Delete models/common.py

models/common.py

@@ -1,2019 +0,0 @@
-import math
-from copy import copy
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import DeformConv2d
-from PIL import Image
-from torch.cuda import amp
-
-from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
-from utils.plots import color_list, plot_one_box
-from utils.torch_utils import time_synchronized
-
-
-##### basic ####
-
-def autopad(k, p=None):  # kernel, padding
-    # Pad to 'same'
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-class MP(nn.Module):
-    def __init__(self, k=2):
-        super(MP, self).__init__()
-        self.m = nn.MaxPool2d(kernel_size=k, stride=k)
-
-    def forward(self, x):
-        return self.m(x)
-
-
-class SP(nn.Module):
-    def __init__(self, k=3, s=1):
-        super(SP, self).__init__()
-        self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
-
-    def forward(self, x):
-        return self.m(x)
-    
-    
-class ReOrg(nn.Module):
-    def __init__(self):
-        super(ReOrg, self).__init__()
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
-
-
-class Concat(nn.Module):
-    def __init__(self, dimension=1):
-        super(Concat, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-class Chuncat(nn.Module):
-    def __init__(self, dimension=1):
-        super(Chuncat, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        x1 = []
-        x2 = []
-        for xi in x:
-            xi1, xi2 = xi.chunk(2, self.d)
-            x1.append(xi1)
-            x2.append(xi2)
-        return torch.cat(x1+x2, self.d)
-
-
-class Shortcut(nn.Module):
-    def __init__(self, dimension=0):
-        super(Shortcut, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return x[0]+x[1]
-
-
-class Foldcut(nn.Module):
-    def __init__(self, dimension=0):
-        super(Foldcut, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        x1, x2 = x.chunk(2, self.d)
-        return x1+x2
-
-
-class Conv(nn.Module):
-    # Standard convolution
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Conv, self).__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def fuseforward(self, x):
-        return self.act(self.conv(x))
-    
-
-class RobustConv(nn.Module):
-    # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs.
-    def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(RobustConv, self).__init__()
-        self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
-        self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True)
-        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
-    def forward(self, x):
-        x = x.to(memory_format=torch.channels_last)
-        x = self.conv1x1(self.conv_dw(x))
-        if self.gamma is not None:
-            x = x.mul(self.gamma.reshape(1, -1, 1, 1)) 
-        return x
-
-
-class RobustConv2(nn.Module):
-    # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP).
-    def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(RobustConv2, self).__init__()
-        self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
-        self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s, 
-                                              padding=0, bias=True, dilation=1, groups=1
-        )
-        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
-    def forward(self, x):
-        x = self.conv_deconv(self.conv_strided(x))
-        if self.gamma is not None:
-            x = x.mul(self.gamma.reshape(1, -1, 1, 1)) 
-        return x
-    
-
-def DWConv(c1, c2, k=1, s=1, act=True):
-    # Depthwise convolution
-    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super(GhostConv, self).__init__()
-        c_ = c2 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, k, s, None, g, act)
-        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
-    def forward(self, x):
-        y = self.cv1(x)
-        return torch.cat([y, self.cv2(y)], 1)
-
-
-class Stem(nn.Module):
-    # Stem
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Stem, self).__init__()
-        c_ = int(c2/2)  # hidden channels
-        self.cv1 = Conv(c1, c_, 3, 2)
-        self.cv2 = Conv(c_, c_, 1, 1)
-        self.cv3 = Conv(c_, c_, 3, 2)
-        self.pool = torch.nn.MaxPool2d(2, stride=2)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1))
-
-
-class DownC(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, n=1, k=2):
-        super(DownC, self).__init__()
-        c_ = int(c1)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2//2, 3, k)
-        self.cv3 = Conv(c1, c2//2, 1, 1)
-        self.mp = nn.MaxPool2d(kernel_size=k, stride=k)
-
-    def forward(self, x):
-        return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1)
-
-
-class SPP(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super(SPP, self).__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-    
-
-class Bottleneck(nn.Module):
-    # Darknet bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Bottleneck, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class Res(nn.Module):
-    # ResNet bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Res, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c_, 3, 1, g=g)
-        self.cv3 = Conv(c_, c2, 1, 1)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
-
-
-class ResX(Res):
-    # ResNet bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__(c1, c2, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-
-
-class Ghost(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super(Ghost, self).__init__()
-        c_ = c2 // 2
-        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
-                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
-                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
-
-    def forward(self, x):
-        return self.conv(x) + self.shortcut(x)
-
-##### end of basic #####
-
-
-##### cspnet #####
-
-class SPPCSPC(nn.Module):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
-        super(SPPCSPC, self).__init__()
-        c_ = int(2 * c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(c_, c_, 3, 1)
