+            }
+        return res
+
+    def to_dict(self, keep_imported: bool = False):
+        """Convert all data in the config to a builtin ``dict``.
+
+        Args:
+            keep_imported (bool): Whether to keep the imported field.
+                Defaults to False
+
+        If you import third-party objects in the config file, all imported
+        objects will be converted to a string like ``torch.optim.SGD``
+        """
+        cfg_dict = self._cfg_dict.to_dict()
+        if hasattr(self, '_imported_names') and not keep_imported:
+            cfg_dict = {
+                key: value
+                for key, value in cfg_dict.items()
+                if key not in self._imported_names
+            }
+        return cfg_dict
\ No newline at end of file
diff --git a/segformer_plusplus/configs/config/lazy.py b/segformer_plusplus/configs/config/lazy.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a3e85baea611fce22b5c3cb01aeecbad1eebe90
--- /dev/null
+++ b/segformer_plusplus/configs/config/lazy.py
@@ -0,0 +1,267 @@
+import importlib
+from typing import Any, Optional, Union, Type
+from collections import abc
+
+
+class LazyObject:
+    """LazyObject is used to lazily initialize the imported module during
+    parsing the configuration file.
+
+    During parsing process, the syntax like:
+
+    Examples:
+        >>> import torch.nn as nn
+        >>> from mmdet.models import RetinaNet
+        >>> import mmcls.models
+        >>> import mmcls.datasets
+        >>> import mmcls
+
+    Will be parsed as:
+
+    Examples:
+        >>> # import torch.nn as nn
+        >>> nn = lazyObject('torch.nn')
+        >>> # from mmdet.models import RetinaNet
+        >>> RetinaNet = lazyObject('mmdet.models', 'RetinaNet')
+        >>> # import mmcls.models; import mmcls.datasets; import mmcls
+        >>> mmcls = lazyObject(['mmcls', 'mmcls.datasets', 'mmcls.models'])
+
+    ``LazyObject`` records all module information and will be further
+    referenced by the configuration file.
+
+    Args:
+        module (str or list or tuple): The module name to be imported.
+        imported (str, optional): The imported module name. Defaults to None.
+        location (str, optional): The filename and line number of the imported
+            module statement happened.
+    """
+
+    def __init__(self,
+                 module: Union[str, list, tuple],
+                 imported: Optional[str] = None,
+                 location: Optional[str] = None):
+        if not isinstance(module, str) and not is_seq_of(module, str):
+            raise TypeError('module should be `str`, `list`, or `tuple`'
+                            f'but got {type(module)}, this might be '
+                            'a bug, please report it')
+        self._module: Union[str, list, tuple] = module
+
+        if not isinstance(imported, str) and imported is not None:
+            raise TypeError('imported should be `str` or None, but got '
+                            f'{type(imported)}, this might be '
+                            'a bug , please report it')
+        self._imported = imported
+        self.location = location
+
+    def build(self) -> Any:
+        """Return imported object.
+
+        Returns:
+            Any: Imported object
+        """
+        if isinstance(self._module, str):
+            try:
+                module = importlib.import_module(self._module)
+            except Exception as e:
+                raise type(e)(f'Failed to import {self._module} '
+                              f'in {self.location} for {e}')
+
+            if self._imported is not None:
+                if hasattr(module, self._imported):
+                    module = getattr(module, self._imported)
+                else:
+                    raise ImportError(
+                        f'Failed to import {self._imported} '
+                        f'from {self._module} in {self.location}')
+
+            return module
+        else:
+            try:
+                for module in self._module:
+                    importlib.import_module(module)  # type: ignore
+                module_name = self._module[0].split('.')[0]
+                return importlib.import_module(module_name)
+            except Exception as e:
+                raise type(e)(f'Failed to import {self.module} '
+                              f'in {self.location} for {e}')
+
+    @property
+    def module(self):
+        if isinstance(self._module, str):
+            return self._module
+        return self._module[0].split('.')[0]
+
+    def __call__(self, *args, **kwargs):
+        raise RuntimeError()
+
+    def __deepcopy__(self, memo):
+        return LazyObject(self._module, self._imported, self.location)
+
+    def __getattr__(self, name):
+        # Cannot locate the line number of the getting attribute.
+        # Therefore only record the filename.
+        if self.location is not None:
+            location = self.location.split(', line')[0]
+        else:
+            location = self.location
+        return LazyAttr(name, self, location)
+
+    def __str__(self) -> str:
+        if self._imported is not None:
+            return self._imported
+        return self.module
+
+    __repr__ = __str__
+
+    # `pickle.dump` will try to get the `__getstate__` and `__setstate__`
+    # methods of the dumped object. If these two methods are not defined,
+    # LazyObject will return a `__getstate__` LazyObject` or `__setstate__`
+    # LazyObject.
+    def __getstate__(self):
+        return self.__dict__
+
+    def __setstate__(self, state):
+        self.__dict__ = state
+
+
+class LazyAttr:
+    """The attribute of the LazyObject.
+
+    When parsing the configuration file, the imported syntax will be
+    parsed as the assignment ``LazyObject``. During the subsequent parsing
+    process, users may reference the attributes of the LazyObject.
+    To ensure that these attributes also contain information needed to
+    reconstruct the attribute itself, LazyAttr was introduced.
+
+    Examples:
+        >>> models = LazyObject(['mmdet.models'])
+        >>> model = dict(type=models.RetinaNet)
+        >>> print(type(model['type']))  # <class 'mmengine.config.lazy.LazyAttr'>
+        >>> print(model['type'].build())  # <class 'mmdet.models.detectors.retinanet.RetinaNet'>
+    """  # noqa: E501
+
+    def __init__(self,
+                 name: str,
+                 source: Union['LazyObject', 'LazyAttr'],
+                 location=None):
+        self.name = name
+        self.source: Union[LazyAttr, LazyObject] = source
+
+        if isinstance(self.source, LazyObject):
+            if isinstance(self.source._module, str):
+                if self.source._imported is None:
+                    # source code:
+                    # from xxx.yyy import zzz
+                    # equivalent code:
+                    # zzz = LazyObject('xxx.yyy', 'zzz')
+                    # The source code of get attribute:
+                    # eee = zzz.eee
+                    # Then, `eee._module` should be "xxx.yyy.zzz"
+                    self._module = self.source._module
+                else:
+                    # source code:
+                    # import xxx.yyy as zzz
+                    # equivalent code:
+                    # zzz = LazyObject('xxx.yyy')
+                    # The source code of get attribute:
+                    # eee = zzz.eee
+                    # Then, `eee._module` should be "xxx.yyy"
+                    self._module = f'{self.source._module}.{self.source}'
+            else:
+                # The source code of LazyObject should be
+                # 1. import xxx.yyy
+                # 2. import xxx.zzz
+                # Equivalent to
+                # xxx = LazyObject(['xxx.yyy', 'xxx.zzz'])
+
+                # The source code of LazyAttr should be
+                # eee = xxx.eee
+                # Then, eee._module = xxx
+                self._module = str(self.source)
+        elif isinstance(self.source, LazyAttr):
+            # 1. import xxx
+            # 2. zzz = xxx.yyy.zzz
+
+            # Equivalent to:
+            # xxx = LazyObject('xxx')
+            # zzz = xxx.yyy.zzz
+            # zzz._module = xxx.yyy._module + zzz.name
+            self._module = f'{self.source._module}.{self.source.name}'
+        self.location = location
+
+    @property
+    def module(self):
+        return self._module
+
+    def __call__(self, *args, **kwargs: Any) -> Any:
+        raise RuntimeError()
+
+    def __getattr__(self, name: str) -> 'LazyAttr':
+        return LazyAttr(name, self)
+
+    def __deepcopy__(self, memo):
+        return LazyAttr(self.name, self.source)
+
+    def build(self) -> Any:
+        """Return the attribute of the imported object.
+
+        Returns:
+            Any: attribute of the imported object.
+        """
+        obj = self.source.build()
+        try:
+            return getattr(obj, self.name)
+        except AttributeError:
+            raise ImportError(f'Failed to import {self.module}.{self.name} in '
+                              f'{self.location}')
+        except ImportError as e:
+            raise e
+
+    def __str__(self) -> str:
+        return self.name
+
+    __repr__ = __str__
+
+    # `pickle.dump` will try to get the `__getstate__` and `__setstate__`
+    # methods of the dumped object. If these two methods are not defined,
+    # LazyAttr will return a `__getstate__` LazyAttr` or `__setstate__`
+    # LazyAttr.
+    def __getstate__(self):
+        return self.__dict__
+
+    def __setstate__(self, state):
+        self.__dict__ = state
+
+
+def is_seq_of(seq: Any,
+              expected_type: Union[Type, tuple],
+              seq_type: Optional[Type] = None) -> bool:
+    """Check whether it is a sequence of some type.
+
+    Args:
+        seq (Sequence): The sequence to be checked.
+        expected_type (type or tuple): Expected type of sequence items.
+        seq_type (type, optional): Expected sequence type. Defaults to None.
+
+    Returns:
+        bool: Return True if ``seq`` is valid else False.
+
+    Examples:
+        >>> from mmengine.utils import is_seq_of
+        >>> seq = ['a', 'b', 'c']
+        >>> is_seq_of(seq, str)
+        True
+        >>> is_seq_of(seq, int)
+        False
+    """
+    if seq_type is None:
+        exp_seq_type = abc.Sequence
+    else:
+        assert isinstance(seq_type, type)
+        exp_seq_type = seq_type
+    if not isinstance(seq, exp_seq_type):
+        return False
+    for item in seq:
+        if not isinstance(item, expected_type):
+            return False
+    return True
\ No newline at end of file
diff --git a/segformer_plusplus/configs/config/utils.py b/segformer_plusplus/configs/config/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b65bbdb1c058bb7e7f1c6c62e4d9eb869b11521d
--- /dev/null
+++ b/segformer_plusplus/configs/config/utils.py
@@ -0,0 +1,647 @@
+import ast
+import os.path as osp
+import re
+import sys
+import warnings
+from collections import defaultdict
+from importlib.util import find_spec
+from typing import List, Optional, Tuple, Union
+from importlib import import_module as real_import_module
+import json
+import pickle
+from pathlib import Path
+from mim.utils import package2module
+
+import yaml
+from omegaconf import OmegaConf
+
+
+PYTHON_ROOT_DIR = osp.dirname(osp.dirname(sys.executable))
+SYSTEM_PYTHON_PREFIX = '/usr/lib/python'
+
+MODULE2PACKAGE = {
+    'mmcls': 'mmcls',
+    'mmdet': 'mmdet',
+    'mmdet3d': 'mmdet3d',
+    'mmseg': 'mmsegmentation',
+    'mmaction': 'mmaction2',
+    'mmtrack': 'mmtrack',
+    'mmpose': 'mmpose',
+    'mmedit': 'mmedit',
+    'mmocr': 'mmocr',
+    'mmgen': 'mmgen',
+    'mmfewshot': 'mmfewshot',
+    'mmrazor': 'mmrazor',
+    'mmflow': 'mmflow',
+    'mmhuman3d': 'mmhuman3d',
+    'mmrotate': 'mmrotate',
+    'mmselfsup': 'mmselfsup',
+    'mmyolo': 'mmyolo',
+    'mmpretrain': 'mmpretrain',
+    'mmagic': 'mmagic',
+}
+
+# PKG2PROJECT is not a proper name to represent the mapping between module name
+# (module import from) and package name (used by pip install). Therefore,
+# PKG2PROJECT will be deprecated and this alias will only be kept until
+# MMEngine v1.0.0
+PKG2PROJECT = MODULE2PACKAGE
+
+
+class ConfigParsingError(RuntimeError):
+    """Raise error when failed to parse pure Python style config files."""
+
+
+def _get_cfg_metainfo(package_path: str, cfg_path: str) -> dict:
+    """Get target meta information from all 'metafile.yml' defined in `mode-
+    index.yml` of external package.
+
+    Args:
+        package_path (str): Path of external package.
+        cfg_path (str): Name of experiment config.
+
+    Returns:
+        dict: Meta information of target experiment.
+    """
+    meta_index_path = osp.join(package_path, '.mim', 'model-index.yml')
+    meta_index = OmegaConf.to_container(OmegaConf.load(meta_index_path), resolve=True)
+    cfg_dict = dict()
+    for meta_path in meta_index['Import']:
+        meta_path = osp.join(package_path, '.mim', meta_path)
+        cfg_meta = OmegaConf.to_container(OmegaConf.load(meta_path), resolve=True)
+        for model_cfg in cfg_meta['Models']:
+            if 'Config' not in model_cfg:
+                warnings.warn(f'There is not `Config` define in {model_cfg}')
+                continue
+            cfg_name = model_cfg['Config'].partition('/')[-1]
+            # Some config could have multiple weights, we only pick the
+            # first one.
+            if cfg_name in cfg_dict:
+                continue
+            cfg_dict[cfg_name] = model_cfg
+    if cfg_path not in cfg_dict:
+        raise ValueError(f'Expected configs: {cfg_dict.keys()}, but got '
+                         f'{cfg_path}')
+    return cfg_dict[cfg_path]
+
+
+def _get_external_cfg_path(package_path: str, cfg_file: str) -> str:
+    """Get config path of external package.
+
+    Args:
+        package_path (str): Path of external package.
+        cfg_file (str): Name of experiment config.
+
+    Returns:
+        str: Absolute config path from external package.
+    """
+    cfg_file = cfg_file.split('.')[0]
+    model_cfg = _get_cfg_metainfo(package_path, cfg_file)
+    cfg_path = osp.join(package_path, model_cfg['Config'])
+    check_file_exist(cfg_path)
+    return cfg_path
+
+
+def _get_external_cfg_base_path(package_path: str, cfg_name: str) -> str:
+    """Get base config path of external package.
+
+    Args:
+        package_path (str): Path of external package.
+        cfg_name (str): External relative config path with 'package::'.
+
+    Returns:
+        str: Absolute config path from external package.
+    """
+    cfg_path = osp.join(package_path, '.mim', 'configs', cfg_name)
+    check_file_exist(cfg_path)
+    return cfg_path
+
+
+def _get_package_and_cfg_path(cfg_path: str) -> Tuple[str, str]:
+    """Get package name and relative config path.
+
+    Args:
+        cfg_path (str): External relative config path with 'package::'.
+
+    Returns:
+        Tuple[str, str]: Package name and config path.
+    """
+    if re.match(r'\w*::\w*/\w*', cfg_path) is None:
+        raise ValueError(
+            '`_get_package_and_cfg_path` is used for get external package, '
+            'please specify the package name and relative config path, just '
+            'like `mmdet::faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py`')
+    package_cfg = cfg_path.split('::')
+    if len(package_cfg) > 2:
+        raise ValueError('`::` should only be used to separate package and '
+                         'config name, but found multiple `::` in '
+                         f'{cfg_path}')
+    package, cfg_path = package_cfg
+    assert package in MODULE2PACKAGE, (
+        f'mmengine does not support to load {package} config.')
+    package = MODULE2PACKAGE[package]
+    return package, cfg_path
+
+
+class RemoveAssignFromAST(ast.NodeTransformer):
+    """Remove Assign node if the target's name match the key.
+
+    Args:
+        key (str): The target name of the Assign node.
+    """
+
+    def __init__(self, key):
+        self.key = key
+
+    def visit_Assign(self, node):
+        if (isinstance(node.targets[0], ast.Name)
+                and node.targets[0].id == self.key):
+            return None
+        else:
+            return node
+
+
+def _is_builtin_module(module_name: str) -> bool:
+    """Check if a module is a built-in module.
+
+    Arg:
+        module_name: name of module.
+    """
+    if module_name.startswith('.'):
+        return False
+    if module_name.startswith('mmengine.config'):
+        return True
+    if module_name in sys.builtin_module_names:
+        return True
+    spec = find_spec(module_name.split('.')[0])
+    # Module not found
+    if spec is None:
+        return False
+    origin_path = getattr(spec, 'origin', None)
+    if origin_path is None:
+        return False
+    origin_path = osp.abspath(origin_path)
+    if ('site-package' in origin_path or 'dist-package' in origin_path
+            or not origin_path.startswith(
+                (PYTHON_ROOT_DIR, SYSTEM_PYTHON_PREFIX))):
+        return False
+    else:
+        return True
+
+
+class ImportTransformer(ast.NodeTransformer):
+    """Convert the import syntax to the assignment of
+    :class:`mmengine.config.LazyObject` and preload the base variable before
+    parsing the configuration file.
+
+    Since you are already looking at this part of the code, I believe you must
+    be interested in the mechanism of the ``lazy_import`` feature of
+    :class:`Config`. In this docstring, we will dive deeper into its
+    principles.
+
+    Most of OpenMMLab users maybe bothered with that:
+
+        * In most of popular IDEs, they cannot navigate to the source code in
+          configuration file
+        * In most of popular IDEs, they cannot jump to the base file in current
+          configuration file, which is much painful when the inheritance
+          relationship is complex.
