DeBiFormer实战：使用DeBiFormer实现图像分类任务（一）

🕗 发布于 2024-11-10 20:07 分类数据挖掘 人工智能

摘要

一、论文介绍

研究背景：视觉Transformer在计算机视觉领域展现出巨大潜力，能够捕获长距离依赖关系，具有高并行性，有利于大型模型的训练和推理。
现有问题：尽管大量研究设计了高效的注意力模式，但查询并非源自语义区域的关键值对，强制所有查询关注不足的一组令牌可能无法产生最优结果。双级路由注意力虽由语义关键值对处理查询，但可能并非在所有情况下都能产生最优结果。
论文目的：提出DeBiFormer，一种带有可变形双级路由注意力（DBRA）的视觉Transformer，旨在优化查询-键-值交互，自适应选择语义相关区域。

二、创新点

可变形双级路由注意力（DBRA）：提出一种注意力中注意力架构，通过可变形点和双级路由机制，实现更高效、有意义的注意力分配。
可变形点感知区域划分：确保每个可变形点仅与键值对的一个小子集进行交互，平衡重要区域和不太重要区域之间的注意力分配。
区域间方法：通过构建有向图建立注意关系，使用topk操作符和路由索引矩阵保留每个区域的topk连接。

三、方法

可变形注意力模块：包含一个偏移网络，为参考点生成偏移量，创建可变形点，这些点以高灵活性和效率向重要区域移动，捕获更多信息性特征。
双层标记到可变形层标记注意力：利用区域路由矩阵，对区域内的每个可变形查询标记执行注意力操作，跨越位于topk路由区域中的所有键值对。
DeBiFormer模型架构：使用四阶段金字塔结构，包含重叠补丁嵌入、补丁合并模块、DeBiFormer块等，用于降低输入空间分辨率，增加通道数，实现跨位置关系建模和每个位置的嵌入。

四、模块作用

可变形双级路由注意力（DBRA）模块：优化查询-键-值交互，自适应选择语义相关区域，实现更高效和有意义的注意力。通过可变形点和双级路由机制，提高模型对重要区域的关注度，同时减少不太重要区域的注意力。
3x3深度卷积：在DeBiFormer块开始时使用，用于隐式编码相对位置信息，增强模型的局部敏感性。
2-ConvFFN模块：用于每个位置的嵌入，扩展模型的特征表示能力。

五、实验结果

图像分类：在ImageNet-1K数据集上从头训练图像分类模型，验证了DeBiFormer的有效性。
语义分割：在ADE20K数据集上对预训练的主干网络进行微调，DeBiFormer表现出色，证明了其在密集预测任务中的性能。
目标检测和实例分割：使用DeBiFormer作为Mask RCNN和RetinaNet框架中的主干网络，在COCO 2017数据集上评估其性能。尽管资源有限，但DeBiFormer在大目标上的性能优于一些最具竞争力的现有方法。
消融研究：验证了DBRA和DeBiFormer的top-k选择的有效性，证明了可变形双级路由注意力对模型性能的贡献。

总结：本文介绍的DeBiFormer是一种专为图像分类和密集预测任务设计的新型分层视觉Transformer。通过提出可变形双级路由注意力（DBRA），优化了查询-键-值交互，自适应选择语义相关区域，实现了更高效和有意义的注意力。实验结果表明，DeBiFormer在多个计算机视觉任务上均表现出色，为设计灵活且语义感知的注意力机制提供了见解。

本文使用DeBiFormer模型实现图像分类任务，模型选择debi_tiny，在植物幼苗分类任务ACC达到了82%+。

在这里插入图片描述

通过深入阅读本文，您将能够掌握以下关键技能与知识：

数据增强的多种策略：包括利用PyTorch的transforms库进行基本增强，以及进阶技巧如CutOut、MixUp、CutMix等，这些方法能显著提升模型泛化能力。
DeBiFormer模型的训练实现：了解如何从头开始构建并训练DeBiFormer（或其他深度学习模型），涵盖模型定义、数据加载、训练循环等关键环节。
混合精度训练：学习如何利用PyTorch自带的混合精度训练功能，加速训练过程同时减少内存消耗。
梯度裁剪技术：掌握梯度裁剪的应用，有效防止梯度爆炸问题，确保训练过程的稳定性。
分布式数据并行（DP）训练：了解如何在多GPU环境下使用PyTorch的分布式数据并行功能，加速大规模模型训练。
可视化训练过程：学习如何绘制训练过程中的loss和accuracy曲线，直观监控模型学习状况。
评估与生成报告：掌握在验证集上评估模型性能的方法，并生成详细的评估报告，包括ACC等指标。
测试脚本编写：学会编写测试脚本，对测试集进行预测，评估模型在实际应用中的表现。
学习率调整策略：理解并应用余弦退火策略动态调整学习率，优化训练效果。
自定义统计工具：使用AverageMeter类或其他工具统计和记录训练过程中的ACC、loss等关键指标，便于后续分析。
深入理解ACC1与ACC5：掌握图像分类任务中ACC1（Top-1准确率）和ACC5（Top-5准确率）的含义及其计算方法。
指数移动平均（EMA）：学习如何在模型训练中应用EMA技术，进一步提升模型在测试集上的表现。

若您在以上任一领域基础尚浅，感到理解困难，推荐您参考我的专栏“经典主干网络精讲与实战”，该专栏从零开始，循序渐进地讲解上述所有知识点，助您轻松掌握深度学习中的这些核心技能。

安装包

安装timm

使用pip就行，命令：

pip install timm

mixup增强和EMA用到了timm

安装einops，执行命令：

pip install einops

数据增强Cutout和Mixup

为了提高模型的泛化能力和性能，我在数据预处理阶段加入了Cutout和Mixup这两种数据增强技术。Cutout通过随机遮挡图像的一部分来强制模型学习更鲁棒的特征，而Mixup则通过混合两张图像及其标签来生成新的训练样本，从而增加数据的多样性。实现这两种增强需要安装torchtoolbox。安装命令：

pip install torchtoolbox

Cutout实现，在transforms中。

from torchtoolbox.transform import Cutout
# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    Cutout(),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])

])

需要导入包：from timm.data.mixup import Mixup，

定义Mixup，和SoftTargetCrossEntropy

  mixup_fn = Mixup(
    mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None,
    prob=0.1, switch_prob=0.5, mode='batch',
    label_smoothing=0.1, num_classes=12)
 criterion_train = SoftTargetCrossEntropy()

Mixup 是一种在图像分类任务中常用的数据增强技术，它通过将两张图像以及其对应的标签进行线性组合来生成新的数据和标签。
参数详解：

mixup_alpha (float): mixup alpha 值，如果 > 0，则 mixup 处于活动状态。

cutmix_alpha (float)：cutmix alpha 值，如果 > 0，cutmix 处于活动状态。

cutmix_minmax (List[float])：cutmix 最小/最大图像比率，cutmix 处于活动状态，如果不是 None，则使用这个 vs alpha。

如果设置了 cutmix_minmax 则cutmix_alpha 默认为1.0

prob (float): 每批次或元素应用 mixup 或 cutmix 的概率。

switch_prob (float): 当两者都处于活动状态时切换cutmix 和mixup 的概率。

mode (str): 如何应用 mixup/cutmix 参数（每个’batch’，‘pair’（元素对），‘elem’（元素）。

correct_lam (bool): 当 cutmix bbox 被图像边框剪裁时应用。 lambda 校正

label_smoothing (float)：将标签平滑应用于混合目标张量。

num_classes (int): 目标的类数。

EMA

EMA（Exponential Moving Average）在深度学习中是一种用于模型参数优化的技术，它通过计算参数的指数移动平均值来平滑模型的学习过程。这种方法有助于提高模型的稳定性和泛化能力，特别是在训练后期。以下是关于EMA的总结，表达进行了优化：

EMA概述

EMA是一种加权移动平均技术，其中每个新的平均值都是前一个平均值和当前值的加权和。在深度学习中，EMA被用于模型参数的更新，以减缓参数在训练过程中的快速波动，从而得到更加平滑和稳定的模型表现。

工作原理

在训练过程中，除了维护当前模型的参数外，还额外保存一份EMA参数。每个训练步骤或每隔一定步骤，根据当前模型参数和EMA参数，按照指数衰减的方式更新EMA参数。具体来说，EMA参数的更新公式通常如下：

$model_parameters \text{EMA}_{\text{new}} = \text{decay} \times \text{EMA}_{\text{old}} + (1 - \text{decay}) \times \text{model\_parameters}$
其中，decay是一个介于0和1之间的超参数，控制着旧EMA值和新模型参数值之间的权重分配。较大的decay值意味着EMA更新时更多地依赖于旧值，即平滑效果更强。

应用优势

稳定性：EMA通过平滑参数更新过程，减少了模型在训练过程中的波动，使得模型更加稳定。
泛化能力：由于EMA参数是历史参数的平滑版本，它往往能捕捉到模型训练过程中的全局趋势，因此在测试或评估时，使用EMA参数往往能获得更好的泛化性能。
快速收敛：虽然EMA本身不直接加速训练过程，但通过稳定模型参数，它可能间接地帮助模型更快地收敛到更优的解。

使用场景

EMA在深度学习中的使用场景广泛，特别是在需要高度稳定性和良好泛化能力的任务中，如图像分类、目标检测等。在训练大型模型时，EMA尤其有用，因为它可以帮助减少过拟合的风险，并提高模型在未见数据上的表现。