-        self.cv4 = Conv(c_, c_, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-        self.cv5 = Conv(4 * c_, c_, 1, 1)
-        self.cv6 = Conv(c_, c_, 3, 1)
-        self.cv7 = Conv(2 * c_, c2, 1, 1)
-
-    def forward(self, x):
-        x1 = self.cv4(self.cv3(self.cv1(x)))
-        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
-        y2 = self.cv2(x)
-        return self.cv7(torch.cat((y1, y2), dim=1))
-
-class GhostSPPCSPC(SPPCSPC):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
-        super().__init__(c1, c2, n, shortcut, g, e, k)
-        c_ = int(2 * c2 * e)  # hidden channels
-        self.cv1 = GhostConv(c1, c_, 1, 1)
-        self.cv2 = GhostConv(c1, c_, 1, 1)
-        self.cv3 = GhostConv(c_, c_, 3, 1)
-        self.cv4 = GhostConv(c_, c_, 1, 1)
-        self.cv5 = GhostConv(4 * c_, c_, 1, 1)
-        self.cv6 = GhostConv(c_, c_, 3, 1)
-        self.cv7 = GhostConv(2 * c_, c2, 1, 1)
-
-
-class GhostStem(Stem):
-    # Stem
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__(c1, c2, k, s, p, g, act)
-        c_ = int(c2/2)  # hidden channels
-        self.cv1 = GhostConv(c1, c_, 3, 2)
-        self.cv2 = GhostConv(c_, c_, 1, 1)
-        self.cv3 = GhostConv(c_, c_, 3, 2)
-        self.cv4 = GhostConv(2 * c_, c2, 1, 1)
-        
-
-class BottleneckCSPA(nn.Module):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSPA, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.m(self.cv1(x))
-        y2 = self.cv2(x)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPB(nn.Module):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSPB, self).__init__()
-        c_ = int(c2)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        x1 = self.cv1(x)
-        y1 = self.m(x1)
-        y2 = self.cv2(x1)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPC(nn.Module):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSPC, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(c_, c_, 1, 1)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(torch.cat((y1, y2), dim=1))
-
-
-class ResCSPA(BottleneckCSPA):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPB(BottleneckCSPB):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPC(BottleneckCSPC):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResXCSPA(ResCSPA):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPB(ResCSPB):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPC(ResCSPC):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class GhostCSPA(BottleneckCSPA):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPB(BottleneckCSPB):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPC(BottleneckCSPC):
-    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-##### end of cspnet #####
-
-
-##### yolor #####
-
-class ImplicitA(nn.Module):
-    def __init__(self, channel, mean=0., std=.02):
-        super(ImplicitA, self).__init__()
-        self.channel = channel
-        self.mean = mean
-        self.std = std
-        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
-        nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
-    def forward(self, x):
-        return self.implicit + x
-    
-
-class ImplicitM(nn.Module):
-    def __init__(self, channel, mean=0., std=.02):
-        super(ImplicitM, self).__init__()
-        self.channel = channel
-        self.mean = mean
-        self.std = std
-        self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
-        nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
-    def forward(self, x):
-        return self.implicit * x
-    
-##### end of yolor #####
-
-
-##### repvgg #####
-
-class RepConv(nn.Module):
-    # Represented convolution
-    # https://arxiv.org/abs/2101.03697
-
-    def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):
-        super(RepConv, self).__init__()
-
-        self.deploy = deploy
-        self.groups = g
-        self.in_channels = c1
-        self.out_channels = c2
-
-        assert k == 3
-        assert autopad(k, p) == 1
-
-        padding_11 = autopad(k, p) - k // 2
-
-        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
-        if deploy:
-            self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)
-
-        else:
-            self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)
-
-            self.rbr_dense = nn.Sequential(
-                nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),
-                nn.BatchNorm2d(num_features=c2),
-            )
-
-            self.rbr_1x1 = nn.Sequential(
-                nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False),
-                nn.BatchNorm2d(num_features=c2),
-            )
-
-    def forward(self, inputs):
-        if hasattr(self, "rbr_reparam"):
-            return self.act(self.rbr_reparam(inputs))
-
-        if self.rbr_identity is None:
-            id_out = 0
-        else:
-            id_out = self.rbr_identity(inputs)
-
-        return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
-    
-    def get_equivalent_kernel_bias(self):
-        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
-        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
-        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
-        return (
-            kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid,
-            bias3x3 + bias1x1 + biasid,
-        )
-
-    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
-        if kernel1x1 is None:
-            return 0
-        else:
-            return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
-
-    def _fuse_bn_tensor(self, branch):
-        if branch is None:
-            return 0, 0
-        if isinstance(branch, nn.Sequential):
-            kernel = branch[0].weight
-            running_mean = branch[1].running_mean
-            running_var = branch[1].running_var
-            gamma = branch[1].weight
-            beta = branch[1].bias
-            eps = branch[1].eps
-        else:
-            assert isinstance(branch, nn.BatchNorm2d)
-            if not hasattr(self, "id_tensor"):
-                input_dim = self.in_channels // self.groups