+
+    In order to solve this problem, we introduce the ``lazy_import`` mode.
+
+    A very intuitive idea for solving this problem is to import the module
+    corresponding to the "type" field using the ``import`` syntax. Similarly,
+    we can also ``import`` base file.
+
+    However, this approach has a significant drawback. It requires triggering
+    the import logic to parse the configuration file, which can be
+    time-consuming. Additionally, it implies downloading numerous dependencies
+    solely for the purpose of parsing the configuration file.
+    However, it's possible that only a portion of the config will actually be
+    used. For instance, the package used in the ``train_pipeline`` may not
+    be necessary for an evaluation task. Forcing users to download these
+    unused packages is not a desirable solution.
+
+    To avoid this problem, we introduce :class:`mmengine.config.LazyObject` and
+    :class:`mmengine.config.LazyAttr`. Before we proceed with further
+    explanations, you may refer to the documentation of these two modules to
+    gain an understanding of their functionalities.
+
+    Actually, one of the functions of ``ImportTransformer`` is to hack the
+    ``import`` syntax. It will replace the import syntax
+    (exclude import the base files) with the assignment of ``LazyObject``.
+
+    As for the import syntax of the base file, we cannot lazy import it since
+    we're eager to merge the fields of current file and base files. Therefore,
+    another function of the ``ImportTransformer`` is to collaborate with
+    ``Config._parse_lazy_import`` to parse the base files.
+
+    Args:
+        global_dict (dict): The global dict of the current configuration file.
+            If we divide ordinary Python syntax into two parts, namely the
+            import section and the non-import section (assuming a simple case
+            with imports at the beginning and the rest of the code following),
+            the variables generated by the import statements are stored in
+            global variables for subsequent code use. In this context,
+            the ``global_dict`` represents the global variables required when
+            executing the non-import code. ``global_dict`` will be filled
+            during visiting the parsed code.
+        base_dict (dict): All variables defined in base files.
+
+            Examples:
+                >>> from mmengine.config import read_base
+                >>>
+                >>>
+                >>> with read_base():
+                >>>     from .._base_.default_runtime import *
+                >>>     from .._base_.datasets.coco_detection import dataset
+
+            In this case, the base_dict will be:
+
+            Examples:
+                >>> base_dict = {
+                >>>     '.._base_.default_runtime': ...
+                >>>     '.._base_.datasets.coco_detection': dataset}
+
+            and `global_dict` will be updated like this:
+
+            Examples:
+                >>> global_dict.update(base_dict['.._base_.default_runtime'])  # `import *` means update all data
+                >>> global_dict.update(dataset=base_dict['.._base_.datasets.coco_detection']['dataset'])  # only update `dataset`
+    """  # noqa: E501
+
+    def __init__(self,
+                 global_dict: dict,
+                 base_dict: Optional[dict] = None,
+                 filename: Optional[str] = None):
+        self.base_dict = base_dict if base_dict is not None else {}
+        self.global_dict = global_dict
+        # In Windows, the filename could be like this:
+        # "C:\\Users\\runneradmin\\AppData\\Local\\"
+        # Although it has been an raw string, ast.parse will firstly escape
+        # it as the executed code:
+        # "C:\Users\runneradmin\AppData\Local\\\"
+        # As you see, the `\U` will be treated as a part of
+        # the escape sequence during code parsing, leading to an
+        # parsing error
+        # Here we use `encode('unicode_escape').decode()` for double escaping
+        if isinstance(filename, str):
+            filename = filename.encode('unicode_escape').decode()
+        self.filename = filename
+        self.imported_obj: set = set()
+        super().__init__()
+
+    def visit_ImportFrom(
+        self, node: ast.ImportFrom
+    ) -> Optional[Union[List[ast.Assign], ast.ImportFrom]]:
+        """Hack the ``from ... import ...`` syntax and update the global_dict.
+
+        Examples:
+            >>> from mmdet.models import RetinaNet
+
+        Will be parsed as:
+
+        Examples:
+            >>> RetinaNet = lazyObject('mmdet.models', 'RetinaNet')
+
+        ``global_dict`` will also be updated by ``base_dict`` as the
+        class docstring says.
+
+        Args:
+            node (ast.AST): The node of the current import statement.
+
+        Returns:
+            Optional[List[ast.Assign]]: There three cases:
+
+                * If the node is a statement of importing base files.
+                  None will be returned.
+                * If the node is a statement of importing a builtin module,
+                  node will be directly returned
+                * Otherwise, it will return the assignment statements of
+                  ``LazyObject``.
+        """
+        # Built-in modules will not be parsed as LazyObject
+        module = f'{node.level*"."}{node.module}'
+        if _is_builtin_module(module):
+            # Make sure builtin module will be added into `self.imported_obj`
+            for alias in node.names:
+                if alias.asname is not None:
+                    self.imported_obj.add(alias.asname)
+                elif alias.name == '*':
+                    raise ConfigParsingError(
+                        'Cannot import * from non-base config')
+                else:
+                    self.imported_obj.add(alias.name)
+            return node
+
+        if module in self.base_dict:
+            for alias_node in node.names:
+                if alias_node.name == '*':
+                    self.global_dict.update(self.base_dict[module])
+                    return None
+                if alias_node.asname is not None:
+                    base_key = alias_node.asname
+                else:
+                    base_key = alias_node.name
+                self.global_dict[base_key] = self.base_dict[module][
+                    alias_node.name]
+            return None
+
+        nodes: List[ast.Assign] = []
+        for alias_node in node.names:
+            # `ast.alias` has lineno attr after Python 3.10,
+            if hasattr(alias_node, 'lineno'):
+                lineno = alias_node.lineno
+            else:
+                lineno = node.lineno
+            if alias_node.name == '*':
+                # TODO: If users import * from a non-config module, it should
+                # fallback to import the real module and raise a warning to
+                # remind users the real module will be imported which will slow
+                # down the parsing speed.
+                raise ConfigParsingError(
+                    'Illegal syntax in config! `from xxx import *` is not '
+                    'allowed to appear outside the `if base:` statement')
+            elif alias_node.asname is not None:
+                # case1:
+                # from mmengine.dataset import BaseDataset as Dataset ->
+                # Dataset = LazyObject('mmengine.dataset', 'BaseDataset')
+                code = f'{alias_node.asname} = LazyObject("{module}", "{alias_node.name}", "{self.filename}, line {lineno}")'  # noqa: E501
+                self.imported_obj.add(alias_node.asname)
+            else:
+                # case2:
+                # from mmengine.model import BaseModel
+                # BaseModel = LazyObject('mmengine.model', 'BaseModel')
+                code = f'{alias_node.name} = LazyObject("{module}", "{alias_node.name}", "{self.filename}, line {lineno}")'  # noqa: E501
+                self.imported_obj.add(alias_node.name)
+            try:
+                nodes.append(ast.parse(code).body[0])  # type: ignore
+            except Exception as e:
+                raise ConfigParsingError(
+                    f'Cannot import {alias_node} from {module}'
+                    '1. Cannot import * from 3rd party lib in the config '
+                    'file\n'
+                    '2. Please check if the module is a base config which '
+                    'should be added to `_base_`\n') from e
+        return nodes
+
+    def visit_Import(self, node) -> Union[ast.Assign, ast.Import]:
+        """Work with ``_gather_abs_import_lazyobj`` to hack the ``import ...``
+        syntax.
+
+        Examples:
+            >>> import mmcls.models
+            >>> import mmcls.datasets
+            >>> import mmcls
+
+        Will be parsed as:
+
+        Examples:
+            >>> # import mmcls.models; import mmcls.datasets; import mmcls
+            >>> mmcls = lazyObject(['mmcls', 'mmcls.datasets', 'mmcls.models'])
+
+        Args:
+            node (ast.AST): The node of the current import statement.
+
+        Returns:
+            ast.Assign: If the import statement is ``import ... as ...``,
+            ast.Assign will be returned, otherwise node will be directly
+            returned.
+        """
+        # For absolute import like: `import mmdet.configs as configs`.
+        # It will be parsed as:
+        # configs = LazyObject('mmdet.configs')
+        # For absolute import like:
+        # `import mmdet.configs`
+        # `import mmdet.configs.default_runtime`
+        # This will be parsed as
+        # mmdet = LazyObject(['mmdet.configs.default_runtime', 'mmdet.configs])
+        # However, visit_Import cannot gather other import information, so
+        # `_gather_abs_import_LazyObject` will gather all import information
+        # from the same module and construct the LazyObject.
+        alias_list = node.names
+        assert len(alias_list) == 1, (
+            'Illegal syntax in config! import multiple modules in one line is '
+            'not supported')
+        # TODO Support multiline import
+        alias = alias_list[0]
+        if alias.asname is not None:
+            self.imported_obj.add(alias.asname)
+            if _is_builtin_module(alias.name.split('.')[0]):
+                return node
+            return ast.parse(  # type: ignore
+                f'{alias.asname} = LazyObject('
+                f'"{alias.name}",'
+                f'location="{self.filename}, line {node.lineno}")').body[0]
+        return node
+
+
+def _gather_abs_import_lazyobj(tree: ast.Module,
+                               filename: Optional[str] = None):
+    """Experimental implementation of gathering absolute import information."""
+    if isinstance(filename, str):
+        filename = filename.encode('unicode_escape').decode()
+    imported = defaultdict(list)
+    abs_imported = set()
+    new_body: List[ast.stmt] = []
+    # module2node is used to get lineno when Python < 3.10
+    module2node: dict = dict()
+    for node in tree.body:
+        if isinstance(node, ast.Import):
+            for alias in node.names:
+                # Skip converting built-in module to LazyObject
+                if _is_builtin_module(alias.name):
+                    new_body.append(node)
+                    continue
+                module = alias.name.split('.')[0]
+                module2node.setdefault(module, node)
+                imported[module].append(alias)
+            continue
+        new_body.append(node)
+
+    for key, value in imported.items():
+        names = [_value.name for _value in value]
+        if hasattr(value[0], 'lineno'):
+            lineno = value[0].lineno
+        else:
+            lineno = module2node[key].lineno
+        lazy_module_assign = ast.parse(
+            f'{key} = LazyObject({names}, location="{filename}, line {lineno}")'  # noqa: E501
+        )  # noqa: E501
+        abs_imported.add(key)
+        new_body.insert(0, lazy_module_assign.body[0])
+    tree.body = new_body
+    return tree, abs_imported
+
+
+def get_installed_path(package: str) -> str:
+    """Get installed path of package.
+
+    Args:
+        package (str): Name of package.
+
+    Example:
+        >>> get_installed_path('mmcls')
+        >>> '.../lib/python3.7/site-packages/mmcls'
+    """
+    import importlib.util
+
+    from pkg_resources import DistributionNotFound, get_distribution
+
+    # if the package name is not the same as module name, module name should be
+    # inferred. For example, mmcv-full is the package name, but mmcv is module
+    # name. If we want to get the installed path of mmcv-full, we should concat
+    # the pkg.location and module name
+    try:
+        pkg = get_distribution(package)
+    except DistributionNotFound as e:
+        # if the package is not installed, package path set in PYTHONPATH
+        # can be detected by `find_spec`
+        spec = importlib.util.find_spec(package)
+        if spec is not None:
+            if spec.origin is not None:
+                return osp.dirname(spec.origin)
+            else:
+                # `get_installed_path` cannot get the installed path of
+                # namespace packages
+                raise RuntimeError(
+                    f'{package} is a namespace package, which is invalid '
+                    'for `get_install_path`')
+        else:
+            raise e
+
+    possible_path = osp.join(pkg.location, package)  # type: ignore
+    if osp.exists(possible_path):
+        return possible_path
+    else:
+        return osp.join(pkg.location, package2module(package))  # type: ignore
+    
+
+def import_modules_from_strings(imports, allow_failed_imports=False):
+    """Import modules from the given list of strings.
+
+    Args:
+        imports (list | str | None): The given module names to be imported.
+        allow_failed_imports (bool): If True, the failed imports will return
+            None. Otherwise, an ImportError is raise. Defaults to False.
+
+    Returns:
+        list[module] | module | None: The imported modules.
+
+    Examples:
+        >>> osp, sys = import_modules_from_strings(
+        ...     ['os.path', 'sys'])
+        >>> import os.path as osp_
+        >>> import sys as sys_
+        >>> assert osp == osp_
+        >>> assert sys == sys_
+    """
+    if not imports:
+        return
+    single_import = False
+    if isinstance(imports, str):
+        single_import = True
+        imports = [imports]
+    if not isinstance(imports, list):
+        raise TypeError(
+            f'custom_imports must be a list but got type {type(imports)}')
+    imported = []
+    for imp in imports:
+        if not isinstance(imp, str):
+            raise TypeError(
+                f'{imp} is of type {type(imp)} and cannot be imported.')
+        try:
+            imported_tmp = import_module(imp)
+        except ImportError:
+            if allow_failed_imports:
+                warnings.warn(f'{imp} failed to import and is ignored.',
+                              UserWarning)
+                imported_tmp = None
+            else:
+                raise ImportError(f'Failed to import {imp}')
+        imported.append(imported_tmp)
+    if single_import:
+        imported = imported[0]
+    return imported
+
+
+def import_module(name, package=None):
+    """Import a module, optionally supporting relative imports."""
+    return real_import_module(name, package)
+
+
+def is_installed(package: str) -> bool:
+    """Check package whether installed.
+
+    Args:
+        package (str): Name of package to be checked.
+    """
+    # When executing `import mmengine.runner`,
+    # pkg_resources will be imported and it takes too much time.
+    # Therefore, import it in function scope to save time.
+    import importlib.util
+    import pkg_resources
+    from pkg_resources import get_distribution
+
+    # refresh the pkg_resources
+    # more datails at https://github.com/pypa/setuptools/issues/373
+    importlib.reload(pkg_resources)
+    try:
+        get_distribution(package)
+        return True
+    except pkg_resources.DistributionNotFound:
+        spec = importlib.util.find_spec(package)
+        if spec is None:
+            return False
+        elif spec.origin is not None:
+            return True
+        else:
+            return False
+    
+
+def dump(obj, file=None, file_format=None, **kwargs):
+    """Dump data to json/yaml/pickle strings or files (mmengine-like replacement)."""