具体实现如下：


import logging
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn

_logger = logging.getLogger(__name__)

class ModelEma:
    def __init__(self, model, decay=0.9999, device='', resume=''):
        # make a copy of the model for accumulating moving average of weights
        self.ema = deepcopy(model)
        self.ema.eval()
        self.decay = decay
        self.device = device  # perform ema on different device from model if set
        if device:
            self.ema.to(device=device)
        self.ema_has_module = hasattr(self.ema, 'module')
        if resume:
            self._load_checkpoint(resume)
        for p in self.ema.parameters():
            p.requires_grad_(False)

    def _load_checkpoint(self, checkpoint_path):
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        assert isinstance(checkpoint, dict)
        if 'state_dict_ema' in checkpoint:
            new_state_dict = OrderedDict()
            for k, v in checkpoint['state_dict_ema'].items():
                # ema model may have been wrapped by DataParallel, and need module prefix
                if self.ema_has_module:
                    name = 'module.' + k if not k.startswith('module') else k
                else:
                    name = k
                new_state_dict[name] = v
            self.ema.load_state_dict(new_state_dict)
            _logger.info("Loaded state_dict_ema")
        else:
            _logger.warning("Failed to find state_dict_ema, starting from loaded model weights")

    def update(self, model):
        # correct a mismatch in state dict keys
        needs_module = hasattr(model, 'module') and not self.ema_has_module
        with torch.no_grad():
            msd = model.state_dict()
            for k, ema_v in self.ema.state_dict().items():
                if needs_module:
                    k = 'module.' + k
                model_v = msd[k].detach()
                if self.device:
                    model_v = model_v.to(device=self.device)
                ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)

加入到模型中。

#初始化
if use_ema:
     model_ema = ModelEma(
            model_ft,
            decay=model_ema_decay,
            device='cpu',
            resume=resume)

# 训练过程中，更新完参数后，同步update shadow weights
def train():
    optimizer.step()
    if model_ema is not None:
        model_ema.update(model)


# 将model_ema传入验证函数中
val(model_ema.ema, DEVICE, test_loader)

针对没有预训练的模型，容易出现EMA不上分的情况，这点大家要注意啊！

项目结构

DeBiFormer_Demo
├─data1
│  ├─Black-grass
│  ├─Charlock
│  ├─Cleavers
│  ├─Common Chickweed
│  ├─Common wheat
│  ├─Fat Hen
│  ├─Loose Silky-bent
│  ├─Maize
│  ├─Scentless Mayweed
│  ├─Shepherds Purse
│  ├─Small-flowered Cranesbill
│  └─Sugar beet
├─models
│  └─debiformer.py
├─mean_std.py
├─makedata.py
├─train.py
└─test.py

mean_std.py：计算mean和std的值。
makedata.py：生成数据集。
train.py：训练models文件下DeBiFormer的模型
models：来源官方代码。

计算mean和std

在深度学习中，特别是在处理图像数据时，计算数据的均值（mean）和标准差（standard deviation, std）并进行归一化（Normalization）是加速模型收敛、提高模型性能的关键步骤之一。这里我将详细解释这两个概念，并讨论它们如何帮助模型学习。

均值（Mean）

均值是所有数值加和后除以数值的个数得到的平均值。在图像处理中，我们通常对每个颜色通道（如RGB图像的三个通道）分别计算均值。这意味着，如果我们的数据集包含多张图像，我们会计算所有图像在R通道上的像素值的均值，同样地，我们也会计算G通道和B通道的均值。

标准差（Standard Deviation, Std）

标准差是衡量数据分布离散程度的统计量。它反映了数据点与均值的偏离程度。在计算图像数据的标准差时，我们也是针对每个颜色通道分别进行的。标准差较大的颜色通道意味着该通道上的像素值变化较大，而标准差较小的通道则相对较为稳定。

归一化（Normalization）

归一化是将数据按比例缩放，使之落入一个小的特定区间，通常是[0, 1]或[-1, 1]。在图像处理中，我们通常会使用计算得到的均值和标准差来进行归一化，公式如下：

$\text{Normalized Value} = \frac{\text{Original Value} - \text{Mean}}{\text{Std}}$

注意，在某些情况下，为了简化计算并确保数据非负，我们可能会选择将数据缩放到[0, 1]区间，这时使用的是最大最小值归一化，而不是基于均值和标准差的归一化。但在这里，我们主要讨论基于均值和标准差的归一化，因为它能保留数据的分布特性。

为什么需要归一化？

加速收敛：归一化后的数据具有相似的尺度，这有助于梯度下降算法更快地找到最优解，因为不同特征的梯度更新将在同一数量级上，从而避免了某些特征因尺度过大或过小而导致的训练缓慢或梯度消失/爆炸问题。
提高精度：归一化可以改善模型的泛化能力，因为它使得模型更容易学习到特征之间的相对关系，而不是被特征的绝对大小所影响。
稳定性：归一化后的数据更加稳定，减少了训练过程中的波动，有助于模型更加稳定地收敛。

如何计算和使用mean和std

计算全局mean和std：在整个数据集上计算mean和std。这通常是在训练开始前进行的，并使用这些值来归一化训练集、验证集和测试集。
使用库函数：许多深度学习框架（如PyTorch、TensorFlow等）提供了计算mean和std的便捷函数，并可以直接用于数据集的归一化。
动态调整：在某些情况下，特别是当数据集非常大或持续更新时，可能需要动态地计算mean和std。这通常涉及到在训练过程中使用移动平均（如EMA）来更新这些统计量。

计算并使用数据的mean和std进行归一化是深度学习中的一项基本且重要的预处理步骤，它对于加速模型收敛、提高模型性能和稳定性具有重要意义。新建mean_std.py,插入代码：

from torchvision.datasets import ImageFolder
import torch
from torchvision import transforms

def get_mean_and_std(train_data):
    train_loader = torch.utils.data.DataLoader(
        train_data, batch_size=1, shuffle=False, num_workers=0,
        pin_memory=True)
    mean = torch.zeros(3)
    std = torch.zeros(3)
    for X, _ in train_loader:
        for d in range(3):
            mean[d] += X[:, d, :, :].mean()
            std[d] += X[:, d, :, :].std()
    mean.div_(len(train_data))
    std.div_(len(train_data))
    return list(mean.numpy()), list(std.numpy())

if __name__ == '__main__':
    train_dataset = ImageFolder(root=r'data1', transform=transforms.ToTensor())
    print(get_mean_and_std(train_dataset))

数据集结构：

运行结果：

([0.3281186, 0.28937867, 0.20702125], [0.09407319, 0.09732835, 0.106712654])

把这个结果记录下来，后面要用！

生成数据集

我们整理还的图像分类的数据集结构是这样的

data
├─Black-grass
├─Charlock
├─Cleavers
├─Common Chickweed
├─Common wheat
├─Fat Hen
├─Loose Silky-bent
├─Maize
├─Scentless Mayweed
├─Shepherds Purse
├─Small-flowered Cranesbill
└─Sugar beet

pytorch和keras默认加载方式是ImageNet数据集格式，格式是

├─data
│  ├─val
│  │   ├─Black-grass
│  │   ├─Charlock
│  │   ├─Cleavers
│  │   ├─Common Chickweed
│  │   ├─Common wheat
│  │   ├─Fat Hen
│  │   ├─Loose Silky-bent
│  │   ├─Maize
│  │   ├─Scentless Mayweed
│  │   ├─Shepherds Purse
│  │   ├─Small-flowered Cranesbill
│  │   └─Sugar beet
│  └─train
│      ├─Black-grass
│      ├─Charlock
│      ├─Cleavers
│      ├─Common Chickweed
│      ├─Common wheat
│      ├─Fat Hen
│      ├─Loose Silky-bent
│      ├─Maize
│      ├─Scentless Mayweed
│      ├─Shepherds Purse
│      ├─Small-flowered Cranesbill
│      └─Sugar beet

新增格式转化脚本makedata.py,插入代码：

import glob
import os
import shutil

image_list=glob.glob('data1/*/*.png')
print(image_list)
file_dir='data'
if os.path.exists(file_dir):
    print('true')
    #os.rmdir(file_dir)
    shutil.rmtree(file_dir)#删除再建立
    os.makedirs(file_dir)
else:
    os.makedirs(file_dir)

from sklearn.model_selection import train_test_split
trainval_files, val_files = train_test_split(image_list, test_size=0.3, random_state=42)
train_dir='train'
val_dir='val'
train_root=os.path.join(file_dir,train_dir)
val_root=os.path.join(file_dir,val_dir)
for file in trainval_files:
    file_class=file.replace("\\","/").split('/')[-2]
    file_name=file.replace("\\","/").split('/')[-1]
    file_class=os.path.join(train_root,file_class)
    if not os.path.isdir(file_class):
        os.makedirs(file_class)
    shutil.copy(file, file_class + '/' + file_name)

for file in val_files:
    file_class=file.replace("\\","/").split('/')[-2]
    file_name=file.replace("\\","/").split('/')[-1]
    file_class=os.path.join(val_root,file_class)
    if not os.path.isdir(file_class):
        os.makedirs(file_class)
    shutil.copy(file, file_class + '/' + file_name)

完成上面的内容就可以开启训练和测试了。

DeBiFormer代码

import numpy as np
from collections import defaultdict
import matplotlib.pyplot as plt
from timm.models.registry import register_model
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision

from torch import Tensor
from typing import Tuple
import numbers
from timm.models.layers import to_2tuple, trunc_normal_
from einops import rearrange
import gc
import torch
import torch.nn as nn
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import Rearrange
from fairscale.nn.checkpoint import checkpoint_wrapper
from timm.models import register_model
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.vision_transformer import _cfg


class LayerNorm2d(nn.Module):

  def __init__(self, 
               channels
               ):
    super().__init__()
    self.ln = nn.LayerNorm(channels)

  def forward(self, x):
    x = rearrange(x, "N C H W -> N H W C")
    x = self.ln(x)
    x = rearrange(x, "N H W C -> N C H W")
    return x



def init_linear(m):
    if isinstance(m, (nn.Conv2d, nn.Linear)):
        nn.init.kaiming_normal_(m.weight)
        if m.bias is not None: nn.init.zeros_(m.bias)
    elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