-                kernel_value = np.zeros(
-                    (self.in_channels, input_dim, 3, 3), dtype=np.float32
-                )
-                for i in range(self.in_channels):
-                    kernel_value[i, i % input_dim, 1, 1] = 1
-                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
-            kernel = self.id_tensor
-            running_mean = branch.running_mean
-            running_var = branch.running_var
-            gamma = branch.weight
-            beta = branch.bias
-            eps = branch.eps
-        std = (running_var + eps).sqrt()
-        t = (gamma / std).reshape(-1, 1, 1, 1)
-        return kernel * t, beta - running_mean * gamma / std
-
-    def repvgg_convert(self):
-        kernel, bias = self.get_equivalent_kernel_bias()
-        return (
-            kernel.detach().cpu().numpy(),
-            bias.detach().cpu().numpy(),
-        )
-
-    def fuse_conv_bn(self, conv, bn):
-
-        std = (bn.running_var + bn.eps).sqrt()
-        bias = bn.bias - bn.running_mean * bn.weight / std
-
-        t = (bn.weight / std).reshape(-1, 1, 1, 1)
-        weights = conv.weight * t
-
-        bn = nn.Identity()
-        conv = nn.Conv2d(in_channels = conv.in_channels,
-                              out_channels = conv.out_channels,
-                              kernel_size = conv.kernel_size,
-                              stride=conv.stride,
-                              padding = conv.padding,
-                              dilation = conv.dilation,
-                              groups = conv.groups,
-                              bias = True,
-                              padding_mode = conv.padding_mode)
-
-        conv.weight = torch.nn.Parameter(weights)
-        conv.bias = torch.nn.Parameter(bias)
-        return conv
-
-    def fuse_repvgg_block(self):    
-        if self.deploy:
-            return
-        print(f"RepConv.fuse_repvgg_block")
-                
-        self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
-        
-        self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
-        rbr_1x1_bias = self.rbr_1x1.bias
-        weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])
-        
-        # Fuse self.rbr_identity
-        if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)):
-            # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")
-            identity_conv_1x1 = nn.Conv2d(
-                    in_channels=self.in_channels,
-                    out_channels=self.out_channels,
-                    kernel_size=1,
-                    stride=1,
-                    padding=0,
-                    groups=self.groups, 
-                    bias=False)
-            identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)
-            identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()
-            # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
-            identity_conv_1x1.weight.data.fill_(0.0)
-            identity_conv_1x1.weight.data.fill_diagonal_(1.0)
-            identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)
-            # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
-
-            identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)
-            bias_identity_expanded = identity_conv_1x1.bias
-            weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1])            
-        else:
-            # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")
-            bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) )
-            weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) )            
-        
-
-        #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")
-        #print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")
-        #print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")
-
-        self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)
-        self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)
-                
-        self.rbr_reparam = self.rbr_dense
-        self.deploy = True
-
-        if self.rbr_identity is not None:
-            del self.rbr_identity
-            self.rbr_identity = None
-
-        if self.rbr_1x1 is not None:
-            del self.rbr_1x1
-            self.rbr_1x1 = None
-
-        if self.rbr_dense is not None:
-            del self.rbr_dense
-            self.rbr_dense = None
-
-
-class RepBottleneck(Bottleneck):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__(c1, c2, shortcut=True, g=1, e=0.5)
-        c_ = int(c2 * e)  # hidden channels
-        self.cv2 = RepConv(c_, c2, 3, 1, g=g)
-
-
-class RepBottleneckCSPA(BottleneckCSPA):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPB(BottleneckCSPB):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPC(BottleneckCSPC):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepRes(Res):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__(c1, c2, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResCSPA(ResCSPA):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPB(ResCSPB):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPC(ResCSPC):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResX(ResX):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__(c1, c2, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResXCSPA(ResXCSPA):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPB(ResXCSPB):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2)  # hidden channels
-        self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPC(ResXCSPC):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-##### end of repvgg #####
-
-
-##### transformer #####
-
-class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
-    def __init__(self, c, num_heads):
-        super().__init__()
-        self.q = nn.Linear(c, c, bias=False)
-        self.k = nn.Linear(c, c, bias=False)
-        self.v = nn.Linear(c, c, bias=False)
-        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
-        self.fc1 = nn.Linear(c, c, bias=False)
-        self.fc2 = nn.Linear(c, c, bias=False)
-
-    def forward(self, x):