+    if isinstance(file, Path):
+        file = str(file)
+
+    # Guess file format if not explicitly given
+    if file_format is None:
+        if isinstance(file, str):
+            file_format = file.split('.')[-1].lower()
+        elif file is None:
+            raise ValueError("file_format must be specified if file is None")
+
+    if file_format not in ['json', 'yaml', 'yml', 'pkl', 'pickle']:
+        raise TypeError(f"Unsupported file format: {file_format}")
+
+    # Convert YAML extension
+    if file_format == 'yml':
+        file_format = 'yaml'
+    if file_format == 'pickle':
+        file_format = 'pkl'
+
+    # Handle output to string
+    if file is None:
+        if file_format == 'json':
+            return json.dumps(obj, indent=4, **kwargs)
+        elif file_format == 'yaml':
+            return yaml.dump(obj, **kwargs)
+        elif file_format == 'pkl':
+            return pickle.dumps(obj, **kwargs)
+
+    # Handle output to file
+    mode = 'w' if file_format in ['json', 'yaml'] else 'wb'
+    with open(file, mode, encoding='utf-8' if 'b' not in mode else None) as f:
+        if file_format == 'json':
+            json.dump(obj, f, indent=4, **kwargs)
+        elif file_format == 'yaml':
+            yaml.dump(obj, f, **kwargs)
+        elif file_format == 'pkl':
+            pickle.dump(obj, f, **kwargs)
+
+    return True
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+    if not osp.isfile(filename):
+        raise FileNotFoundError(msg_tmpl.format(filename))
\ No newline at end of file
diff --git a/segformer_plusplus/configs/segformer_mit_b0.py b/segformer_plusplus/configs/segformer_mit_b0.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4eb059010ad97d0360e779cbcca82e3a2b50717
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b0.py
@@ -0,0 +1,28 @@
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+backbone = dict(
+    type='MixVisionTransformer',
+    in_channels=3,
+    embed_dims=32,
+    num_stages=4,
+    num_layers=[2, 2, 2, 2],
+    num_heads=[1, 2, 5, 8],
+    patch_sizes=[7, 3, 3, 3],
+    sr_ratios=[8, 4, 2, 1],
+    out_indices=(0, 1, 2, 3),
+    mlp_ratio=4,
+    qkv_bias=True,
+    drop_rate=0.0,
+    attn_drop_rate=0.0,
+    drop_path_rate=0.1
+)
+decode_head = dict(
+    type='SegformerHead',
+    in_channels=[32, 64, 160, 256],
+    in_index=[0, 1, 2, 3],
+    channels=256,
+    dropout_ratio=0.1,
+    out_channels=19,
+    norm_cfg=norm_cfg,
+    align_corners=False,
+    interpolate_mode='bilinear'
+)
diff --git a/segformer_plusplus/configs/segformer_mit_b1.py b/segformer_plusplus/configs/segformer_mit_b1.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ec3214aff7f298cf429b836e51e84cea5aeb771
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b1.py
@@ -0,0 +1,8 @@
+_base_ = ['./segformer_mit_b0.py']
+
+backbone = dict(
+        embed_dims=64,
+)
+decode_head = dict(
+    in_channels=[64, 128, 320, 512]
+)
diff --git a/segformer_plusplus/configs/segformer_mit_b2.py b/segformer_plusplus/configs/segformer_mit_b2.py
new file mode 100644
index 0000000000000000000000000000000000000000..230e1e50daba53993126d58efc9bed642cb9f4ca
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b2.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 4, 6, 3]
+)
diff --git a/segformer_plusplus/configs/segformer_mit_b3.py b/segformer_plusplus/configs/segformer_mit_b3.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c877f9d12459c9508a7627a3131144e94768856
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b3.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 4, 18, 3]
+)
diff --git a/segformer_plusplus/configs/segformer_mit_b4.py b/segformer_plusplus/configs/segformer_mit_b4.py
new file mode 100644
index 0000000000000000000000000000000000000000..897e59765578c96a5a9a17ffb8cf6aceecc81e3b
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b4.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 8, 27, 3]
+)
diff --git a/segformer_plusplus/configs/segformer_mit_b5.py b/segformer_plusplus/configs/segformer_mit_b5.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f0762237dd35e601e3153ca2e52a89eac365e1d
--- /dev/null
+++ b/segformer_plusplus/configs/segformer_mit_b5.py
@@ -0,0 +1,6 @@
+_base_ = ['./segformer_mit_b1.py']
+
+backbone = dict(
+        embed_dims=64,
+        num_layers=[3, 6, 40, 3]
+)
diff --git a/segformer_plusplus/model/__init__.py b/segformer_plusplus/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b680692d5ff945d85311a8c90c70766275444498
--- /dev/null
+++ b/segformer_plusplus/model/__init__.py
@@ -0,0 +1 @@
+__all__ = []
\ No newline at end of file
diff --git a/segformer_plusplus/model/backbone/__init__.py b/segformer_plusplus/model/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fc6fa95ea87f1419517325a0a49915b94996d74
--- /dev/null
+++ b/segformer_plusplus/model/backbone/__init__.py
@@ -0,0 +1,3 @@
+from .mit import MixVisionTransformer
+
+__all__ = ['MixVisionTransformer']
\ No newline at end of file
diff --git a/segformer_plusplus/model/backbone/mit.py b/segformer_plusplus/model/backbone/mit.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dabcde1b48b834a1721a136ddbee1d0a56b8266
--- /dev/null
+++ b/segformer_plusplus/model/backbone/mit.py
@@ -0,0 +1,477 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from tomesd.merge import bipartite_soft_matching_random2d
+
+from ...utils import PatchEmbed
+from ...utils import nchw_to_nlc, nlc_to_nchw
+from ...utils import MODELS
+from ...utils import Conv2d, build_activation_layer, build_norm_layer, build_dropout
+from ..base_module import BaseModule, MultiheadAttention, ModuleList, Sequential
+from ..weight_init import (constant_init, normal_init,
+                                        trunc_normal_init)
+
+
+class MixFFN(BaseModule):
+    """An implementation of MixFFN of Segformer.
+
+    The differences between MixFFN & FFN:
+        1. Use 1X1 Conv to replace Linear layer.
+        2. Introduce 3X3 Conv to encode positional information.
+    Args:
+        embed_dims (int): The feature dimension. Same as
+            `MultiheadAttention`. Defaults: 256.
+        feedforward_channels (int): The hidden dimension of FFNs.
+            Defaults: 1024.
+        act_cfg (dict, optional): The activation config for FFNs.
+            Default: dict(type='ReLU')
+        ffn_drop (float, optional): Probability of an element to be
+            zeroed in FFN. Default 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 feedforward_channels,
+                 act_cfg=dict(type='GELU'),
+                 ffn_drop=0.,
+                 dropout_layer=None,
+                 init_cfg=None):
+        super().__init__(init_cfg)
+
+        self.embed_dims = embed_dims
+        self.feedforward_channels = feedforward_channels
+        self.act_cfg = act_cfg
+        self.activate = build_activation_layer(act_cfg)
+
+        in_channels = embed_dims
+        fc1 = Conv2d(
+            in_channels=in_channels,
+            out_channels=feedforward_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        # 3x3 depth wise conv to provide positional encode information
+        pe_conv = Conv2d(
+            in_channels=feedforward_channels,
+            out_channels=feedforward_channels,
+            kernel_size=3,
+            stride=1,
+            padding=(3 - 1) // 2,
+            bias=True,
+            groups=feedforward_channels)
+        fc2 = Conv2d(
+            in_channels=feedforward_channels,
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        drop = nn.Dropout(ffn_drop)
+        layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
+        self.layers = Sequential(*layers)
+        self.dropout_layer = build_dropout(
+            dropout_layer) if dropout_layer else torch.nn.Identity()
+
+    def forward(self, x, hw_shape, identity=None):
+        out = nlc_to_nchw(x, hw_shape)
+        out = self.layers(out)
+        out = nchw_to_nlc(out)
+        if identity is None:
+            identity = x
+        return identity + self.dropout_layer(out)
+
+
+class EfficientMultiheadAttention(MultiheadAttention):
+    """An implementation of Efficient Multi-head Attention of Segformer.
+
+    This module is modified from MultiheadAttention which is a module from
+    mmcv.cnn.bricks.transformer.
+    Args:
+        embed_dims (int): The embedding dimension.
+        num_heads (int): Parallel attention heads.
+        attn_drop (float): A Dropout layer on attn_output_weights.
+            Default: 0.0.
+        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
+            Default: 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut. Default: None.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+        batch_first (bool): Key, Query and Value are shape of
+            (batch, n, embed_dim)
+            or (n, batch, embed_dim). Default: False.
+        qkv_bias (bool): enable bias for qkv if True. Default True.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
+            Attention of Segformer. Default: 1.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 attn_drop=0.,
+                 proj_drop=0.,
+                 dropout_layer=None,
+                 init_cfg=None,
+                 batch_first=True,
+                 qkv_bias=False,
+                 tome_cfg=dict(),
+                 norm_cfg=dict(type='LN'),
+                 sr_ratio=1):
+        super().__init__(
+            embed_dims,
+            num_heads,
+            attn_drop,
+            proj_drop,
+            dropout_layer=dropout_layer,
+            init_cfg=init_cfg,
+            batch_first=batch_first,
+            bias=qkv_bias)
+
+        self.q_mode = tome_cfg.get('q_mode')
+        self.kv_mode = tome_cfg.get('kv_mode')
+        self.tome_cfg = tome_cfg
+
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1:
+            self.sr = Conv2d(
+                in_channels=embed_dims,
+                out_channels=embed_dims,
+                kernel_size=sr_ratio,
+                stride=sr_ratio)
+            # The ret[0] of build_norm_layer is norm name.
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+
+    def forward(self, x, hw_shape, identity=None):
+        x_q = x
+
+        if self.sr_ratio > 1:
+            x_kv = nlc_to_nchw(x, hw_shape)
+            x_kv = self.sr(x_kv)
+            x_kv = nchw_to_nlc(x_kv)
+            x_kv = self.norm(x_kv)
+        else:
+            x_kv = x
+
+        # 2D Neighbour Merging KV
+        if self.kv_mode == 'n2d':
+            kv_hw_shape = (int(hw_shape[0] / self.sr_ratio), int(hw_shape[1] / self.sr_ratio))
+            x_kv = nlc_to_nchw(x_kv, kv_hw_shape)
+            x_kv = torch.nn.functional.avg_pool2d(x_kv, kernel_size=self.tome_cfg['kv_s'],
+                                                  stride=self.tome_cfg['kv_s'],
+                                                  ceil_mode=True)
+            x_kv = nchw_to_nlc(x_kv)
+
+        # Bipartite Soft Matching (tomesd) KV
+        if self.kv_mode == 'bsm':
+            w_kv = int(hw_shape[1] / self.sr_ratio)
+            h_kv = int(hw_shape[0] / self.sr_ratio)
+            merge, unmerge = bipartite_soft_matching_random2d(metric=x_kv, w=w_kv, h=h_kv,
+                                                              r=int(x_kv.size()[1] * self.tome_cfg['kv_r']),
+                                                              sx=self.tome_cfg['kv_sx'], sy=self.tome_cfg['kv_sy'],
+                                                              no_rand=True)
+            x_kv = merge(x_kv)
+
+        if identity is None:
+            identity = x_q
+
+        # 1D Neighbor Merging Q
+        if self.q_mode == 'n1d':
+            x_q = x_q.transpose(-2, -1)
+            x_q = torch.nn.functional.avg_pool1d(x_q, kernel_size=self.tome_cfg['q_s'],
+                                                 stride=self.tome_cfg['q_s'],
+                                                 ceil_mode=True)
+            x_q = x_q.transpose(-2, -1)
+
+        # 2D Neighbor Merging Q
+        if self.q_mode == 'n2d':
+            reduced_hw = (int(torch.ceil(torch.tensor(hw_shape[0] / self.tome_cfg['q_s'][0]))),
+                          int(torch.ceil(torch.tensor(hw_shape[1] / self.tome_cfg['q_s'][1]))))
+            x_q = nlc_to_nchw(x_q, hw_shape)
+            x_q = torch.nn.functional.avg_pool2d(x_q, kernel_size=self.tome_cfg['q_s'],
+                                                 stride=self.tome_cfg['q_s'],
+                                                 ceil_mode=True)
+            x_q = nchw_to_nlc(x_q)
+
+        # Bipartite Soft Matching (tomesd) Q
+        if self.q_mode == 'bsm':
+            merge, unmerge = bipartite_soft_matching_random2d(metric=x_q, w=hw_shape[1], h=hw_shape[0],
+                                                              r=int(x_q.size()[1] * self.tome_cfg['q_r']),
+                                                              sx=self.tome_cfg['q_sx'], sy=self.tome_cfg['q_sy'],
+                                                              no_rand=True)
+            x_q = merge(x_q)
+
+        # Because the dataflow('key', 'query', 'value') of
+        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
+        # embed_dims), We should adjust the shape of dataflow from
+        # batch_first (batch, num_query, embed_dims) to num_query_first
+        # (num_query ,batch, embed_dims), and recover ``attn_output``
+        # from num_query_first to batch_first.
+
+        if self.batch_first:
+            x_q = x_q.transpose(0, 1)
+            x_kv = x_kv.transpose(0, 1)
+        out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]
+        if self.batch_first:
+            out = out.transpose(0, 1)
+
+        # Unmerging BSM (tome+tomesd)
+        if self.q_mode == 'bsm':
+            out = unmerge(out)
+
+        # Unmerging 1D Neighbour Merging
+        if self.q_mode == 'n1d':
+            out = out.transpose(-2, -1)
+            out = torch.nn.functional.interpolate(out, size=identity.size()[-2])
+            out = out.transpose(-2, -1)
+
+        # Unmerging 2D Neighbor Merging
+        if self.q_mode == 'n2d':
+            out = nlc_to_nchw(out, reduced_hw)
+            out = torch.nn.functional.interpolate(out, size=hw_shape)
+            out = nchw_to_nlc(out)
+
+        return identity + self.dropout_layer(self.proj_drop(out))
+
+
+class TransformerEncoderLayer(BaseModule):
+    """Implements one encoder layer in Segformer.
+
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        drop_rate (float): Probability of an element to be zeroed.
+            after the feed forward layer. Default 0.0.
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default 0.0.
+        drop_path_rate (float): stochastic depth rate. Default 0.0.
+        qkv_bias (bool): enable bias for qkv if True.
+            Default: True.
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        batch_first (bool): Key, Query and Value are shape of
+            (batch, n, embed_dim)
+            or (n, batch, embed_dim). Default: False.
+        init_cfg (dict, optional): Initialization config dict.
+            Default:None.
+        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
+            Attention of Segformer. Default: 1.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save
+            some memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 qkv_bias=True,
+                 tome_cfg=dict(),
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 batch_first=True,
+                 sr_ratio=1,
+                 with_cp=False):
+        super().__init__()
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.attn = EfficientMultiheadAttention(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            attn_drop=attn_drop_rate,
+            proj_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            batch_first=batch_first,
+            qkv_bias=qkv_bias,
+            tome_cfg=tome_cfg,
+            norm_cfg=norm_cfg,
+            sr_ratio=sr_ratio)
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.ffn = MixFFN(
+            embed_dims=embed_dims,
+            feedforward_channels=feedforward_channels,
+            ffn_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            act_cfg=act_cfg)
+
+        self.with_cp = with_cp
+
+    def forward(self, x, hw_shape):
+
+        def _inner_forward(x):
+            x = self.attn(self.norm1(x), hw_shape, identity=x)
+            x = self.ffn(self.norm2(x), hw_shape, identity=x)
+            return x
+
+        if self.with_cp and x.requires_grad:
+            x = cp.checkpoint(_inner_forward, x)
+        else:
+            x = _inner_forward(x)
+        return x
+
+
+@MODELS.register_module()
+class MixVisionTransformer(BaseModule):
+    """The backbone of Segformer.
+
+    This backbone is the implementation of `SegFormer: Simple and
+    Efficient Design for Semantic Segmentation with
+    Transformers <https://arxiv.org/abs/2105.15203>`_.
+    Args:
+        in_channels (int): Number of input channels. Default: 3.
+        embed_dims (int): Embedding dimension. Default: 768.
+        num_stags (int): The num of stages. Default: 4.
+        num_layers (Sequence[int]): The layer number of each transformer encode
+            layer. Default: [3, 4, 6, 3].
+        num_heads (Sequence[int]): The attention heads of each transformer
+            encode layer. Default: [1, 2, 4, 8].
+        patch_sizes (Sequence[int]): The patch_size of each overlapped patch
+            embedding. Default: [7, 3, 3, 3].
+        strides (Sequence[int]): The stride of each overlapped patch embedding.
+            Default: [4, 2, 2, 2].
+        sr_ratios (Sequence[int]): The spatial reduction rate of each
+            transformer encode layer. Default: [8, 4, 2, 1].
+        out_indices (Sequence[int] | int): Output from which stages.
+            Default: (0, 1, 2, 3).
+        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
+            Default: 4.
+        qkv_bias (bool): Enable bias for qkv if True. Default: True.
+        drop_rate (float): Probability of an element to be zeroed.
+            Default 0.0
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default 0.0
+        drop_path_rate (float): stochastic depth rate. Default 0.0
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN')
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        pretrained (str, optional): model pretrained path. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save
+            some memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 embed_dims=64,
+                 num_stages=4,
+                 num_layers=[3, 4, 6, 3],
+                 num_heads=[1, 2, 4, 8],
+                 patch_sizes=[7, 3, 3, 3],
+                 strides=[4, 2, 2, 2],
+                 sr_ratios=[8, 4, 2, 1],
+                 out_indices=(0, 1, 2, 3),
+                 mlp_ratio=4,
+                 qkv_bias=True,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 tome_cfg=[dict(), dict(), dict(), dict()],
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN', eps=1e-6),
+                 init_cfg=None,
+                 with_cp=False,
+                 down_sample=False):
+        super().__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        self.num_stages = num_stages
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.patch_sizes = patch_sizes
+        self.strides = strides
+        self.sr_ratios = sr_ratios
+        self.with_cp = with_cp
+        self.down_sample = down_sample
+        assert num_stages == len(num_layers) == len(num_heads) \
+               == len(patch_sizes) == len(strides) == len(sr_ratios)
+
+        self.out_indices = out_indices
+        assert max(out_indices) < self.num_stages
+
+        # transformer encoder
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, drop_path_rate, sum(num_layers))
+        ]  # stochastic num_layer decay rule
+
+        cur = 0
+        self.layers = ModuleList()
+        for i, num_layer in enumerate(num_layers):
+            embed_dims_i = embed_dims * num_heads[i]
+            patch_embed = PatchEmbed(
+                in_channels=in_channels,
+                embed_dims=embed_dims_i,
+                kernel_size=patch_sizes[i],
+                stride=strides[i],
+                padding=patch_sizes[i] // 2,
+                norm_cfg=norm_cfg)
+            layer = ModuleList([
+                TransformerEncoderLayer(
+                    embed_dims=embed_dims_i,
+                    num_heads=num_heads[i],
+                    feedforward_channels=mlp_ratio * embed_dims_i,
+                    drop_rate=drop_rate,
+                    attn_drop_rate=attn_drop_rate,
+                    drop_path_rate=dpr[cur + idx],
+                    qkv_bias=qkv_bias,
+                    tome_cfg=tome_cfg[i],
+                    act_cfg=act_cfg,
+                    norm_cfg=norm_cfg,
+                    with_cp=with_cp,
+                    sr_ratio=sr_ratios[i]) for idx in range(num_layer)
+            ])
+            in_channels = embed_dims_i
+            # The ret[0] of build_norm_layer is norm name.