#def to_4d(x,s,h,w):
#    return rearrange(x, 'b (s h w) c -> b c s h w',s=s,h=h,w=w)

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

#def to_3d(x):
#    return rearrange(x, 'b c s h w -> b (s h w) c')

class Partial:
    def __init__(self, module, *args, **kwargs):
        self.module = module
        self.args = args
        self.kwargs = kwargs

    def __call__(self, *args_c, **kwargs_c):
        return self.module(*args_c, *self.args, **kwargs_c, **self.kwargs)



class LayerNormChannels(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.norm = nn.LayerNorm(channels)

    def forward(self, x):
        x = x.transpose(1, -1)
        x = self.norm(x)
        x = x.transpose(-1, 1)
        return x

class LayerNormProxy(nn.Module):
    
    def __init__(self, dim):
        
        super().__init__()
        self.norm = nn.LayerNorm(dim)

    def forward(self, x):

        x = rearrange(x, 'b c h w -> b h w c')
        x = self.norm(x)
        return rearrange(x, 'b h w c -> b c h w')
    
    
class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)
    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)
    
    
#class LayerNorm(nn.Module):
#    def __init__(self, dim, LayerNorm_type):
#        super(LayerNorm, self).__init__()
#        if LayerNorm_type =='BiasFree':
#            self.body = BiasFree_LayerNorm(dim)
#        else:
#            self.body = WithBias_LayerNorm(dim)
#    def forward(self, x):
#        s, h, w = x.shape[-3:]
#        return to_4d(self.body(to_3d(x)),s, h, w)
    

class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x):
        """
        x: NHWC tensor
        """
        x = x.permute(0, 3, 1, 2) #NCHW
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1) #NHWC

        return x
    
class ConvFFN(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 1, 1, 0)

    def forward(self, x):
        """
        x: NHWC tensor
        """
        x = x.permute(0, 3, 1, 2) #NCHW
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1) #NHWC

        return x
    


class Attention(nn.Module):
    """
    vanilla attention
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        """
        args:
            x: NHWC tensor
        return:
            NHWC tensor
        """
        _, H, W, _ = x.size()
        x = rearrange(x, 'n h w c -> n (h w) c')

        #######################################
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        #######################################

        x = rearrange(x, 'n (h w) c -> n h w c', h=H, w=W)
        return x

class AttentionLePE(nn.Module):
    """
    vanilla attention
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., side_dwconv=5):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.lepe = nn.Conv2d(dim, dim, kernel_size=side_dwconv, stride=1, padding=side_dwconv//2, groups=dim) if side_dwconv > 0 else \
                    lambda x: torch.zeros_like(x)

    def forward(self, x):
        """
        args:
            x: NHWC tensor
        return:
            NHWC tensor
        """
        _, H, W, _ = x.size()
        x = rearrange(x, 'n h w c -> n (h w) c')

        #######################################
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        lepe = self.lepe(rearrange(x, 'n (h w) c -> n c h w', h=H, w=W))
        lepe = rearrange(lepe, 'n c h w -> n (h w) c')

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = x + lepe

        x = self.proj(x)
        x = self.proj_drop(x)
        #######################################

        x = rearrange(x, 'n (h w) c -> n h w c', h=H, w=W)
        return x



class nchwAttentionLePE(nn.Module):
    """
    Attention with LePE, takes nchw input
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., side_dwconv=5):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = qk_scale or self.head_dim ** -0.5

        self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Conv2d(dim, dim, kernel_size=1)
        self.proj_drop = nn.Dropout(proj_drop)
        self.lepe = nn.Conv2d(dim, dim, kernel_size=side_dwconv, stride=1, padding=side_dwconv//2, groups=dim) if side_dwconv > 0 else \
                    lambda x: torch.zeros_like(x)

    def forward(self, x:torch.Tensor):
        """
        args:
            x: NCHW tensor
        return:
            NCHW tensor
        """
        B, C, H, W = x.size()
        q, k, v = self.qkv.forward(x).chunk(3, dim=1) # B, C, H, W

        attn = q.view(B, self.num_heads, self.head_dim, H*W).transpose(-1, -2) @ \
               k.view(B, self.num_heads, self.head_dim, H*W)
        attn = torch.softmax(attn*self.scale, dim=-1)
        attn = self.attn_drop(attn)

        # (B, nhead, HW, HW) @ (B, nhead, HW, head_dim) -> (B, nhead, HW, head_dim)
        output:torch.Tensor = attn @ v.view(B, self.num_heads, self.head_dim, H*W).transpose(-1, -2)
        output = output.permute(0, 1, 3, 2).reshape(B, C, H, W)
        output = output + self.lepe(v)

        output = self.proj_drop(self.proj(output))

        return output


class TopkRouting(nn.Module):
    """
    differentiable topk routing with scaling
    Args:
        qk_dim: int, feature dimension of query and key
        topk: int, the 'topk'
        qk_scale: int or None, temperature (multiply) of softmax activation
        with_param: bool, wether inorporate learnable params in routing unit
        diff_routing: bool, wether make routing differentiable
        soft_routing: bool, wether make output value multiplied by routing weights
    """
    def __init__(self, qk_dim, topk=4, qk_scale=None, param_routing=False, diff_routing=False):
        super().__init__()
        self.topk = topk
        self.qk_dim = qk_dim
        self.scale = qk_scale or qk_dim ** -0.5
        self.diff_routing = diff_routing
        # TODO: norm layer before/after linear?
        self.emb = nn.Linear(qk_dim, qk_dim) if param_routing else nn.Identity()
        # routing activation
        self.routing_act = nn.Softmax(dim=-1)

    def forward(self, query:Tensor, key:Tensor)->Tuple[Tensor]:
        """
        Args:
            q, k: (n, p^2, c) tensor
        Return:
            r_weight, topk_index: (n, p^2, topk) tensor
        """
        if not self.diff_routing:
            query, key = query.detach(), key.detach()
        query_hat, key_hat = self.emb(query), self.emb(key) # per-window pooling -> (n, p^2, c)
        attn_logit = (query_hat*self.scale) @ key_hat.transpose(-2, -1) # (n, p^2, p^2)
        topk_attn_logit, topk_index = torch.topk(attn_logit, k=self.topk, dim=-1) # (n, p^2, k), (n, p^2, k)
        r_weight = self.routing_act(topk_attn_logit) # (n, p^2, k)

        return r_weight, topk_index


class KVGather(nn.Module):
    def __init__(self, mul_weight='none'):
        super().__init__()
        assert mul_weight in ['none', 'soft', 'hard']
        self.mul_weight = mul_weight

    def forward(self, r_idx:Tensor, r_weight:Tensor, kv:Tensor):
        """
        r_idx: (n, p^2, topk) tensor
        r_weight: (n, p^2, topk) tensor
        kv: (n, p^2, w^2, c_kq+c_v)
        Return:
            (n, p^2, topk, w^2, c_kq+c_v) tensor
        """
        # select kv according to routing index
        n, p2, w2, c_kv = kv.size()
        topk = r_idx.size(-1)
        # print(r_idx.size(), r_weight.size())
        # FIXME: gather consumes much memory (topk times redundancy), write cuda kernel?
        topk_kv = torch.gather(kv.view(n, 1, p2, w2, c_kv).expand(-1, p2, -1, -1, -1), # (n, p^2, p^2, w^2, c_kv) without mem cpy
                                dim=2,
                                index=r_idx.view(n, p2, topk, 1, 1).expand(-1, -1, -1, w2, c_kv) # (n, p^2, k, w^2, c_kv)
                               )

        if self.mul_weight == 'soft':
            topk_kv = r_weight.view(n, p2, topk, 1, 1) * topk_kv # (n, p^2, k, w^2, c_kv)
        elif self.mul_weight == 'hard':
            raise NotImplementedError('differentiable hard routing TBA')
        # else: #'none'
        #     topk_kv = topk_kv # do nothing

        return topk_kv

class QKVLinear(nn.Module):
    def __init__(self, dim, qk_dim, bias=True):
        super().__init__()
        self.dim = dim
        self.qk_dim = qk_dim
        self.qkv = nn.Linear(dim, qk_dim + qk_dim + dim, bias=bias)

    def forward(self, x):
        q, kv = self.qkv(x).split([self.qk_dim, self.qk_dim+self.dim], dim=-1)
        return q, kv
        # q, k, v = self.qkv(x).split([self.qk_dim, self.qk_dim, self.dim], dim=-1)
        # return q, k, v


class QKVConv(nn.Module):
    def __init__(self, dim, qk_dim, bias=True):
        super().__init__()
        self.dim = dim
        self.qk_dim = qk_dim
        self.qkv = nn.Conv2d(dim,  qk_dim + qk_dim + dim, 1, 1, 0)

    def forward(self, x):
        q, kv = self.qkv(x).split([self.qk_dim, self.qk_dim+self.dim], dim=1)
        return q, kv
    

        
class BiLevelRoutingAttention(nn.Module):
    """
    n_win: number of windows in one side (so the actual number of windows is n_win*n_win)
    kv_per_win: for kv_downsample_mode='ada_xxxpool' only, number of key/values per window. Similar to n_win, the actual number is kv_per_win*kv_per_win.
    topk: topk for window filtering
    param_attention: 'qkvo'-linear for q,k,v and o, 'none': param free attention
    param_routing: extra linear for routing
    diff_routing: wether to set routing differentiable
    soft_routing: wether to multiply soft routing weights 
    """
    def __init__(self, dim, num_heads=8, n_win=7, qk_dim=None, qk_scale=None,
                 kv_per_win=4, kv_downsample_ratio=4, kv_downsample_kernel=None, kv_downsample_mode='identity',
                 topk=4, param_attention="qkvo", param_routing=False, diff_routing=False, soft_routing=False, side_dwconv=3,
                 auto_pad=False):
        super().__init__()
        # local attention setting
        self.dim = dim
        self.n_win = n_win  # Wh, Ww
        self.num_heads = num_heads
        self.qk_dim = qk_dim or dim
        assert self.qk_dim % num_heads == 0 and self.dim % num_heads==0, 'qk_dim and dim must be divisible by num_heads!'
        self.scale = qk_scale or self.qk_dim ** -0.5