-        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
-        x = self.fc2(self.fc1(x)) + x
-        return x
-
-
-class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
-    def __init__(self, c1, c2, num_heads, num_layers):
-        super().__init__()
-        self.conv = None
-        if c1 != c2:
-            self.conv = Conv(c1, c2)
-        self.linear = nn.Linear(c2, c2)  # learnable position embedding
-        self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
-        self.c2 = c2
-
-    def forward(self, x):
-        if self.conv is not None:
-            x = self.conv(x)
-        b, _, w, h = x.shape
-        p = x.flatten(2)
-        p = p.unsqueeze(0)
-        p = p.transpose(0, 3)
-        p = p.squeeze(3)
-        e = self.linear(p)
-        x = p + e
-
-        x = self.tr(x)
-        x = x.unsqueeze(3)
-        x = x.transpose(0, 3)
-        x = x.reshape(b, self.c2, w, h)
-        return x
-
-##### end of transformer #####
-
-
-##### yolov5 #####
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Focus, self).__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
-        # self.contract = Contract(gain=2)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
-        # return self.conv(self.contract(x))
-        
-
-class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * 4, c2, 1, 1)
-        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        y1 = self.m(x)
-        y2 = self.m(y1)
-        return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
-    
-    
-class Contract(nn.Module):
-    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
-        s = self.gain
-        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
-        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
-        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
-
-
-class Expand(nn.Module):
-    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        N, C, H, W = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
-        s = self.gain
-        x = x.view(N, s, s, C // s ** 2, H, W)  # x(1,2,2,16,80,80)
-        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
-        return x.view(N, C // s ** 2, H * s, W * s)  # x(1,16,160,160)
-
-
-class NMS(nn.Module):
-    # Non-Maximum Suppression (NMS) module
-    conf = 0.25  # confidence threshold
-    iou = 0.45  # IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def __init__(self):
-        super(NMS, self).__init__()
-
-    def forward(self, x):
-        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class autoShape(nn.Module):
-    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def __init__(self, model):
-        super(autoShape, self).__init__()
-        self.model = model.eval()
-
-    def autoshape(self):
-        print('autoShape already enabled, skipping... ')  # model already converted to model.autoshape()
-        return self
-
-    @torch.no_grad()
-    def forward(self, imgs, size=640, augment=False, profile=False):
-        # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
-        #   filename:   imgs = 'data/samples/zidane.jpg'
-        #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
-        #   PIL:             = Image.open('image.jpg')  # HWC x(640,1280,3)
-        #   numpy:           = np.zeros((640,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
-        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        t = [time_synchronized()]
-        p = next(self.model.parameters())  # for device and type
-        if isinstance(imgs, torch.Tensor):  # torch
-            with amp.autocast(enabled=p.device.type != 'cpu'):
-                return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
-
-        # Pre-process
-        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
-        shape0, shape1, files = [], [], []  # image and inference shapes, filenames
-        for i, im in enumerate(imgs):
-            f = f'image{i}'  # filename
-            if isinstance(im, str):  # filename or uri
-                im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
-            elif isinstance(im, Image.Image):  # PIL Image
-                im, f = np.asarray(im), getattr(im, 'filename', f) or f
-            files.append(Path(f).with_suffix('.jpg').name)
-            if im.shape[0] < 5:  # image in CHW
-                im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-            im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3)  # enforce 3ch input
-            s = im.shape[:2]  # HWC
-            shape0.append(s)  # image shape
-            g = (size / max(s))  # gain
-            shape1.append([y * g for y in s])
-            imgs[i] = im  # update
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
-        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
-        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
-        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
-        t.append(time_synchronized())
-
-        with amp.autocast(enabled=p.device.type != 'cpu'):
-            # Inference
-            y = self.model(x, augment, profile)[0]  # forward
-            t.append(time_synchronized())
-
-            # Post-process
-            y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
-            for i in range(n):
-                scale_coords(shape1, y[i][:, :4], shape0[i])
-
-            t.append(time_synchronized())
-            return Detections(imgs, y, files, t, self.names, x.shape)
-
-
-class Detections:
-    # detections class for YOLOv5 inference results
-    def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
-        super(Detections, self).__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
-        self.imgs = imgs  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.files = files  # image filenames
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)  # number of images (batch size)
-        self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
-        self.s = shape  # inference BCHW shape
-
-    def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