+            norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
+            self.layers.append(ModuleList([patch_embed, layer, norm]))
+            cur += num_layer
+
+    def init_weights(self):
+        if self.init_cfg is None:
+            for m in self.modules():
+                if isinstance(m, nn.Linear):
+                    trunc_normal_init(m, std=.02, bias=0.)
+                elif isinstance(m, nn.LayerNorm):
+                    constant_init(m, val=1.0, bias=0.)
+                elif isinstance(m, nn.Conv2d):
+                    fan_out = m.kernel_size[0] * m.kernel_size[
+                        1] * m.out_channels
+                    fan_out //= m.groups
+                    normal_init(
+                        m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
+        else:
+            super().init_weights()
+
+    def forward(self, x):
+        if self.down_sample:
+            x = torch.nn.functional.interpolate(x, scale_factor=(0.5, 0.5))
+        outs = []
+
+        for i, layer in enumerate(self.layers):
+            x, hw_shape = layer[0](x)
+            for block in layer[1]:
+                x = block(x, hw_shape)
+            x = layer[2](x)
+            x = nlc_to_nchw(x, hw_shape)
+            if i in self.out_indices:
+                outs.append(x)
+
+        return outs
diff --git a/segformer_plusplus/model/base_module.py b/segformer_plusplus/model/base_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9948e6a1f89808c85935544187e49e7f95de7f5
--- /dev/null
+++ b/segformer_plusplus/model/base_module.py
@@ -0,0 +1,390 @@
+import copy
+from abc import ABCMeta
+from collections import defaultdict
+from typing import Iterable, List, Optional, Union, Callable
+import warnings
+from inspect import getfullargspec
+import functools
+import torch.nn as nn
+
+from .utils import is_model_wrapper
+from .weight_init import PretrainedInit, initialize, update_init_info
+from ..utils.activation import build_dropout
+from ..utils.registry import MODELS
+
+
+class BaseModule(nn.Module, metaclass=ABCMeta):
+    """Base module for all modules in openmmlab. ``BaseModule`` is a wrapper of
+    ``torch.nn.Module`` with additional functionality of parameter
+    initialization. Compared with ``torch.nn.Module``, ``BaseModule`` mainly
+    adds three attributes.
+
+    - ``init_cfg``: the config to control the initialization.
+    - ``init_weights``: The function of parameter initialization and recording
+      initialization information.
+    - ``_params_init_info``: Used to track the parameter initialization
+      information. This attribute only exists during executing the
+      ``init_weights``.
+
+    Note:
+        :obj:`PretrainedInit` has a higher priority than any other
+        initializer. The loaded pretrained weights will overwrite
+        the previous initialized weights.
+
+    Args:
+        init_cfg (dict or List[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self, init_cfg: Union[dict, List[dict], None] = None):
+        """Initialize BaseModule, inherited from `torch.nn.Module`"""
+
+        # NOTE init_cfg can be defined in different levels, but init_cfg
+        # in low levels has a higher priority.
+
+        super().__init__()
+        # define default value of init_cfg instead of hard code
+        # in init_weights() function
+        self._is_init = False
+
+        self.init_cfg = copy.deepcopy(init_cfg)
+
+        # Backward compatibility in derived classes
+        # if pretrained is not None:
+        #     warnings.warn('DeprecationWarning: pretrained is a deprecated \
+        #         key, please consider using init_cfg')
+        #     self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+
+    @property
+    def is_init(self):
+        return self._is_init
+
+    @is_init.setter
+    def is_init(self, value):
+        self._is_init = value
+
+    def init_weights(self):
+        """Initialize the weights."""
+
+        is_top_level_module = False
+        # check if it is top-level module
+        if not hasattr(self, '_params_init_info'):
+            # The `_params_init_info` is used to record the initialization
+            # information of the parameters
+            # the key should be the obj:`nn.Parameter` of model and the value
+            # should be a dict containing
+            # - init_info (str): The string that describes the initialization.
+            # - tmp_mean_value (FloatTensor): The mean of the parameter,
+            #       which indicates whether the parameter has been modified.
+            # this attribute would be deleted after all parameters
+            # is initialized.
+            self._params_init_info = defaultdict(dict)
+            is_top_level_module = True
+
+            # Initialize the `_params_init_info`,
+            # When detecting the `tmp_mean_value` of
+            # the corresponding parameter is changed, update related
+            # initialization information
+            for name, param in self.named_parameters():
+                self._params_init_info[param][
+                    'init_info'] = f'The value is the same before and ' \
+                                   f'after calling `init_weights` ' \
+                                   f'of {self.__class__.__name__} '
+                self._params_init_info[param][
+                    'tmp_mean_value'] = param.data.mean().cpu()
+
+            # pass `params_init_info` to all submodules
+            # All submodules share the same `params_init_info`,
+            # so it will be updated when parameters are
+            # modified at any level of the model.
+            for sub_module in self.modules():
+                sub_module._params_init_info = self._params_init_info
+
+        module_name = self.__class__.__name__
+        if not self._is_init:
+            if self.init_cfg:
+
+                init_cfgs = self.init_cfg
+                if isinstance(self.init_cfg, dict):
+                    init_cfgs = [self.init_cfg]
+
+                # PretrainedInit has higher priority than any other init_cfg.
+                # Therefore we initialize `pretrained_cfg` last to overwrite
+                # the previous initialized weights.
+                # See details in https://github.com/open-mmlab/mmengine/issues/691 # noqa E501
+                other_cfgs = []
+                pretrained_cfg = []
+                for init_cfg in init_cfgs:
+                    assert isinstance(init_cfg, dict)
+                    if (init_cfg['type'] == 'Pretrained'
+                            or init_cfg['type'] is PretrainedInit):
+                        pretrained_cfg.append(init_cfg)
+                    else:
+                        other_cfgs.append(init_cfg)
+
+                initialize(self, other_cfgs)
+
+            for m in self.children():
+                if is_model_wrapper(m) and not hasattr(m, 'init_weights'):
+                    m = m.module
+                if hasattr(m, 'init_weights') and not getattr(
+                        m, 'is_init', False):
+                    m.init_weights()
+                    # users may overload the `init_weights`
+                    update_init_info(
+                        m,
+                        init_info=f'Initialized by '
+                        f'user-defined `init_weights`'
+                        f' in {m.__class__.__name__} ')
+            if self.init_cfg and pretrained_cfg:
+                initialize(self, pretrained_cfg)
+            self._is_init = True
+
+        if is_top_level_module:
+            self._dump_init_info()
+
+            for sub_module in self.modules():
+                del sub_module._params_init_info
+
+    def __repr__(self):
+        s = super().__repr__()
+        if self.init_cfg:
+            s += f'\ninit_cfg={self.init_cfg}'
+        return s
+    
+
+def deprecated_api_warning(name_dict: dict,
+                           cls_name: Optional[str] = None) -> Callable:
+    """A decorator to check if some arguments are deprecate and try to replace
+    deprecate src_arg_name to dst_arg_name.
+
+    Args:
+        name_dict(dict):
+            key (str): Deprecate argument names.
+            val (str): Expected argument names.
+
+    Returns:
+        func: New function.
+    """
+
+    def api_warning_wrapper(old_func):
+
+        @functools.wraps(old_func)
+        def new_func(*args, **kwargs):
+            # get the arg spec of the decorated method
+            args_info = getfullargspec(old_func)
+            # get name of the function
+            func_name = old_func.__name__
+            if cls_name is not None:
+                func_name = f'{cls_name}.{func_name}'
+            if args:
+                arg_names = args_info.args[:len(args)]
+                for src_arg_name, dst_arg_name in name_dict.items():
+                    if src_arg_name in arg_names:
+                        warnings.warn(
+                            f'"{src_arg_name}" is deprecated in '
+                            f'`{func_name}`, please use "{dst_arg_name}" '
+                            'instead', DeprecationWarning)
+                        arg_names[arg_names.index(src_arg_name)] = dst_arg_name
+            if kwargs:
+                for src_arg_name, dst_arg_name in name_dict.items():
+                    if src_arg_name in kwargs:
+                        assert dst_arg_name not in kwargs, (
+                            f'The expected behavior is to replace '
+                            f'the deprecated key `{src_arg_name}` to '
+                            f'new key `{dst_arg_name}`, but got them '
+                            f'in the arguments at the same time, which '
+                            f'is confusing. `{src_arg_name} will be '
+                            f'deprecated in the future, please '
+                            f'use `{dst_arg_name}` instead.')
+
+                        warnings.warn(
+                            f'"{src_arg_name}" is deprecated in '
+                            f'`{func_name}`, please use "{dst_arg_name}" '
+                            'instead', DeprecationWarning)
+                        kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
+
+            # apply converted arguments to the decorated method
+            output = old_func(*args, **kwargs)
+            return output
+
+        return new_func
+
+    return api_warning_wrapper
+    
+
+@MODELS.register_module()
+class MultiheadAttention(BaseModule):
+    """A wrapper for ``torch.nn.MultiheadAttention``.
+
+    This module implements MultiheadAttention with identity connection,
+    and positional encoding  is also passed as input.
+
+    Args:
+        embed_dims (int): The embedding dimension.
+        num_heads (int): Parallel attention heads.
+        attn_drop (float): A Dropout layer on attn_output_weights.
+            Default: 0.0.
+        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
+            Default: 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+        batch_first (bool): When it is True,  Key, Query and Value are shape of
+            (batch, n, embed_dim), otherwise (n, batch, embed_dim).
+             Default to False.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 attn_drop=0.,
+                 proj_drop=0.,
+                 dropout_layer=dict(type='Dropout', drop_prob=0.),
+                 init_cfg=None,
+                 batch_first=False,
+                 **kwargs):
+        super().__init__(init_cfg)
+        if 'dropout' in kwargs:
+            warnings.warn(
+                'The arguments `dropout` in MultiheadAttention '
+                'has been deprecated, now you can separately '
+                'set `attn_drop`(float), proj_drop(float), '
+                'and `dropout_layer`(dict) ', DeprecationWarning)
+            attn_drop = kwargs['dropout']
+            dropout_layer['drop_prob'] = kwargs.pop('dropout')
+
+        self.embed_dims = embed_dims
+        self.num_heads = num_heads
+        self.batch_first = batch_first
+
+        self.attn = nn.MultiheadAttention(embed_dims, num_heads, attn_drop,
+                                          **kwargs)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.dropout_layer = build_dropout(
+            dropout_layer) if dropout_layer else nn.Identity()
+
+    @deprecated_api_warning({'residual': 'identity'},
+                            cls_name='MultiheadAttention')
+    def forward(self,
+                query,
+                key=None,
+                value=None,
+                identity=None,
+                query_pos=None,
+                key_pos=None,
+                attn_mask=None,
+                key_padding_mask=None,
+                **kwargs):
+        """Forward function for `MultiheadAttention`.
+
+        **kwargs allow passing a more general data flow when combining
+        with other operations in `transformerlayer`.
+
+        Args:
+            query (Tensor): The input query with shape [num_queries, bs,
+                embed_dims] if self.batch_first is False, else
+                [bs, num_queries embed_dims].
+            key (Tensor): The key tensor with shape [num_keys, bs,
+                embed_dims] if self.batch_first is False, else
+                [bs, num_keys, embed_dims] .
+                If None, the ``query`` will be used. Defaults to None.
+            value (Tensor): The value tensor with same shape as `key`.
+                Same in `nn.MultiheadAttention.forward`. Defaults to None.
+                If None, the `key` will be used.
+            identity (Tensor): This tensor, with the same shape as x,
+                will be used for the identity link.
+                If None, `x` will be used. Defaults to None.
+            query_pos (Tensor): The positional encoding for query, with
+                the same shape as `x`. If not None, it will
+                be added to `x` before forward function. Defaults to None.
+            key_pos (Tensor): The positional encoding for `key`, with the
+                same shape as `key`. Defaults to None. If not None, it will
+                be added to `key` before forward function. If None, and
+                `query_pos` has the same shape as `key`, then `query_pos`
+                will be used for `key_pos`. Defaults to None.
+            attn_mask (Tensor): ByteTensor mask with shape [num_queries,
+                num_keys]. Same in `nn.MultiheadAttention.forward`.
+                Defaults to None.
+            key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys].
+                Defaults to None.
+
+        Returns:
+            Tensor: forwarded results with shape
+            [num_queries, bs, embed_dims]
+            if self.batch_first is False, else
+            [bs, num_queries embed_dims].
+        """
+
+        if key is None:
+            key = query
+        if value is None:
+            value = key
+        if identity is None:
+            identity = query
+        if key_pos is None:
+            if query_pos is not None:
+                # use query_pos if key_pos is not available
+                if query_pos.shape == key.shape:
+                    key_pos = query_pos
+        if query_pos is not None:
+            query = query + query_pos
+        if key_pos is not None:
+            key = key + key_pos
+
+        # Because the dataflow('key', 'query', 'value') of
+        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
+        # embed_dims), We should adjust the shape of dataflow from
+        # batch_first (batch, num_query, embed_dims) to num_query_first
+        # (num_query ,batch, embed_dims), and recover ``attn_output``
+        # from num_query_first to batch_first.
+        if self.batch_first:
+            query = query.transpose(0, 1)
+            key = key.transpose(0, 1)
+            value = value.transpose(0, 1)
+
+        out = self.attn(
+            query=query,
+            key=key,
+            value=value,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask)[0]
+
+        if self.batch_first:
+            out = out.transpose(0, 1)
+
+        return identity + self.dropout_layer(self.proj_drop(out))
+
+
+class ModuleList(BaseModule, nn.ModuleList):
+    """ModuleList in openmmlab.
+
+    Ensures that all modules in ``ModuleList`` have a different initialization
+    strategy than the outer model
+
+    Args:
+        modules (iterable, optional): An iterable of modules to add.
+        init_cfg (dict, optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 modules: Optional[Iterable] = None,
+                 init_cfg: Optional[dict] = None):
+        BaseModule.__init__(self, init_cfg)
+        nn.ModuleList.__init__(self, modules)
+
+
+class Sequential(BaseModule, nn.Sequential):
+    """Sequential module in openmmlab.
+
+    Ensures that all modules in ``Sequential`` have a different initialization
+    strategy than the outer model
+
+    Args:
+        init_cfg (dict, optional): Initialization config dict.
+    """
+
+    def __init__(self, *args, init_cfg: Optional[dict] = None):
+        BaseModule.__init__(self, init_cfg)
+        nn.Sequential.__init__(self, *args)
\ No newline at end of file
diff --git a/segformer_plusplus/model/head/__init__.py b/segformer_plusplus/model/head/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..939f5552815b4e712af13103e34e72469f240ec9
--- /dev/null
+++ b/segformer_plusplus/model/head/__init__.py
@@ -0,0 +1,3 @@
+from .segformer_head import SegformerHead
+
+__all__ = ['SegformerHead']
diff --git a/segformer_plusplus/model/head/segformer_head.py b/segformer_plusplus/model/head/segformer_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..6729bc6ab9784f7a2916ac91f233debba5000827
--- /dev/null
+++ b/segformer_plusplus/model/head/segformer_head.py
@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+
+from ...utils import MODELS
+from ...utils import resize
+from ..base_module import BaseModule
+from ...utils.activation import ConvModule
+
+
+@MODELS.register_module()
+class SegformerHead(BaseModule):
+    """The all mlp Head of segformer.
+
+    This head is the implementation of
+    `Segformer <https://arxiv.org/abs/2105.15203>` _.
+
+    Args:
+        interpolate_mode: The interpolate mode of MLP head upsample operation.
+            Default: 'bilinear'.
+    """
+
+    def __init__(self,
+                 in_channels=[32, 64, 160, 256],
+                 in_index=[0, 1, 2, 3],
+                 channels=256,
+                 dropout_ratio=0.1,
+                 out_channels=19,
+                 norm_cfg=None,
+                 align_corners=False,
+                 interpolate_mode='bilinear'):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.in_index = in_index
+        self.channels = channels
+        self.dropout_ratio = dropout_ratio
+        self.out_channels = out_channels
+        self.norm_cfg = norm_cfg
+        self.align_corners = align_corners
+        self.interpolate_mode = interpolate_mode
+
+        self.act_cfg = dict(type='ReLU')
+        self.conv_seg = nn.Conv2d(channels, self.out_channels, kernel_size=1)
+        if dropout_ratio > 0:
+            self.dropout = nn.Dropout2d(dropout_ratio)
+        else:
+            self.dropout = None
+
+        num_inputs = len(self.in_channels)
+
+        assert num_inputs == len(self.in_index)
+
+        self.convs = nn.ModuleList()
+        for i in range(num_inputs):
+            self.convs.append(
+                ConvModule(
+                    in_channels=self.in_channels[i],
+                    out_channels=self.channels,
+                    kernel_size=1,
+                    stride=1,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg))
+
+        self.fusion_conv = ConvModule(
+            in_channels=self.channels * num_inputs,
+            out_channels=self.channels,
+            kernel_size=1,
+            norm_cfg=self.norm_cfg)
+
+    def cls_seg(self, feat):
+        """Classify each pixel."""