        ################side_dwconv (i.e. LCE in ShuntedTransformer)###########
        self.lepe = nn.Conv2d(dim, dim, kernel_size=side_dwconv, stride=1, padding=side_dwconv//2, groups=dim) if side_dwconv > 0 else \
                    lambda x: torch.zeros_like(x)
        
        ################ global routing setting #################
        self.topk = topk
        self.param_routing = param_routing
        self.diff_routing = diff_routing
        self.soft_routing = soft_routing
        # router
        assert not (self.param_routing and not self.diff_routing) # cannot be with_param=True and diff_routing=False
        self.router = TopkRouting(qk_dim=self.qk_dim,
                                  qk_scale=self.scale,
                                  topk=self.topk,
                                  diff_routing=self.diff_routing,
                                  param_routing=self.param_routing)
        if self.soft_routing: # soft routing, always diffrentiable (if no detach)
            mul_weight = 'soft'
        elif self.diff_routing: # hard differentiable routing
            mul_weight = 'hard'
        else:  # hard non-differentiable routing
            mul_weight = 'none'
        self.kv_gather = KVGather(mul_weight=mul_weight)

        # qkv mapping (shared by both global routing and local attention)
        self.param_attention = param_attention
        if self.param_attention == 'qkvo':
            self.qkv = QKVLinear(self.dim, self.qk_dim)
            self.wo = nn.Linear(dim, dim)
        elif self.param_attention == 'qkv':
            self.qkv = QKVLinear(self.dim, self.qk_dim)
            self.wo = nn.Identity()
        else:
            raise ValueError(f'param_attention mode {self.param_attention} is not surpported!')
        
        self.kv_downsample_mode = kv_downsample_mode
        self.kv_per_win = kv_per_win
        self.kv_downsample_ratio = kv_downsample_ratio
        self.kv_downsample_kenel = kv_downsample_kernel
        if self.kv_downsample_mode == 'ada_avgpool':
            assert self.kv_per_win is not None
            self.kv_down = nn.AdaptiveAvgPool2d(self.kv_per_win)
        elif self.kv_downsample_mode == 'ada_maxpool':
            assert self.kv_per_win is not None
            self.kv_down = nn.AdaptiveMaxPool2d(self.kv_per_win)
        elif self.kv_downsample_mode == 'maxpool':
            assert self.kv_downsample_ratio is not None
            self.kv_down = nn.MaxPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity()
        elif self.kv_downsample_mode == 'avgpool':
            assert self.kv_downsample_ratio is not None
            self.kv_down = nn.AvgPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity()
        elif self.kv_downsample_mode == 'identity': # no kv downsampling
            self.kv_down = nn.Identity()
        elif self.kv_downsample_mode == 'fracpool':
            # assert self.kv_downsample_ratio is not None
            # assert self.kv_downsample_kenel is not None
            # TODO: fracpool
            # 1. kernel size should be input size dependent
            # 2. there is a random factor, need to avoid independent sampling for k and v 
            raise NotImplementedError('fracpool policy is not implemented yet!')
        elif kv_downsample_mode == 'conv':
            # TODO: need to consider the case where k != v so that need two downsample modules
            raise NotImplementedError('conv policy is not implemented yet!')
        else:
            raise ValueError(f'kv_down_sample_mode {self.kv_downsaple_mode} is not surpported!')

        # softmax for local attention
        self.attn_act = nn.Softmax(dim=-1)

        self.auto_pad=auto_pad

    def forward(self, x, ret_attn_mask=False):
        """
        x: NHWC tensor

        Return:
            NHWC tensor
        """
         # NOTE: use padding for semantic segmentation
        
        ###################################################
        if self.auto_pad:
            N, H_in, W_in, C = x.size()

            pad_l = pad_t = 0
            pad_r = (self.n_win - W_in % self.n_win) % self.n_win
            pad_b = (self.n_win - H_in % self.n_win) % self.n_win
            x = F.pad(x, (0, 0, # dim=-1
                          pad_l, pad_r, # dim=-2
                          pad_t, pad_b)) # dim=-3
            _, H, W, _ = x.size() # padded size
        else:
            N, H, W, C = x.size()
            
            #assert H%self.n_win == 0 and W%self.n_win == 0 #
        ###################################################


        # patchify, (n, p^2, w, w, c), keep 2d window as we need 2d pooling to reduce kv size
        x = rearrange(x, "n (j h) (i w) c -> n (j i) h w c", j=self.n_win, i=self.n_win)

        #################qkv projection###################
        # q: (n, p^2, w, w, c_qk)
        # kv: (n, p^2, w, w, c_qk+c_v)
        # NOTE: separte kv if there were memory leak issue caused by gather
        q, kv = self.qkv(x) 

        # pixel-wise qkv
        # q_pix: (n, p^2, w^2, c_qk)
        # kv_pix: (n, p^2, h_kv*w_kv, c_qk+c_v)
        q_pix = rearrange(q, 'n p2 h w c -> n p2 (h w) c')
        kv_pix = self.kv_down(rearrange(kv, 'n p2 h w c -> (n p2) c h w'))
        kv_pix = rearrange(kv_pix, '(n j i) c h w -> n (j i) (h w) c', j=self.n_win, i=self.n_win)

        q_win, k_win = q.mean([2, 3]), kv[..., 0:self.qk_dim].mean([2, 3]) # window-wise qk, (n, p^2, c_qk), (n, p^2, c_qk)

        ##################side_dwconv(lepe)##################
        # NOTE: call contiguous to avoid gradient warning when using ddp
        lepe = self.lepe(rearrange(kv[..., self.qk_dim:], 'n (j i) h w c -> n c (j h) (i w)', j=self.n_win, i=self.n_win).contiguous())
        lepe = rearrange(lepe, 'n c (j h) (i w) -> n (j h) (i w) c', j=self.n_win, i=self.n_win)

        ############ gather q dependent k/v #################

        r_weight, r_idx = self.router(q_win, k_win) # both are (n, p^2, topk) tensors

        kv_pix_sel = self.kv_gather(r_idx=r_idx, r_weight=r_weight, kv=kv_pix) #(n, p^2, topk, h_kv*w_kv, c_qk+c_v)
        k_pix_sel, v_pix_sel = kv_pix_sel.split([self.qk_dim, self.dim], dim=-1)
        # kv_pix_sel: (n, p^2, topk, h_kv*w_kv, c_qk)
        # v_pix_sel: (n, p^2, topk, h_kv*w_kv, c_v)
        
        ######### do attention as normal ####################
        k_pix_sel = rearrange(k_pix_sel, 'n p2 k w2 (m c) -> (n p2) m c (k w2)', m=self.num_heads) # flatten to BMLC, (n*p^2, m, topk*h_kv*w_kv, c_kq//m) transpose here?
        v_pix_sel = rearrange(v_pix_sel, 'n p2 k w2 (m c) -> (n p2) m (k w2) c', m=self.num_heads) # flatten to BMLC, (n*p^2, m, topk*h_kv*w_kv, c_v//m)
        q_pix = rearrange(q_pix, 'n p2 w2 (m c) -> (n p2) m w2 c', m=self.num_heads) # to BMLC tensor (n*p^2, m, w^2, c_qk//m)

        # param-free multihead attention
        attn_weight = (q_pix * self.scale) @ k_pix_sel # (n*p^2, m, w^2, c) @ (n*p^2, m, c, topk*h_kv*w_kv) -> (n*p^2, m, w^2, topk*h_kv*w_kv)
        attn_weight = self.attn_act(attn_weight)
        out = attn_weight @ v_pix_sel # (n*p^2, m, w^2, topk*h_kv*w_kv) @ (n*p^2, m, topk*h_kv*w_kv, c) -> (n*p^2, m, w^2, c)
        out = rearrange(out, '(n j i) m (h w) c -> n (j h) (i w) (m c)', j=self.n_win, i=self.n_win,
                        h=H//self.n_win, w=W//self.n_win)

        out = out + lepe
        # output linear
        out = self.wo(out)