-        colors = color_list()
-        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
-            str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
-            if pred is not None:
-                for c in pred[:, -1].unique():
-                    n = (pred[:, -1] == c).sum()  # detections per class
-                    str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
-                if show or save or render:
-                    for *box, conf, cls in pred:  # xyxy, confidence, class
-                        label = f'{self.names[int(cls)]} {conf:.2f}'
-                        plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
-            img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
-            if pprint:
-                print(str.rstrip(', '))
-            if show:
-                img.show(self.files[i])  # show
-            if save:
-                f = self.files[i]
-                img.save(Path(save_dir) / f)  # save
-                print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
-            if render:
-                self.imgs[i] = np.asarray(img)
-
-    def print(self):
-        self.display(pprint=True)  # print results
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
-
-    def show(self):
-        self.display(show=True)  # show results
-
-    def save(self, save_dir='runs/hub/exp'):
-        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp')  # increment save_dir
-        Path(save_dir).mkdir(parents=True, exist_ok=True)
-        self.display(save=True, save_dir=save_dir)  # save results
-
-    def render(self):
-        self.display(render=True)  # render results
-        return self.imgs
-
-    def pandas(self):
-        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
-        new = copy(self)  # return copy
-        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
-        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
-        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
-            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
-        return new
-
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
-        for d in x:
-            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-                setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
-
-    def __len__(self):
-        return self.n
-
-
-class Classify(nn.Module):
-    # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Classify, self).__init__()
-        self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g)  # to x(b,c2,1,1)
-        self.flat = nn.Flatten()
-
-    def forward(self, x):
-        z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1)  # cat if list
-        return self.flat(self.conv(z))  # flatten to x(b,c2)
-
-##### end of yolov5 ######
-
-
-##### orepa #####
-
-def transI_fusebn(kernel, bn):
-    gamma = bn.weight
-    std = (bn.running_var + bn.eps).sqrt()
-    return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
-    
-    
-class ConvBN(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size,
-                             stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None):
-        super().__init__()
-        if nonlinear is None:
-            self.nonlinear = nn.Identity()
-        else:
-            self.nonlinear = nonlinear
-        if deploy:
-            self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
-                                      stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
-        else:
-            self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
-                                            stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)
-            self.bn = nn.BatchNorm2d(num_features=out_channels)
-
-    def forward(self, x):
-        if hasattr(self, 'bn'):
-            return self.nonlinear(self.bn(self.conv(x)))
-        else:
-            return self.nonlinear(self.conv(x))
-
-    def switch_to_deploy(self):
-        kernel, bias = transI_fusebn(self.conv.weight, self.bn)
-        conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size,
-                                      stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True)
-        conv.weight.data = kernel
-        conv.bias.data = bias
-        for para in self.parameters():
-            para.detach_()
-        self.__delattr__('conv')
-        self.__delattr__('bn')
-        self.conv = conv    
-
-class OREPA_3x3_RepConv(nn.Module):
-
-    def __init__(self, in_channels, out_channels, kernel_size,
-                 stride=1, padding=0, dilation=1, groups=1,
-                 internal_channels_1x1_3x3=None,
-                 deploy=False, nonlinear=None, single_init=False):
-        super(OREPA_3x3_RepConv, self).__init__()
-        self.deploy = deploy
-
-        if nonlinear is None:
-            self.nonlinear = nn.Identity()
-        else:
-            self.nonlinear = nonlinear
-
-        self.kernel_size = kernel_size
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.groups = groups
-        assert padding == kernel_size // 2
-
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-
-        self.branch_counter = 0
-
-        self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size))
-        nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0))
-        self.branch_counter += 1
-
-
-        if groups < out_channels:
-            self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
-            self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
-            nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0)
-            nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0)
-            self.weight_rbr_avg_conv.data
-            self.weight_rbr_pfir_conv.data
-            self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size))
-            self.branch_counter += 1
-
-        else:
-            raise NotImplementedError
-        self.branch_counter += 1
-
-        if internal_channels_1x1_3x3 is None:
-            internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels   # For mobilenet, it is better to have 2X internal channels