+        if self.dropout is not None:
+            feat = self.dropout(feat)
+        output = self.conv_seg(feat)
+        return output
+
+    def forward(self, inputs):
+        # Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
+        outs = []
+        for idx in range(len(inputs)):
+            x = inputs[idx]
+            conv = self.convs[idx]
+            outs.append(
+                resize(
+                    input=conv(x),
+                    size=inputs[0].shape[2:],
+                    mode=self.interpolate_mode,
+                    align_corners=self.align_corners))
+
+        out = self.fusion_conv(torch.cat(outs, dim=1))
+
+        out = self.cls_seg(out)
+
+        return out
diff --git a/segformer_plusplus/model/utils.py b/segformer_plusplus/model/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..10e071a71bd0e8565ed85e8ff215842bbbe12e30
--- /dev/null
+++ b/segformer_plusplus/model/utils.py
@@ -0,0 +1,27 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+
+from ..utils.registry import Registry
+
+
+MODEL_WRAPPERS = Registry('model_wrapper')
+
+def is_model_wrapper(model: nn.Module, registry: Registry = MODEL_WRAPPERS):
+    """Check if a module is a model wrapper.
+
+    Args:
+        model (nn.Module): The model to be checked.
+        registry (Registry): The parent registry to search for model wrappers.
+
+    Returns:
+        bool: True if the input model is a model wrapper.
+    """
+    module_wrappers = tuple(registry.module_dict.values())
+    if isinstance(model, module_wrappers):
+        return True
+
+    if not registry.children:
+        return False
+
+    return any(
+        is_model_wrapper(model, child) for child in registry.children.values())
diff --git a/segformer_plusplus/model/weight_init.py b/segformer_plusplus/model/weight_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..53b438be5fabd0fcdd122960fdd2ae7248fb76c4
--- /dev/null
+++ b/segformer_plusplus/model/weight_init.py
@@ -0,0 +1,413 @@
+
+import copy
+import math
+import warnings
+import inspect
+from typing import Any, Optional, Union
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from ..configs.config.config import Config, ConfigDict
+from ..utils.registry import Registry
+from ..utils.manager import ManagerMixin
+
+
+WEIGHT_INITIALIZERS = Registry('weight initializer')
+
+@WEIGHT_INITIALIZERS.register_module(name='Pretrained')
+class PretrainedInit:
+    """Initialize module by loading a pretrained model.
+
+    Args:
+        checkpoint (str): the checkpoint file of the pretrained model should
+            be load.
+        prefix (str, optional): the prefix of a sub-module in the pretrained
+            model. it is for loading a part of the pretrained model to
+            initialize. For example, if we would like to only load the
+            backbone of a detector model, we can set ``prefix='backbone.'``.
+            Defaults to None.
+        map_location (str): map tensors into proper locations. Defaults to cpu.
+    """
+
+    def __init__(self, checkpoint, prefix=None, map_location='cpu'):
+        self.checkpoint = checkpoint
+        self.prefix = prefix
+        self.map_location = map_location
+
+    def __call__(self, module):
+        from mmengine.runner.checkpoint import (_load_checkpoint_with_prefix,
+                                                load_checkpoint,
+                                                load_state_dict)
+        if self.prefix is None:
+            load_checkpoint(
+                module,
+                self.checkpoint,
+                map_location=self.map_location,
+                strict=False,
+                logger='current')
+        else:
+            state_dict = _load_checkpoint_with_prefix(
+                self.prefix, self.checkpoint, map_location=self.map_location)
+            load_state_dict(module, state_dict, strict=False, logger='current')
+
+        if hasattr(module, '_params_init_info'):
+            update_init_info(module, init_info=self._get_init_info())
+
+    def _get_init_info(self):
+        info = f'{self.__class__.__name__}: load from {self.checkpoint}'
+        return info
+    
+
+def update_init_info(module, init_info):
+    """Update the `_params_init_info` in the module if the value of parameters
+    are changed.
+
+    Args:
+        module (obj:`nn.Module`): The module of PyTorch with a user-defined
+            attribute `_params_init_info` which records the initialization
+            information.
+        init_info (str): The string that describes the initialization.
+    """
+    assert hasattr(
+        module,
+        '_params_init_info'), f'Can not find `_params_init_info` in {module}'
+    for name, param in module.named_parameters():
+
+        assert param in module._params_init_info, (
+            f'Find a new :obj:`Parameter` '
+            f'named `{name}` during executing the '
+            f'`init_weights` of '
+            f'`{module.__class__.__name__}`. '
+            f'Please do not add or '
+            f'replace parameters during executing '
+            f'the `init_weights`. ')
+
+        # The parameter has been changed during executing the
+        # `init_weights` of module
+        mean_value = param.data.mean().cpu()
+        if module._params_init_info[param]['tmp_mean_value'] != mean_value:
+            module._params_init_info[param]['init_info'] = init_info
+            module._params_init_info[param]['tmp_mean_value'] = mean_value
+
+
+def initialize(module, init_cfg):
+    r"""Initialize a module.
+
+    Args:
+        module (``torch.nn.Module``): the module will be initialized.
+        init_cfg (dict | list[dict]): initialization configuration dict to
+            define initializer. OpenMMLab has implemented 6 initializers
+            including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
+            ``Kaiming``, and ``Pretrained``.
+
+    Example:
+        >>> module = nn.Linear(2, 3, bias=True)
+        >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
+        >>> initialize(module, init_cfg)
+        >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
+        >>> # define key ``'layer'`` for initializing layer with different
+        >>> # configuration
+        >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
+                dict(type='Constant', layer='Linear', val=2)]
+        >>> initialize(module, init_cfg)
+        >>> # define key``'override'`` to initialize some specific part in
+        >>> # module
+        >>> class FooNet(nn.Module):
+        >>>     def __init__(self):
+        >>>         super().__init__()
+        >>>         self.feat = nn.Conv2d(3, 16, 3)
+        >>>         self.reg = nn.Conv2d(16, 10, 3)
+        >>>         self.cls = nn.Conv2d(16, 5, 3)
+        >>> model = FooNet()
+        >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
+        >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
+        >>> initialize(model, init_cfg)
+        >>> model = ResNet(depth=50)
+        >>> # Initialize weights with the pretrained model.
+        >>> init_cfg = dict(type='Pretrained',
+                checkpoint='torchvision://resnet50')
+        >>> initialize(model, init_cfg)
+        >>> # Initialize weights of a sub-module with the specific part of
+        >>> # a pretrained model by using "prefix".
+        >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
+        >>>     'retinanet_r50_fpn_1x_coco/'\
+        >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
+        >>> init_cfg = dict(type='Pretrained',
+                checkpoint=url, prefix='backbone.')
+    """
+    if not isinstance(init_cfg, (dict, list)):
+        raise TypeError(f'init_cfg must be a dict or a list of dict, \
+                but got {type(init_cfg)}')
+
+    if isinstance(init_cfg, dict):
+        init_cfg = [init_cfg]
+
+    for cfg in init_cfg:
+        # should deeply copy the original config because cfg may be used by
+        # other modules, e.g., one init_cfg shared by multiple bottleneck
+        # blocks, the expected cfg will be changed after pop and will change
+        # the initialization behavior of other modules
+        cp_cfg = copy.deepcopy(cfg)
+        override = cp_cfg.pop('override', None)
+        _initialize(module, cp_cfg)
+
+        if override is not None:
+            cp_cfg.pop('layer', None)
+            _initialize_override(module, override, cp_cfg)
+        else:
+            # All attributes in module have same initialization.
+            pass
+
+
+def _initialize(module, cfg, wholemodule=False):
+    func = build_from_cfg(cfg, WEIGHT_INITIALIZERS)
+    # wholemodule flag is for override mode, there is no layer key in override
+    # and initializer will give init values for the whole module with the name
+    # in override.
+    func.wholemodule = wholemodule
+    func(module)
+
+
+def _initialize_override(module, override, cfg):
+    if not isinstance(override, (dict, list)):
+        raise TypeError(f'override must be a dict or a list of dict, \
+                but got {type(override)}')
+
+    override = [override] if isinstance(override, dict) else override
+
+    for override_ in override:
+
+        cp_override = copy.deepcopy(override_)
+        name = cp_override.pop('name', None)
+        if name is None:
+            raise ValueError('`override` must contain the key "name",'
+                             f'but got {cp_override}')
+        # if override only has name key, it means use args in init_cfg
+        if not cp_override:
+            cp_override.update(cfg)
+        # if override has name key and other args except type key, it will
+        # raise error
+        elif 'type' not in cp_override.keys():
+            raise ValueError(
+                f'`override` need "type" key, but got {cp_override}')
+
+        if hasattr(module, name):
+            _initialize(getattr(module, name), cp_override, wholemodule=True)
+        else:
+            raise RuntimeError(f'module did not have attribute {name}, '
+                               f'but init_cfg is {cp_override}.')
+        
+
+def build_from_cfg(
+        cfg: Union[dict, ConfigDict, Config],
+        registry: Registry,
+        default_args: Optional[Union[dict, ConfigDict, Config]] = None) -> Any:
+    """Build a module from config dict when it is a class configuration, or
+    call a function from config dict when it is a function configuration.
+
+    If the global variable default scope (:obj:`DefaultScope`) exists,
+    :meth:`build` will firstly get the responding registry and then call
+    its own :meth:`build`.
+
+    At least one of the ``cfg`` and ``default_args`` contains the key "type",
+    which should be either str or class. If they all contain it, the key
+    in ``cfg`` will be used because ``cfg`` has a high priority than
+    ``default_args`` that means if a key exists in both of them, the value of
+    the key will be ``cfg[key]``. They will be merged first and the key "type"
+    will be popped up and the remaining keys will be used as initialization
+    arguments.
+
+    Examples:
+        >>> from mmengine import Registry, build_from_cfg
+        >>> MODELS = Registry('models')
+        >>> @MODELS.register_module()
+        >>> class ResNet:
+        >>>     def __init__(self, depth, stages=4):
+        >>>         self.depth = depth
+        >>>         self.stages = stages
+        >>> cfg = dict(type='ResNet', depth=50)
+        >>> model = build_from_cfg(cfg, MODELS)
+        >>> # Returns an instantiated object
+        >>> @MODELS.register_module()
+        >>> def resnet50():
+        >>>     pass
+        >>> resnet = build_from_cfg(dict(type='resnet50'), MODELS)
+        >>> # Return a result of the calling function
+
+    Args:
+        cfg (dict or ConfigDict or Config): Config dict. It should at least
+            contain the key "type".
+        registry (:obj:`Registry`): The registry to search the type from.
+        default_args (dict or ConfigDict or Config, optional): Default
+            initialization arguments. Defaults to None.
+
+    Returns:
+        object: The constructed object.
+    """
+    if not isinstance(cfg, (dict, ConfigDict, Config)):
+        raise TypeError(
+            f'cfg should be a dict, ConfigDict or Config, but got {type(cfg)}')
+
+    if 'type' not in cfg:
+        if default_args is None or 'type' not in default_args:
+            raise KeyError(
+                '`cfg` or `default_args` must contain the key "type", '
+                f'but got {cfg}\n{default_args}')
+
+    if not isinstance(registry, Registry):
+        raise TypeError('registry must be a mmengine.Registry object, '
+                        f'but got {type(registry)}')
+
+    if not (isinstance(default_args,
+                       (dict, ConfigDict, Config)) or default_args is None):
+        raise TypeError(
+            'default_args should be a dict, ConfigDict, Config or None, '
+            f'but got {type(default_args)}')
+
+    args = cfg.copy()
+    if default_args is not None:
+        for name, value in default_args.items():
+            args.setdefault(name, value)
+
+    # Instance should be built under target scope, if `_scope_` is defined
+    # in cfg, current default scope should switch to specified scope
+    # temporarily.
+    scope = args.pop('_scope_', None)
+    with registry.switch_scope_and_registry(scope) as registry:
+        obj_type = args.pop('type')
+        if isinstance(obj_type, str):
+            obj_cls = registry.get(obj_type)
+            if obj_cls is None:
+                raise KeyError(
+                    f'{obj_type} is not in the {registry.scope}::{registry.name} registry. '  # noqa: E501
+                    f'Please check whether the value of `{obj_type}` is '
+                    'correct or it was registered as expected. More details '
+                    'can be found at '
+                    'https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'  # noqa: E501
+                )
+        # this will include classes, functions, partial functions and more
+        elif callable(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        # If `obj_cls` inherits from `ManagerMixin`, it should be
+        # instantiated by `ManagerMixin.get_instance` to ensure that it
+        # can be accessed globally.
+        if inspect.isclass(obj_cls) and \
+                issubclass(obj_cls, ManagerMixin):  # type: ignore
+            obj = obj_cls.get_instance(**args)  # type: ignore
+        else:
+            obj = obj_cls(**args)  # type: ignore
+        return obj
+    
+
+def constant_init(module, val, bias=0):
+    if hasattr(module, 'weight') and module.weight is not None:
+        nn.init.constant_(module.weight, val)
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)
+
+
+def normal_init(module, mean=0, std=1, bias=0):
+    if hasattr(module, 'weight') and module.weight is not None:
+        nn.init.normal_(module.weight, mean, std)
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)
+
+
+def trunc_normal_init(module: nn.Module,
+                      mean: float = 0,
+                      std: float = 1,
+                      a: float = -2,
+                      b: float = 2,
+                      bias: float = 0) -> None:
+    if hasattr(module, 'weight') and module.weight is not None:
+        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)  # type: ignore
+
+
+def kaiming_init(module,
+                 a=0,
+                 mode='fan_out',
+                 nonlinearity='relu',
+                 bias=0,
+                 distribution='normal'):
+    assert distribution in ['uniform', 'normal']
+    if hasattr(module, 'weight') and module.weight is not None:
+        if distribution == 'uniform':
+            nn.init.kaiming_uniform_(
+                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
+        else:
+            nn.init.kaiming_normal_(
+                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)
+
+
+def trunc_normal_(tensor: Tensor,
+                  mean: float = 0.,
+                  std: float = 1.,
+                  a: float = -2.,
+                  b: float = 2.) -> Tensor:
+    r"""Fills the input Tensor with values drawn from a truncated normal
+    distribution. The values are effectively drawn from the normal distribution
+    :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside
+    :math:`[a, b]` redrawn until they are within the bounds. The method used
+    for generating the random values works best when :math:`a \leq \text{mean}
+    \leq b`.
+
+    Modified from
+    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
+
+    Args:
+        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
+        mean (float): the mean of the normal distribution.
+        std (float): the standard deviation of the normal distribution.
+        a (float): the minimum cutoff value.
+        b (float): the maximum cutoff value.
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
+                           b: float) -> Tensor:
+    # Method based on
+    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    # Modified from
+    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
+            'The distribution of values may be incorrect.',
+            stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        lower = norm_cdf((a - mean) / std)
+        upper = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [lower, upper], then translate
+        # to [2lower-1, 2upper-1].
+        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
\ No newline at end of file
diff --git a/segformer_plusplus/random_benchmark.py b/segformer_plusplus/random_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cb1628279a081dd2c64635faea0d154d458ddd9
--- /dev/null
+++ b/segformer_plusplus/random_benchmark.py
@@ -0,0 +1,61 @@
+from typing import Union, List, Tuple
+
+import numpy as np
+import torch
+
+from .utils import benchmark
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+
+def random_benchmark(
+        model: torch.nn.Module,
+        batch_size: Union[int, List[int]] = 1,
+        image_size: Union[Tuple[int], List[Tuple[int]]] = (3, 1024, 1024),
+):
+    """
+    Calculate the FPS of a given model using randomly generated tensors.