        # NOTE: use padding for semantic segmentation
        # crop padded region
        if self.auto_pad and (pad_r > 0 or pad_b > 0):
            out = out[:, :H_in, :W_in, :].contiguous()

        if ret_attn_mask:
            return out, r_weight, r_idx, attn_weight
        else:
            return out
        
        
class TransformerMLPWithConv(nn.Module):

    def __init__(self, channels, expansion, drop):
        
        super().__init__()
        
        self.dim1 = channels
        self.dim2 = channels * expansion
        self.linear1 = nn.Sequential(
            nn.Conv2d(self.dim1, self.dim2, 1, 1, 0),
            # nn.GELU(),
            # nn.BatchNorm2d(self.dim2, eps=1e-5)
        )
        self.drop1 = nn.Dropout(drop, inplace=True)
        self.act = nn.GELU()
        # self.bn = nn.BatchNorm2d(self.dim2, eps=1e-5)
        self.linear2 = nn.Sequential(
            nn.Conv2d(self.dim2, self.dim1, 1, 1, 0),
            # nn.BatchNorm2d(self.dim1, eps=1e-5)
        )
        self.drop2 = nn.Dropout(drop, inplace=True)
        self.dwc = nn.Conv2d(self.dim2, self.dim2, 3, 1, 1, groups=self.dim2)
    
    def forward(self, x):
        
        x = self.linear1(x)
        x = self.drop1(x)
        x = x + self.dwc(x)
        x = self.act(x)
        # x = self.bn(x)
        x = self.linear2(x)
        x = self.drop2(x)
        
        return x
    
        
class DeBiLevelRoutingAttention(nn.Module):
    """
    n_win: number of windows in one side (so the actual number of windows is n_win*n_win)
    kv_per_win: for kv_downsample_mode='ada_xxxpool' only, number of key/values per window. Similar to n_win, the actual number is kv_per_win*kv_per_win.
    topk: topk for window filtering
    param_attention: 'qkvo'-linear for q,k,v and o, 'none': param free attention
    param_routing: extra linear for routing
    diff_routing: wether to set routing differentiable
    soft_routing: wether to multiply soft routing weights
    """
    def __init__(self, dim, num_heads=8, n_win=7, qk_dim=None, qk_scale=None,
                 kv_per_win=4, kv_downsample_ratio=4, kv_downsample_kernel=None, kv_downsample_mode='identity',
                 topk=4, param_attention="qkvo", param_routing=False, diff_routing=False, soft_routing=False, side_dwconv=3,
                 auto_pad=False, param_size='small'):
        super().__init__()
        # local attention setting
        self.dim = dim
        self.n_win = n_win  # Wh, Ww
        self.num_heads = num_heads
        self.qk_dim = qk_dim or dim

#############################################################
        if param_size=='tiny':
            if self.dim == 64 :
                self.n_groups = 1
                self.top_k_def = 16   # 2    128
                self.kk = 9
                self.stride_def = 8
                self.expain_ratio = 3
                self.q_size=to_2tuple(56)

            if self.dim == 128 :
                self.n_groups = 2
                self.top_k_def = 16   # 4    256
                self.kk = 7
                self.stride_def = 4
                self.expain_ratio = 3
                self.q_size=to_2tuple(28)

            if self.dim == 256 :
                self.n_groups = 4
                self.top_k_def = 4   # 8    512
                self.kk = 5
                self.stride_def = 2
                self.expain_ratio = 3
                self.q_size=to_2tuple(14)

            if self.dim == 512 :
                self.n_groups = 8
                self.top_k_def = 49   # 8    512
                self.kk = 3
                self.stride_def = 1
                self.expain_ratio = 3
                self.q_size=to_2tuple(7)
#############################################################
        if param_size=='small':
            if self.dim == 64 :
                self.n_groups = 1
                self.top_k_def = 16   # 2    128
                self.kk = 9
                self.stride_def = 8
                self.expain_ratio = 3
                self.q_size=to_2tuple(56)

            if self.dim == 128 :
                self.n_groups = 2
                self.top_k_def = 16   # 4    256
                self.kk = 7
                self.stride_def = 4
                self.expain_ratio = 3
                self.q_size=to_2tuple(28)

            if self.dim == 256 :
                self.n_groups = 4
                self.top_k_def = 4   # 8    512
                self.kk = 5
                self.stride_def = 2
                self.expain_ratio = 3
                self.q_size=to_2tuple(14)

            if self.dim == 512 :
                self.n_groups = 8
                self.top_k_def = 49   # 8    512
                self.kk = 3
                self.stride_def = 1
                self.expain_ratio = 1
                self.q_size=to_2tuple(7)
#############################################################
        if param_size=='base':
            if self.dim == 96 :
                self.n_groups = 1
                self.top_k_def = 16   # 2    128
                self.kk = 9
                self.stride_def = 8
                self.expain_ratio = 3
                self.q_size=to_2tuple(56)

            if self.dim == 192 :
                self.n_groups = 2
                self.top_k_def = 16   # 4    256
                self.kk = 7
                self.stride_def = 4
                self.expain_ratio = 3
                self.q_size=to_2tuple(28)

            if self.dim == 384 :
                self.n_groups = 3
                self.top_k_def = 4   # 8    512
                self.kk = 5
                self.stride_def = 2
                self.expain_ratio = 3
                self.q_size=to_2tuple(14)
                
            if self.dim == 768 :
                self.n_groups = 6
                self.top_k_def = 49   # 8    512
                self.kk = 3
                self.stride_def = 1
                self.expain_ratio = 3
                self.q_size=to_2tuple(7)
            

        self.q_h, self.q_w = self.q_size
        
        self.kv_h, self.kv_w = self.q_h // self.stride_def, self.q_w // self.stride_def
        self.n_group_channels = self.dim // self.n_groups
        self.n_group_heads = self.num_heads // self.n_groups
        self.n_group_channels = self.dim // self.n_groups
        
        self.offset_range_factor = -1
        self.head_channels = dim // num_heads

        self.n_group_heads = self.num_heads // self.n_groups

        #assert self.qk_dim % num_heads == 0 and self.dim % num_heads==0, 'qk_dim and dim must be divisible by num_heads!'
        self.scale = qk_scale or self.qk_dim ** -0.5
        
        
        
        
        self.rpe_table = nn.Parameter(
                    torch.zeros(self.num_heads, self.q_h * 2 - 1, self.q_w * 2 - 1)
                )
        trunc_normal_(self.rpe_table, std=0.01)




        ################side_dwconv (i.e. LCE in ShuntedTransformer)###########
        self.lepe1 = nn.Conv2d(dim, dim, kernel_size=side_dwconv, stride=self.stride_def, padding=side_dwconv//2, groups=dim) if side_dwconv > 0 else \
                    lambda x: torch.zeros_like(x)


        ################ global routing setting #################
        self.topk = topk
        self.param_routing = param_routing
        self.diff_routing = diff_routing
        self.soft_routing = soft_routing


        # router
        #assert not (self.param_routing and not self.diff_routing) # cannot be with_param=True and diff_routing=False
        self.router = TopkRouting(qk_dim=self.qk_dim,
                                  qk_scale=self.scale,
                                  topk=self.topk,
                                  diff_routing=self.diff_routing,
                                  param_routing=self.param_routing)

        if self.soft_routing: # soft routing, always diffrentiable (if no detach)
            mul_weight = 'soft'
        elif self.diff_routing: # hard differentiable routing
            mul_weight = 'hard'
        else:  # hard non-differentiable routing
            mul_weight = 'none'
        self.kv_gather = KVGather(mul_weight=mul_weight)




        # qkv mapping (shared by both global routing and local attention)
        self.param_attention = param_attention
        if self.param_attention == 'qkvo':
            #self.qkv = QKVLinear(self.dim, self.qk_dim)
            self.qkv_conv = QKVConv(self.dim, self.qk_dim)
            #self.wo = nn.Linear(dim, dim)
        elif self.param_attention == 'qkv':
            #self.qkv = QKVLinear(self.dim, self.qk_dim)
            self.qkv_conv = QKVConv(self.dim, self.qk_dim)
            #self.wo = nn.Identity()
        else:
            raise ValueError(f'param_attention mode {self.param_attention} is not surpported!')




        self.kv_downsample_mode = kv_downsample_mode
        self.kv_per_win = kv_per_win
        self.kv_downsample_ratio = kv_downsample_ratio
        self.kv_downsample_kenel = kv_downsample_kernel
        if self.kv_downsample_mode == 'ada_avgpool':
            assert self.kv_per_win is not None
            self.kv_down = nn.AdaptiveAvgPool2d(self.kv_per_win)
        elif self.kv_downsample_mode == 'ada_maxpool':
            assert self.kv_per_win is not None
            self.kv_down = nn.AdaptiveMaxPool2d(self.kv_per_win)
        elif self.kv_downsample_mode == 'maxpool':
            assert self.kv_downsample_ratio is not None
            self.kv_down = nn.MaxPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity()
        elif self.kv_downsample_mode == 'avgpool':
            assert self.kv_downsample_ratio is not None
            self.kv_down = nn.AvgPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity()
        elif self.kv_downsample_mode == 'identity': # no kv downsampling
            self.kv_down = nn.Identity()
        elif self.kv_downsample_mode == 'fracpool':
            raise NotImplementedError('fracpool policy is not implemented yet!')
        elif kv_downsample_mode == 'conv':
            raise NotImplementedError('conv policy is not implemented yet!')
        else:
            raise ValueError(f'kv_down_sample_mode {self.kv_downsaple_mode} is not surpported!')

        self.attn_act = nn.Softmax(dim=-1)

        self.auto_pad=auto_pad




##########################################################################################

        self.proj_q = nn.Conv2d(
            dim, dim,
            kernel_size=1, stride=1, padding=0
        )
        self.proj_k = nn.Conv2d(
            dim, dim,
            kernel_size=1, stride=1, padding=0
        )
        self.proj_v = nn.Conv2d(
            dim, dim,
            kernel_size=1, stride=1, padding=0
        )
        self.proj_out = nn.Conv2d(
            dim, dim,
            kernel_size=1, stride=1, padding=0
        )
        
        self.unifyheads1 = nn.Conv2d(
            dim, dim,
            kernel_size=1, stride=1, padding=0
        )

        self.conv_offset_q = nn.Sequential(
                        nn.Conv2d(self.n_group_channels, self.n_group_channels, (self.kk,self.kk), (self.stride_def,self.stride_def), (self.kk//2,self.kk//2), groups=self.n_group_channels, bias=False),
                        LayerNormProxy(self.n_group_channels),
                        nn.GELU(),
                        nn.Conv2d(self.n_group_channels, 1, 1, 1, 0, bias=False),
                )


### FFN

        self.norm = nn.LayerNorm(dim, eps=1e-6)

        self.norm2 = nn.LayerNorm(dim, eps=1e-6)

        self.mlp =TransformerMLPWithConv(dim, self.expain_ratio, 0.)