-
-        if internal_channels_1x1_3x3 == in_channels:
-            self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1))
-            id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1))
-            for i in range(in_channels):
-                id_value[i, i % int(in_channels/self.groups), 0, 0] = 1
-            id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1)
-            self.register_buffer('id_tensor', id_tensor)
-
-        else:
-            self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1))
-            nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0))
-        self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size))
-        nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0))
-        self.branch_counter += 1
-
-        expand_ratio = 8
-        self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size))
-        self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1))
-        nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0))
-        nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0))
-        self.branch_counter += 1
-
-        if out_channels == in_channels and stride == 1:
-            self.branch_counter += 1
-
-        self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels))
-        self.bn = nn.BatchNorm2d(out_channels)
-
-        self.fre_init()
-
-        nn.init.constant_(self.vector[0, :], 0.25)    #origin
-        nn.init.constant_(self.vector[1, :], 0.25)      #avg
-        nn.init.constant_(self.vector[2, :], 0.0)      #prior
-        nn.init.constant_(self.vector[3, :], 0.5)    #1x1_kxk
-        nn.init.constant_(self.vector[4, :], 0.5)     #dws_conv
-
-
-    def fre_init(self):
-        prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size)
-        half_fg = self.out_channels/2
-        for i in range(self.out_channels):
-            for h in range(3):
-                for w in range(3):
-                    if i < half_fg:
-                        prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3)
-                    else:
-                        prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3)
-
-        self.register_buffer('weight_rbr_prior', prior_tensor)
-
-    def weight_gen(self):
-
-        weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :])
-
-        weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :])
-        
-        weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :])
-
-        weight_rbr_1x1_kxk_conv1 = None
-        if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'):
-            weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze()
-        elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'):
-            weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze()
-        else:
-            raise NotImplementedError
-        weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2
-
-        if self.groups > 1:
-            g = self.groups
-            t, ig = weight_rbr_1x1_kxk_conv1.size()
-            o, tg, h, w = weight_rbr_1x1_kxk_conv2.size()
-            weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig)
-            weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w)
-            weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w)
-        else:
-            weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2)
-
-        weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :])
-
-        weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels)
-        weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :])    
-
-        weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv
-
-        return weight
-
-    def dwsc2full(self, weight_dw, weight_pw, groups):
-        
-        t, ig, h, w = weight_dw.size()
-        o, _, _, _ = weight_pw.size()
-        tg = int(t/groups)
-        i = int(ig*groups)
-        weight_dw = weight_dw.view(groups, tg, ig, h, w)
-        weight_pw = weight_pw.squeeze().view(o, groups, tg)
-        
-        weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw)
-        return weight_dsc.view(o, i, h, w)
-
-    def forward(self, inputs):
-        weight = self.weight_gen()
-        out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups)
-
-        return self.nonlinear(self.bn(out))
-
-class RepConv_OREPA(nn.Module):
-
-    def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()):
-        super(RepConv_OREPA, self).__init__()
-        self.deploy = deploy
-        self.groups = groups
-        self.in_channels = c1
-        self.out_channels = c2
-
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-
-        assert k == 3
-        assert padding == 1
-
-        padding_11 = padding - k // 2
-
-        if nonlinear is None:
-            self.nonlinearity = nn.Identity()
-        else:
-            self.nonlinearity = nonlinear
-
-        if use_se:
-            self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16)
-        else:
-            self.se = nn.Identity()
-
-        if deploy:
-            self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s,
-                                      padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
-
-        else:
-            self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None
-            self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1)
-            self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1)
-            print('RepVGG Block, identity = ', self.rbr_identity)
-
-
-    def forward(self, inputs):
-        if hasattr(self, 'rbr_reparam'):
-            return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
-
-        if self.rbr_identity is None:
-            id_out = 0
-        else:
-            id_out = self.rbr_identity(inputs)
-
-        out1 = self.rbr_dense(inputs)
-        out2 = self.rbr_1x1(inputs)
-        out3 = id_out
-        out = out1 + out2 + out3
-
-        return self.nonlinearity(self.se(out))
-
-
-    #   Optional. This improves the accuracy and facilitates quantization.
