--- a
+++ b/CaraNet/pretrain/Res2Net_v1b.py
@@ -0,0 +1,234 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 22 16:13:07 2021
+
+@author: angelou
+"""
+
+import torch.nn as nn
+import math
+import torch.utils.model_zoo as model_zoo
+import torch
+import torch.nn.functional as F
+
+__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s']
+
+model_urls = {
+    'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth',
+    'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth',
+}
+
+
+class Bottle2neck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal'):
+        """ Constructor
+        Args:
+            inplanes: input channel dimensionality
+            planes: output channel dimensionality
+            stride: conv stride. Replaces pooling layer.
+            downsample: None when stride = 1
+            baseWidth: basic width of conv3x3
+            scale: number of scale.
+            type: 'normal': normal set. 'stage': first block of a new stage.
+        """
+        super(Bottle2neck, self).__init__()
+
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+
+        if scale == 1:
+            self.nums = 1
+        else:
+            self.nums = scale - 1
+        if stype == 'stage':
+            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, bias=False))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+
+        self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stype = stype
+        self.scale = scale
+        self.width = width
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0 or self.stype == 'stage':
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+        if self.scale != 1 and self.stype == 'normal':
+            out = torch.cat((out, spx[self.nums]), 1)
+        elif self.scale != 1 and self.stype == 'stage':
+            out = torch.cat((out, self.pool(spx[self.nums])), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Res2Net(nn.Module):
+
+    def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
+        self.inplanes = 64
+        super(Res2Net, self).__init__()
+        self.baseWidth = baseWidth
+        self.scale = scale
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 32, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 32, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, 3, 1, 1, bias=False)
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.AvgPool2d(kernel_size=stride, stride=stride,
+                             ceil_mode=True, count_include_pad=False),
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=1, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample=downsample,
+                            stype='stage', baseWidth=self.baseWidth, scale=self.scale))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+def res2net50_v1b(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b lib.
+    Res2Net-50 refers to the Res2Net-50_v1b_26w_4s.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
+    return model
+
+
+def res2net101_v1b(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s lib.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
+    return model
+
+
+def res2net50_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s lib.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model_state = torch.load('D:/HarDNet-MSEG-master/res2net50_v1b_26w_4s-3cf99910.pth')
+        model.load_state_dict(model_state)
+        # lib.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
+    return model
+
+
+def res2net101_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s lib.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
+    return model
+
+
+def res2net152_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s lib.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net152_v1b_26w_4s']))
+    return model
+
+
+if __name__ == '__main__':
+    images = torch.rand(1, 3, 224, 224).cuda(0)
+    model = res2net50_v1b_26w_4s(pretrained=True)
+    model = model.cuda(0)
+    print(model(images).size())
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