    @torch.no_grad()
    def _get_ref_points(self, H_key, W_key, B, dtype, device):

        ref_y, ref_x = torch.meshgrid(
            torch.linspace(0.5, H_key - 0.5, H_key, dtype=dtype, device=device),
            torch.linspace(0.5, W_key - 0.5, W_key, dtype=dtype, device=device)
        )
        ref = torch.stack((ref_y, ref_x), -1)
        ref[..., 1].div_(W_key).mul_(2).sub_(1)
        ref[..., 0].div_(H_key).mul_(2).sub_(1)
        ref = ref[None, ...].expand(B * self.n_groups, -1, -1, -1) # B * g H W 2

        return ref

    @torch.no_grad()
    def _get_q_grid(self, H, W, B, dtype, device):

        ref_y, ref_x = torch.meshgrid(
            torch.arange(0, H, dtype=dtype, device=device),
            torch.arange(0, W, dtype=dtype, device=device),
            indexing='ij'
        )
        ref = torch.stack((ref_y, ref_x), -1)
        ref[..., 1].div_(W - 1.0).mul_(2.0).sub_(1.0)
        ref[..., 0].div_(H - 1.0).mul_(2.0).sub_(1.0)
        ref = ref[None, ...].expand(B * self.n_groups, -1, -1, -1) # B * g H W 2

        return ref

    def forward(self, x, ret_attn_mask=False):
        dtype, device = x.dtype, x.device
        """
        x: NHWC tensor
        Return:
            NHWC tensor
        """
# NOTE: use padding for semantic segmentation
###################################################
        if self.auto_pad:
            N, H_in, W_in, C = x.size()

            pad_l = pad_t = 0
            pad_r = (self.n_win - W_in % self.n_win) % self.n_win
            pad_b = (self.n_win - H_in % self.n_win) % self.n_win
            x = F.pad(x, (0, 0, # dim=-1
                          pad_l, pad_r, # dim=-2
                          pad_t, pad_b)) # dim=-3
            _, H, W, _ = x.size() # padded size
        else:
            N, H, W, C = x.size()
            assert H%self.n_win == 0 and W%self.n_win == 0 #
            
        #print("X_in")
        #print(x.shape)
        
###################################################
        #q=self.proj_q_def(x)
        x_res = rearrange(x, "n h w c -> n c h w")
#################qkv projection###################
        
        q,kv = self.qkv_conv(x.permute(0, 3, 1, 2))
        q_bi = rearrange(q, "n c (j h) (i w) -> n (j i) h w c", j=self.n_win, i=self.n_win)
        kv = rearrange(kv, "n c (j h) (i w) -> n (j i) h w c", j=self.n_win, i=self.n_win)
        
        
        q_pix = rearrange(q_bi, 'n p2 h w c -> n p2 (h w) c')
        kv_pix = self.kv_down(rearrange(kv, 'n p2 h w c -> (n p2) c h w'))
        kv_pix = rearrange(kv_pix, '(n j i) c h w -> n (j i) (h w) c', j=self.n_win, i=self.n_win)

##################side_dwconv(lepe)##################
        # NOTE: call contiguous to avoid gradient warning when using ddp
        lepe1 = self.lepe1(rearrange(kv[..., self.qk_dim:], 'n (j i) h w c -> n c (j h) (i w)', j=self.n_win, i=self.n_win).contiguous())


#################################################################   Offset Q
        
        q_off = rearrange(q, 'b (g c) h w -> (b g) c h w', g=self.n_groups, c=self.n_group_channels)
        offset_q = self.conv_offset_q(q_off).contiguous() # B * g 2 Sg HWg
        Hk, Wk = offset_q.size(2), offset_q.size(3)
        n_sample = Hk * Wk

        if self.offset_range_factor > 0:
            offset_range = torch.tensor([1.0 / Hk, 1.0 / Wk], device=device).reshape(1, 2, 1, 1)
            offset_q = offset_q.tanh().mul(offset_range).mul(self.offset_range_factor)

        offset_q = rearrange(offset_q, 'b p h w -> b h w p') # B * g 2 Hg Wg -> B*g Hg Wg 2
        reference = self._get_ref_points(Hk, Wk, N, dtype, device)

        if self.offset_range_factor >= 0:
            pos_k = offset_q + reference
        else:
            pos_k = (offset_q + reference).clamp(-1., +1.)

        x_sampled_q = F.grid_sample(
            input=x_res.reshape(N * self.n_groups, self.n_group_channels, H, W),
            grid=pos_k[..., (1, 0)], # y, x -> x, y
            mode='bilinear', align_corners=True) # B * g, Cg, Hg, Wg

        q_sampled = x_sampled_q.reshape(N, C, Hk, Wk)


########　　Bi-LEVEL Gathering

        if self.auto_pad:
            q_sampled=q_sampled.permute(0, 2, 3, 1)
            Ng, Hg, Wg, Cg = q_sampled.size()
        
            pad_l = pad_t = 0
            pad_rg = (self.n_win - Wg % self.n_win) % self.n_win
            pad_bg = (self.n_win - Hg % self.n_win) % self.n_win
            q_sampled = F.pad(q_sampled, (0, 0, # dim=-1
                          pad_l, pad_rg, # dim=-2
                          pad_t, pad_bg)) # dim=-3
            _, Hg, Wg, _ = q_sampled.size() # padded size
            
            q_sampled=q_sampled.permute(0, 3, 1, 2)
            
            lepe1 = F.pad(lepe1.permute(0, 2, 3, 1), (0, 0, # dim=-1
                          pad_l, pad_rg, # dim=-2
                          pad_t, pad_bg)) # dim=-3
            lepe1=lepe1.permute(0, 3, 1, 2)
            
            pos_k = F.pad(pos_k, (0, 0, # dim=-1
                          pad_l, pad_rg, # dim=-2
                          pad_t, pad_bg)) # dim=-3

            
        queries_def = self.proj_q(q_sampled)  #Linnear projection

        queries_def = rearrange(queries_def, "n c (j h) (i w) -> n (j i) h w c", j=self.n_win, i=self.n_win).contiguous()

        q_win, k_win = queries_def.mean([2, 3]), kv[..., 0:(self.qk_dim)].mean([2, 3])
        r_weight, r_idx = self.router(q_win, k_win)
        kv_gather = self.kv_gather(r_idx=r_idx, r_weight=r_weight, kv=kv_pix)  # (n, p^2, topk, h_kv*w_kv, c )

        k_gather, v_gather = kv_gather.split([self.qk_dim, self.dim], dim=-1)

        ###     Bi-level Routing MHA
        k = rearrange(k_gather, 'n p2 k hw (m c) -> (n p2) m c (k hw)', m=self.num_heads)
        v = rearrange(v_gather, 'n p2 k hw (m c) -> (n p2) m (k hw) c', m=self.num_heads)
        q_def = rearrange(queries_def,  'n p2 h w (m c)-> (n p2) m (h w) c',m=self.num_heads)

        attn_weight = (q_def * self.scale) @ k
        attn_weight = self.attn_act(attn_weight)
        out = attn_weight @ v

        out_def = rearrange(out, '(n j i) m (h w) c -> n (m c) (j h) (i w)', j=self.n_win, i=self.n_win, h=Hg//self.n_win, w=Wg//self.n_win).contiguous()

        out_def = out_def + lepe1

        out_def = self.unifyheads1(out_def)
        
        out_def = q_sampled + out_def
        
        out_def = out_def + self.mlp(self.norm2(out_def.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)) # (N, C, H, W)


#############################################################################################




########　　　Deformable Gathering
#############################################################################################  
 
        out_def = self.norm(out_def.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        
        k = self.proj_k(out_def)
        v = self.proj_v(out_def)
             
        k_pix_sel = rearrange(k, 'n (m c) h w -> (n m) c (h w)', m=self.num_heads)
        v_pix_sel = rearrange(v, 'n (m c) h w -> (n m) c (h w)', m=self.num_heads)
        q_pix = rearrange(q, 'n (m c) h w -> (n m) c (h w)', m=self.num_heads)
        
        attn = torch.einsum('b c m, b c n -> b m n', q_pix, k_pix_sel) # B * h, HW, Ns
        attn = attn.mul(self.scale)
        
        ### Bias
        rpe_table = self.rpe_table
        rpe_bias = rpe_table[None, ...].expand(N, -1, -1, -1)
        q_grid = self._get_q_grid(H, W, N, dtype, device)
        displacement = (q_grid.reshape(N * self.n_groups, H * W, 2).unsqueeze(2) - pos_k.reshape(N * self.n_groups, Hg*Wg, 2).unsqueeze(1)).mul(0.5)
        attn_bias = F.grid_sample(
                    input=rearrange(rpe_bias, 'b (g c) h w -> (b g) c h w', c=self.n_group_heads, g=self.n_groups),
                    grid=displacement[..., (1, 0)],
                    mode='bilinear', align_corners=True) # B * g, h_g, HW, Ns
        attn_bias = attn_bias.reshape(N * self.num_heads, H * W, Hg*Wg)
        attn = attn + attn_bias
        ### 
        attn = F.softmax(attn, dim=2)
        out = torch.einsum('b m n, b c n -> b c m', attn, v_pix_sel)
        out = out.reshape(N,C,H,W).contiguous()
        out = self.proj_out(out).permute(0,2,3,1)