-    #   1.  Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
-    #   2.  Use like this.
-    #       loss = criterion(....)
-    #       for every RepVGGBlock blk:
-    #           loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
-    #       optimizer.zero_grad()
-    #       loss.backward()
-
-    # Not used for OREPA
-    def get_custom_L2(self):
-        K3 = self.rbr_dense.weight_gen()
-        K1 = self.rbr_1x1.conv.weight
-        t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
-        t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
-
-        l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum()      # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
-        eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1                           # The equivalent resultant central point of 3x3 kernel.
-        l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum()        # Normalize for an L2 coefficient comparable to regular L2.
-        return l2_loss_eq_kernel + l2_loss_circle
-
-    def get_equivalent_kernel_bias(self):
-        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
-        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
-        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
-        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
-
-    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
-        if kernel1x1 is None:
-            return 0
-        else:
-            return torch.nn.functional.pad(kernel1x1, [1,1,1,1])
-
-    def _fuse_bn_tensor(self, branch):
-        if branch is None:
-            return 0, 0
-        if not isinstance(branch, nn.BatchNorm2d):
-            if isinstance(branch, OREPA_3x3_RepConv):
-                kernel = branch.weight_gen()
-            elif isinstance(branch, ConvBN):
-                kernel = branch.conv.weight
-            else:
-                raise NotImplementedError
-            running_mean = branch.bn.running_mean
-            running_var = branch.bn.running_var
-            gamma = branch.bn.weight
-            beta = branch.bn.bias
-            eps = branch.bn.eps
-        else:
-            if not hasattr(self, 'id_tensor'):
-                input_dim = self.in_channels // self.groups
-                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
-                for i in range(self.in_channels):
-                    kernel_value[i, i % input_dim, 1, 1] = 1
-                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
-            kernel = self.id_tensor
-            running_mean = branch.running_mean
-            running_var = branch.running_var
-            gamma = branch.weight
-            beta = branch.bias
-            eps = branch.eps
-        std = (running_var + eps).sqrt()
-        t = (gamma / std).reshape(-1, 1, 1, 1)
-        return kernel * t, beta - running_mean * gamma / std
-
-    def switch_to_deploy(self):
-        if hasattr(self, 'rbr_reparam'):
-            return
-        print(f"RepConv_OREPA.switch_to_deploy")
-        kernel, bias = self.get_equivalent_kernel_bias()
-        self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels,
-                                     kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride,
-                                     padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True)
-        self.rbr_reparam.weight.data = kernel
-        self.rbr_reparam.bias.data = bias
-        for para in self.parameters():
-            para.detach_()
-        self.__delattr__('rbr_dense')
-        self.__delattr__('rbr_1x1')
-        if hasattr(self, 'rbr_identity'):
-            self.__delattr__('rbr_identity') 
-
-##### end of orepa #####
-
-
-##### swin transformer #####    
-    
-class WindowAttention(nn.Module):
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        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)
-
-        nn.init.normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-
-        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)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        # print(attn.dtype, v.dtype)
-        try:
-            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        except:
-            #print(attn.dtype, v.dtype)
-            x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-class Mlp(nn.Module):
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-def window_partition(x, window_size):
-
-    B, H, W, C = x.shape
-    assert H % window_size == 0, 'feature map h and w can not divide by window size'
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-def window_reverse(windows, window_size, H, W):
-    
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class SwinTransformerLayer(nn.Module):
-
-    def __init__(self, dim, num_heads, window_size=8, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.SiLU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        # if min(self.input_resolution) <= self.window_size:
-        #     # if window size is larger than input resolution, we don't partition windows
-        #     self.shift_size = 0
-        #     self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def create_mask(self, H, W):
-        # calculate attention mask for SW-MSA
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x):
-        # reshape x[b c h w] to x[b l c]
-        _, _, H_, W_ = x.shape
-
-        Padding = False
-        if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
-            Padding = True
-            # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
-            pad_r = (self.window_size - W_ % self.window_size) % self.window_size
-            pad_b = (self.window_size - H_ % self.window_size) % self.window_size
-            x = F.pad(x, (0, pad_r, 0, pad_b))
-
-        # print('2', x.shape)
-        B, C, H, W = x.shape
-        L = H * W
-        x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C)  # b, L, c
-
-        # create mask from init to forward
-        if self.shift_size > 0:
-            attn_mask = self.create_mask(H, W).to(x.device)
-        else:
-            attn_mask = None
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W)  # b c h w