+
+    Args:
+        model: instance of a model (e.g. SegFormer)
+        batch_size: the batch size(s) at which to calculate the FPS (e.g. 1 or [1, 2, 4])
+        image_size: the size of the images to use (e.g. (3, 1024, 1024))
+
+    Returns: the FPS values calculated for all image sizes and batch sizes in the form of a dictionary
+
+    """
+    if isinstance(batch_size, int):
+        batch_size = [batch_size]
+    if isinstance(image_size, tuple):
+        image_size = [image_size]
+
+    values = {}
+    throughput_values = []
+
+    for i in image_size:
+        # fill with fps for each batch size
+        fps = []
+        for b in batch_size:
+            for _ in range(4):
+                # Baseline benchmark
+                if i[1] >= 1024:
+                    r = 16
+                else:
+                    r = 32
+                baseline_throughput = benchmark(
+                    model.to(device),
+                    device=device,
+                    verbose=True,
+                    runs=r,
+                    batch_size=b,
+                    input_size=i
+                )
+                throughput_values.append(baseline_throughput)
+            throughput_values = np.asarray(throughput_values)
+            throughput = np.around(np.mean(throughput_values), decimals=2)
+            print('Im_size:', i, 'Batch_size:', b, 'Mean:', throughput, 'Std:',
+                  np.around(np.std(throughput_values), decimals=2))
+            throughput_values = []
+            fps.append({b: throughput})
+        values[i] = fps
+    return values
diff --git a/segformer_plusplus/start_cityscape_benchmark.py b/segformer_plusplus/start_cityscape_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..f140c50cf2329c3bb97c6ab5a7f200feb4c945d9
--- /dev/null
+++ b/segformer_plusplus/start_cityscape_benchmark.py
@@ -0,0 +1,53 @@
+import os
+import torch
+import numpy as np
+import argparse
+
+from .build_model import create_model
+from .cityscape_benchmark import cityscape_benchmark
+
+parser = argparse.ArgumentParser(description="Segformer Benchmarking Script")
+parser.add_argument('--backbone', type=str, default='b0', choices=['b0', 'b1', 'b2', 'b3', 'b4', 'b5'], help='Model backbone version')
+parser.add_argument('--head', type=str, default='bsm_hq', choices=['bsm_hq', 'bsm_fast', 'n2d_2x2'], help='Model head type')
+parser.add_argument('--checkpoint', type=str, default=None, help='Path to .pth checkpoint file (optional)')
+args = parser.parse_args()
+
+model = create_model(args.backbone, args.head, pretrained=True)
+
+if args.checkpoint:
+    checkpoint_path = os.path.expanduser(args.checkpoint)
+    print(f"Loading checkpoint: {checkpoint_path}")
+    checkpoint = torch.load(checkpoint_path)
+    model.load_state_dict(checkpoint)
+else:
+    print("No checkpoint provided – using model as initialized.")
+
+image_path = os.path.expanduser('~/SegformerPlusPlus/mmsegmentation/data/cityscapes/leftImg8bit/test/berlin/berlin_000543_000019_leftImg8bit.png')
+result = cityscape_benchmark(model, image_path)
+
+print("Cityscapes Benchmark Results:", result)
+
+reference_txt_path = os.path.expanduser('~/SegformerPlusPlus/model/cityscapes_prediction_output_reference.txt')
+generated_txt_path = os.path.expanduser('~/SegformerPlusPlus/model/cityscapes_prediction_output.txt')
+
+if os.path.exists(reference_txt_path) and os.path.exists(generated_txt_path):
+    ref_arr = np.loadtxt(reference_txt_path, dtype=int)
+    gen_arr = np.loadtxt(generated_txt_path, dtype=int)
+
+    if ref_arr.shape != gen_arr.shape:
+        print(f"Files have different shapes: {ref_arr.shape} vs. {gen_arr.shape}")
+    else:
+        total_elements = ref_arr.size
+        equal_elements = np.sum(ref_arr == gen_arr)
+        similarity = equal_elements / total_elements
+
+        threshold = 0.999
+        if similarity >= threshold:
+            print(f"Outputs are {similarity*100:.4f}% identical (>= {threshold*100}%).")
+        else:
+            print(f"Outputs differ by {100 - similarity*100:.4f}%.")
+else:
+    if not os.path.exists(reference_txt_path):
+        print(f"Reference file not found: {reference_txt_path}")
+    if not os.path.exists(generated_txt_path):
+        print(f"Generated output file not found: {generated_txt_path}")
diff --git a/segformer_plusplus/start_random_benchmark.py b/segformer_plusplus/start_random_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..fec133ddef4836e4d2549849adc95e8ffb0aabf3
--- /dev/null
+++ b/segformer_plusplus/start_random_benchmark.py
@@ -0,0 +1,5 @@
+from .build_model import create_model
+from .random_benchmark import random_benchmark
+
+model = create_model('b5', 'bsm_hq', pretrained=True)
+v = random_benchmark(model)
\ No newline at end of file
diff --git a/segformer_plusplus/utils/__init__.py b/segformer_plusplus/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a16c7795ef0e3cddd17a38475e9c2a1601c7984f
--- /dev/null
+++ b/segformer_plusplus/utils/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .embed import PatchEmbed
+from .shape_convert import nchw_to_nlc, nlc_to_nchw
+from .wrappers import resize, Conv2d
+from .tome_presets import tome_presets
+from .registry import MODELS
+from .imagenet_weights import imagenet_weights
+from .benchmark import benchmark
+from .activation import build_activation_layer, build_norm_layer, build_dropout
+from .version_utils import digit_version
+
+
+__all__ = [
+    'PatchEmbed', 'nchw_to_nlc', 'nlc_to_nchw', 'resize', 'Conv2d', 'tome_presets', 'MODELS', 'imagenet_weights', 'benchmark', 'build_activation_layer', 'build_norm_layer', 'build_dropout', 'digit_version'
+]
diff --git a/segformer_plusplus/utils/activation.py b/segformer_plusplus/utils/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..43f037b1f3ed0a02184d470ffe1b02c4ad89a121
--- /dev/null
+++ b/segformer_plusplus/utils/activation.py
@@ -0,0 +1,430 @@
+import warnings
+from typing import Dict, Tuple, Union, Any, Optional
+import inspect
+import torch.nn as nn
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+from torch.nn.modules.instancenorm import _InstanceNorm
+
+from ..model.weight_init import constant_init, kaiming_init
+from ..utils.registry import MODELS
+from .build_functions import SyncBatchNorm
+
+
+TORCH_VERSION = torch.__version__
+
+# register norm-layers
+MODELS.register_module('BN', module=nn.BatchNorm2d)
+MODELS.register_module('BN1d', module=nn.BatchNorm1d)
+MODELS.register_module('BN2d', module=nn.BatchNorm2d)
+MODELS.register_module('BN3d', module=nn.BatchNorm3d)
+MODELS.register_module('SyncBN', module=SyncBatchNorm)
+MODELS.register_module('GN', module=nn.GroupNorm)
+MODELS.register_module('LN', module=nn.LayerNorm)
+MODELS.register_module('IN', module=nn.InstanceNorm2d)
+MODELS.register_module('IN1d', module=nn.InstanceNorm1d)
+MODELS.register_module('IN2d', module=nn.InstanceNorm2d)
+MODELS.register_module('IN3d', module=nn.InstanceNorm3d)
+# register conv-layers
+MODELS.register_module('Conv1d', module=nn.Conv1d)
+MODELS.register_module('Conv2d', module=nn.Conv2d)
+MODELS.register_module('Conv3d', module=nn.Conv3d)
+MODELS.register_module('Conv', module=nn.Conv2d)
+# register activation-functions
+MODELS.register_module('GELU', module=nn.GELU)
+MODELS.register_module('ReLU', module=nn.ReLU)
+
+def build_activation_layer(cfg: Dict) -> nn.Module:
+    """Build activation layer.
+
+    Args:
+        cfg (dict): The activation layer config, which should contain:
+
+            - type (str): Layer type.
+            - layer args: Args needed to instantiate an activation layer.
+
+    Returns:
+        nn.Module: Created activation layer.
+    """
+    return MODELS.build(cfg)
+
+
+def build_norm_layer(cfg: Dict,
+                     num_features: int,
+                     postfix: Union[int, str] = '') -> Tuple[str, nn.Module]:
+    """Build normalization layer.
+
+    Args:
+        cfg (dict): The norm layer config, which should contain:
+
+            - type (str): Layer type.
+            - layer args: Args needed to instantiate a norm layer.
+            - requires_grad (bool, optional): Whether stop gradient updates.
+        num_features (int): Number of input channels.
+        postfix (int | str): The postfix to be appended into norm abbreviation
+            to create named layer.
+
+    Returns:
+        tuple[str, nn.Module]: The first element is the layer name consisting
+        of abbreviation and postfix, e.g., bn1, gn. The second element is the
+        created norm layer.
+    """
+    if not isinstance(cfg, dict):
+        raise TypeError('cfg must be a dict')
+    if 'type' not in cfg:
+        raise KeyError('the cfg dict must contain the key "type"')
+    cfg_ = cfg.copy()
+
+    layer_type = cfg_.pop('type')
+
+    # Switch registry to the target scope. If `norm_layer` cannot be found
+    # in the registry, fallback to search `norm_layer` in the
+    # mmengine.MODELS.
+    with MODELS.switch_scope_and_registry(None) as registry:
+        norm_layer = registry.get(layer_type)
+    if norm_layer is None:
+        raise KeyError(f'Cannot find {norm_layer} in registry under scope '
+                       f'name {registry.scope}')
+    abbr = infer_abbr(norm_layer)
+
+    assert isinstance(postfix, (int, str))
+    name = abbr + str(postfix)
+
+    requires_grad = cfg_.pop('requires_grad', True)
+    cfg_.setdefault('eps', 1e-5)
+    if layer_type != 'GN':
+        layer = norm_layer(num_features, **cfg_)
+        if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'):
+            layer._specify_ddp_gpu_num(1)
+    else:
+        assert 'num_groups' in cfg_
+        layer = norm_layer(num_channels=num_features, **cfg_)
+
+    for param in layer.parameters():
+        param.requires_grad = requires_grad
+
+    return name, layer
+
+
+def infer_abbr(class_type):
+    """Infer abbreviation from the class name.
+
+    When we build a norm layer with `build_norm_layer()`, we want to preserve
+    the norm type in variable names, e.g, self.bn1, self.gn. This method will
+    infer the abbreviation to map class types to abbreviations.
+
+    Rule 1: If the class has the property "_abbr_", return the property.
+    Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or
+    InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and
+    "in" respectively.
+    Rule 3: If the class name contains "batch", "group", "layer" or "instance",
+    the abbreviation of this layer will be "bn", "gn", "ln" and "in"
+    respectively.
+    Rule 4: Otherwise, the abbreviation falls back to "norm".
+
+    Args:
+        class_type (type): The norm layer type.
+
+    Returns:
+        str: The inferred abbreviation.
+    """
+    if not inspect.isclass(class_type):
+        raise TypeError(
+            f'class_type must be a type, but got {type(class_type)}')
+    if hasattr(class_type, '_abbr_'):
+        return class_type._abbr_
+    if issubclass(class_type, _InstanceNorm):  # IN is a subclass of BN
+        return 'in'
+    elif issubclass(class_type, _BatchNorm):
+        return 'bn'
+    elif issubclass(class_type, nn.GroupNorm):
+        return 'gn'
+    elif issubclass(class_type, nn.LayerNorm):
+        return 'ln'
+    else:
+        class_name = class_type.__name__.lower()
+        if 'batch' in class_name:
+            return 'bn'
+        elif 'group' in class_name:
+            return 'gn'
+        elif 'layer' in class_name:
+            return 'ln'
+        elif 'instance' in class_name:
+            return 'in'
+        else:
+            return 'norm_layer'
+        
+
+def build_dropout(cfg: Dict, default_args: Optional[Dict] = None) -> Any:
+    """Builder for drop out layers."""
+    return MODELS.build(cfg, default_args=default_args)
+
+
+def build_conv_layer(cfg: Optional[Dict], *args, **kwargs) -> nn.Module:
+    """Build convolution layer.
+
+    Args:
+        cfg (None or dict): The conv layer config, which should contain:
+            - type (str): Layer type.
+            - layer args: Args needed to instantiate an conv layer.
+        args (argument list): Arguments passed to the `__init__`
+            method of the corresponding conv layer.
+        kwargs (keyword arguments): Keyword arguments passed to the `__init__`
+            method of the corresponding conv layer.
+
+    Returns:
+        nn.Module: Created conv layer.
+    """
+    if cfg is None:
+        cfg_ = dict(type='Conv2d')
+    else:
+        if not isinstance(cfg, dict):
+            raise TypeError('cfg must be a dict')
+        if 'type' not in cfg:
+            raise KeyError('the cfg dict must contain the key "type"')
+        cfg_ = cfg.copy()
+
+    layer_type = cfg_.pop('type')
+
+    # Switch registry to the target scope. If `conv_layer` cannot be found
+    # in the registry, fallback to search `conv_layer` in the
+    # mmengine.MODELS.
+    with MODELS.switch_scope_and_registry(None) as registry:
+        conv_layer = registry.get(layer_type)
+    if conv_layer is None:
+        raise KeyError(f'Cannot find {conv_layer} in registry under scope '
+                       f'name {registry.scope}')
+    layer = conv_layer(*args, **kwargs, **cfg_)
+
+    return layer
+
+
+def build_padding_layer(cfg: Dict, *args, **kwargs) -> nn.Module:
+    """Build padding layer.
+
+    Args:
+        cfg (dict): The padding layer config, which should contain:
+            - type (str): Layer type.
+            - layer args: Args needed to instantiate a padding layer.
+
+    Returns:
+        nn.Module: Created padding layer.
+    """
+    if not isinstance(cfg, dict):
+        raise TypeError('cfg must be a dict')
+    if 'type' not in cfg:
+        raise KeyError('the cfg dict must contain the key "type"')
+
+    cfg_ = cfg.copy()
+    padding_type = cfg_.pop('type')
+
+    # Switch registry to the target scope. If `padding_layer` cannot be found
+    # in the registry, fallback to search `padding_layer` in the
+    # mmengine.MODELS.
+    with MODELS.switch_scope_and_registry(None) as registry:
+        padding_layer = registry.get(padding_type)
+    if padding_layer is None:
+        raise KeyError(f'Cannot find {padding_layer} in registry under scope '
+                       f'name {registry.scope}')
+    layer = padding_layer(*args, **kwargs, **cfg_)
+
+    return layer
+
+
+@MODELS.register_module()
+class ConvModule(nn.Module):
+    """A conv block that bundles conv/norm/activation layers.
+
+    This block simplifies the usage of convolution layers, which are commonly
+    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
+    It is based upon three build methods: `build_conv_layer()`,
+    `build_norm_layer()` and `build_activation_layer()`.
+
+    Besides, we add some additional features in this module.
+    1. Automatically set `bias` of the conv layer.
+    2. Spectral norm is supported.
+    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
+    supports zero and circular padding, and we add "reflect" padding mode.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+            Same as that in ``nn._ConvNd``.
+        out_channels (int): Number of channels produced by the convolution.
+            Same as that in ``nn._ConvNd``.
+        kernel_size (int | tuple[int]): Size of the convolving kernel.
+            Same as that in ``nn._ConvNd``.
+        stride (int | tuple[int]): Stride of the convolution.
+            Same as that in ``nn._ConvNd``.
+        padding (int | tuple[int]): Zero-padding added to both sides of
+            the input. Same as that in ``nn._ConvNd``.
+        dilation (int | tuple[int]): Spacing between kernel elements.
+            Same as that in ``nn._ConvNd``.
+        groups (int): Number of blocked connections from input channels to
+            output channels. Same as that in ``nn._ConvNd``.
+        bias (bool | str): If specified as `auto`, it will be decided by the
+            norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise
+            False. Default: "auto".
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        inplace (bool): Whether to use inplace mode for activation.
+            Default: True.
+        with_spectral_norm (bool): Whether use spectral norm in conv module.
+            Default: False.
+        padding_mode (str): If the `padding_mode` has not been supported by
+            current `Conv2d` in PyTorch, we will use our own padding layer
+            instead. Currently, we support ['zeros', 'circular'] with official
+            implementation and ['reflect'] with our own implementation.
+            Default: 'zeros'.
+        order (tuple[str]): The order of conv/norm/activation layers. It is a
+            sequence of "conv", "norm" and "act". Common examples are
+            ("conv", "norm", "act") and ("act", "conv", "norm").
+            Default: ('conv', 'norm', 'act').
+    """
+
+    _abbr_ = 'conv_block'
+
+    def __init__(self,
+                 in_channels: int,
+                 out_channels: int,
+                 kernel_size: Union[int, Tuple[int, int]],
+                 stride: Union[int, Tuple[int, int]] = 1,
+                 padding: Union[int, Tuple[int, int]] = 0,
+                 dilation: Union[int, Tuple[int, int]] = 1,
+                 groups: int = 1,
+                 bias: Union[bool, str] = 'auto',
+                 conv_cfg: Optional[Dict] = None,
+                 norm_cfg: Optional[Dict] = None,
+                 act_cfg: Optional[Dict] = dict(type='ReLU'),
+                 inplace: bool = True,
+                 with_spectral_norm: bool = False,
+                 padding_mode: str = 'zeros',
+                 order: tuple = ('conv', 'norm', 'act')):
+        super().__init__()
+        assert conv_cfg is None or isinstance(conv_cfg, dict)
+        assert norm_cfg is None or isinstance(norm_cfg, dict)
+        assert act_cfg is None or isinstance(act_cfg, dict)
+        official_padding_mode = ['zeros', 'circular']
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.inplace = inplace
+        self.with_spectral_norm = with_spectral_norm
+        self.with_explicit_padding = padding_mode not in official_padding_mode
+        self.order = order
+        assert isinstance(self.order, tuple) and len(self.order) == 3
+        assert set(order) == {'conv', 'norm', 'act'}
+
+        self.with_norm = norm_cfg is not None
+        self.with_activation = act_cfg is not None
+        # if the conv layer is before a norm layer, bias is unnecessary.