#############################################################################################
        
        # NOTE: use padding for semantic segmentation
        # crop padded region
        if self.auto_pad and (pad_r > 0 or pad_b > 0):
            out = out[:, :H_in, :W_in, :].contiguous()

        if ret_attn_mask:
            return out, r_weight, r_idx, attn_weight
        else:
            return out




def get_pe_layer(emb_dim, pe_dim=None, name='none'):
    if name == 'none':
        return nn.Identity()
    else:
        raise ValueError(f'PE name {name} is not surpported!')


class Block(nn.Module):
    def __init__(self, dim, drop_path=0., layer_scale_init_value=-1,
                       num_heads=8, n_win=7, qk_dim=None, qk_scale=None,
                       kv_per_win=4, kv_downsample_ratio=4, 
                       kv_downsample_kernel=None, kv_downsample_mode='ada_avgpool',
                       topk=4, param_attention="qkvo", param_routing=False, 
                       diff_routing=False, soft_routing=False, mlp_ratio=4, param_size='small',mlp_dwconv=False,
                       side_dwconv=5, before_attn_dwconv=3, pre_norm=True, auto_pad=False):
        super().__init__()
        qk_dim = qk_dim or dim

        # modules
        if before_attn_dwconv > 0:
            self.pos_embed1 = nn.Conv2d(dim, dim,  kernel_size=before_attn_dwconv, padding=1, groups=dim)
            self.pos_embed2 = nn.Conv2d(dim, dim,  kernel_size=before_attn_dwconv, padding=1, groups=dim)
        else:
            self.pos_embed = lambda x: 0
            
        self.norm1 = nn.LayerNorm(dim, eps=1e-6) # important to avoid attention collapsing
        #if topk > 0:
        if topk == 4:
            self.attn1 = BiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=1, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        auto_pad=auto_pad)
            
            self.attn2 = DeBiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=topk, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        auto_pad=auto_pad,param_size=param_size)
            
            
        elif topk == 8:
            self.attn1 = BiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=4, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        auto_pad=auto_pad)
            
            self.attn2 = DeBiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=topk, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        uto_pad=auto_pad,param_size=param_size)
            
        elif topk == 16:
            self.attn1 = BiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=16, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        auto_pad=auto_pad)
            
            self.attn2 = DeBiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=topk, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        uto_pad=auto_pad,param_size=param_size)
            
            
            
            
        elif topk == -1:
            self.attn = Attention(dim=dim)
        elif topk == -2:
            self.attn1 = DeBiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=49, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        uto_pad=auto_pad,param_size=param_size)
            
            self.attn2 = DeBiLevelRoutingAttention(dim=dim, num_heads=num_heads, n_win=n_win, qk_dim=qk_dim,
                                        qk_scale=qk_scale, kv_per_win=kv_per_win, kv_downsample_ratio=kv_downsample_ratio,
                                        kv_downsample_kernel=kv_downsample_kernel, kv_downsample_mode=kv_downsample_mode,
                                        topk=49, param_attention=param_attention, param_routing=param_routing,
                                        diff_routing=diff_routing, soft_routing=soft_routing, side_dwconv=side_dwconv,
                                        uto_pad=auto_pad,param_size=param_size)
            
        elif topk == 0:
            self.attn = nn.Sequential(Rearrange('n h w c -> n c h w'), # compatiability
                                      nn.Conv2d(dim, dim, 1), # pseudo qkv linear
                                      nn.Conv2d(dim, dim, 5, padding=2, groups=dim), # pseudo attention
                                      nn.Conv2d(dim, dim, 1), # pseudo out linear
                                      Rearrange('n c h w -> n h w c')
                                     )
            
        
        self.norm2 = nn.LayerNorm(dim, eps=1e-6)
        
        self.mlp1 = TransformerMLPWithConv(dim, mlp_ratio, 0.)
        
        
        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        
        self.norm3 = nn.LayerNorm(dim, eps=1e-6)
        self.norm4 = nn.LayerNorm(dim, eps=1e-6)
        
        self.mlp2 =TransformerMLPWithConv(dim, mlp_ratio, 0.)

        
        # tricks: layer scale & pre_norm/post_norm
        if layer_scale_init_value > 0:
            self.use_layer_scale = True
            self.gamma1 = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.gamma2 = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.gamma3 = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            self.gamma4 = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
        else:
            self.use_layer_scale = False
        self.pre_norm = pre_norm


    def forward(self, x):
        """
        x: NCHW tensor
        """
        # conv pos embedding
        x = x + self.pos_embed1(x)
        # permute to NHWC tensor for attention & mlp
        x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)

        # attention & mlp
        if self.pre_norm:
            if self.use_layer_scale:
                x = x + self.drop_path1(self.gamma1 * self.attn1(self.norm1(x))) # (N, H, W, C)
                x = x + self.drop_path1(self.gamma2 * self.mlp1(self.norm2(x))) # (N, H, W, C)
                
                # conv pos embedding
                x = x + self.pos_embed2(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
        
                x = x + self.drop_path2(self.gamma3 * self.attn2(self.norm3(x))) # (N, H, W, C)
                x = x + self.drop_path2(self.gamma4 * self.mlp2(self.norm4(x))) # (N, H, W, C)
                
            else:
                x = x + self.drop_path1(self.attn1(self.norm1(x))) # (N, H, W, C)
                x = x + self.drop_path1(self.mlp1(self.norm2(x).permute(0, 3, 1, 2)).permute(0, 2, 3, 1)) # (N, H, W, C)
                
                # conv pos embedding
                x = x + self.pos_embed2(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
                
                x = x + self.drop_path2(self.attn2(self.norm3(x))) # (N, H, W, C)
                x = x + self.drop_path2(self.mlp2(self.norm4(x).permute(0, 3, 1, 2)).permute(0, 2, 3, 1)) # (N, H, W, C)
                
        else: # https://kexue.fm/archives/9009
            if self.use_layer_scale:
                x = self.norm1(x + self.drop_path1(self.gamma1 * self.attn1(x))) # (N, H, W, C)
                x = self.norm2(x + self.drop_path1(self.gamma2 * self.mlp1(x))) # (N, H, W, C)
                
                # conv pos embedding
                x = x + self.pos_embed2(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
                
                x = self.norm3(x + self.drop_path2(self.gamma3 * self.attn2(x))) # (N, H, W, C)
                x = self.norm4(x + self.drop_path2(self.gamma4 * self.mlp2(x))) # (N, H, W, C)
                
                
            else:
                x = self.norm1(x + self.drop_path1(self.attn1(x))) # (N, H, W, C)
                x = x + self.drop_path1(self.mlp1(self.norm2(x).permute(0, 3, 1, 2)).permute(0, 2, 3, 1)) # (N, H, W, C)
                
                # conv pos embedding
                x = x + self.pos_embed2(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
                
                x = self.norm3(x + self.drop_path2(self.attn2(x))) # (N, H, W, C)
                x = x + self.drop_path2(self.mlp2(self.norm4(x).permute(0, 3, 1, 2)).permute(0, 2, 3, 1)) # (N, H, W, C)

        # permute back
        x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
        return x


class DeBiFormer(nn.Module):
    def __init__(self, depth=[3, 4, 8, 3], in_chans=3, num_classes=1000, embed_dim=[64, 128, 320, 512],
                 head_dim=64, qk_scale=None, representation_size=None,
                 drop_path_rate=0., drop_rate=0.,
                 use_checkpoint_stages=[],
                 ########
                 n_win=7,
                 kv_downsample_mode='ada_avgpool',
                 kv_per_wins=[2, 2, -1, -1],
                 topks=[8, 8, -1, -1],
                 side_dwconv=5,
                 layer_scale_init_value=-1,
                 qk_dims=[None, None, None, None],
                 param_routing=False, diff_routing=False, soft_routing=False,
                 pre_norm=True,
                 pe=None,
                 pe_stages=[0],
                 before_attn_dwconv=3,
                 auto_pad=False,
                 #-----------------------
                 kv_downsample_kernels=[4, 2, 1, 1],
                 kv_downsample_ratios=[4, 2, 1, 1], # -> kv_per_win = [2, 2, 2, 1]
                 mlp_ratios=[4, 4, 4, 4],
                 param_attention='qkvo',
                 param_size='small',
                 mlp_dwconv=False):
        """
        Args:
            depth (list): depth of each stage
            img_size (int, tuple): input image size
            in_chans (int): number of input channels
            num_classes (int): number of classes for classification head
            embed_dim (list): embedding dimension of each stage
            head_dim (int): head dimension
            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
            qkv_bias (bool): enable bias for qkv if True
            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
            drop_rate (float): dropout rate
            attn_drop_rate (float): attention dropout rate
            drop_path_rate (float): stochastic depth rate
            norm_layer (nn.Module): normalization layer
            conv_stem (bool): whether use overlapped patch stem
        """
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        ############ downsample layers (patch embeddings) ######################
        self.downsample_layers = nn.ModuleList()
        # NOTE: uniformer uses two 3*3 conv, while in many other transformers this is one 7*7 conv
        stem = nn.Sequential(
            nn.Conv2d(in_chans, embed_dim[0] // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
            nn.BatchNorm2d(embed_dim[0] // 2),
            nn.GELU(),
            nn.Conv2d(embed_dim[0] // 2, embed_dim[0], kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
            nn.BatchNorm2d(embed_dim[0]),
        )
        if (pe is not None) and 0 in pe_stages:
            stem.append(get_pe_layer(emb_dim=embed_dim[0], name=pe))
        if use_checkpoint_stages:
            stem = checkpoint_wrapper(stem)
        self.downsample_layers.append(stem)