-
-        if Padding:
-            x = x[:, :, :H_, :W_]  # reverse padding
-
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    def __init__(self, c1, c2, num_heads, num_layers, window_size=8):
-        super().__init__()
-        self.conv = None
-        if c1 != c2:
-            self.conv = Conv(c1, c2)
-
-        # remove input_resolution
-        self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
-    def forward(self, x):
-        if self.conv is not None:
-            x = self.conv(x)
-        x = self.blocks(x)
-        return x
-
-
-class STCSPA(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(STCSPA, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformerBlock(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.m(self.cv1(x))
-        y2 = self.cv2(x)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPB(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(STCSPB, self).__init__()
-        c_ = int(c2)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformerBlock(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        x1 = self.cv1(x)
-        y1 = self.m(x1)
-        y2 = self.cv2(x1)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPC(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(STCSPC, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(c_, c_, 1, 1)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformerBlock(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer #####   
-
-
-##### swin transformer v2 ##### 
-  
-class WindowAttention_v2(nn.Module):
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
-                                     nn.ReLU(inplace=True),
-                                     nn.Linear(512, num_heads, bias=False))
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
-        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(
-            torch.meshgrid([relative_coords_h,
-                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
-        else:
-            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))
-            self.v_bias = nn.Parameter(torch.zeros(dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
-        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        try:
-            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        except:
-            x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
-            
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, ' \
-               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-    
-class Mlp_v2(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition_v2(x, window_size):
-    
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse_v2(windows, window_size, H, W):
-    
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class SwinTransformerLayer_v2(nn.Module):
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
-        super().__init__()
-        self.dim = dim
-        #self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        #if min(self.input_resolution) <= self.window_size:
-        #    # if window size is larger than input resolution, we don't partition windows
-        #    self.shift_size = 0
-        #    self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention_v2(
-            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
-            pretrained_window_size=(pretrained_window_size, pretrained_window_size))
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def create_mask(self, H, W):
-        # calculate attention mask for SW-MSA
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x):
-        # reshape x[b c h w] to x[b l c]
-        _, _, H_, W_ = x.shape
-
-        Padding = False
-        if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
-            Padding = True
-            # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
-            pad_r = (self.window_size - W_ % self.window_size) % self.window_size
-            pad_b = (self.window_size - H_ % self.window_size) % self.window_size
-            x = F.pad(x, (0, pad_r, 0, pad_b))
-
-        # print('2', x.shape)
-        B, C, H, W = x.shape
-        L = H * W
-        x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C)  # b, L, c
-
-        # create mask from init to forward
-        if self.shift_size > 0:
-            attn_mask = self.create_mask(H, W).to(x.device)
-        else:
-            attn_mask = None
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition_v2(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-        x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W)  # b c h w
-        
-        if Padding:
-            x = x[:, :, :H_, :W_]  # reverse padding
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class SwinTransformer2Block(nn.Module):
-    def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
-        super().__init__()
-        self.conv = None
-        if c1 != c2:
-            self.conv = Conv(c1, c2)
-
-        # remove input_resolution
-        self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
-    def forward(self, x):
-        if self.conv is not None:
-            x = self.conv(x)
-        x = self.blocks(x)
-        return x
-
-
-class ST2CSPA(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(ST2CSPA, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformer2Block(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.m(self.cv1(x))
-        y2 = self.cv2(x)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPB(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(ST2CSPB, self).__init__()
-        c_ = int(c2)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformer2Block(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        x1 = self.cv1(x)
-        y1 = self.m(x1)
-        y2 = self.cv2(x1)
-        return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPC(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(ST2CSPC, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(c_, c_, 1, 1)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        num_heads = c_ // 32
-        self.m = SwinTransformer2Block(c_, c_, num_heads, n)
-        #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer v2 #####