+        if bias == 'auto':
+            bias = not self.with_norm
+        self.with_bias = bias
+
+        if self.with_explicit_padding:
+            pad_cfg = dict(type=padding_mode)
+            self.padding_layer = build_padding_layer(pad_cfg, padding)
+
+        # reset padding to 0 for conv module
+        conv_padding = 0 if self.with_explicit_padding else padding
+        # build convolution layer
+        self.conv = build_conv_layer(
+            conv_cfg,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=conv_padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias)
+        # export the attributes of self.conv to a higher level for convenience
+        self.in_channels = self.conv.in_channels
+        self.out_channels = self.conv.out_channels
+        self.kernel_size = self.conv.kernel_size
+        self.stride = self.conv.stride
+        self.padding = padding
+        self.dilation = self.conv.dilation
+        self.transposed = self.conv.transposed
+        self.output_padding = self.conv.output_padding
+        self.groups = self.conv.groups
+
+        if self.with_spectral_norm:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+        # build normalization layers
+        if self.with_norm:
+            # norm layer is after conv layer
+            if order.index('norm') > order.index('conv'):
+                norm_channels = out_channels
+            else:
+                norm_channels = in_channels
+            self.norm_name, norm = build_norm_layer(
+                norm_cfg, norm_channels)  # type: ignore
+            self.add_module(self.norm_name, norm)
+            if self.with_bias:
+                if isinstance(norm, (_BatchNorm, _InstanceNorm)):
+                    warnings.warn(
+                        'Unnecessary conv bias before batch/instance norm')
+        else:
+            self.norm_name = None  # type: ignore
+
+        # build activation layer
+        if self.with_activation:
+            act_cfg_ = act_cfg.copy()  # type: ignore
+            # nn.Tanh has no 'inplace' argument
+            if act_cfg_['type'] not in [
+                    'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish', 'GELU'
+            ]:
+                act_cfg_.setdefault('inplace', inplace)
+            self.activate = build_activation_layer(act_cfg_)
+
+        # Use msra init by default
+        self.init_weights()
+
+    @property
+    def norm(self):
+        if self.norm_name:
+            return getattr(self, self.norm_name)
+        else:
+            return None
+
+    def init_weights(self):
+        # 1. It is mainly for customized conv layers with their own
+        #    initialization manners by calling their own ``init_weights()``,
+        #    and we do not want ConvModule to override the initialization.
+        # 2. For customized conv layers without their own initialization
+        #    manners (that is, they don't have their own ``init_weights()``)
+        #    and PyTorch's conv layers, they will be initialized by
+        #    this method with default ``kaiming_init``.
+        # Note: For PyTorch's conv layers, they will be overwritten by our
+        #    initialization implementation using default ``kaiming_init``.
+        if not hasattr(self.conv, 'init_weights'):
+            if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':
+                nonlinearity = 'leaky_relu'
+                a = self.act_cfg.get('negative_slope', 0.01)
+            else:
+                nonlinearity = 'relu'
+                a = 0
+            kaiming_init(self.conv, a=a, nonlinearity=nonlinearity)
+        if self.with_norm:
+            constant_init(self.norm, 1, bias=0)
+
+    def forward(self,
+                x: torch.Tensor,
+                activate: bool = True,
+                norm: bool = True) -> torch.Tensor:
+        for layer in self.order:
+            if layer == 'conv':
+                if self.with_explicit_padding:
+                    x = self.padding_layer(x)
+                x = self.conv(x)
+            elif layer == 'norm' and norm and self.with_norm:
+                x = self.norm(x)
+            elif layer == 'act' and activate and self.with_activation:
+                x = self.activate(x)
+        return x
+
diff --git a/segformer_plusplus/utils/benchmark.py b/segformer_plusplus/utils/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0b20c51f7c83d33e40d5210da9f1d4bbabb87d
--- /dev/null
+++ b/segformer_plusplus/utils/benchmark.py
@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# Source: https://github.com/facebookresearch/ToMe/blob/main/tome/utils.py
+# --------------------------------------------------------
+
+import time
+from typing import Tuple
+import torch
+from tqdm import tqdm
+
+
+def benchmark(
+        model: torch.nn.Module,
+        device: torch.device = 0,
+        input_size: Tuple[int] = (3, 224, 224),
+        batch_size: int = 64,
+        runs: int = 40,
+        throw_out: float = 0.25,
+        use_fp16: bool = False,
+        verbose: bool = False,
+) -> float:
+    """
+    Benchmark the given model with random inputs at the given batch size.
+
+    Args:
+     - model: the module to benchmark
+     - device: the device to use for benchmarking
+     - input_size: the input size to pass to the model (channels, h, w)
+     - batch_size: the batch size to use for evaluation
+     - runs: the number of total runs to do
+     - throw_out: the percentage of runs to throw out at the start of testing
+     - use_fp16: whether or not to benchmark with float16 and autocast
+     - verbose: whether or not to use tqdm to print progress / print throughput at end
+
+    Returns:
+     - the throughput measured in images / second
+    """
+    
+    if not isinstance(device, torch.device):
+        device = torch.device(device)
+    is_cuda = torch.device(device).type == "cuda"
+
+    model = model.eval().to(device)
+    input = torch.rand(batch_size, *input_size, device=device)
+    if use_fp16:
+        input = input.half()
+
+    warm_up = int(runs * throw_out)
+    total = 0
+    start = time.time()
+
+    with torch.autocast(device.type, enabled=use_fp16):
+        with torch.no_grad():
+            for i in tqdm(range(runs), disable=not verbose, desc="Benchmarking"):
+                if i == warm_up:
+                    if is_cuda:
+                        torch.cuda.synchronize()
+                    total = 0
+                    start = time.time()
+
+                model(input)
+                total += batch_size
+
+    if is_cuda:
+        torch.cuda.synchronize()
+
+    end = time.time()
+    elapsed = end - start
+
+    throughput = total / elapsed
+
+    if verbose:
+        print(f"Throughput: {throughput:.2f} im/s")
+
+    return throughput
diff --git a/segformer_plusplus/utils/build_functions.py b/segformer_plusplus/utils/build_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f5f92a42c55f5ba927e3c9be01fd88d495a5695
--- /dev/null
+++ b/segformer_plusplus/utils/build_functions.py
@@ -0,0 +1,156 @@
+from typing import Any, Optional, Union
+import inspect
+import torch.nn as nn
+import torch
+
+from ..configs.config.config import Config, ConfigDict
+from .registry import Registry
+from ..utils.manager import ManagerMixin
+
+
+TORCH_VERSION = torch.__version__
+
+def build_from_cfg(
+        cfg: Union[dict, ConfigDict, Config],
+        registry: Registry,
+        default_args: Optional[Union[dict, ConfigDict, Config]] = None) -> Any:
+    """Build a module from config dict when it is a class configuration, or
+    call a function from config dict when it is a function configuration.
+
+    If the global variable default scope (:obj:`DefaultScope`) exists,
+    :meth:`build` will firstly get the responding registry and then call
+    its own :meth:`build`.
+
+    At least one of the ``cfg`` and ``default_args`` contains the key "type",
+    which should be either str or class. If they all contain it, the key
+    in ``cfg`` will be used because ``cfg`` has a high priority than
+    ``default_args`` that means if a key exists in both of them, the value of
+    the key will be ``cfg[key]``. They will be merged first and the key "type"
+    will be popped up and the remaining keys will be used as initialization
+    arguments.
+
+    Examples:
+        >>> from mmengine import Registry, build_from_cfg
+        >>> MODELS = Registry('models')
+        >>> @MODELS.register_module()
+        >>> class ResNet:
+        >>>     def __init__(self, depth, stages=4):
+        >>>         self.depth = depth
+        >>>         self.stages = stages
+        >>> cfg = dict(type='ResNet', depth=50)
+        >>> model = build_from_cfg(cfg, MODELS)
+        >>> # Returns an instantiated object
+        >>> @MODELS.register_module()
+        >>> def resnet50():
+        >>>     pass
+        >>> resnet = build_from_cfg(dict(type='resnet50'), MODELS)
+        >>> # Return a result of the calling function
+
+    Args:
+        cfg (dict or ConfigDict or Config): Config dict. It should at least
+            contain the key "type".
+        registry (:obj:`Registry`): The registry to search the type from.
+        default_args (dict or ConfigDict or Config, optional): Default
+            initialization arguments. Defaults to None.
+
+    Returns:
+        object: The constructed object.
+    """
+
+    if not isinstance(cfg, (dict, ConfigDict, Config)):
+        raise TypeError(
+            f'cfg should be a dict, ConfigDict or Config, but got {type(cfg)}')
+
+    if 'type' not in cfg:
+        if default_args is None or 'type' not in default_args:
+            raise KeyError(
+                '`cfg` or `default_args` must contain the key "type", '
+                f'but got {cfg}\n{default_args}')
+
+    if not isinstance(registry, Registry):
+        raise TypeError('registry must be a mmengine.Registry object, '
+                        f'but got {type(registry)}')
+
+    if not (isinstance(default_args,
+                       (dict, ConfigDict, Config)) or default_args is None):
+        raise TypeError(
+            'default_args should be a dict, ConfigDict, Config or None, '
+            f'but got {type(default_args)}')
+
+    args = cfg.copy()
+    if default_args is not None:
+        for name, value in default_args.items():
+            args.setdefault(name, value)
+
+    # Instance should be built under target scope, if `_scope_` is defined
+    # in cfg, current default scope should switch to specified scope
+    # temporarily.
+    scope = args.pop('_scope_', None)
+    with registry.switch_scope_and_registry(scope) as registry:
+        obj_type = args.pop('type')
+        if isinstance(obj_type, str):
+            obj_cls = registry.get(obj_type)
+            if obj_cls is None:
+                raise KeyError(
+                    f'{obj_type} is not in the {registry.scope}::{registry.name} registry. '  # noqa: E501
+                    f'Please check whether the value of `{obj_type}` is '
+                    'correct or it was registered as expected. More details '
+                    'can be found at '
+                    'https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'  # noqa: E501
+                )
+        # this will include classes, functions, partial functions and more
+        elif callable(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        # If `obj_cls` inherits from `ManagerMixin`, it should be
+        # instantiated by `ManagerMixin.get_instance` to ensure that it
+        # can be accessed globally.
+        if inspect.isclass(obj_cls) and \
+                issubclass(obj_cls, ManagerMixin):  # type: ignore
+            obj = obj_cls.get_instance(**args)  # type: ignore
+        else:
+            obj = obj_cls(**args)  # type: ignore
+        return obj
+
+
+def build_model_from_cfg(
+    cfg: Union[dict, ConfigDict, Config],
+    registry: Registry,
+    default_args: Optional[Union[dict, 'ConfigDict', 'Config']] = None
+) -> 'nn.Module':
+    """Build a PyTorch model from config dict(s). Different from
+    ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built.
+
+    Args:
+        cfg (dict, list[dict]): The config of modules, which is either a config
+            dict or a list of config dicts. If cfg is a list, the built
+            modules will be wrapped with ``nn.Sequential``.
+        registry (:obj:`Registry`): A registry the module belongs to.
+        default_args (dict, optional): Default arguments to build the module.
+            Defaults to None.
+
+    Returns:
+        nn.Module: A built nn.Module.
+    """
+    from ..model.base_module import Sequential
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(_cfg, registry, default_args) for _cfg in cfg
+        ]
+        return Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+    
+
+class SyncBatchNorm(torch.nn.SyncBatchNorm):  # type: ignore
+
+    def _check_input_dim(self, input):
+        if TORCH_VERSION == 'parrots':
+            if input.dim() < 2:
+                raise ValueError(
+                    f'expected at least 2D input (got {input.dim()}D input)')
+        else:
+            super()._check_input_dim(input)
\ No newline at end of file
diff --git a/segformer_plusplus/utils/embed.py b/segformer_plusplus/utils/embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cfeb32bc108c3c02f7452c01b1cf5f6ab5bf516
--- /dev/null
+++ b/segformer_plusplus/utils/embed.py
@@ -0,0 +1,217 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+from typing import Sequence
+from itertools import repeat
+import collections.abc
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..model.base_module import BaseModule
+from .activation import build_conv_layer, build_norm_layer
+
+
+class AdaptivePadding(nn.Module):
+    """Applies padding to input (if needed) so that input can get fully covered
+    by filter you specified. It supports two modes "same" and "corner". The
+    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
+    input. The "corner"  mode would pad zero to bottom right.
+
+    Args:
+        kernel_size (int | tuple): Size of the kernel:
+        stride (int | tuple): Stride of the filter. Default: 1:
+        dilation (int | tuple): Spacing between kernel elements.
+            Default: 1.
+        padding (str): Support "same" and "corner", "corner" mode
+            would pad zero to bottom right, and "same" mode would
+            pad zero around input. Default: "corner".
+    Example:
+        >>> kernel_size = 16
+        >>> stride = 16
+        >>> dilation = 1
+        >>> input = torch.rand(1, 1, 15, 17)
+        >>> adap_pad = AdaptivePadding(
+        >>>     kernel_size=kernel_size,
+        >>>     stride=stride,
+        >>>     dilation=dilation,
+        >>>     padding="corner")
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+        >>> input = torch.rand(1, 1, 16, 17)
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+    """
+
+    def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
+
+        super().__init__()
+
+        assert padding in ('same', 'corner')
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+
+    def get_pad_shape(self, input_shape):
+        input_h, input_w = input_shape
+        kernel_h, kernel_w = self.kernel_size
+        stride_h, stride_w = self.stride
+        output_h = math.ceil(input_h / stride_h)
+        output_w = math.ceil(input_w / stride_w)
+        pad_h = max((output_h - 1) * stride_h +
+                    (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
+        pad_w = max((output_w - 1) * stride_w +
+                    (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+        return pad_h, pad_w
+
+    def forward(self, x):
+        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
+        if pad_h > 0 or pad_w > 0:
+            if self.padding == 'corner':
+                x = F.pad(x, [0, pad_w, 0, pad_h])
+            elif self.padding == 'same':
+                x = F.pad(x, [
+                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
+                    pad_h - pad_h // 2
+                ])
+        return x
+
+
+class PatchEmbed(BaseModule):
+    """Image to Patch Embedding.
+
+    We use a conv layer to implement PatchEmbed.
+
+    Args:
+        in_channels (int): The num of input channels. Default: 3
+        embed_dims (int): The dimensions of embedding. Default: 768
+        conv_type (str): The config dict for embedding
+            conv layer type selection. Default: "Conv2d".
+        kernel_size (int): The kernel_size of embedding conv. Default: 16.
+        stride (int, optional): The slide stride of embedding conv.
+            Default: None (Would be set as `kernel_size`).
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int): The dilation rate of embedding conv. Default: 1.
+        bias (bool): Bias of embed conv. Default: True.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        input_size (int | tuple | None): The size of input, which will be
+            used to calculate the out size. Only work when `dynamic_size`
+            is False. Default: None.
+        init_cfg (`mmengine.ConfigDict`, optional): The Config for
+            initialization. Default: None.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 embed_dims=768,
+                 conv_type='Conv2d',
+                 kernel_size=16,
+                 stride=None,
+                 padding='corner',
+                 dilation=1,
+                 bias=True,
+                 norm_cfg=None,
+                 input_size=None,
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        if stride is None:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of conv
+            padding = 0
+        else:
+            self.adap_padding = None
+        padding = to_2tuple(padding)
+
+        self.projection = build_conv_layer(
+            dict(type=conv_type),
+            in_channels=in_channels,
+            out_channels=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+        else:
+            self.norm = None
+
+        if input_size:
+            input_size = to_2tuple(input_size)
+            # `init_out_size` would be used outside to
+            # calculate the num_patches
+            # when `use_abs_pos_embed` outside
+            self.init_input_size = input_size
+            if self.adap_padding:
+                pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
+                input_h, input_w = input_size
+                input_h = input_h + pad_h
+                input_w = input_w + pad_w
+                input_size = (input_h, input_w)
+
+            # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+            h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
+                     (kernel_size[0] - 1) - 1) // stride[0] + 1
+            w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
+                     (kernel_size[1] - 1) - 1) // stride[1] + 1
+            self.init_out_size = (h_out, w_out)
+        else:
+            self.init_input_size = None
+            self.init_out_size = None
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (out_h, out_w).