        for i in range(3):
            downsample_layer = nn.Sequential(
                nn.Conv2d(embed_dim[i], embed_dim[i+1], kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
                nn.BatchNorm2d(embed_dim[i+1])
            )
            if (pe is not None) and i+1 in pe_stages:
                downsample_layer.append(get_pe_layer(emb_dim=embed_dim[i+1], name=pe))
            if use_checkpoint_stages:
                downsample_layer = checkpoint_wrapper(downsample_layer)
            self.downsample_layers.append(downsample_layer)
        ##########################################################################

        self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
        nheads= [dim // head_dim for dim in qk_dims]
        dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))]
        cur = 0
        for i in range(4):
            stage = nn.Sequential(
                *[Block(dim=embed_dim[i], drop_path=dp_rates[cur + j],
                        layer_scale_init_value=layer_scale_init_value,
                        topk=topks[i],
                        num_heads=nheads[i],
                        n_win=n_win,
                        qk_dim=qk_dims[i],
                        qk_scale=qk_scale,
                        kv_per_win=kv_per_wins[i],
                        kv_downsample_ratio=kv_downsample_ratios[i],
                        kv_downsample_kernel=kv_downsample_kernels[i],
                        kv_downsample_mode=kv_downsample_mode,
                        param_attention=param_attention,
                        param_size=param_size,
                        param_routing=param_routing,
                        diff_routing=diff_routing,
                        soft_routing=soft_routing,
                        mlp_ratio=mlp_ratios[i],
                        mlp_dwconv=mlp_dwconv,
                        side_dwconv=side_dwconv,
                        before_attn_dwconv=before_attn_dwconv,
                        pre_norm=pre_norm,
                        auto_pad=auto_pad) for j in range(depth[i])],
            )
            if i in use_checkpoint_stages:
                stage = checkpoint_wrapper(stage)
            self.stages.append(stage)
            cur += depth[i]

        ##########################################################################
        self.norm = nn.BatchNorm2d(embed_dim[-1])
        # Representation layer
        if representation_size:
            self.num_features = representation_size
            self.pre_logits = nn.Sequential(OrderedDict([
                ('fc', nn.Linear(embed_dim, representation_size)),
                ('act', nn.Tanh())
            ]))
        else:
            self.pre_logits = nn.Identity()

        # Classifier head
        self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()

            
        self.reset_parameters()
    def reset_parameters(self):
        for m in self.parameters():
            if isinstance(m, (nn.Linear, nn.Conv2d)):
                nn.init.kaiming_normal_(m.weight)
                nn.init.zeros_(m.bias)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        for i in range(4):
            x = self.downsample_layers[i](x) # res = (56, 28, 14, 7), wins = (64, 16, 4, 1)
            x = self.stages[i](x)
        x = self.norm(x)
        x = self.pre_logits(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = x.flatten(2).mean(-1)
        x = self.head(x)
        return x            
    


@register_model
def debi_tiny(pretrained=False, pretrained_cfg=None, **kwargs):
    model = DeBiFormer(
            depth=[1, 1, 4, 1],
            embed_dim=[64, 128, 256, 512], mlp_ratios=[3, 3, 3, 3],
            param_size='tiny',
            drop_path_rate=0.,  #Drop rate
            #------------------------------
            n_win=7,
            kv_downsample_mode='identity',
            kv_per_wins=[-1, -1, -1, -1],
            topks=[4, 8, 16, -2],
            side_dwconv=5,
            before_attn_dwconv=3,
            layer_scale_init_value=-1,
            qk_dims=[64, 128, 256, 512],
            head_dim=32,
            param_routing=False, diff_routing=False, soft_routing=False,
            pre_norm=True,
            pe=None)
    return model




@register_model
def debi_small(pretrained=False, pretrained_cfg=None, **kwargs):
    model = DeBiFormer(
            depth=[2, 2, 9, 3],
            embed_dim=[64, 128, 256, 512], mlp_ratios=[3, 3, 3, 2],
            param_size='small',
            drop_path_rate=0.3,  #Drop rate
            #------------------------------
            n_win=7,
            kv_downsample_mode='identity',
            kv_per_wins=[-1, -1, -1, -1],
            topks=[4, 8, 16, -2],
            side_dwconv=5,
            before_attn_dwconv=3,
            layer_scale_init_value=-1,
            qk_dims=[64, 128, 256, 512],
            head_dim=32,
            param_routing=False, diff_routing=False, soft_routing=False,
            pre_norm=True,
            pe=None)
    return model



@register_model
def debi_base(pretrained=False, pretrained_cfg=None, **kwargs):
    model = DeBiFormer(
            depth=[2, 2, 9, 2],
            embed_dim=[96, 192, 384, 768], mlp_ratios=[3, 3, 3, 3],
            param_size='base',
            drop_path_rate=0.4,  #Drop rate
            #------------------------------
            n_win=7,
            kv_downsample_mode='identity',
            kv_per_wins=[-1, -1, -1, -1],
            topks=[4, 8, 16, -2],
            side_dwconv=5,
            before_attn_dwconv=3,
            layer_scale_init_value=-1,
            qk_dims=[96, 192, 384, 768],
            head_dim=32,
            param_routing=False, diff_routing=False, soft_routing=False,
            pre_norm=True,
            pe=None)
    return model


if __name__ == '__main__':
    
    from mmcv.cnn.utils import flops_counter

    model = DeBiFormer(
            depth=[2, 2, 9, 1],
            embed_dim=[64, 128, 256, 512], mlp_ratios=[3, 3, 3, 2],
            #------------------------------
            n_win=7,
            kv_downsample_mode='identity',
            kv_per_wins=[-1, -1, -1, -1],
            topks=[4, 8, 16, -2],
            side_dwconv=5,
            before_attn_dwconv=3,
            layer_scale_init_value=-1,
            qk_dims=[64, 128, 256, 512],
            head_dim=32,
            param_routing=False, diff_routing=False, soft_routing=False,
            pre_norm=True,
            pe=None)

    input_shape = (3, 224, 224)
    flops_counter.get_model_complexity_info(model, input_shape)

原文地址：https://blog.csdn.net/m0_47867638/article/details/143651348

免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！

上一篇：内容产品项目（21年项目版本）
下一篇：L8.【LeetCode笔记】回文数

解决 Spring Boot 中 `Ambiguous mapping. Cannot map ‘xxxController‘ method` 错误
在使用 Spring Boot 开发 Web 应用时，经常会遇到各种各样的错误。其中一种常见的错误是。本文将详细介绍这个错误的原因及解决方法，帮助开发者快速定位并解决问题。解决错误的关键在于确保每个方
阅读更多2024-11-15
数据分析案例-笔记本电脑价格数据可视化分析
本实验数据集来源于Kaggle，原始数据集共有1303条数据，13个变量，各变量含义如下：0 laptop_ID-数字-产品ID1 Company-字符串-笔记本电脑制造商2 Product-字符串-
阅读更多2024-11-15
Linux 如何查看当前系统版本的详细信息？
系统中基本都存在，所以是一种标准的获取系统信息的方式。命令以标准的方式提供详细的发行版信息。发行版的说明文件一般位于。
阅读更多2024-11-15
初级数据结构——栈
数据结构栈（Stack）是一种线性的数据结构，它只允许在序列的一端（称为栈顶）进行插入和删除操作。这种特性使得栈成为许多算法和问题解决中的有力工具。栈是一种简单而强大的数据结构，它遵循后进先出的原则，
阅读更多2024-11-15
网络安全练习之 ctfshow_web
根据前面得到的qq邮箱中的qq号查询用户，目前电脑版的QQ添加好友好像不能查看详细信息，手机版的可以看到对方所在地为陕西西安。路径是：/editor/attached/file/tmp/html/no
阅读更多2024-11-15
Python 三种方式实现自动化任务
本文介绍了用Python实现机器人过程自动化的三个包，方便读者选择、对比学习。
阅读更多2024-11-15
C++基础：Pimpl设计模式的实现
PIMPL （ Private Implementation 或 Pointer to Implementation ）是通过一个私有的成员指针，将指针所指向的类的内部实现数据进行隐藏。
阅读更多2024-11-15
【flutter】flutter2升级到3.
以这个 https://github.com/aa286211636/Flutter_QQ 为例子，升级下看看。只有登录界面能正常显示，别的页面都是报错。flutter这版本变动，基本不能直接ru
阅读更多2024-11-15
【JAVA毕业设计】基于Vue和SpringBoot的宠物咖啡馆平台
基于Vue.js和SpringBoot的宠物咖啡馆平台是一个综合性的在线服务系统，旨在为宠物爱好者提供一个便捷的宠物护理和社交场所。该平台分为管理后台和用户网页端，以满足不同用户群体的需求。管理后台主
阅读更多2024-11-15
认证鉴权框架SpringSecurity-2--重点组件和过滤器链篇
这4个接口中，每一个都是当认证或者授权过程中发生结果后触发，可以是失败的场景也可以是成功后触发。1个成功后执行，3个为失败后促发执行。
阅读更多2024-11-15