+        """
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+
+        x = self.projection(x)
+        out_size = (x.shape[2], x.shape[3])
+        x = x.flatten(2).transpose(1, 2)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x, out_size
+
+# From PyTorch internals
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+to_2tuple = _ntuple(2)
diff --git a/segformer_plusplus/utils/imagenet_weights.py b/segformer_plusplus/utils/imagenet_weights.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9ef1d12b01027e22b2053c796f12a9a2e554ef5
--- /dev/null
+++ b/segformer_plusplus/utils/imagenet_weights.py
@@ -0,0 +1,8 @@
+imagenet_weights = {
+    'b0': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth',
+    'b1': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth',
+    'b2': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth',
+    'b3': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth',
+    'b4': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth',
+    'b5': 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
+}
\ No newline at end of file
diff --git a/segformer_plusplus/utils/manager.py b/segformer_plusplus/utils/manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..33482836c748d9fedab64762d30094fe174565ef
--- /dev/null
+++ b/segformer_plusplus/utils/manager.py
@@ -0,0 +1,149 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import inspect
+import threading
+import warnings
+from collections import OrderedDict
+from typing import Type, TypeVar
+
+_lock = threading.RLock()
+T = TypeVar('T')
+
+
+def _accquire_lock() -> None:
+    """Acquire the module-level lock for serializing access to shared data.
+
+    This should be released with _release_lock().
+    """
+    if _lock:
+        _lock.acquire()
+
+
+def _release_lock() -> None:
+    """Release the module-level lock acquired by calling _accquire_lock()."""
+    if _lock:
+        _lock.release()
+
+
+class ManagerMeta(type):
+    """The metaclass for global accessible class.
+
+    The subclasses inheriting from ``ManagerMeta`` will manage their
+    own ``_instance_dict`` and root instances. The constructors of subclasses
+    must contain the ``name`` argument.
+
+    Examples:
+        >>> class SubClass1(metaclass=ManagerMeta):
+        >>>     def __init__(self, *args, **kwargs):
+        >>>         pass
+        AssertionError: <class '__main__.SubClass1'>.__init__ must have the
+        name argument.
+        >>> class SubClass2(metaclass=ManagerMeta):
+        >>>     def __init__(self, name):
+        >>>         pass
+        >>> # valid format.
+    """
+
+    def __init__(cls, *args):
+        cls._instance_dict = OrderedDict()
+        params = inspect.getfullargspec(cls)
+        params_names = params[0] if params[0] else []
+        assert 'name' in params_names, f'{cls} must have the `name` argument'
+        super().__init__(*args)
+
+
+class ManagerMixin(metaclass=ManagerMeta):
+    """``ManagerMixin`` is the base class for classes that have global access
+    requirements.
+
+    The subclasses inheriting from ``ManagerMixin`` can get their
+    global instances.
+
+    Examples:
+        >>> class GlobalAccessible(ManagerMixin):
+        >>>     def __init__(self, name=''):
+        >>>         super().__init__(name)
+        >>>
+        >>> GlobalAccessible.get_instance('name')
+        >>> instance_1 = GlobalAccessible.get_instance('name')
+        >>> instance_2 = GlobalAccessible.get_instance('name')
+        >>> assert id(instance_1) == id(instance_2)
+
+    Args:
+        name (str): Name of the instance. Defaults to ''.
+    """
+
+    def __init__(self, name: str = ''):
+        assert isinstance(name, str) and name, \
+            'name argument must be an non-empty string.'
+        self._instance_name = name
+
+    @classmethod
+    def get_instance(cls: Type[T], name: str, **kwargs) -> T:
+        """Get subclass instance by name if the name exists.
+
+        If corresponding name instance has not been created, ``get_instance``
+        will create an instance, otherwise ``get_instance`` will return the
+        corresponding instance.
+
+        Examples
+            >>> instance1 = GlobalAccessible.get_instance('name1')
+            >>> # Create name1 instance.
+            >>> instance.instance_name
+            name1
+            >>> instance2 = GlobalAccessible.get_instance('name1')
+            >>> # Get name1 instance.
+            >>> assert id(instance1) == id(instance2)
+
+        Args:
+            name (str): Name of instance. Defaults to ''.
+
+        Returns:
+            object: Corresponding name instance, the latest instance, or root
+            instance.
+        """
+        _accquire_lock()
+        assert isinstance(name, str), \
+            f'type of name should be str, but got {type(cls)}'
+        instance_dict = cls._instance_dict  # type: ignore
+        # Get the instance by name.
+        if name not in instance_dict:
+            instance = cls(name=name, **kwargs)  # type: ignore
+            instance_dict[name] = instance  # type: ignore
+        elif kwargs:
+            warnings.warn(
+                f'{cls} instance named of {name} has been created, '
+                'the method `get_instance` should not accept any other '
+                'arguments')
+        # Get latest instantiated instance or root instance.
+        _release_lock()
+        return instance_dict[name]
+
+    @classmethod
+    def get_current_instance(cls):
+        """Get latest created instance.
+
+        Before calling ``get_current_instance``, The subclass must have called
+        ``get_instance(xxx)`` at least once.
+
+        Examples
+            >>> instance = GlobalAccessible.get_current_instance()
+            AssertionError: At least one of name and current needs to be set
+            >>> instance = GlobalAccessible.get_instance('name1')
+            >>> instance.instance_name
+            name1
+            >>> instance = GlobalAccessible.get_current_instance()
+            >>> instance.instance_name
+            name1
+
+        Returns:
+            object: Latest created instance.
+        """
+        _accquire_lock()
+        if not cls._instance_dict:
+            raise RuntimeError(
+                f'Before calling {cls.__name__}.get_current_instance(), you '
+                'should call get_instance(name=xxx) at least once.')
+        name = next(iter(reversed(cls._instance_dict)))
+        _release_lock()
+        return cls._instance_dict[name]
+
diff --git a/segformer_plusplus/utils/registry.py b/segformer_plusplus/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbe5984fb753bb466ad50fdae86314a15601910a
--- /dev/null
+++ b/segformer_plusplus/utils/registry.py
@@ -0,0 +1,7 @@
+
+from ..Registry.registry import Registry
+
+MODELS = Registry(
+    'models',
+    locations=['segformer_plusplus.model.backbone', 'segformer_plusplus.model.head']
+)
diff --git a/segformer_plusplus/utils/shape_convert.py b/segformer_plusplus/utils/shape_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..cce1e220b645d4b02df1ec2d9ed3137c8acba707
--- /dev/null
+++ b/segformer_plusplus/utils/shape_convert.py
@@ -0,0 +1,107 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+def nlc_to_nchw(x, hw_shape):
+    """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, L, C] before conversion.
+        hw_shape (Sequence[int]): The height and width of output feature map.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W] after conversion.
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len doesn\'t match H, W'
+    return x.transpose(1, 2).reshape(B, C, H, W)
+
+
+def nchw_to_nlc(x):
+    """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, C, H, W] before conversion.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C] after conversion.
+    """
+    assert len(x.shape) == 4
+    return x.flatten(2).transpose(1, 2).contiguous()
+
+
+def nchw2nlc2nchw(module, x, contiguous=False, **kwargs):
+    """Flatten [N, C, H, W] shape tensor `x` to [N, L, C] shape tensor. Use the
+    reshaped tensor as the input of `module`, and the convert the output of
+    `module`, whose shape is.
+
+    [N, L, C], to [N, C, H, W].
+
+    Args:
+        module (Callable): A callable object the takes a tensor
+            with shape [N, L, C] as input.
+        x (Tensor): The input tensor of shape [N, C, H, W].
+                contiguous:
+        contiguous (Bool): Whether to make the tensor contiguous
+            after each shape transform.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W].
+
+    Example:
+        >>> import torch
+        >>> import torch.nn as nn
+        >>> norm = nn.LayerNorm(4)
+        >>> feature_map = torch.rand(4, 4, 5, 5)
+        >>> output = nchw2nlc2nchw(norm, feature_map)
+    """
+    B, C, H, W = x.shape
+    if not contiguous:
+        x = x.flatten(2).transpose(1, 2)
+        x = module(x, **kwargs)
+        x = x.transpose(1, 2).reshape(B, C, H, W)
+    else:
+        x = x.flatten(2).transpose(1, 2).contiguous()
+        x = module(x, **kwargs)
+        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+    return x
+
+
+def nlc2nchw2nlc(module, x, hw_shape, contiguous=False, **kwargs):
+    """Convert [N, L, C] shape tensor `x` to [N, C, H, W] shape tensor. Use the
+    reshaped tensor as the input of `module`, and convert the output of
+    `module`, whose shape is.
+
+    [N, C, H, W], to [N, L, C].
+
+    Args:
+        module (Callable): A callable object the takes a tensor
+            with shape [N, C, H, W] as input.
+        x (Tensor): The input tensor of shape [N, L, C].
+        hw_shape: (Sequence[int]): The height and width of the
+            feature map with shape [N, C, H, W].
+        contiguous (Bool): Whether to make the tensor contiguous
+            after each shape transform.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C].
+
+    Example:
+        >>> import torch
+        >>> import torch.nn as nn
+        >>> conv = nn.Conv2d(16, 16, 3, 1, 1)
+        >>> feature_map = torch.rand(4, 25, 16)
+        >>> output = nlc2nchw2nlc(conv, feature_map, (5, 5))
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len doesn\'t match H, W'
+    if not contiguous:
+        x = x.transpose(1, 2).reshape(B, C, H, W)
+        x = module(x, **kwargs)
+        x = x.flatten(2).transpose(1, 2)
+    else:
+        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+        x = module(x, **kwargs)
+        x = x.flatten(2).transpose(1, 2).contiguous()
+    return x
diff --git a/segformer_plusplus/utils/tome_presets.py b/segformer_plusplus/utils/tome_presets.py
new file mode 100644
index 0000000000000000000000000000000000000000..152c88689834f48338198657974de42db406957b
--- /dev/null
+++ b/segformer_plusplus/utils/tome_presets.py
@@ -0,0 +1,20 @@
+tome_presets = {
+    'bsm_hq': [
+        dict(q_mode=None, kv_mode='bsm', kv_r=0.6, kv_sx=2, kv_sy=2),
+        dict(q_mode=None, kv_mode='bsm', kv_r=0.6, kv_sx=2, kv_sy=2),
+        dict(q_mode='bsm', kv_mode=None, q_r=0.8, q_sx=4, q_sy=4),
+        dict(q_mode='bsm', kv_mode=None, q_r=0.8, q_sx=4, q_sy=4)
+    ],
+    'bsm_fast': [
+        dict(q_mode=None, kv_mode='bsm_r2D', kv_r=0.9, kv_sx=4, kv_sy=4),
+        dict(q_mode=None, kv_mode='bsm_r2D', kv_r=0.9, kv_sx=4, kv_sy=4),
+        dict(q_mode='bsm_r2D', kv_mode=None, q_r=0.9, q_sx=4, q_sy=4),
+        dict(q_mode='bsm_r2D', kv_mode=None, q_r=0.9, q_sx=4, q_sy=4)
+    ],
+    'n2d_2x2': [
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2)),
+        dict(q_mode='neighbor_2D', kv_mode=None, q_s=(2, 2))
+    ]
+}
diff --git a/segformer_plusplus/utils/version_utils.py b/segformer_plusplus/utils/version_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4b43624fa811026d32bc86c73ace88df594ade7
--- /dev/null
+++ b/segformer_plusplus/utils/version_utils.py
@@ -0,0 +1,64 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import subprocess
+import warnings
+
+from packaging.version import parse
+
+
+def digit_version(version_str: str, length: int = 4):
+    """Convert a version string into a tuple of integers.
+
+    This method is usually used for comparing two versions. For pre-release
+    versions: alpha < beta < rc.
+
+    Args:
+        version_str (str): The version string.
+        length (int): The maximum number of version levels. Defaults to 4.
+
+    Returns:
+        tuple[int]: The version info in digits (integers).
+    """
+    assert 'parrots' not in version_str
+    version = parse(version_str)
+    assert version.release, f'failed to parse version {version_str}'
+    release = list(version.release)
+    release = release[:length]
+    if len(release) < length:
+        release = release + [0] * (length - len(release))
+    if version.is_prerelease:
+        mapping = {'a': -3, 'b': -2, 'rc': -1}
+        val = -4
+        # version.pre can be None
+        if version.pre:
+            if version.pre[0] not in mapping:
+                warnings.warn(f'unknown prerelease version {version.pre[0]}, '
+                              'version checking may go wrong')
+            else:
+                val = mapping[version.pre[0]]
+            release.extend([val, version.pre[-1]])
+        else:
+            release.extend([val, 0])
+
+    elif version.is_postrelease:
+        release.extend([1, version.post])  # type: ignore
+    else:
+        release.extend([0, 0])
+    return tuple(release)
+
+
+def _minimal_ext_cmd(cmd):
+    # construct minimal environment
+    env = {}
+    for k in ['SYSTEMROOT', 'PATH', 'HOME']:
+        v = os.environ.get(k)
+        if v is not None:
+            env[k] = v
+    # LANGUAGE is used on win32
+    env['LANGUAGE'] = 'C'
+    env['LANG'] = 'C'
+    env['LC_ALL'] = 'C'
+    out, err = subprocess.Popen(
+        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+        env=env).communicate()
+    return out
diff --git a/segformer_plusplus/utils/wrappers.py b/segformer_plusplus/utils/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..183f0fc4912ea211f4de702c20e4f7b858367f12
--- /dev/null
+++ b/segformer_plusplus/utils/wrappers.py
@@ -0,0 +1,109 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .registry import MODELS
+
+
+TORCH_VERSION = torch.__version__
+
+def resize(input,
+           size=None,
+           scale_factor=None,
+           mode='nearest',
+           align_corners=None,
+           warning=True):
+    if warning:
+        if size is not None and align_corners:
+            input_h, input_w = tuple(int(x) for x in input.shape[2:])
+            output_h, output_w = tuple(int(x) for x in size)
+            if output_h > input_h or output_w > output_h:
+                if ((output_h > 1 and output_w > 1 and input_h > 1
+                     and input_w > 1) and (output_h - 1) % (input_h - 1)
+                        and (output_w - 1) % (input_w - 1)):
+                    warnings.warn(
+                        f'When align_corners={align_corners}, '
+                        'the output would more aligned if '
+                        f'input size {(input_h, input_w)} is `x+1` and '
+                        f'out size {(output_h, output_w)} is `nx+1`')
+    return F.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+@MODELS.register_module('Conv', force=True)
+class Conv2d(nn.Conv2d):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
+            out_shape = [x.shape[0], self.out_channels]
+            for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size,
+                                     self.padding, self.stride, self.dilation):
+                o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1
+                out_shape.append(o)
+            empty = NewEmptyTensorOp.apply(x, out_shape)
+            if self.training:
+                # produce dummy gradient to avoid DDP warning.
+                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
+                return empty + dummy
+            else:
+                return empty
+
+        return super().forward(x)
+    
+
+class NewEmptyTensorOp(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, new_shape: tuple) -> torch.Tensor:
+        ctx.shape = x.shape
+        return x.new_empty(new_shape)
+
+    @staticmethod
+    def backward(ctx, grad: torch.Tensor) -> tuple:
+        shape = ctx.shape
+        return NewEmptyTensorOp.apply(grad, shape), None
+    
+
+def obsolete_torch_version(torch_version, version_threshold) -> bool:
+    return torch_version == 'parrots' or torch_version <= version_threshold
+
+
+@MODELS.register_module()
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
+    residual blocks).
+
+    We follow the implementation
+    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
+
+    Args:
+        drop_prob (float): Probability of the path to be zeroed. Default: 0.1
+    """
+
+    def __init__(self, drop_prob: float = 0.1):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return drop_path(x, self.drop_prob, self.training)
+    
+
+def drop_path(x: torch.Tensor,
+              drop_prob: float = 0.,
+              training: bool = False) -> torch.Tensor:
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of
+    residual blocks).
+
+    We follow the implementation
+    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    # handle tensors with different dimensions, not just 4D tensors.
+    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
+    random_tensor = keep_prob + torch.rand(
+        shape, dtype=x.dtype, device=x.device)
+    output = x.div(keep_prob) * random_tensor.floor()
+    return output
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..c07b49e770642b514e773574902828a1c0c699ed
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,12 @@
+from setuptools import find_packages, setup
+
+setup(
+    name="segformer_plusplus",
+    version="0.2",
+    author="Marco Kantonis",
+    description="Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation",
+    install_requires=['torch>=2.0.1', 'tomesd','omegaconf', 'pyyaml'],
+    packages=find_packages(),
+    license='MIT',
+    long_description="https://arxiv.org/abs/2405.14467"
+)